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Kaur J, Sharma A, Mundlia P, Sood V, Pandey A, Singh G, Barnwal RP. RNA-Small-Molecule Interaction: Challenging the "Undruggable" Tag. J Med Chem 2024. [PMID: 38498010 DOI: 10.1021/acs.jmedchem.3c01354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
RNA targeting, specifically with small molecules, is a relatively new and rapidly emerging avenue with the promise to expand the target space in the drug discovery field. From being "disregarded" as an "undruggable" messenger molecule to FDA approval of an RNA-targeting small-molecule drug Risdiplam, a radical change in perspective toward RNA has been observed in the past decade. RNAs serve important regulatory functions beyond canonical protein synthesis, and their dysregulation has been reported in many diseases. A deeper understanding of RNA biology reveals that RNA molecules can adopt a variety of structures, carrying defined binding pockets that can accommodate small-molecule drugs. Due to its functional diversity and structural complexity, RNA can be perceived as a prospective target for therapeutic intervention. This perspective highlights the proof of concept of RNA-small-molecule interactions, exemplified by targeting of various transcripts with functional modulators. The advent of RNA-oriented knowledge would help expedite drug discovery.
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Affiliation(s)
- Jaskirat Kaur
- Department of Biophysics, Panjab University, Chandigarh 160014, India
| | - Akanksha Sharma
- Department of Biophysics, Panjab University, Chandigarh 160014, India
- University Institute of Pharmaceutical Sciences, Panjab University, Chandigarh 160014, India
| | - Poonam Mundlia
- Department of Biophysics, Panjab University, Chandigarh 160014, India
| | - Vikas Sood
- Department of Biochemistry, Jamia Hamdard, New Delhi 110062, India
| | - Ankur Pandey
- Department of Chemistry, Panjab University, Chandigarh 160014, India
| | - Gurpal Singh
- University Institute of Pharmaceutical Sciences, Panjab University, Chandigarh 160014, India
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Kirsch R, Seemann SE, Ruzzo WL, Cohen SM, Stadler PF, Gorodkin J. Identification and characterization of novel conserved RNA structures in Drosophila. BMC Genomics 2018; 19:899. [PMID: 30537930 PMCID: PMC6288889 DOI: 10.1186/s12864-018-5234-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 11/08/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Comparative genomics approaches have facilitated the discovery of many novel non-coding and structured RNAs (ncRNAs). The increasing availability of related genomes now makes it possible to systematically search for compensatory base changes - and thus for conserved secondary structures - even in genomic regions that are poorly alignable in the primary sequence. The wealth of available transcriptome data can add valuable insight into expression and possible function for new ncRNA candidates. Earlier work identifying ncRNAs in Drosophila melanogaster made use of sequence-based alignments and employed a sliding window approach, inevitably biasing identification toward RNAs encoded in the more conserved parts of the genome. RESULTS To search for conserved RNA structures (CRSs) that may not be highly conserved in sequence and to assess the expression of CRSs, we conducted a genome-wide structural alignment screen of 27 insect genomes including D. melanogaster and integrated this with an extensive set of tiling array data. The structural alignment screen revealed ∼30,000 novel candidate CRSs at an estimated false discovery rate of less than 10%. With more than one quarter of all individual CRS motifs showing sequence identities below 60%, the predicted CRSs largely complement the findings of sliding window approaches applied previously. While a sixth of the CRSs were ubiquitously expressed, we found that most were expressed in specific developmental stages or cell lines. Notably, most statistically significant enrichment of CRSs were observed in pupae, mainly in exons of untranslated regions, promotors, enhancers, and long ncRNAs. Interestingly, cell lines were found to express a different set of CRSs than were found in vivo. Only a small fraction of intergenic CRSs were co-expressed with the adjacent protein coding genes, which suggests that most intergenic CRSs are independent genetic units. CONCLUSIONS This study provides a more comprehensive view of the ncRNA transcriptome in fly as well as evidence for differential expression of CRSs during development and in cell lines.
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Affiliation(s)
- Rebecca Kirsch
- Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, Frederiksberg C, DK-1870 Denmark
- Department of Veterinary and Animal Science, University of Copenhagen, Grønnegårdsvej 3, Frederiksberg C, DK-1870 Denmark
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16–18, Leipzig, D-04107 Germany
| | - Stefan E. Seemann
- Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, Frederiksberg C, DK-1870 Denmark
- Department of Veterinary and Animal Science, University of Copenhagen, Grønnegårdsvej 3, Frederiksberg C, DK-1870 Denmark
| | - Walter L. Ruzzo
- Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, Frederiksberg C, DK-1870 Denmark
- School of Computer Science and Engineering, University of Washington, Box 352350, Seattle, 98195-2350 WA USA
- Department of Genome Sciences, University of Washington, Box 355065, Seattle, 98195-5065 WA USA
- Fred Hutchinson Cancer Research Center, 1100 Fairview Ave. N., Seattle, 98109-1024 WA USA
| | - Stephen M. Cohen
- Department of Cellular and Molecular Medicine, University of Copenhagen, Blegdamsvej 3, Copenhagen N, DK-2200 Denmark
| | - Peter F. Stadler
- Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, Frederiksberg C, DK-1870 Denmark
- Bioinformatics Group, Department of Computer Science, and Interdisciplinary Center for Bioinformatics, Universität Leipzig, Härtelstraße 16–18, Leipzig, D-04107 Germany
- Max Planck Institute for Mathematics in the Sciences, Inselstraße 22, Leipzig, D-04103 Germany
- Faculdad de Ciencias, Universidad Nacional de Colombia, Sede Bogotá, Ciudad Universitaria, Bogotá, COL-111321 D.C. Colombia
- Department of Theoretical Chemistry, University of Vienna, Währinger Straße 17, Vienna, A-1090 Austria
- Santa Fe Institute, 1399 Hyde Park Rd., Santa Fe, NM87501 USA
| | - Jan Gorodkin
- Center for non-coding RNA in Technology and Health, University of Copenhagen, Grønnegårdsvej 3, Frederiksberg C, DK-1870 Denmark
- Department of Veterinary and Animal Science, University of Copenhagen, Grønnegårdsvej 3, Frederiksberg C, DK-1870 Denmark
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Chen B, Zhang Y, Zhang X, Jia S, Chen S, Kang L. Genome-wide identification and developmental expression profiling of long noncoding RNAs during Drosophila metamorphosis. Sci Rep 2016; 6:23330. [PMID: 26996731 PMCID: PMC4800424 DOI: 10.1038/srep23330] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2015] [Accepted: 02/29/2016] [Indexed: 12/27/2022] Open
Abstract
An increasing number of long noncoding RNAs (lncRNAs) have been discovered with the recent advances in RNA-sequencing technologies. lncRNAs play key roles across diverse biological processes, and are involved in developmental regulation. However, knowledge about how the genome-wide expression of lncRNAs is developmentally regulated is still limited. We here performed a whole-genome identification of lncRNAs followed by a global expression profiling of these lncRNAs during development in Drosophila melanogaster. We combined bioinformatic prediction of lncRNAs with stringent filtering of protein-coding transcripts and experimental validation to define a high-confidence set of Drosophila lncRNAs. We identified 1,077 lncRNAs in the given transcriptomes that contain 43,967 transcripts; among these, 646 lncRNAs are novel. In vivo expression profiling of these lncRNAs in 27 developmental processes revealed that the expression of lncRNAs is highly temporally restricted relative to that of protein-coding genes. Remarkably, 21% and 42% lncRNAs were significantly upregulated at late embryonic and larval stage, the critical time for developmental transition. The results highlight the developmental specificity of lncRNA expression, and reflect the regulatory significance of a large subclass of lncRNAs for the onset of metamorphosis. The systematic annotation and expression analysis of lncRNAs during Drosophila development form the foundation for future functional exploration.
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Affiliation(s)
- Bing Chen
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yi Zhang
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.,Department of Mathematics, Hebei University of Science and Technology/Hebei Laboratory of Pharmaceutical Molecular Chemistry, Shijiazhuang 050018, China
| | - Xia Zhang
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.,Beijing Institutes of Life Sciences, Chinese Academy of Sciences, Beijing 100101, China
| | - Shili Jia
- Department of Mathematics, Hebei University of Science and Technology/Hebei Laboratory of Pharmaceutical Molecular Chemistry, Shijiazhuang 050018, China
| | - Shuang Chen
- Chengdu Institute of Biology, Chinese Academy of Sciences, Chengdu 610041, China
| | - Le Kang
- State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China.,Beijing Institutes of Life Sciences, Chinese Academy of Sciences, Beijing 100101, China
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Han X, Yang F, Cao H, Liang Z. Malat1
regulates serum response factor through miR‐133 as a competing endogenous RNA in myogenesis. FASEB J 2015; 29:3054-64. [DOI: 10.1096/fj.14-259952] [Citation(s) in RCA: 116] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2014] [Accepted: 03/17/2015] [Indexed: 01/26/2023]
Affiliation(s)
- Xiaorui Han
- Institute of Molecular Medicine, Peking UniversityBeijingChina
| | - Feng Yang
- Institute of Molecular Medicine, Peking UniversityBeijingChina
| | - Huiqing Cao
- Institute of Molecular Medicine, Peking UniversityBeijingChina
| | - Zicai Liang
- Institute of Molecular Medicine, Peking UniversityBeijingChina
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Abstract
Transcriptomics experiments and computational predictions both enable systematic discovery of new functional RNAs. However, many putative noncoding transcripts arise instead from artifacts and biological noise, and current computational prediction methods have high false positive rates. I discuss prospects for improving computational methods for analyzing and identifying functional RNAs, with a focus on detecting signatures of conserved RNA secondary structure. An interesting new front is the application of chemical and enzymatic experiments that probe RNA structure on a transcriptome-wide scale. I review several proposed approaches for incorporating structure probing data into the computational prediction of RNA secondary structure. Using probabilistic inference formalisms, I show how all these approaches can be unified in a well-principled framework, which in turn allows RNA probing data to be easily integrated into a wide range of analyses that depend on RNA secondary structure inference. Such analyses include homology search and genome-wide detection of new structural RNAs.
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Affiliation(s)
- Sean R Eddy
- Howard Hughes Medical Institute Janelia Farm Research Campus, Ashburn, Virginia 20147;
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Abstract
Recent genome-wide computational screens that search for conservation of RNA secondary structure in whole-genome alignments (WGAs) have predicted thousands of structural noncoding RNAs (ncRNAs). The sensitivity of such approaches, however, is limited, due to their reliance on sequence-based whole-genome aligners, which regularly misalign structural ncRNAs. This suggests that many more structural ncRNAs may remain undetected. Structure-based alignment, which could increase the sensitivity, has been prohibitive for genome-wide screens due to its extreme computational costs. Breaking this barrier, we present the pipeline REAPR (RE-Alignment for Prediction of structural ncRNA), which efficiently realigns whole genomes based on RNA sequence and structure, thus allowing us to boost the performance of de novo ncRNA predictors, such as RNAz. Key to the pipeline's efficiency is the development of a novel banding technique for multiple RNA alignment. REAPR significantly outperforms the widely used predictors RNAz and EvoFold in genome-wide screens; in direct comparison to the most recent RNAz screen on D. melanogaster, REAPR predicts twice as many high-confidence ncRNA candidates. Moreover, modENCODE RNA-seq experiments confirm a substantial number of its predictions as transcripts. REAPR's advancement of de novo structural characterization of ncRNAs complements the identification of transcripts from rapidly accumulating RNA-seq data.
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Affiliation(s)
- Sebastian Will
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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Dhanasekaran K, Kumari S, Kanduri C. Noncoding RNAs in chromatin organization and transcription regulation: an epigenetic view. Subcell Biochem 2013; 61:343-72. [PMID: 23150258 DOI: 10.1007/978-94-007-4525-4_15] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The Genome of a eukaryotic cell harbors genetic material in the form of DNA which carries the hereditary information encoded in their bases. Nucleotide bases of DNA are transcribed into complimentary RNA bases which are further translated into protein, performing defined set of functions. The central dogma of life ensures sequential flow of genetic information among these biopolymers. Noncoding RNAs (ncRNAs) serve as exceptions for this principle as they do not code for any protein. Nevertheless, a major portion of the human transcriptome comprises noncoding RNAs. These RNAs vary in size, as well as they vary in the spatio-temporal distribution. These ncRnAs are functional and are shown to be involved in diverse cellular activities. Precise location and expression of ncRNA is essential for the cellular homeostasis. Failures of these events ultimately results in numerous disease conditions including cancer. The present review lists out the various classes of ncRNAs with a special emphasis on their role in chromatin organization and transcription regulation.
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Affiliation(s)
- Karthigeyan Dhanasekaran
- Transcription and Disease Laboratory, Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur P.O., Bangalore, 560064, India
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Searls DB. A primer in macromolecular linguistics. Biopolymers 2012; 99:203-17. [PMID: 23034580 DOI: 10.1002/bip.22101] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2012] [Accepted: 05/25/2012] [Indexed: 01/01/2023]
Abstract
Polymeric macromolecules, when viewed abstractly as strings of symbols, can be treated in terms of formal language theory, providing a mathematical foundation for characterizing such strings both as collections and in terms of their individual structures. In addition this approach offers a framework for analysis of macromolecules by tools and conventions widely used in computational linguistics. This article introduces the ways that linguistics can be and has been applied to molecular biology, covering the relevant formal language theory at a relatively nontechnical level. Analogies between macromolecules and human natural language are used to provide intuitive insights into the relevance of grammars, parsing, and analysis of language complexity to biology.
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Westesson O, Holmes I. Developing and applying heterogeneous phylogenetic models with XRate. PLoS One 2012; 7:e36898. [PMID: 22693624 PMCID: PMC3367922 DOI: 10.1371/journal.pone.0036898] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2011] [Accepted: 04/09/2012] [Indexed: 12/17/2022] Open
Abstract
Modeling sequence evolution on phylogenetic trees is a useful technique in computational biology. Especially powerful are models which take account of the heterogeneous nature of sequence evolution according to the "grammar" of the encoded gene features. However, beyond a modest level of model complexity, manual coding of models becomes prohibitively labor-intensive. We demonstrate, via a set of case studies, the new built-in model-prototyping capabilities of XRate (macros and Scheme extensions). These features allow rapid implementation of phylogenetic models which would have previously been far more labor-intensive. XRate 's new capabilities for lineage-specific models, ancestral sequence reconstruction, and improved annotation output are also discussed. XRate 's flexible model-specification capabilities and computational efficiency make it well-suited to developing and prototyping phylogenetic grammar models. XRate is available as part of the DART software package: http://biowiki.org/DART.
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Affiliation(s)
| | - Ian Holmes
- Department of Bioengineering, University of California, Berkeley, California, United States of America
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Westesson O, Lunter G, Paten B, Holmes I. Accurate reconstruction of insertion-deletion histories by statistical phylogenetics. PLoS One 2012; 7:e34572. [PMID: 22536326 PMCID: PMC3335033 DOI: 10.1371/journal.pone.0034572] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2012] [Accepted: 03/05/2012] [Indexed: 11/24/2022] Open
Abstract
The Multiple Sequence Alignment (MSA) is a computational abstraction that represents a partial summary either of indel history, or of structural similarity. Taking the former view (indel history), it is possible to use formal automata theory to generalize the phylogenetic likelihood framework for finite substitution models (Dayhoff's probability matrices and Felsenstein's pruning algorithm) to arbitrary-length sequences. In this paper, we report results of a simulation-based benchmark of several methods for reconstruction of indel history. The methods tested include a relatively new algorithm for statistical marginalization of MSAs that sums over a stochastically-sampled ensemble of the most probable evolutionary histories. For mammalian evolutionary parameters on several different trees, the single most likely history sampled by our algorithm appears less biased than histories reconstructed by other MSA methods. The algorithm can also be used for alignment-free inference, where the MSA is explicitly summed out of the analysis. As an illustration of our method, we discuss reconstruction of the evolutionary histories of human protein-coding genes.
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Affiliation(s)
- Oscar Westesson
- University of California Berkeley and University of California San Francisco Graduate Program in Bioengineering, University of California, Berkeley, California, United States of America
| | - Gerton Lunter
- Wellcome Trust Center for Human Genetics, Oxford, Oxford, United Kingdom
| | - Benedict Paten
- Baskin School of Engineering, University of California Santa Cruz, Santa Cruz, California, United States of America
| | - Ian Holmes
- University of California Berkeley and University of California San Francisco Graduate Program in Bioengineering, University of California, Berkeley, California, United States of America
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