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Calvanese F, Lambert CN, Nghe P, Zamponi F, Weigt M. Towards parsimonious generative modeling of RNA families. Nucleic Acids Res 2024; 52:5465-5477. [PMID: 38661206 PMCID: PMC11162787 DOI: 10.1093/nar/gkae289] [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: 10/14/2023] [Revised: 03/05/2024] [Accepted: 04/05/2024] [Indexed: 04/26/2024] Open
Abstract
Generative probabilistic models emerge as a new paradigm in data-driven, evolution-informed design of biomolecular sequences. This paper introduces a novel approach, called Edge Activation Direct Coupling Analysis (eaDCA), tailored to the characteristics of RNA sequences, with a strong emphasis on simplicity, efficiency, and interpretability. eaDCA explicitly constructs sparse coevolutionary models for RNA families, achieving performance levels comparable to more complex methods while utilizing a significantly lower number of parameters. Our approach demonstrates efficiency in generating artificial RNA sequences that closely resemble their natural counterparts in both statistical analyses and SHAPE-MaP experiments, and in predicting the effect of mutations. Notably, eaDCA provides a unique feature: estimating the number of potential functional sequences within a given RNA family. For example, in the case of cyclic di-AMP riboswitches (RF00379), our analysis suggests the existence of approximately 1039 functional nucleotide sequences. While huge compared to the known <4000 natural sequences, this number represents only a tiny fraction of the vast pool of nearly 1082 possible nucleotide sequences of the same length (136 nucleotides). These results underscore the promise of sparse and interpretable generative models, such as eaDCA, in enhancing our understanding of the expansive RNA sequence space.
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Affiliation(s)
- Francesco Calvanese
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratoire de Biologie Computationnelle et Quantitative – LCQB, Paris, France
- Laboratoire de Biophysique et Evolution, UMR CNRS-ESPCI 8231 Chimie Biologie Innovation, PSL University, Paris, France
| | - Camille N Lambert
- Laboratoire de Biophysique et Evolution, UMR CNRS-ESPCI 8231 Chimie Biologie Innovation, PSL University, Paris, France
| | - Philippe Nghe
- Laboratoire de Biophysique et Evolution, UMR CNRS-ESPCI 8231 Chimie Biologie Innovation, PSL University, Paris, France
| | - Francesco Zamponi
- Dipartimento di Fisica, Sapienza Università di Roma, Rome, Italy
- Laboratoire de Physique de l’Ecole Normale Supérieure, ENS, Université PSL, CNRS, Sorbonne Université, Université de Paris, Paris, France
| | - Martin Weigt
- Sorbonne Université, CNRS, Institut de Biologie Paris-Seine, Laboratoire de Biologie Computationnelle et Quantitative – LCQB, Paris, France
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2
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Raden M, Miladi M. How to do RNA-RNA Interaction Prediction? A Use-Case Driven Handbook Using IntaRNA. Methods Mol Biol 2024; 2726:209-234. [PMID: 38780733 DOI: 10.1007/978-1-0716-3519-3_9] [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
Computational prediction of RNA-RNA interactions (RRI) is a central methodology for the specific investigation of inter-molecular RNA interactions and regulatory effects of non-coding RNAs like eukaryotic microRNAs or prokaryotic small RNAs. Available methods can be classified according to their underlying prediction strategies, each implicating specific capabilities and restrictions often not transparent to the non-expert user. Within this work, we review seven classes of RRI prediction strategies and discuss the advantages and limitations of respective tools, since such knowledge is essential for selecting the right tool in the first place.Among the RRI prediction strategies, accessibility-based approaches have been shown to provide the most reliable predictions. Here, we describe how IntaRNA, as one of the state-of-the-art accessibility-based tools, can be applied in various use cases for the task of computational RRI prediction. Detailed hands-on examples for individual RRI predictions as well as large-scale target prediction scenarios are provided. We illustrate the flexibility and capabilities of IntaRNA through the examples. Each example is designed using real-life data from the literature and is accompanied by instructions on interpreting the respective results from IntaRNA output. Our use-case driven instructions enable non-expert users to comprehensively understand and utilize IntaRNA's features for effective RRI predictions.
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Affiliation(s)
- Martin Raden
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.
| | - Milad Miladi
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
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3
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Monroy-Eklund A, Taylor C, Weidmann CA, Burch C, Laederach A. Structural analysis of MALAT1 long noncoding RNA in cells and in evolution. RNA (NEW YORK, N.Y.) 2023; 29:691-704. [PMID: 36792358 PMCID: PMC10159000 DOI: 10.1261/rna.079388.122] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 02/02/2023] [Indexed: 05/06/2023]
Abstract
Although not canonically polyadenylated, the long noncoding RNA MALAT1 (metastasis-associated lung adenocarcinoma transcript 1) is stabilized by a highly conserved 76-nt triple helix structure on its 3' end. The entire MALAT1 transcript is over 8000 nt long in humans. The strongest structural conservation signal in MALAT1 (as measured by covariation of base pairs) is in the triple helix structure. Primary sequence analysis of covariation alone does not reveal the degree of structural conservation of the entire full-length transcript, however. Furthermore, RNA structure is often context dependent; RNA binding proteins that are differentially expressed in different cell types may alter structure. We investigate here the in-cell and cell-free structures of the full-length human and green monkey (Chlorocebus sabaeus) MALAT1 transcripts in multiple tissue-derived cell lines using SHAPE chemical probing. Our data reveal levels of uniform structural conservation in different cell lines, in cells and cell-free, and even between species, despite significant differences in primary sequence. The uniformity of the structural conservation across the entire transcript suggests that, despite seeing covariation signals only in the triple helix junction of the lncRNA, the rest of the transcript's structure is remarkably conserved, at least in primates and across multiple cell types and conditions.
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Affiliation(s)
- Anais Monroy-Eklund
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Colin Taylor
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Chase A Weidmann
- Department of Biological Chemistry, University of Michigan Medical School, Center for RNA Biomedicine, Rogel Cancer Center, Ann Arbor, Michigan 48109, USA
| | - Christina Burch
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
| | - Alain Laederach
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA
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4
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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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5
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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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6
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Lackey L, Coria A, Ghosh AJ, Grayeski P, Hatfield A, Shankar V, Platig J, Xu Z, Ramos SBV, Silverman EK, Ortega VE, Cho MH, Hersh CP, Hobbs BD, Castaldi P, Laederach A. Alternative poly-adenylation modulates α1-antitrypsin expression in chronic obstructive pulmonary disease. PLoS Genet 2021; 17:e1009912. [PMID: 34784346 PMCID: PMC8631626 DOI: 10.1371/journal.pgen.1009912] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 11/30/2021] [Accepted: 10/25/2021] [Indexed: 01/07/2023] Open
Abstract
α1-anti-trypsin (A1AT), encoded by SERPINA1, is a neutrophil elastase inhibitor that controls the inflammatory response in the lung. Severe A1AT deficiency increases risk for Chronic Obstructive Pulmonary Disease (COPD), however, the role of A1AT in COPD in non-deficient individuals is not well known. We identify a 2.1-fold increase (p = 2.5x10-6) in the use of a distal poly-adenylation site in primary lung tissue RNA-seq in 82 COPD cases when compared to 64 controls and replicate this in an independent study of 376 COPD and 267 controls. This alternative polyadenylation event involves two sites, a proximal and distal site, 61 and 1683 nucleotides downstream of the A1AT stop codon. To characterize this event, we measured the distal ratio in human primary tissue short read RNA-seq data and corroborated our results with long read RNA-seq data. Integrating these results with 3' end RNA-seq and nanoluciferase reporter assay experiments we show that use of the distal site yields mRNA transcripts with over 50-fold decreased translation efficiency and A1AT expression. We identified seven RNA binding proteins using enhanced CrossLinking and ImmunoPrecipitation precipitation (eCLIP) with one or more binding sites in the SERPINA1 3' UTR. We combined these data with measurements of the distal ratio in shRNA knockdown experiments, nuclear and cytoplasmic fractionation, and chemical RNA structure probing. We identify Quaking Homolog (QKI) as a modulator of SERPINA1 mRNA translation and confirm the role of QKI in SERPINA1 translation with luciferase reporter assays. Analysis of single-cell RNA-seq showed differences in the distribution of the SERPINA1 distal ratio among hepatocytes, macrophages, αβ-Tcells and plasma cells in the liver. Alveolar Type 1,2, dendritic cells and macrophages also vary in their distal ratio in the lung. Our work reveals a complex post-transcriptional mechanism that regulates alternative polyadenylation and A1AT expression in COPD.
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Affiliation(s)
- Lela Lackey
- Department of Genetics and Biochemistry, Center for Human Genetics, Clemson University, Greenwood, South Carolina, United States of America
| | - Aaztli Coria
- Department of Biology, University of North Carolina, Chapel Hill, North Carolina, United States of America
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Auyon J. Ghosh
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Phil Grayeski
- Curriculum in Genetics and Molecular Biology, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Abigail Hatfield
- Department of Genetics and Biochemistry, Center for Human Genetics, Clemson University, Greenwood, South Carolina, United States of America
| | - Vijay Shankar
- Department of Genetics and Biochemistry, Center for Human Genetics, Clemson University, Greenwood, South Carolina, United States of America
| | - John Platig
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Zhonghui Xu
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Silvia B. V. Ramos
- Department of Biochemistry and Biophysics, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Edwin K. Silverman
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Victor E. Ortega
- Department of Internal Medicine, Division of Respiratory Medicine, Center for Individualized Medicine, Mayo Clinic, Scottsdale, Arizona, United States of America
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Craig P. Hersh
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Brian D. Hobbs
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Peter Castaldi
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
- Division of Internal Medicine and Primary Care, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Alain Laederach
- Department of Biology, University of North Carolina, Chapel Hill, North Carolina, United States of America
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7
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Largy E, König A, Ghosh A, Ghosh D, Benabou S, Rosu F, Gabelica V. Mass Spectrometry of Nucleic Acid Noncovalent Complexes. Chem Rev 2021; 122:7720-7839. [PMID: 34587741 DOI: 10.1021/acs.chemrev.1c00386] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Nucleic acids have been among the first targets for antitumor drugs and antibiotics. With the unveiling of new biological roles in regulation of gene expression, specific DNA and RNA structures have become very attractive targets, especially when the corresponding proteins are undruggable. Biophysical assays to assess target structure as well as ligand binding stoichiometry, affinity, specificity, and binding modes are part of the drug development process. Mass spectrometry offers unique advantages as a biophysical method owing to its ability to distinguish each stoichiometry present in a mixture. In addition, advanced mass spectrometry approaches (reactive probing, fragmentation techniques, ion mobility spectrometry, ion spectroscopy) provide more detailed information on the complexes. Here, we review the fundamentals of mass spectrometry and all its particularities when studying noncovalent nucleic acid structures, and then review what has been learned thanks to mass spectrometry on nucleic acid structures, self-assemblies (e.g., duplexes or G-quadruplexes), and their complexes with ligands.
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Affiliation(s)
- Eric Largy
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, IECB, F-33600 Pessac, France
| | - Alexander König
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, IECB, F-33600 Pessac, France
| | - Anirban Ghosh
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, IECB, F-33600 Pessac, France
| | - Debasmita Ghosh
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, IECB, F-33600 Pessac, France
| | - Sanae Benabou
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, IECB, F-33600 Pessac, France
| | - Frédéric Rosu
- Univ. Bordeaux, CNRS, INSERM, IECB, UMS 3033, F-33600 Pessac, France
| | - Valérie Gabelica
- Univ. Bordeaux, CNRS, INSERM, ARNA, UMR 5320, U1212, IECB, F-33600 Pessac, France
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8
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Townshend RJL, Eismann S, Watkins AM, Rangan R, Karelina M, Das R, Dror RO. Geometric deep learning of RNA structure. Science 2021; 373:1047-1051. [PMID: 34446608 PMCID: PMC9829186 DOI: 10.1126/science.abe5650] [Citation(s) in RCA: 146] [Impact Index Per Article: 48.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Accepted: 07/14/2021] [Indexed: 01/28/2023]
Abstract
RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond.
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Affiliation(s)
| | - Stephan Eismann
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Department of Applied Physics, Stanford University, Stanford, CA, USA
| | - Andrew M Watkins
- Department of Biochemistry, Stanford University, Stanford, CA, USA
| | - Ramya Rangan
- Department of Biochemistry, Stanford University, Stanford, CA, USA
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Masha Karelina
- Department of Computer Science, Stanford University, Stanford, CA, USA
- Biophysics Program, Stanford University, Stanford, CA, USA
| | - Rhiju Das
- Department of Biochemistry, Stanford University, Stanford, CA, USA.
- Department of Physics, Stanford University, Stanford, CA, USA
| | - Ron O Dror
- Department of Computer Science, Stanford University, Stanford, CA, USA.
- Department of Structural Biology, Stanford University, Stanford, CA, USA
- Department of Molecular and Cellular Physiology, Stanford University, Stanford, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
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9
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Cao J, Xue Y. Characteristic chemical probing patterns of loop motifs improve prediction accuracy of RNA secondary structures. Nucleic Acids Res 2021; 49:4294-4307. [PMID: 33849076 PMCID: PMC8096282 DOI: 10.1093/nar/gkab250] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 03/24/2021] [Accepted: 04/10/2021] [Indexed: 12/14/2022] Open
Abstract
RNA structures play a fundamental role in nearly every aspect of cellular physiology and pathology. Gaining insights into the functions of RNA molecules requires accurate predictions of RNA secondary structures. However, the existing thermodynamic folding models remain less accurate than desired, even when chemical probing data, such as selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) reactivities, are used as restraints. Unlike most SHAPE-directed algorithms that only consider SHAPE restraints for base pairing, we extract two-dimensional structural features encoded in SHAPE data and establish robust relationships between characteristic SHAPE patterns and loop motifs of various types (hairpin, internal, and bulge) and lengths (2-11 nucleotides). Such characteristic SHAPE patterns are closely related to the sugar pucker conformations of loop residues. Based on these patterns, we propose a computational method, SHAPELoop, which refines the predicted results of the existing methods, thereby further improving their prediction accuracy. In addition, SHAPELoop can provide information about local or global structural rearrangements (including pseudoknots) and help researchers to easily test their hypothesized secondary structures.
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Affiliation(s)
- Jingyi Cao
- School of Life Sciences, Tsinghua-Peking Joint Center for Life Sciences, Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
| | - Yi Xue
- School of Life Sciences, Tsinghua-Peking Joint Center for Life Sciences, Beijing Advanced Innovation Center for Structural Biology, Tsinghua University, Beijing 100084, China
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10
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Andrzejewska A, Zawadzka M, Gumna J, Garfinkel DJ, Pachulska-Wieczorek K. In vivo structure of the Ty1 retrotransposon RNA genome. Nucleic Acids Res 2021; 49:2878-2893. [PMID: 33621339 PMCID: PMC7969010 DOI: 10.1093/nar/gkab090] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 01/28/2021] [Accepted: 02/02/2021] [Indexed: 12/25/2022] Open
Abstract
Long terminal repeat (LTR)-retrotransposons constitute a significant part of eukaryotic genomes and influence their function and evolution. Like other RNA viruses, LTR-retrotransposons efficiently utilize their RNA genome to interact with host cell machinery during replication. Here, we provide the first genome-wide RNA secondary structure model for a LTR-retrotransposon in living cells. Using SHAPE probing, we explore the secondary structure of the yeast Ty1 retrotransposon RNA genome in its native in vivo state and under defined in vitro conditions. Comparative analyses reveal the strong impact of the cellular environment on folding of Ty1 RNA. In vivo, Ty1 genome RNA is significantly less structured and more dynamic but retains specific well-structured regions harboring functional cis-acting sequences. Ribosomes participate in the unfolding and remodeling of Ty1 RNA, and inhibition of translation initiation stabilizes Ty1 RNA structure. Together, our findings support the dual role of Ty1 genomic RNA as a template for protein synthesis and reverse transcription. This study also contributes to understanding how a complex multifunctional RNA genome folds in vivo, and strengthens the need for studying RNA structure in its natural cellular context.
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Affiliation(s)
- Angelika Andrzejewska
- Department of Structure and Function of Retrotransposons, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| | - Małgorzata Zawadzka
- Department of Structure and Function of Retrotransposons, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| | - Julita Gumna
- Department of Structure and Function of Retrotransposons, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
| | - David J Garfinkel
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA
| | - Katarzyna Pachulska-Wieczorek
- Department of Structure and Function of Retrotransposons, Institute of Bioorganic Chemistry, Polish Academy of Sciences, Noskowskiego 12/14, 61-704 Poznan, Poland
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11
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Abzhanova A, Hirschi A, Reiter NJ. An exon-biased biophysical approach and NMR spectroscopy define the secondary structure of a conserved helical element within the HOTAIR long non-coding RNA. J Struct Biol 2021; 213:107728. [PMID: 33753203 DOI: 10.1016/j.jsb.2021.107728] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 02/16/2021] [Accepted: 03/17/2021] [Indexed: 11/16/2022]
Abstract
HOTAIR is a large, multi-exon spliced non-coding RNA proposed to function as a molecular scaffold and competes with chromatin to bind to histone modification enzymes. Previous sequence analysis and biochemical experiments identified potential conserved regions and characterized the full length HOTAIR secondary structure. Here, we examine the thermodynamic folding properties and structural propensity of the individual exonic regions of HOTAIR using an array of biophysical methods and NMR spectroscopy. We demonstrate that different exons of HOTAIR contain variable degrees of heterogeneity, and identify one exonic region, exon 4, that adopts a stable and compact fold under low magnesium concentrations. Close agreement of NMR spectroscopy and chemical probing unambiguously confirm conserved base pair interactions within the structural element, termed helix 10 of exon 4, located within domain I of human HOTAIR. This combined exon-biased and integrated biophysical approach introduces a new strategy to examine conformational heterogeneity in lncRNAs and emphasizes NMR as a key method to validate base pair interactions and corroborate large RNA secondary structures.
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Affiliation(s)
- Ainur Abzhanova
- Department of Chemistry, Marquette University, Milwaukee 53233, WI, United States
| | - Alexander Hirschi
- Department of Biochemistry, Vanderbilt University Medical Center, Nashville 37205-0146, TN, United States
| | - Nicholas J Reiter
- Department of Chemistry, Marquette University, Milwaukee 53233, WI, United States.
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12
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Abdelsattar AS, Mansour Y, Aboul-Ela F. The Perturbed Free-Energy Landscape: Linking Ligand Binding to Biomolecular Folding. Chembiochem 2021; 22:1499-1516. [PMID: 33351206 DOI: 10.1002/cbic.202000695] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 12/19/2020] [Indexed: 12/24/2022]
Abstract
The effects of ligand binding on biomolecular conformation are crucial in drug design, enzyme mechanisms, the regulation of gene expression, and other biological processes. Descriptive models such as "lock and key", "induced fit", and "conformation selection" are common ways to interpret such interactions. Another historical model, linked equilibria, proposes that the free-energy landscape (FEL) is perturbed by the addition of ligand binding energy for the bound population of biomolecules. This principle leads to a unified, quantitative theory of ligand-induced conformation change, building upon the FEL concept. We call the map of binding free energy over biomolecular conformational space the "binding affinity landscape" (BAL). The perturbed FEL predicts/explains ligand-induced conformational changes conforming to all common descriptive models. We review recent experimental and computational studies that exemplify the perturbed FEL, with emphasis on RNA. This way of understanding ligand-induced conformation dynamics motivates new experimental and theoretical approaches to ligand design, structural biology and systems biology.
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Affiliation(s)
- Abdallah S Abdelsattar
- Center for X-Ray Determination of the Structure of Matter, Zewail City of Science and Technology, Ahmed Zewail Road, October Gardens, 12578, Giza, Egypt
| | - Youssef Mansour
- Center for X-Ray Determination of the Structure of Matter, Zewail City of Science and Technology, Ahmed Zewail Road, October Gardens, 12578, Giza, Egypt
| | - Fareed Aboul-Ela
- Center for X-Ray Determination of the Structure of Matter, Zewail City of Science and Technology, Ahmed Zewail Road, October Gardens, 12578, Giza, Egypt
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13
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Madden EA, Plante KS, Morrison CR, Kutchko KM, Sanders W, Long KM, Taft-Benz S, Cruz Cisneros MC, White AM, Sarkar S, Reynolds G, Vincent HA, Laederach A, Moorman NJ, Heise MT. Using SHAPE-MaP To Model RNA Secondary Structure and Identify 3'UTR Variation in Chikungunya Virus. J Virol 2020; 94:e00701-20. [PMID: 32999019 PMCID: PMC7925192 DOI: 10.1128/jvi.00701-20] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 09/17/2020] [Indexed: 01/04/2023] Open
Abstract
Chikungunya virus (CHIKV) is a mosquito-borne alphavirus associated with debilitating arthralgia in humans. RNA secondary structure in the viral genome plays an important role in the lifecycle of alphaviruses; however, the specific role of RNA structure in regulating CHIKV replication is poorly understood. Our previous studies found little conservation in RNA secondary structure between alphaviruses, and this structural divergence creates unique functional structures in specific alphavirus genomes. Therefore, to understand the impact of RNA structure on CHIKV biology, we used SHAPE-MaP to inform the modeling of RNA secondary structure throughout the genome of a CHIKV isolate from the 2013 Caribbean outbreak. We then analyzed regions of the genome with high levels of structural specificity to identify potentially functional RNA secondary structures and identified 23 regions within the CHIKV genome with higher than average structural stability, including four previously identified, functionally important CHIKV RNA structures. We also analyzed the RNA flexibility and secondary structures of multiple 3'UTR variants of CHIKV that are known to affect virus replication in mosquito cells. This analysis found several novel RNA structures within these 3'UTR variants. A duplication in the 3'UTR that enhances viral replication in mosquito cells led to an overall increase in the amount of unstructured RNA in the 3'UTR. This analysis demonstrates that the CHIKV genome contains a number of unique, specific RNA secondary structures and provides a strategy for testing these secondary structures for functional importance in CHIKV replication and pathogenesis.IMPORTANCE Chikungunya virus (CHIKV) is a mosquito-borne RNA virus that causes febrile illness and debilitating arthralgia in humans. CHIKV causes explosive outbreaks but there are no approved therapies to treat or prevent CHIKV infection. The CHIKV genome contains functional RNA secondary structures that are essential for proper virus replication. Since RNA secondary structures have only been defined for a small portion of the CHIKV genome, we used a chemical probing method to define the RNA secondary structures of CHIKV genomic RNA. We identified 23 highly specific structured regions of the genome, and confirmed the functional importance of one structure using mutagenesis. Furthermore, we defined the RNA secondary structure of three CHIKV 3'UTR variants that differ in their ability to replicate in mosquito cells. Our study highlights the complexity of the CHIKV genome and describes new systems for designing compensatory mutations to test the functional relevance of viral RNA secondary structures.
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Affiliation(s)
- Emily A Madden
- Department of Microbiology and Immunology, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kenneth S Plante
- Department of Genetics, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Clayton R Morrison
- Department of Genetics, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Katrina M Kutchko
- Biology Department, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
- Curriculum in Bioinformatics and Computational Biology, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Wes Sanders
- Department of Microbiology and Immunology, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Kristin M Long
- Department of Genetics, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Sharon Taft-Benz
- Department of Genetics, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
| | | | | | - Sanjay Sarkar
- Department of Genetics, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Grace Reynolds
- Department of Genetics, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Heather A Vincent
- Department of Microbiology and Immunology, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Alain Laederach
- Biology Department, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Nathanial J Moorman
- Department of Microbiology and Immunology, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
- Lineberger Comprehensive Cancer Center, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
| | - Mark T Heise
- Department of Microbiology and Immunology, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Genetics, UNC-Chapel Hill, Chapel Hill, North Carolina, USA
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14
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Miladi M, Montaseri S, Backofen R, Raden M. Integration of accessibility data from structure probing into RNA-RNA interaction prediction. Bioinformatics 2020; 35:2862-2864. [PMID: 30590479 PMCID: PMC6691327 DOI: 10.1093/bioinformatics/bty1029] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 11/20/2018] [Accepted: 12/18/2018] [Indexed: 12/12/2022] Open
Abstract
SUMMARY Experimental structure probing data has been shown to improve thermodynamics-based RNA secondary structure prediction. To this end, chemical reactivity information (as provided e.g. by SHAPE) is incorporated, which encodes whether or not individual nucleotides are involved in intra-molecular structure. Since inter-molecular RNA-RNA interactions are often confined to unpaired RNA regions, SHAPE data is even more promising to improve interaction prediction. Here, we show how such experimental data can be incorporated seamlessly into accessibility-based RNA-RNA interaction prediction approaches, as implemented in IntaRNA. This is possible via the computation and use of unpaired probabilities that incorporate the structure probing information. We show that experimental SHAPE data can significantly improve RNA-RNA interaction prediction. We evaluate our approach by investigating interactions of a spliceosomal U1 snRNA transcript with its target splice sites. When SHAPE data is incorporated, known target sites are predicted with increased precision and specificity. AVAILABILITY AND IMPLEMENTATION https://github.com/BackofenLab/IntaRNA. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Milad Miladi
- Department of Computer Science, Bioinformatics Group, University of Freiburg, Freiburg D-79110, Germany
| | - Soheila Montaseri
- Department of Computer Science, Bioinformatics Group, University of Freiburg, Freiburg D-79110, Germany
| | - Rolf Backofen
- Department of Computer Science, Bioinformatics Group, University of Freiburg, Freiburg D-79110, Germany.,Center for Biological Signaling Studies (BIOSS), University of Freiburg, Freiburg D-79104, Germany
| | - Martin Raden
- Department of Computer Science, Bioinformatics Group, University of Freiburg, Freiburg D-79110, Germany
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15
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Mautner S, Montaseri S, Miladi M, Raden M, Costa F, Backofen R. ShaKer: RNA SHAPE prediction using graph kernel. Bioinformatics 2020; 35:i354-i359. [PMID: 31510707 PMCID: PMC6612843 DOI: 10.1093/bioinformatics/btz395] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023] Open
Abstract
Summary SHAPE experiments are used to probe the structure of RNA molecules. We present ShaKer to predict SHAPE data for RNA using a graph-kernel-based machine learning approach that is trained on experimental SHAPE information. While other available methods require a manually curated reference structure, ShaKer predicts reactivity data based on sequence input only and by sampling the ensemble of possible structures. Thus, ShaKer is well placed to enable experiment-driven, transcriptome-wide SHAPE data prediction to enable the study of RNA structuredness and to improve RNA structure and RNA–RNA interaction prediction. For performance evaluation, we use accuracy and accessibility comparing to experimental SHAPE data and competing methods. We can show that Shaker outperforms its competitors and is able to predict high quality SHAPE annotations even when no reference structure is provided. Availability and implementation ShaKer is freely available at https://github.com/BackofenLab/ShaKer.
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Affiliation(s)
- Stefan Mautner
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Soheila Montaseri
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Milad Miladi
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Martin Raden
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany
| | - Fabrizio Costa
- Department Computer Science, University of Exeter, Exeter, UK
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Freiburg, Germany.,Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
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16
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Alaidi O, Aboul‐ela F. Statistical mechanical prediction of ligand perturbation to RNA secondary structure and application to riboswitches. J Comput Chem 2020; 41:1521-1537. [DOI: 10.1002/jcc.26195] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2019] [Revised: 01/03/2020] [Accepted: 03/09/2020] [Indexed: 02/04/2023]
Affiliation(s)
- Osama Alaidi
- Biocomplexity for Research and Consulting Cairo Egypt
| | - Fareed Aboul‐ela
- Center for X‐Ray Determination of the Structure of MatterZewail City of Science and Technology Giza Egypt
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17
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Miladi M, Sokhoyan E, Houwaart T, Heyne S, Costa F, Grüning B, Backofen R. GraphClust2: Annotation and discovery of structured RNAs with scalable and accessible integrative clustering. Gigascience 2019; 8:giz150. [PMID: 31808801 PMCID: PMC6897289 DOI: 10.1093/gigascience/giz150] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 08/23/2019] [Accepted: 11/20/2019] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND RNA plays essential roles in all known forms of life. Clustering RNA sequences with common sequence and structure is an essential step towards studying RNA function. With the advent of high-throughput sequencing techniques, experimental and genomic data are expanding to complement the predictive methods. However, the existing methods do not effectively utilize and cope with the immense amount of data becoming available. RESULTS Hundreds of thousands of non-coding RNAs have been detected; however, their annotation is lagging behind. Here we present GraphClust2, a comprehensive approach for scalable clustering of RNAs based on sequence and structural similarities. GraphClust2 bridges the gap between high-throughput sequencing and structural RNA analysis and provides an integrative solution by incorporating diverse experimental and genomic data in an accessible manner via the Galaxy framework. GraphClust2 can efficiently cluster and annotate large datasets of RNAs and supports structure-probing data. We demonstrate that the annotation performance of clustering functional RNAs can be considerably improved. Furthermore, an off-the-shelf procedure is introduced for identifying locally conserved structure candidates in long RNAs. We suggest the presence and the sparseness of phylogenetically conserved local structures for a collection of long non-coding RNAs. CONCLUSIONS By clustering data from 2 cross-linking immunoprecipitation experiments, we demonstrate the benefits of GraphClust2 for motif discovery under the presence of biological and methodological biases. Finally, we uncover prominent targets of double-stranded RNA binding protein Roquin-1, such as BCOR's 3' untranslated region that contains multiple binding stem-loops that are evolutionary conserved.
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Affiliation(s)
- Milad Miladi
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Eteri Sokhoyan
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
| | - Torsten Houwaart
- Institute of Medical Microbiology and Hospital Hygiene, University of Dusseldorf, Universitaetsstr. 1, 40225 Dusseldorf, Germany
| | - Steffen Heyne
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Stuebeweg 51, 79108 Freiburg, Germany
| | - Fabrizio Costa
- Department of Computer Science, University of Exeter, North Park Road, EX4 4QF Exeter, UK
| | - Björn Grüning
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
- ZBSA Centre for Biological Systems Analysis, University of Freiburg, Hauptstr. 1, 79104 Freiburg, Germany
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, Georges-Koehler-Allee 106, 79110 Freiburg, Germany
- ZBSA Centre for Biological Systems Analysis, University of Freiburg, Hauptstr. 1, 79104 Freiburg, Germany
- Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Schaenzlestr. 18, 79104 Freiburg, Germany
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18
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Abstract
RNA is a versatile biomolecule with a broad range of biological functions that go far beyond its initially described role as a simple information carrier. The development of chemical methods to control, manipulate and modify RNA has the potential to yield new insights into its many functions and properties. Traditionally, most of these methods involved the chemical modification of RNA structure using solid-state synthesis or enzymatic transformations. However, over the past 15 years, the direct functionalization of RNA by selective acylation of the 2'-hydroxyl (2'-OH) group has emerged as a powerful alternative that enables the simple modification of both synthetic and transcribed RNAs. In this Review, we discuss the chemical properties and design of effective reagents for RNA 2'-OH acylation, highlighting the unique problem of 2'-OH reactivity in the presence of water. We elaborate on how RNA 2'-OH acylation is being exploited to develop selective chemical probes that enable interrogation of RNA structure and function, and describe new developments and applications in the field.
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19
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Choudhary K, Lai YH, Tran EJ, Aviran S. dStruct: identifying differentially reactive regions from RNA structurome profiling data. Genome Biol 2019; 20:40. [PMID: 30791935 PMCID: PMC6385470 DOI: 10.1186/s13059-019-1641-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2018] [Accepted: 01/24/2019] [Indexed: 12/16/2022] Open
Abstract
RNA biology is revolutionized by recent developments of diverse high-throughput technologies for transcriptome-wide profiling of molecular RNA structures. RNA structurome profiling data can be used to identify differentially structured regions between groups of samples. Existing methods are limited in scope to specific technologies and/or do not account for biological variation. Here, we present dStruct which is the first broadly applicable method for differential analysis accounting for biological variation in structurome profiling data. dStruct is compatible with diverse profiling technologies, is validated with experimental data and simulations, and outperforms existing methods.
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Affiliation(s)
- Krishna Choudhary
- Department of Biomedical Engineering and Genome Center, University of California, Davis, One Shields Avenue, Davis, 95616 CA USA
| | - Yu-Hsuan Lai
- Department of Biochemistry, Purdue University, BCHM 305, 175 S. University Street, West Lafayette, 47907-2063 IN USA
| | - Elizabeth J. Tran
- Department of Biochemistry, Purdue University, BCHM 305, 175 S. University Street, West Lafayette, 47907-2063 IN USA
- Purdue University Center for Cancer Research, Purdue University, Hansen Life Sciences Research Building, Room 141, 201 S. University Street, West Lafayette, 47907-2064 IN USA
| | - Sharon Aviran
- Department of Biomedical Engineering and Genome Center, University of California, Davis, One Shields Avenue, Davis, 95616 CA USA
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20
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Mailler E, Paillart JC, Marquet R, Smyth RP, Vivet-Boudou V. The evolution of RNA structural probing methods: From gels to next-generation sequencing. WILEY INTERDISCIPLINARY REVIEWS-RNA 2018; 10:e1518. [PMID: 30485688 DOI: 10.1002/wrna.1518] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 09/13/2018] [Accepted: 10/17/2018] [Indexed: 01/09/2023]
Abstract
RNA molecules are important players in all domains of life and the study of the relationship between their multiple flexible states and the associated biological roles has increased in recent years. For several decades, chemical and enzymatic structural probing experiments have been used to determine RNA structure. During this time, there has been a steady improvement in probing reagents and experimental methods, and today the structural biologist community has a large range of tools at its disposal to probe the secondary structure of RNAs in vitro and in cells. Early experiments used radioactive labeling and polyacrylamide gel electrophoresis as read-out methods. This was superseded by capillary electrophoresis, and more recently by next-generation sequencing. Today, powerful structural probing methods can characterize RNA structure on a genome-wide scale. In this review, we will provide an overview of RNA structural probing methodologies from a historical and technical perspective. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics, and Chemistry RNA Methods > RNA Analyses in vitro and In Silico RNA Methods > RNA Analyses in Cells.
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Affiliation(s)
- Elodie Mailler
- Architecture et Réactivité de l'ARN, Université de Strasbourg, CNRS, Strasbourg, France
| | | | - Roland Marquet
- Architecture et Réactivité de l'ARN, Université de Strasbourg, CNRS, Strasbourg, France
| | - Redmond P Smyth
- Architecture et Réactivité de l'ARN, Université de Strasbourg, CNRS, Strasbourg, France
| | - Valerie Vivet-Boudou
- Architecture et Réactivité de l'ARN, Université de Strasbourg, CNRS, Strasbourg, France
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21
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Extracting information from RNA SHAPE data: Kalman filtering approach. PLoS One 2018; 13:e0207029. [PMID: 30462682 PMCID: PMC6248965 DOI: 10.1371/journal.pone.0207029] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2018] [Accepted: 10/23/2018] [Indexed: 01/26/2023] Open
Abstract
RNA SHAPE experiments have become important and successful sources of information for RNA structure prediction. In such experiments, chemical reagents are used to probe RNA backbone flexibility at the nucleotide level, which in turn provides information on base pairing and therefore secondary structure. Little is known, however, about the statistics of such SHAPE data. In this work, we explore different representations of noise in SHAPE data and propose a statistically sound framework for extracting reliable reactivity information from multiple SHAPE replicates. Our analyses of RNA SHAPE experiments underscore that a normal noise model is not adequate to represent their data. We propose instead a log-normal representation of noise and discuss its relevance. Under this assumption, we observe that processing simulated SHAPE data by directly averaging different replicates leads to bias. Such bias can be reduced by analyzing the data following a log transformation, either by log-averaging or Kalman filtering. Application of Kalman filtering has the additional advantage that a prior on the nucleotide reactivities can be introduced. We show that the performance of Kalman filtering is then directly dependent on the quality of that prior. We conclude the paper with guidelines on signal processing of RNA SHAPE data.
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22
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Abel GR, Korshoj LE, Otoupal PB, Khan S, Chatterjee A, Nagpal P. Nucleotide and structural label identification in single RNA molecules with quantum tunneling spectroscopy. Chem Sci 2018; 10:1052-1063. [PMID: 30774901 PMCID: PMC6346406 DOI: 10.1039/c8sc03354d] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2018] [Accepted: 11/03/2018] [Indexed: 01/04/2023] Open
Abstract
Although a number of advances have been made in RNA sequencing and structural characterization, the lack of a method for directly determining the sequence and structure of single RNA molecules has limited our ability to probe heterogeneity in gene expression at the level of single cells. Here we present a method for direct nucleotide identification and structural label mapping of single RNA molecules via Quantum Molecular Sequencing (QMSeq). The method combines non-perturbative quantum tunneling spectroscopy to probe the molecular orbitals of ribonucleotides, new experimental biophysical parameters that fingerprint these molecular orbitals, and a machine learning classification algorithm to distinguish between the ribonucleotides. The algorithm uses tunneling spectroscopy measurements on an unknown ribonucleotide to determine its chemical identity and the presence of local chemical modifications. Combining this with structure-dependent chemical labeling presents the possibility of mapping both the sequence and local structure of individual RNA molecules. By optimizing the base-calling algorithm, we show a high accuracy for both ribonucleotide discrimination (>99.8%) and chemical label identification (>98%) with a relatively modest molecular coverage (35 repeat measurements). This lays the groundwork for simultaneous sequencing and structural mapping of single unknown RNA molecules, and paves the way for probing the sequence-structure-function relationship within the transcriptome at an unprecedented level of detail.
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Affiliation(s)
- Gary R Abel
- Department of Chemical and Biological Engineering , University of Colorado Boulder , USA . .,Renewable and Sustainable Energy Institute (RASEI) , University of Colorado Boulder , USA
| | - Lee E Korshoj
- Department of Chemical and Biological Engineering , University of Colorado Boulder , USA . .,Renewable and Sustainable Energy Institute (RASEI) , University of Colorado Boulder , USA
| | - Peter B Otoupal
- Department of Chemical and Biological Engineering , University of Colorado Boulder , USA .
| | - Sajida Khan
- Department of Chemical and Biological Engineering , University of Colorado Boulder , USA . .,Renewable and Sustainable Energy Institute (RASEI) , University of Colorado Boulder , USA
| | - Anushree Chatterjee
- Department of Chemical and Biological Engineering , University of Colorado Boulder , USA .
| | - Prashant Nagpal
- Department of Chemical and Biological Engineering , University of Colorado Boulder , USA . .,Renewable and Sustainable Energy Institute (RASEI) , University of Colorado Boulder , USA.,Materials Science and Engineering , University of Colorado Boulder , USA
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23
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A Functional riboSNitch in the 3' Untranslated Region of FKBP5 Alters MicroRNA-320a Binding Efficiency and Mediates Vulnerability to Chronic Post-Traumatic Pain. J Neurosci 2018; 38:8407-8420. [PMID: 30150364 DOI: 10.1523/jneurosci.3458-17.2018] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 07/11/2018] [Accepted: 07/13/2018] [Indexed: 01/30/2023] Open
Abstract
Previous studies have shown that common variants of the gene coding for FK506-binding protein 51 (FKBP5), a critical regulator of glucocorticoid sensitivity, affect vulnerability to stress-related disorders. In a previous report, FKBP5 rs1360780 was identified as a functional variant because of its effect on gene methylation. Here we report evidence for a novel functional FKBP5 allele, rs3800373. This study assessed the association between rs3800373 and post-traumatic chronic pain in 1607 women and men from two ethnically diverse human cohorts. The molecular mechanism through which rs3800373 affects adverse outcomes was established via in silico, in vivo, and in vitro analyses. The rs3800373 minor allele predicted worse adverse outcomes after trauma exposure, such that individuals with the minor (risk) allele developed more severe post-traumatic chronic musculoskeletal pain. Among these individuals, peritraumatic circulating FKBP5 expression levels increased as cortisol and glucocorticoid receptor (NR3C1) mRNA levels increased, consistent with increased glucocorticoid resistance. Bioinformatic, in vitro, and mutational analyses indicate that the rs3800373 minor allele reduces the binding of a stress- and pain-associated microRNA, miR-320a, to FKBP5 via altering the FKBP5 mRNA 3'UTR secondary structure (i.e., is a riboSNitch). This results in relatively greater FKBP5 translation, unchecked by miR-320a. Overall, these results identify an important gene-miRNA interaction influencing chronic pain risk in vulnerable individuals and suggest that exogenous methods to achieve targeted reduction in poststress FKBP5 mRNA expression may constitute useful therapeutic strategies.SIGNIFICANCE STATEMENT FKBP5 is a critical regulator of the stress response. Previous studies have shown that dysregulation of the expression of this gene plays a role in the pathogenesis of chronic pain development as well as a number of comorbid neuropsychiatric disorders. In the current study, we identified a functional allele (rs3800373) in the 3'UTR of FKBP5 that influences vulnerability to chronic post-traumatic pain in two ethnic cohorts. Using multiple complementary experimental approaches, we show that the FKBP5 rs3800373 minor allele alters the secondary structure of FKBP5 mRNA, decreasing the binding of a stress- and pain-associated microRNA, miR-320a. This results in relatively greater FKBP5 translation, unchecked by miR-320a, increasing glucocorticoid resistance and increasing vulnerability to post-traumatic pain.
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24
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Choudhary K, Ruan L, Deng F, Shih N, Aviran S. SEQualyzer: interactive tool for quality control and exploratory analysis of high-throughput RNA structural profiling data. Bioinformatics 2018; 33:441-443. [PMID: 28172632 DOI: 10.1093/bioinformatics/btw627] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 09/25/2016] [Accepted: 09/26/2016] [Indexed: 11/14/2022] Open
Abstract
Summary To serve numerous functional roles, RNA must fold into specific structures. Determining these structures is thus of paramount importance. The recent advent of high-throughput sequencing-based structure profiling experiments has provided important insights into RNA structure and widened the scope of RNA studies. However, as a broad range of approaches continues to emerge, a universal framework is needed to quantitatively ensure consistent and high-quality data. We present SEQualyzer, a visual and interactive application that makes it easy and efficient to gauge data quality, screen for transcripts with high-quality information and identify discordant replicates in structure profiling experiments. Our methods rely on features common to a wide range of protocols and can serve as standards for quality control and analyses. Availability and Implementation SEQualyzer is written in R, is platform-independent, and is freely available at http://bme.ucdavis.edu/aviranlab/SEQualyzer. Contact saviran@ucdavis.edu Supplementary Informantion Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Krishna Choudhary
- Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA
| | - Luyao Ruan
- Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA
| | - Fei Deng
- Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA
| | - Nathan Shih
- Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA
| | - Sharon Aviran
- Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA, USA
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25
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Automated Recognition of RNA Structure Motifs by Their SHAPE Data Signatures. Genes (Basel) 2018; 9:genes9060300. [PMID: 29904019 PMCID: PMC6027059 DOI: 10.3390/genes9060300] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 06/04/2018] [Accepted: 06/13/2018] [Indexed: 02/03/2023] Open
Abstract
High-throughput structure profiling (SP) experiments that provide information at nucleotide resolution are revolutionizing our ability to study RNA structures. Of particular interest are RNA elements whose underlying structures are necessary for their biological functions. We previously introduced patteRNA, an algorithm for rapidly mining SP data for patterns characteristic of such motifs. This work provided a proof-of-concept for the detection of motifs and the capability of distinguishing structures displaying pronounced conformational changes. Here, we describe several improvements and automation routines to patteRNA. We then consider more elaborate biological situations starting with the comparison or integration of results from searches for distinct motifs and across datasets. To facilitate such analyses, we characterize patteRNA’s outputs and describe a normalization framework that regularizes results. We then demonstrate that our algorithm successfully discerns between highly similar structural variants of the human immunodeficiency virus type 1 (HIV-1) Rev response element (RRE) and readily identifies its exact location in whole-genome structure profiles of HIV-1. This work highlights the breadth of information that can be gleaned from SP data and broadens the utility of data-driven methods as tools for the detection of novel RNA elements.
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26
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Kutchko KM, Madden EA, Morrison C, Plante KS, Sanders W, Vincent HA, Cruz Cisneros MC, Long KM, Moorman NJ, Heise MT, Laederach A. Structural divergence creates new functional features in alphavirus genomes. Nucleic Acids Res 2018; 46:3657-3670. [PMID: 29361131 PMCID: PMC6283419 DOI: 10.1093/nar/gky012] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2017] [Revised: 12/10/2017] [Accepted: 01/05/2018] [Indexed: 12/03/2022] Open
Abstract
Alphaviruses are mosquito-borne pathogens that cause human diseases ranging from debilitating arthritis to lethal encephalitis. Studies with Sindbis virus (SINV), which causes fever, rash, and arthralgia in humans, and Venezuelan equine encephalitis virus (VEEV), which causes encephalitis, have identified RNA structural elements that play key roles in replication and pathogenesis. However, a complete genomic structural profile has not been established for these viruses. We used the structural probing technique SHAPE-MaP to identify structured elements within the SINV and VEEV genomes. Our SHAPE-directed structural models recapitulate known RNA structures, while also identifying novel structural elements, including a new functional element in the nsP1 region of SINV whose disruption causes a defect in infectivity. Although RNA structural elements are important for multiple aspects of alphavirus biology, we found the majority of RNA structures were not conserved between SINV and VEEV. Our data suggest that alphavirus RNA genomes are highly divergent structurally despite similar genomic architecture and sequence conservation; still, RNA structural elements are critical to the viral life cycle. These findings reframe traditional assumptions about RNA structure and evolution: rather than structures being conserved, alphaviruses frequently evolve new structures that may shape interactions with host immune systems or co-evolve with viral proteins.
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Affiliation(s)
- Katrina M Kutchko
- Department of Biology, UNC-Chapel Hill, USA
- Curriculum in Bioinformatics and Computational Biology, UNC-Chapel Hill, USA
| | - Emily A Madden
- Department of Microbiology and Immunology, UNC-Chapel Hill, USA
| | | | | | - Wes Sanders
- Department of Microbiology and Immunology, UNC-Chapel Hill, USA
- Lineberger Comprehensive Cancer Center, UNC-Chapel Hill, USA
| | | | | | | | - Nathaniel J Moorman
- Department of Microbiology and Immunology, UNC-Chapel Hill, USA
- Lineberger Comprehensive Cancer Center, UNC-Chapel Hill, USA
| | - Mark T Heise
- Department of Microbiology and Immunology, UNC-Chapel Hill, USA
- Department of Genetics, UNC-Chapel Hill, USA
| | - Alain Laederach
- Department of Biology, UNC-Chapel Hill, USA
- Curriculum in Bioinformatics and Computational Biology, UNC-Chapel Hill, USA
- Lineberger Comprehensive Cancer Center, UNC-Chapel Hill, USA
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27
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Woods CT, Laederach A. Classification of RNA structure change by 'gazing' at experimental data. Bioinformatics 2018; 33:1647-1655. [PMID: 28130241 PMCID: PMC5447233 DOI: 10.1093/bioinformatics/btx041] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Accepted: 01/20/2017] [Indexed: 11/12/2022] Open
Abstract
Motivation Mutations (or Single Nucleotide Variants) in folded RiboNucleic Acid structures that cause local or global conformational change are riboSNitches. Predicting riboSNitches is challenging, as it requires making two, albeit related, structure predictions. The data most often used to experimentally validate riboSNitch predictions is Selective 2' Hydroxyl Acylation by Primer Extension, or SHAPE. Experimentally establishing a riboSNitch requires the quantitative comparison of two SHAPE traces: wild-type (WT) and mutant. Historically, SHAPE data was collected on electropherograms and change in structure was evaluated by 'gel gazing.' SHAPE data is now routinely collected with next generation sequencing and/or capillary sequencers. We aim to establish a classifier capable of simulating human 'gazing' by identifying features of the SHAPE profile that human experts agree 'looks' like a riboSNitch. Results We find strong quantitative agreement between experts when RNA scientists 'gaze' at SHAPE data and identify riboSNitches. We identify dynamic time warping and seven other features predictive of the human consensus. The classSNitch classifier reported here accurately reproduces human consensus for 167 mutant/WT comparisons with an Area Under the Curve (AUC) above 0.8. When we analyze 2019 mutant traces for 17 different RNAs, we find that features of the WT SHAPE reactivity allow us to improve thermodynamic structure predictions of riboSNitches. This is significant, as accurate RNA structural analysis and prediction is likely to become an important aspect of precision medicine. Availability and Implementation The classSNitch R package is freely available at http://classsnitch.r-forge.r-project.org . Contact alain@email.unc.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Chanin Tolson Woods
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alain Laederach
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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28
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Ledda M, Aviran S. PATTERNA: transcriptome-wide search for functional RNA elements via structural data signatures. Genome Biol 2018; 19:28. [PMID: 29495968 PMCID: PMC5833111 DOI: 10.1186/s13059-018-1399-z] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 01/30/2018] [Indexed: 02/08/2023] Open
Abstract
Establishing a link between RNA structure and function remains a great challenge in RNA biology. The emergence of high-throughput structure profiling experiments is revolutionizing our ability to decipher structure, yet principled approaches for extracting information on structural elements directly from these data sets are lacking. We present PATTERNA, an unsupervised pattern recognition algorithm that rapidly mines RNA structure motifs from profiling data. We demonstrate that PATTERNA detects motifs with an accuracy comparable to commonly used thermodynamic models and highlight its utility in automating data-directed structure modeling from large data sets. PATTERNA is versatile and compatible with diverse profiling techniques and experimental conditions.
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Affiliation(s)
- Mirko Ledda
- Department of Biomedical Engineering and Genome Center, UC Davis, 1 Shields Ave, Davis, 95616 USA
- Integrative Genetics and Genomics Graduate Group, UC Davis, 1 Shields Ave, Davis, 95616 USA
| | - Sharon Aviran
- Department of Biomedical Engineering and Genome Center, UC Davis, 1 Shields Ave, Davis, 95616 USA
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29
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Statistical modeling of RNA structure profiling experiments enables parsimonious reconstruction of structure landscapes. Nat Commun 2018; 9:606. [PMID: 29426922 PMCID: PMC5807309 DOI: 10.1038/s41467-018-02923-8] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2017] [Accepted: 01/09/2018] [Indexed: 11/23/2022] Open
Abstract
RNA plays key regulatory roles in diverse cellular processes, where its functionality often derives from folding into and converting between structures. Many RNAs further rely on co-existence of alternative structures, which govern their response to cellular signals. However, characterizing heterogeneous landscapes is difficult, both experimentally and computationally. Recently, structure profiling experiments have emerged as powerful and affordable structure characterization methods, which improve computational structure prediction. To date, efforts have centered on predicting one optimal structure, with much less progress made on multiple-structure prediction. Here, we report a probabilistic modeling approach that predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data. We demonstrate robust landscape reconstruction and quantitative insights into structural dynamics by analyzing numerous data sets. This work establishes a framework for data-directed characterization of structure landscapes to aid experimentalists in performing structure-function studies. Different experimental and computational approaches can be used to study RNA structures. Here, the authors present a computational method for data-directed reconstruction of complex RNA structure landscapes, which predicts a parsimonious set of co-existing structures and estimates their abundances from structure profiling data.
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30
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Mlýnský V, Bussi G. Molecular Dynamics Simulations Reveal an Interplay between SHAPE Reagent Binding and RNA Flexibility. J Phys Chem Lett 2018; 9:313-318. [PMID: 29265824 PMCID: PMC5830694 DOI: 10.1021/acs.jpclett.7b02921] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 12/21/2017] [Indexed: 05/10/2023]
Abstract
The function of RNA molecules usually depends on their overall fold and on the presence of specific structural motifs. Chemical probing methods are routinely used in combination with nearest-neighbor models to determine RNA secondary structure. Among the available methods, SHAPE is relevant due to its capability to probe all RNA nucleotides and the possibility to be used in vivo. However, the structural determinants for SHAPE reactivity and its mechanism of reaction are still unclear. Here molecular dynamics simulations and enhanced sampling techniques are used to predict the accessibility of nucleotide analogs and larger RNA structural motifs to SHAPE reagents. We show that local RNA reconformations are crucial in allowing reagents to reach the 2'-OH group of a particular nucleotide and that sugar pucker is a major structural factor influencing SHAPE reactivity.
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Affiliation(s)
- Vojtěch Mlýnský
- Scuola Internazionale Superiore di
Studi Avanzati, SISSA, via Bonomea 265, 34136 Trieste, Italy
| | - Giovanni Bussi
- Scuola Internazionale Superiore di
Studi Avanzati, SISSA, via Bonomea 265, 34136 Trieste, Italy
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31
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Schlick T, Pyle AM. Opportunities and Challenges in RNA Structural Modeling and Design. Biophys J 2017; 113:225-234. [PMID: 28162235 PMCID: PMC5529161 DOI: 10.1016/j.bpj.2016.12.037] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Revised: 12/08/2016] [Accepted: 12/19/2016] [Indexed: 01/27/2023] Open
Abstract
We describe opportunities and challenges in RNA structural modeling and design, as recently discussed during the second Telluride Science Research Center workshop organized in June 2016. Topics include fundamental processes of RNA, such as structural assemblies (hierarchical folding, multiple conformational states and their clustering), RNA motifs, and chemical reactivity of RNA, as used for structural prediction and functional inference. We also highlight the software and database issues associated with RNA structures, such as the multiple approaches for motif annotation, the need for frequent database updating, and the importance of quality control of RNA structures. We discuss various modeling approaches for structure prediction, mechanistic analysis of RNA reactions, and RNA design, and the complementary roles that both atomistic and coarse-grained approaches play in such simulations. Collectively, as scientists from varied disciplines become familiar and drawn into these unique challenges, new approaches and collaborative efforts will undoubtedly be catalyzed.
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Affiliation(s)
- Tamar Schlick
- Department of Chemistry, New York University, New York, New York; Courant Institute of Mathematical Sciences, New York University, New York, New York.
| | - Anna Marie Pyle
- Department of Molecular and Cellular and Developmental Biology and Department of Chemistry, Yale University; Howard Hughes Medical Institute, New Haven, Connecticut.
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32
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Woods CT, Lackey L, Williams B, Dokholyan NV, Gotz D, Laederach A. Comparative Visualization of the RNA Suboptimal Conformational Ensemble In Vivo. Biophys J 2017. [PMID: 28625696 PMCID: PMC5529173 DOI: 10.1016/j.bpj.2017.05.031] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
When a ribonucleic acid (RNA) molecule folds, it often does not adopt a single, well-defined conformation. The folding energy landscape of an RNA is highly dependent on its nucleotide sequence and molecular environment. Cellular molecules sometimes alter the energy landscape, thereby changing the ensemble of likely low-energy conformations. The effects of these energy landscape changes on the conformational ensemble are particularly challenging to visualize for large RNAs. We have created a robust approach for visualizing the conformational ensemble of RNAs that is well suited for in vitro versus in vivo comparisons. Our method creates a stable map of conformational space for a given RNA sequence. We first identify single point mutations in the RNA that maximally sample suboptimal conformational space based on the ensemble’s partition function. Then, we cluster these diverse ensembles to identify the most diverse partition functions for Boltzmann stochastic sampling. By using, to our knowledge, a novel nestedness distance metric, we iteratively add mutant suboptimal ensembles to converge on a stable 2D map of conformational space. We then compute the selective 2′ hydroxyl acylation by primer extension (SHAPE)-directed ensemble for the RNA folding under different conditions, and we project these ensembles on the map to visualize. To validate our approach, we established a conformational map of the Vibrio vulnificus add adenine riboswitch that reveals five classes of structures. In the presence of adenine, projection of the SHAPE-directed sampling correctly identified the on-conformation; without the ligand, only off-conformations were visualized. We also collected the whole-transcript in vitro and in vivo SHAPE-MaP for human β-actin messenger RNA that revealed similar global folds in both conditions. Nonetheless, a comparison of in vitro and in vivo data revealed that specific regions exhibited significantly different SHAPE-MaP profiles indicative of structural rearrangements, including rearrangement consistent with binding of the zipcode protein in a region distal to the stop codon.
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Affiliation(s)
- Chanin T Woods
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Lela Lackey
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Benfeard Williams
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Nikolay V Dokholyan
- Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - David Gotz
- Carolina Health Informatics Program, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Alain Laederach
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.
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33
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Choudhary K, Deng F, Aviran S. Comparative and integrative analysis of RNA structural profiling data: current practices and emerging questions. QUANTITATIVE BIOLOGY 2017; 5:3-24. [PMID: 28717530 PMCID: PMC5510538 DOI: 10.1007/s40484-017-0093-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2016] [Revised: 12/08/2016] [Accepted: 12/15/2016] [Indexed: 12/30/2022]
Abstract
BACKGROUND Structure profiling experiments provide single-nucleotide information on RNA structure. Recent advances in chemistry combined with application of high-throughput sequencing have enabled structure profiling at transcriptome scale and in living cells, creating unprecedented opportunities for RNA biology. Propelled by these experimental advances, massive data with ever-increasing diversity and complexity have been generated, which give rise to new challenges in interpreting and analyzing these data. RESULTS We review current practices in analysis of structure profiling data with emphasis on comparative and integrative analysis as well as highlight emerging questions. Comparative analysis has revealed structural patterns across transcriptomes and has become an integral component of recent profiling studies. Additionally, profiling data can be integrated into traditional structure prediction algorithms to improve prediction accuracy. CONCLUSIONS To keep pace with experimental developments, methods to facilitate, enhance and refine such analyses are needed. Parallel advances in analysis methodology will complement profiling technologies and help them reach their full potential.
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Affiliation(s)
| | | | - Sharon Aviran
- Department of Biomedical Engineering and Genome Center, University of California at Davis, Davis, CA 95616, USA
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