1
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Biayna J, Dumbović G. Decoding subcellular RNA localization one molecule at a time. Genome Biol 2025; 26:45. [PMID: 40033325 PMCID: PMC11874642 DOI: 10.1186/s13059-025-03507-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Accepted: 02/13/2025] [Indexed: 03/05/2025] Open
Abstract
Eukaryotic cells are highly structured and composed of multiple membrane-bound and membraneless organelles. Subcellular RNA localization is a critical regulator of RNA function, influencing various biological processes. At any given moment, RNAs must accurately navigate the three-dimensional subcellular environment to ensure proper localization and function, governed by numerous factors, including splicing, RNA stability, modifications, and localizing sequences. Aberrant RNA localization can contribute to the development of numerous diseases. Here, we explore diverse RNA localization mechanisms and summarize advancements in methods for determining subcellular RNA localization, highlighting imaging techniques transforming our ability to study RNA dynamics at the single-molecule level.
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Affiliation(s)
- Josep Biayna
- Goethe University Frankfurt, Center for Molecular Medicine, Institute for Cardiovascular Regeneration, Frankfurt, Germany
| | - Gabrijela Dumbović
- Goethe University Frankfurt, Center for Molecular Medicine, Institute for Cardiovascular Regeneration, Frankfurt, Germany.
- Cardio-Pulmonary Institute (CPI), Goethe University, Frankfurt, Frankfurt, Germany.
- German Center of Cardiovascular Research (DZHK), Partner Site Rhein/Main, Frankfurt, Germany.
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2
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Du C, Fan W, Zhou Y. Integrated Biochemical and Computational Methods for Deciphering RNA-Processing Codes. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1875. [PMID: 39523464 DOI: 10.1002/wrna.1875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 09/23/2024] [Accepted: 10/21/2024] [Indexed: 11/16/2024]
Abstract
RNA processing involves steps such as capping, splicing, polyadenylation, modification, and nuclear export. These steps are essential for transforming genetic information in DNA into proteins and contribute to RNA diversity and complexity. Many biochemical methods have been developed to profile and quantify RNAs, as well as to identify the interactions between RNAs and RNA-binding proteins (RBPs), especially when coupled with high-throughput sequencing technologies. With the rapid accumulation of diverse data, it is crucial to develop computational methods to convert the big data into biological knowledge. In particular, machine learning and deep learning models are commonly utilized to learn the rules or codes governing the transformation from DNA sequences to intriguing RNAs based on manually designed or automatically extracted features. When precise enough, the RNA codes can be incredibly useful for predicting RNA products, decoding the molecular mechanisms, forecasting the impact of disease variants on RNA processing events, and identifying driver mutations. In this review, we systematically summarize the biochemical and computational methods for deciphering five important RNA codes related to alternative splicing, alternative polyadenylation, RNA localization, RNA modifications, and RBP binding. For each code, we review the main types of experimental methods used to generate training data, as well as the key features, strategic model structures, and advantages of representative tools. We also discuss the challenges encountered in developing predictive models using large language models and extensive domain knowledge. Additionally, we highlight useful resources and propose ways to improve computational tools for studying RNA codes.
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Affiliation(s)
- Chen Du
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, RNA Institute, Wuhan University, Wuhan, China
| | - Weiliang Fan
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, RNA Institute, Wuhan University, Wuhan, China
| | - Yu Zhou
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, RNA Institute, Wuhan University, Wuhan, China
- Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan, China
- State Key Laboratory of Virology, Wuhan University, Wuhan, China
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3
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Babaiha NS, Aghdam R, Ghiam S, Eslahchi C. NN-RNALoc: Neural network-based model for prediction of mRNA sub-cellular localization using distance-based sub-sequence profiles. PLoS One 2023; 18:e0258793. [PMID: 37708177 PMCID: PMC10501558 DOI: 10.1371/journal.pone.0258793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 05/12/2023] [Indexed: 09/16/2023] Open
Abstract
The localization of messenger RNAs (mRNAs) is a frequently observed phenomenon and a crucial aspect of gene expression regulation. It is also a mechanism for targeting proteins to a specific cellular region. Moreover, prior research and studies have shown the significance of intracellular RNA positioning during embryonic and neural dendrite formation. Incorrect RNA localization, which can be caused by a variety of factors, such as mutations in trans-regulatory elements, has been linked to the development of certain neuromuscular diseases and cancer. In this study, we introduced NN-RNALoc, a neural network-based method for predicting the cellular location of mRNA using novel features extracted from mRNA sequence data and protein interaction patterns. In fact, we developed a distance-based subsequence profile for RNA sequence representation that is more memory and time-efficient than well-known k-mer sequence representation. Combining protein-protein interaction data, which is essential for numerous biological processes, with our novel distance-based subsequence profiles of mRNA sequences produces more accurate features. On two benchmark datasets, CeFra-Seq and RNALocate, the performance of NN-RNALoc is compared to powerful predictive models proposed in previous works (mRNALoc, RNATracker, mLoc-mRNA, DM3Loc, iLoc-mRNA, and EL-RMLocNet), and a ground neural (DNN5-mer) network. Compared to the previous methods, NN-RNALoc significantly reduces computation time and also outperforms them in terms of accuracy. This study's source code and datasets are freely accessible at https://github.com/NeginBabaiha/NN-RNALoc.
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Affiliation(s)
- Negin Sadat Babaiha
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
- Bonn-Aachen International Center for Information Technology (B-IT), University of Bonn, Bonn, Germany
| | - Rosa Aghdam
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, United States of America
| | - Shokoofeh Ghiam
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
| | - Changiz Eslahchi
- Department of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran
- School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
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4
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Yuan GH, Wang Y, Wang GZ, Yang L. RNAlight: a machine learning model to identify nucleotide features determining RNA subcellular localization. Brief Bioinform 2022; 24:6868526. [PMID: 36464487 PMCID: PMC9851306 DOI: 10.1093/bib/bbac509] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 10/25/2022] [Indexed: 12/12/2022] Open
Abstract
Different RNAs have distinct subcellular localizations. However, nucleotide features that determine these distinct distributions of lncRNAs and mRNAs have yet to be fully addressed. Here, we develop RNAlight, a machine learning model based on LightGBM, to identify nucleotide k-mers contributing to the subcellular localizations of mRNAs and lncRNAs. With the Tree SHAP algorithm, RNAlight extracts nucleotide features for cytoplasmic or nuclear localization of RNAs, indicating the sequence basis for distinct RNA subcellular localizations. By assembling k-mers to sequence features and subsequently mapping to known RBP-associated motifs, different types of sequence features and their associated RBPs were additionally uncovered for lncRNAs and mRNAs with distinct subcellular localizations. Finally, we extended RNAlight to precisely predict the subcellular localizations of other types of RNAs, including snRNAs, snoRNAs and different circular RNA transcripts, suggesting the generality of using RNAlight for RNA subcellular localization prediction.
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Affiliation(s)
| | | | - Guang-Zhong Wang
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Li Yang
- Corresponding author. Li Yang, Center for Molecular Medicine, Children's Hospital, Fudan University and Shanghai Key Laboratory of Medical Epigenetics, International Laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Dong-An Road, 131, Shanghai, China. Tel: +86-021-54237325; E-mail:
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5
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Wei A, Wang L. Prediction of Synaptically Localized RNAs in Human Neurons Using Developmental Brain Gene Expression Data. Genes (Basel) 2022; 13:1488. [PMID: 36011399 PMCID: PMC9408096 DOI: 10.3390/genes13081488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/16/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022] Open
Abstract
In the nervous system, synapses are special and pervasive structures between axonal and dendritic terminals, which facilitate electrical and chemical communications among neurons. Extensive studies have been conducted in mice and rats to explore the RNA pool at synapses and investigate RNA transport, local protein synthesis, and synaptic plasticity. However, owing to the experimental difficulties of studying human synaptic transcriptomes, the full pool of human synaptic RNAs remains largely unclear. We developed a new machine learning method, called PredSynRNA, to predict the synaptic localization of human RNAs. Training instances of dendritically localized RNAs were compiled from previous rodent studies, overcoming the shortage of empirical instances of human synaptic RNAs. Using RNA sequence and gene expression data as features, various models with different learning algorithms were constructed and evaluated. Strikingly, the models using the developmental brain gene expression features achieved superior performance for predicting synaptically localized RNAs. We examined the relevant expression features learned by PredSynRNA and used an independent test dataset to further validate the model performance. PredSynRNA models were then applied to the prediction and prioritization of candidate RNAs localized to human synapses, providing valuable targets for experimental investigations into neuronal mechanisms and brain disorders.
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Affiliation(s)
- Anqi Wei
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA
- Center for Human Genetics, Clemson University, Greenwood, SC 29646, USA
| | - Liangjiang Wang
- Department of Genetics and Biochemistry, Clemson University, Clemson, SC 29634, USA
- Center for Human Genetics, Clemson University, Greenwood, SC 29646, USA
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6
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Le P, Ahmed N, Yeo GW. Illuminating RNA biology through imaging. Nat Cell Biol 2022; 24:815-824. [PMID: 35697782 PMCID: PMC11132331 DOI: 10.1038/s41556-022-00933-9] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 05/06/2022] [Indexed: 12/14/2022]
Abstract
RNA processing plays a central role in accurately transmitting genetic information into functional RNA and protein regulators. To fully appreciate the RNA life-cycle, tools to observe RNA with high spatial and temporal resolution are critical. Here we review recent advances in RNA imaging and highlight how they will propel the field of RNA biology. We discuss current trends in RNA imaging and their potential to elucidate unanswered questions in RNA biology.
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Affiliation(s)
- Phuong Le
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Stem Cell Program, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
| | - Noorsher Ahmed
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA
- Stem Cell Program, University of California San Diego, La Jolla, CA, USA
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA
| | - Gene W Yeo
- Department of Cellular and Molecular Medicine, University of California San Diego, La Jolla, CA, USA.
- Stem Cell Program, University of California San Diego, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California San Diego, La Jolla, CA, USA.
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA.
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7
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Abstract
Most of the transcribed human genome codes for noncoding RNAs (ncRNAs), and long noncoding RNAs (lncRNAs) make for the lion's share of the human ncRNA space. Despite growing interest in lncRNAs, because there are so many of them, and because of their tissue specialization and, often, lower abundance, their catalog remains incomplete and there are multiple ongoing efforts to improve it. Consequently, the number of human lncRNA genes may be lower than 10,000 or higher than 200,000. A key open challenge for lncRNA research, now that so many lncRNA species have been identified, is the characterization of lncRNA function and the interpretation of the roles of genetic and epigenetic alterations at their loci. After all, the most important human genes to catalog and study are those that contribute to important cellular functions-that affect development or cell differentiation and whose dysregulation may play a role in the genesis and progression of human diseases. Multiple efforts have used screens based on RNA-mediated interference (RNAi), antisense oligonucleotide (ASO), and CRISPR screens to identify the consequences of lncRNA dysregulation and predict lncRNA function in select contexts, but these approaches have unresolved scalability and accuracy challenges. Instead-as was the case for better-studied ncRNAs in the past-researchers often focus on characterizing lncRNA interactions and investigating their effects on genes and pathways with known functions. Here, we focus most of our review on computational methods to identify lncRNA interactions and to predict the effects of their alterations and dysregulation on human disease pathways.
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8
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Nicolet BP, Zandhuis ND, Lattanzio VM, Wolkers MC. Sequence determinants as key regulators in gene expression of T cells. Immunol Rev 2021; 304:10-29. [PMID: 34486113 PMCID: PMC9292449 DOI: 10.1111/imr.13021] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Revised: 08/09/2021] [Accepted: 08/17/2021] [Indexed: 12/12/2022]
Abstract
T cell homeostasis, T cell differentiation, and T cell effector function rely on the constant fine-tuning of gene expression. To alter the T cell state, substantial remodeling of the proteome is required. This remodeling depends on the intricate interplay of regulatory mechanisms, including post-transcriptional gene regulation. In this review, we discuss how the sequence of a transcript influences these post-transcriptional events. In particular, we review how sequence determinants such as sequence conservation, GC content, and chemical modifications define the levels of the mRNA and the protein in a T cell. We describe the effect of different forms of alternative splicing on mRNA expression and protein production, and their effect on subcellular localization. In addition, we discuss the role of sequences and structures as binding hubs for miRNAs and RNA-binding proteins in T cells. The review thus highlights how the intimate interplay of post-transcriptional mechanisms dictate cellular fate decisions in T cells.
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Affiliation(s)
- Benoit P. Nicolet
- Department of HematopoiesisSanquin Research and Landsteiner LaboratoryAmsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
| | - Nordin D. Zandhuis
- Department of HematopoiesisSanquin Research and Landsteiner LaboratoryAmsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
| | - V. Maria Lattanzio
- Department of HematopoiesisSanquin Research and Landsteiner LaboratoryAmsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
| | - Monika C. Wolkers
- Department of HematopoiesisSanquin Research and Landsteiner LaboratoryAmsterdam UMCUniversity of AmsterdamAmsterdamThe Netherlands
- Oncode InstituteUtrechtThe Netherlands
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9
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Dumbović G, Braunschweig U, Langner HK, Smallegan M, Biayna J, Hass EP, Jastrzebska K, Blencowe B, Cech TR, Caruthers MH, Rinn JL. Nuclear compartmentalization of TERT mRNA and TUG1 lncRNA is driven by intron retention. Nat Commun 2021; 12:3308. [PMID: 34083519 PMCID: PMC8175569 DOI: 10.1038/s41467-021-23221-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Accepted: 04/07/2021] [Indexed: 12/27/2022] Open
Abstract
The spatial partitioning of the transcriptome in the cell is an important form of gene-expression regulation. Here, we address how intron retention influences the spatio-temporal dynamics of transcripts from two clinically relevant genes: TERT (Telomerase Reverse Transcriptase) pre-mRNA and TUG1 (Taurine-Upregulated Gene 1) lncRNA. Single molecule RNA FISH reveals that nuclear TERT transcripts uniformly and robustly retain specific introns. Our data suggest that the splicing of TERT retained introns occurs during mitosis. In contrast, TUG1 has a bimodal distribution of fully spliced cytoplasmic and intron-retained nuclear transcripts. We further test the functionality of intron-retention events using RNA-targeting thiomorpholino antisense oligonucleotides to block intron excision. We show that intron retention is the driving force for the nuclear compartmentalization of these RNAs. For both RNAs, altering this splicing-driven subcellular distribution has significant effects on cell viability. Together, these findings show that stable retention of specific introns can orchestrate spatial compartmentalization of these RNAs within the cell. This process reveals that modulating RNA localization via targeted intron retention can be utilized for RNA-based therapies.
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Affiliation(s)
- Gabrijela Dumbović
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO, USA.
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA.
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany.
| | | | - Heera K Langner
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO, USA
| | - Michael Smallegan
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA
- Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Josep Biayna
- Institute for Research in Biomedicine, Parc Científic de Barcelona, Barcelona, Spain
| | - Evan P Hass
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO, USA
| | - Katarzyna Jastrzebska
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO, USA
- Department of Bioorganic Chemistry, Centre of Molecular and Macromolecular Studies, Polish Academy of Sciences, Lodz, Poland
| | | | - Thomas R Cech
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO, USA
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA
- Howard Hughes Medical Institute, University of Colorado Boulder, Boulder, CO, USA
| | - Marvin H Caruthers
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO, USA
| | - John L Rinn
- Department of Biochemistry, University of Colorado Boulder, Boulder, CO, USA.
- BioFrontiers Institute, University of Colorado Boulder, Boulder, CO, USA.
- Howard Hughes Medical Institute, University of Colorado Boulder, Boulder, CO, USA.
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10
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Aillaud M, Schulte LN. Emerging Roles of Long Noncoding RNAs in the Cytoplasmic Milieu. Noncoding RNA 2020; 6:ncrna6040044. [PMID: 33182489 PMCID: PMC7711603 DOI: 10.3390/ncrna6040044] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 10/26/2020] [Accepted: 11/05/2020] [Indexed: 02/06/2023] Open
Abstract
While the important functions of long noncoding RNAs (lncRNAs) in nuclear organization are well documented, their orchestrating and architectural roles in the cytoplasmic environment have long been underestimated. However, recently developed fractionation and proximity labelling approaches have shown that a considerable proportion of cellular lncRNAs is exported into the cytoplasm and associates nonrandomly with proteins in the cytosol and organelles. The functions of these lncRNAs range from the control of translation and mitochondrial metabolism to the anchoring of cellular components on the cytoskeleton and regulation of protein degradation at the proteasome. In the present review, we provide an overview of the functions of lncRNAs in cytoplasmic structures and machineries und discuss their emerging roles in the coordination of the dense intracellular milieu. It is becoming apparent that further research into the functions of these lncRNAs will lead to an improved understanding of the spatiotemporal organization of cytoplasmic processes during homeostasis and disease.
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Affiliation(s)
- Michelle Aillaud
- Institute for Lung Research, Philipps University Marburg, 35043 Marburg, Germany;
| | - Leon N Schulte
- Institute for Lung Research, Philipps University Marburg, 35043 Marburg, Germany;
- German Center for Lung Research (DZL), 35392 Giessen, Germany
- Correspondence:
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11
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Wu KE, Fazal FM, Parker KR, Zou J, Chang HY. RNA-GPS Predicts SARS-CoV-2 RNA Residency to Host Mitochondria and Nucleolus. Cell Syst 2020; 11:102-108.e3. [PMID: 32673562 PMCID: PMC7305881 DOI: 10.1016/j.cels.2020.06.008] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Revised: 05/20/2020] [Accepted: 06/17/2020] [Indexed: 02/06/2023]
Abstract
SARS-CoV-2 genomic and subgenomic RNA (sgRNA) transcripts hijack the host cell's machinery. Subcellular localization of its viral RNA could, thus, play important roles in viral replication and host antiviral immune response. We perform computational modeling of SARS-CoV-2 viral RNA subcellular residency across eight subcellular neighborhoods. We compare hundreds of SARS-CoV-2 genomes with the human transcriptome and other coronaviruses. We predict the SARS-CoV-2 RNA genome and sgRNAs to be enriched toward the host mitochondrial matrix and nucleolus, and that the 5' and 3' viral untranslated regions contain the strongest, most distinct localization signals. We interpret the mitochondrial residency signal as an indicator of intracellular RNA trafficking with respect to double-membrane vesicles, a critical stage in the coronavirus life cycle. Our computational analysis serves as a hypothesis generation tool to suggest models for SARS-CoV-2 biology and inform experimental efforts to combat the virus. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.
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Affiliation(s)
- Kevin E Wu
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA; Center for Personal and Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Furqan M Fazal
- Center for Personal and Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Kevin R Parker
- Center for Personal and Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - James Zou
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA.
| | - Howard Y Chang
- Center for Personal and Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA.
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12
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Fazal FM, Chang HY. Subcellular Spatial Transcriptomes: Emerging Frontier for Understanding Gene Regulation. COLD SPRING HARBOR SYMPOSIA ON QUANTITATIVE BIOLOGY 2020; 84:31-45. [PMID: 32482897 PMCID: PMC7426137 DOI: 10.1101/sqb.2019.84.040352] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
RNAs are trafficked and localized with exquisite precision inside the cell. Studies of candidate messenger RNAs have shown the vital importance of RNA subcellular location in development and cellular function. New sequencing- and imaging-based methods are providing complementary insights into subcellular localization of RNAs transcriptome-wide. APEX-seq and ribosome profiling as well as proximity-labeling approaches have revealed thousands of transcript isoforms are localized to distinct cytotopic locations, including locations that defy biochemical fractionation and hence were missed by prior studies. Sequences in the 3' and 5' untranslated regions (UTRs) serve as "zip codes" to direct transcripts to particular locales, and it is clear that intronic and retrotransposable sequences within transcripts have been co-opted by cells to control localization. Molecular motors, nuclear-to-cytosol RNA export, liquid-liquid phase separation, RNA modifications, and RNA structure dynamically shape the subcellular transcriptome. Location-based RNA regulation continues to pose new mysteries for the field, yet promises to reveal insights into fundamental cell biology and disease mechanisms.
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Affiliation(s)
- Furqan M Fazal
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, California 94305, USA
| | - Howard Y Chang
- Center for Personal Dynamic Regulomes, Stanford University, Stanford, California 94305, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, California 94305, USA
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13
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Wu K, Zou J, Chang HY. RNA-GPS Predicts SARS-CoV-2 RNA Localization to Host Mitochondria and Nucleolus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.04.28.065201. [PMID: 32511373 PMCID: PMC7263502 DOI: 10.1101/2020.04.28.065201] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The SARS-CoV-2 coronavirus is driving a global pandemic, but its biological mechanisms are less well understood. SARS-CoV-2 is an RNA virus whose multiple genomic and subgenomic RNA (sgRNA) transcripts hijack the host cell's machinery, located across distinct cytotopic locations. Subcellular localization of its viral RNA could play important roles in viral replication and host antiviral immune response. Here we perform computational modeling of SARS-CoV-2 viral RNA localization across eight subcellular neighborhoods. We compare hundreds of SARS-CoV-2 genomes to the human transcriptome and other coronaviruses and perform systematic sub-sequence analyses to identify the responsible signals. Using state-of-the-art machine learning models, we predict that the SARS-CoV-2 RNA genome and all sgRNAs are enriched in the host mitochondrial matrix and nucleolus. The 5' and 3' viral untranslated regions possess the strongest and most distinct localization signals. We discuss the mitochondrial localization signal in relation to the formation of double-membrane vesicles, a critical stage in the coronavirus life cycle. Our computational analysis serves as a hypothesis generation tool to suggest models for SARS-CoV-2 biology and inform experimental efforts to combat the virus.
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Affiliation(s)
- Kevin Wu
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
- Center for Personal and Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - James Zou
- Department of Computer Science, Stanford University, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Howard Y Chang
- Center for Personal and Dynamic Regulomes, Stanford University School of Medicine, Stanford, CA 94305, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA 94305, USA
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