1
|
Chen Y, Du Z, Ren X, Pan C, Zhu Y, Li Z, Meng T, Yao X. mRNA-CLA: An interpretable deep learning approach for predicting mRNA subcellular localization. Methods 2024; 227:17-26. [PMID: 38705502 DOI: 10.1016/j.ymeth.2024.04.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/30/2024] [Accepted: 04/28/2024] [Indexed: 05/07/2024] Open
Abstract
Messenger RNA (mRNA) is vital for post-transcriptional gene regulation, acting as the direct template for protein synthesis. However, the methods available for predicting mRNA subcellular localization need to be improved and enhanced. Notably, few existing algorithms can annotate mRNA sequences with multiple localizations. In this work, we propose the mRNA-CLA, an innovative multi-label subcellular localization prediction framework for mRNA, leveraging a deep learning approach with a multi-head self-attention mechanism. The framework employs a multi-scale convolutional layer to extract sequence features across different regions and uses a self-attention mechanism explicitly designed for each sequence. Paired with Position Weight Matrices (PWMs) derived from the convolutional neural network layers, our model offers interpretability in the analysis. In particular, we perform a base-level analysis of mRNA sequences from diverse subcellular localizations to determine the nucleotide specificity corresponding to each site. Our evaluations demonstrate that the mRNA-CLA model substantially outperforms existing methods and tools.
Collapse
Affiliation(s)
- Yifan Chen
- Institute of Artificial Intelligence Application, College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
| | - Zhenya Du
- Guangzhou Xinhua University, 510520, Guangzhou, China
| | - Xuanbai Ren
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, China
| | - Chu Pan
- College of Information Science and Engineering, Hunan University, Changsha, Hunan, China
| | - Yangbin Zhu
- Manufacturing and Electronic Engineering, Wenzhou University of Technology, 325027, Wenzhou, China.
| | - Zhen Li
- Institute of Computational Science and Technology, Guangzhou University, Guangzhou, 510006, China.
| | - Tao Meng
- Institute of Artificial Intelligence Application, College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, Hunan 410004, China
| | - Xiaojun Yao
- Faculty of Applied Sciences, Macao Polytechnic University, 999078, Macao.
| |
Collapse
|
2
|
Ntasis VF, Guigó R. Studying relative RNA localization From nucleus to the cytosol. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.06.583744. [PMID: 38559161 PMCID: PMC10979850 DOI: 10.1101/2024.03.06.583744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
The precise coordination of important biological processes, such as differentiation and development, is highly dependent on the regulation of expression of the genetic information. The flow of the genetic information is tightly regulated on multiple levels. Among them, RNA export to cytosol is an essential step for the production of proteins in eukaryotic cells. Hence, estimating the relative concentration of RNA molecules of a given transcript species in the nucleus and in the cytosol is of major significance as it contributes to the understanding of the dynamics of RNA trafficking between the nucleus and the cytosol. The most efficient way to estimate the levels of RNA species genome-wide is through RNA sequencing (RNAseq). While RNAseq can be performed separately in the nucleus and in the cytosol, because measured transcript levels are relative to the total volume of RNA in these compartments, and because this volume is usually unknown, the transcript levels in the nucleus and in the cytosol cannot be directly compared. Here we show theoretically that if, in addition to nuclear and cytosolic RNA-seq, whole cell RNA-seq is also performed, then accurate estimations of the localization of transcripts can be obtained. Based on this, we designed a method that estimates, first the fraction of the total RNA volume in the cytosol (nucleus), and then, this fraction for every transcript. We evaluate our methodology on simulated data and nuclear and cytosolic single cell data available. Finally, we use our method to investigate the cellular localization of transcripts using bulk RNAseq data from the ENCODE project.
Collapse
Affiliation(s)
- Vasilis F. Ntasis
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
| | - Roderic Guigó
- Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, Barcelona, Catalonia, Spain
- Department of Experimental and Health Sciences (DCEXS), Universitat Pompeu Fabra (UPF), Barcelona, Catalonia, Spain
| |
Collapse
|
3
|
Musleh S, Arif M, Alajez NM, Alam T. Unified mRNA Subcellular Localization Predictor based on machine learning techniques. BMC Genomics 2024; 25:151. [PMID: 38326777 PMCID: PMC10848524 DOI: 10.1186/s12864-024-10077-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 02/01/2024] [Indexed: 02/09/2024] Open
Abstract
BACKGROUND The mRNA subcellular localization bears substantial impact in the regulation of gene expression, cellular migration, and adaptation. However, the methods employed for experimental determination of this localization are arduous, time-intensive, and come with a high cost. METHODS In this research article, we tackle the essential challenge of predicting the subcellular location of messenger RNAs (mRNAs) through Unified mRNA Subcellular Localization Predictor (UMSLP), a machine learning (ML) based approach. We embrace an in silico strategy that incorporate four distinct feature sets: kmer, pseudo k-tuple nucleotide composition, nucleotide physicochemical attributes, and the 3D sequence depiction achieved via Z-curve transformation for predicting subcellular localization in benchmark dataset across five distinct subcellular locales, encompassing nucleus, cytoplasm, extracellular region (ExR), mitochondria, and endoplasmic reticulum (ER). RESULTS The proposed ML model UMSLP attains cutting-edge outcomes in predicting mRNA subcellular localization. On independent testing dataset, UMSLP ahcieved over 87% precision, 94% specificity, and 94% accuracy. Compared to other existing tools, UMSLP outperformed mRNALocator, mRNALoc, and SubLocEP by 11%, 21%, and 32%, respectively on average prediction accuracy for all five locales. SHapley Additive exPlanations analysis highlights the dominance of k-mer features in predicting cytoplasm, nucleus, ER, and ExR localizations, while Z-curve based features play pivotal roles in mitochondria subcellular localization detection. AVAILABILITY We have shared datasets, code, Docker API for users in GitHub at: https://github.com/smusleh/UMSLP .
Collapse
Affiliation(s)
- Saleh Musleh
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Muhammad Arif
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Nehad M Alajez
- Translational Cancer and Immunity Center (TCIC), Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Tanvir Alam
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
| |
Collapse
|
4
|
Bai T, Liu B. ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning. Brief Funct Genomics 2023; 22:442-452. [PMID: 37122147 DOI: 10.1093/bfgp/elad007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 12/31/2022] [Accepted: 01/31/2023] [Indexed: 05/02/2023] Open
Abstract
Subcellular localizations of ncRNAs are associated with specific functions. Currently, an increasing number of biological researchers are focusing on computational approaches to identify subcellular localizations of ncRNAs. However, the performance of the existing computational methods is low and needs to be further studied. First, most prediction models are trained with outdated databases. Second, only a few predictors can identify multiple subcellular localizations simultaneously. In this work, we establish three human ncRNA subcellular datasets based on the latest RNALocate, including lncRNA, miRNA and snoRNA, and then we propose a novel multi-label classification model based on ensemble learning called ncRNALocate-EL to identify multi-label subcellular localizations of three ncRNAs. The results show that the ncRNALocate-EL outperforms previous methods. Our method achieved an average precision of 0.709,0.977 and 0.730 on three human ncRNA datasets. The web server of ncRNALocate-EL has been established, which can be accessed at https://bliulab.net/ncRNALocate-EL.
Collapse
|
5
|
Padilla JCA, Barutcu S, Malet L, Deschamps-Francoeur G, Calderon V, Kwon E, Lécuyer E. Profiling the polyadenylated transcriptome of extracellular vesicles with long-read nanopore sequencing. BMC Genomics 2023; 24:564. [PMID: 37736705 PMCID: PMC10514964 DOI: 10.1186/s12864-023-09552-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 08/03/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND While numerous studies have described the transcriptomes of extracellular vesicles (EVs) in different cellular contexts, these efforts have typically relied on sequencing methods requiring RNA fragmentation, which limits interpretations on the integrity and isoform diversity of EV-targeted RNA populations. It has been assumed that mRNA signatures in EVs are likely to be fragmentation products of the cellular mRNA material, and the extent to which full-length mRNAs are present within EVs remains to be clarified. RESULTS Using long-read nanopore RNA sequencing, we sought to characterize the full-length polyadenylated (poly-A) transcriptome of EVs released by human chronic myelogenous leukemia K562 cells. We detected 443 and 280 RNAs that were respectively enriched or depleted in EVs. EV-enriched poly-A transcripts consist of a variety of biotypes, including mRNAs, long non-coding RNAs, and pseudogenes. Our analysis revealed that 10.58% of all EV reads, and 18.67% of all cellular (WC) reads, corresponded to known full-length transcripts, with mRNAs representing the largest biotype for each group (EV = 58.13%, WC = 43.93%). We also observed that for many well-represented coding and non-coding genes, diverse full-length transcript isoforms were present in EV specimens, and these isoforms were reflective-of but often in different ratio compared to cellular samples. CONCLUSION This work provides novel insights into the compositional diversity of poly-A transcript isoforms enriched within EVs, while also underscoring the potential usefulness of nanopore sequencing to interrogate secreted RNA transcriptomes.
Collapse
Affiliation(s)
- Juan-Carlos A Padilla
- Institut de Recherches Cliniques de Montréal (IRCM), 110 Avenue des Pins, Ouest, Montréal, QC, H2W 1R7, Canada
- Division of Experimental Medicine, McGill University, Montréal, QC, H4A 3J1, Canada
| | - Seda Barutcu
- Institut de Recherches Cliniques de Montréal (IRCM), 110 Avenue des Pins, Ouest, Montréal, QC, H2W 1R7, Canada
| | - Ludovic Malet
- Institut de Recherches Cliniques de Montréal (IRCM), 110 Avenue des Pins, Ouest, Montréal, QC, H2W 1R7, Canada
| | | | - Virginie Calderon
- Institut de Recherches Cliniques de Montréal (IRCM), 110 Avenue des Pins, Ouest, Montréal, QC, H2W 1R7, Canada
| | - Eunjeong Kwon
- Institut de Recherches Cliniques de Montréal (IRCM), 110 Avenue des Pins, Ouest, Montréal, QC, H2W 1R7, Canada
| | - Eric Lécuyer
- Institut de Recherches Cliniques de Montréal (IRCM), 110 Avenue des Pins, Ouest, Montréal, QC, H2W 1R7, Canada.
- Division of Experimental Medicine, McGill University, Montréal, QC, H4A 3J1, Canada.
- Département de Biochimie et de Médecine Moléculaire, Université de Montréal, Montréal, QC, H3T 1J4, Canada.
| |
Collapse
|
6
|
Musleh S, Islam MT, Qureshi R, Alajez N, Alam T. MSLP: mRNA subcellular localization predictor based on machine learning techniques. BMC Bioinformatics 2023; 24:109. [PMID: 36949389 PMCID: PMC10035125 DOI: 10.1186/s12859-023-05232-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 03/15/2023] [Indexed: 03/24/2023] Open
Abstract
BACKGROUND Subcellular localization of messenger RNA (mRNAs) plays a pivotal role in the regulation of gene expression, cell migration as well as in cellular adaptation. Experiment techniques for pinpointing the subcellular localization of mRNAs are laborious, time-consuming and expensive. Therefore, in silico approaches for this purpose are attaining great attention in the RNA community. METHODS In this article, we propose MSLP, a machine learning-based method to predict the subcellular localization of mRNA. We propose a novel combination of four types of features representing k-mer, pseudo k-tuple nucleotide composition (PseKNC), physicochemical properties of nucleotides, and 3D representation of sequences based on Z-curve transformation to feed into machine learning algorithm to predict the subcellular localization of mRNAs. RESULTS Considering the combination of the above-mentioned features, ennsemble-based models achieved state-of-the-art results in mRNA subcellular localization prediction tasks for multiple benchmark datasets. We evaluated the performance of our method in ten subcellular locations, covering cytoplasm, nucleus, endoplasmic reticulum (ER), extracellular region (ExR), mitochondria, cytosol, pseudopodium, posterior, exosome, and the ribosome. Ablation study highlighted k-mer and PseKNC to be more dominant than other features for predicting cytoplasm, nucleus, and ER localizations. On the other hand, physicochemical properties and Z-curve based features contributed the most to ExR and mitochondria detection. SHAP-based analysis revealed the relative importance of features to provide better insights into the proposed approach. AVAILABILITY We have implemented a Docker container and API for end users to run their sequences on our model. Datasets, the code of API and the Docker are shared for the community in GitHub at: https://github.com/smusleh/MSLP .
Collapse
Affiliation(s)
- Saleh Musleh
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | | | - Rizwan Qureshi
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar
| | - Nihad Alajez
- Translational Cancer and Immunity Center (TCIC), Qatar Biomedical Research Institute (QBRI), Hamad Bin Khalifa University, Doha, Qatar
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | - Tanvir Alam
- College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.
| |
Collapse
|
7
|
Zhang J, Lin X, Chen Y, Li T, Lee AC, Chow EY, Cho WC, Chan T. LAFITE Reveals the Complexity of Transcript Isoforms in Subcellular Fractions. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2203480. [PMID: 36461702 PMCID: PMC9875686 DOI: 10.1002/advs.202203480] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/28/2022] [Indexed: 06/17/2023]
Abstract
Characterization of the subcellular distribution of RNA is essential for understanding the molecular basis of biological processes. Here, the subcellular nanopore direct RNA-sequencing (DRS) of four lung cancer cell lines (A549, H1975, H358, and HCC4006) is performed, coupled with a computational pipeline, Low-abundance Aware Full-length Isoform clusTEr (LAFITE), to comprehensively analyze the full-length cytoplasmic and nuclear transcriptome. Using additional DRS and orthogonal data sets, it is shown that LAFITE outperforms current methods for detecting full-length transcripts, particularly for low-abundance isoforms that are usually overlooked due to poor read coverage. Experimental validation of six novel isoforms exclusively identified by LAFITE further confirms the reliability of this pipeline. By applying LAFITE to subcellular DRS data, the complexity of the nuclear transcriptome is revealed in terms of isoform diversity, 3'-UTR usage, m6A modification patterns, and intron retention. Overall, LAFITE provides enhanced full-length isoform identification and enables a high-resolution view of the RNA landscape at the isoform level.
Collapse
Affiliation(s)
- Jizhou Zhang
- School of Life SciencesThe Chinese University of Hong KongShatinHong Kong SARChina
- State Key Laboratory of AgrobiotechnologyThe Chinese University of Hong KongShatinHong Kong SARChina
| | - Xiao Lin
- School of Life SciencesThe Chinese University of Hong KongShatinHong Kong SARChina
- State Key Laboratory of AgrobiotechnologyThe Chinese University of Hong KongShatinHong Kong SARChina
| | - Yuelong Chen
- School of Life SciencesThe Chinese University of Hong KongShatinHong Kong SARChina
| | - Tsz‐Ho Li
- School of Life SciencesThe Chinese University of Hong KongShatinHong Kong SARChina
- State Key Laboratory of AgrobiotechnologyThe Chinese University of Hong KongShatinHong Kong SARChina
| | - Alan Chun‐Kit Lee
- School of Life SciencesThe Chinese University of Hong KongShatinHong Kong SARChina
| | | | | | - Ting‐Fung Chan
- School of Life SciencesThe Chinese University of Hong KongShatinHong Kong SARChina
- State Key Laboratory of AgrobiotechnologyThe Chinese University of Hong KongShatinHong Kong SARChina
| |
Collapse
|
8
|
Uszczynska-Ratajczak B, Sugunan S, Kwiatkowska M, Migdal M, Carbonell-Sala S, Sokol A, Winata CL, Chacinska A. Profiling subcellular localization of nuclear-encoded mitochondrial gene products in zebrafish. Life Sci Alliance 2022; 6:6/1/e202201514. [PMID: 36283702 PMCID: PMC9595208 DOI: 10.26508/lsa.202201514] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Revised: 09/30/2022] [Accepted: 10/04/2022] [Indexed: 11/08/2022] Open
Abstract
Most mitochondrial proteins are encoded by nuclear genes, synthetized in the cytosol and targeted into the organelle. To characterize the spatial organization of mitochondrial gene products in zebrafish (Danio rerio), we sequenced RNA from different cellular fractions. Our results confirmed the presence of nuclear-encoded mRNAs in the mitochondrial fraction, which in unperturbed conditions, are mainly transcripts encoding large proteins with specific properties, like transmembrane domains. To further explore the principles of mitochondrial protein compartmentalization in zebrafish, we quantified the transcriptomic changes for each subcellular fraction triggered by the chchd4a -/- mutation, causing the disorders in the mitochondrial protein import. Our results indicate that the proteostatic stress further restricts the population of transcripts on the mitochondrial surface, allowing only the largest and the most evolutionary conserved proteins to be synthetized there. We also show that many nuclear-encoded mitochondrial transcripts translated by the cytosolic ribosomes stay resistant to the global translation shutdown. Thus, vertebrates, in contrast to yeast, are not likely to use localized translation to facilitate synthesis of mitochondrial proteins under proteostatic stress conditions.
Collapse
Affiliation(s)
- Barbara Uszczynska-Ratajczak
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland .,Centre of New Technologies, University of Warsaw, Warsaw, Poland
| | - Sreedevi Sugunan
- ReMedy International Research Agenda Unit, University of Warsaw, Warsaw, Poland,International Institute of Molecular and Cell Biology, Warsaw, Poland
| | - Monika Kwiatkowska
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Poznan, Poland,Centre of New Technologies, University of Warsaw, Warsaw, Poland,International Institute of Molecular and Cell Biology, Warsaw, Poland
| | - Maciej Migdal
- International Institute of Molecular and Cell Biology, Warsaw, Poland
| | - Silvia Carbonell-Sala
- Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Anna Sokol
- Department of Developmental Genetics, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany,Biomolecular Mass Spectrometry, Max Planck Institute for Heart and Lung Research, Bad Nauheim, Germany
| | - Cecilia L Winata
- International Institute of Molecular and Cell Biology, Warsaw, Poland
| | - Agnieszka Chacinska
- ReMedy International Research Agenda Unit, IMol Polish Academy of Sciences, Warsaw, Poland
| |
Collapse
|
9
|
Li R, Zou Z, Wang W, Zou P. Metabolic incorporation of electron-rich ribonucleosides enhances APEX-seq for profiling spatially restricted nascent transcriptome. Cell Chem Biol 2022; 29:1218-1231.e8. [PMID: 35245437 DOI: 10.1016/j.chembiol.2022.02.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2020] [Revised: 07/30/2021] [Accepted: 02/10/2022] [Indexed: 12/13/2022]
Abstract
The spatial arrangement of newly synthesized transcriptome in eukaryotic cells underlies various biological processes including cell proliferation and differentiation. In this study, we combine metabolic incorporation of electron-rich ribonucleosides (e.g., 6-thioguanosine and 4-thiouridine) with a peroxidase-mediated proximity-dependent RNA labeling technique (APEX-seq) to develop a sensitive method, termed MERR APEX-seq, for selectively profiling newly transcribed RNAs at specific subcellular locations in live cells. We demonstrate that MERR APEX-seq is 20-fold more efficient than APEX-seq and offers both high spatial specificity and high coverage in mitochondrial matrix. At the ER membrane, 91% of the transcripts captured by MERR APEX-seq encode for secretory pathway proteins, thus demonstrating the high spatial specificity of MERR APEX-seq in open subcellular compartments. Application of MERR APEX-seq to the nuclear lamina of human cells reveals a local transcriptome of 1,012 RNAs, many of which encode for nuclear proteins involved in histone modification, chromosomal structure maintenance, and RNA processing.
Collapse
Affiliation(s)
- Ran Li
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Zhongyu Zou
- College of Chemistry and Molecular Engineering, Synthetic and Functional Biomolecules Center Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing 100871, China
| | - Wentao Wang
- College of Chemistry and Molecular Engineering, Synthetic and Functional Biomolecules Center Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing 100871, China
| | - Peng Zou
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; College of Chemistry and Molecular Engineering, Synthetic and Functional Biomolecules Center Beijing National Laboratory for Molecular Sciences, Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing 100871, China; PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China; Chinese Institute for Brain Research (CIBR), Beijing 102206, China.
| |
Collapse
|
10
|
Xiang JS, Mueller JR, Luo EC, Yee BA, Schafer D, Schmok JC, Tan FE, Rothamel K, McVicar RN, Kwong EM, Jones KL, Her HL, Chen CY, Vu AQ, Jin W, Park SS, Le P, Brannan KW, Kofman ER, Li Y, Tankka AT, Dong KD, Song Y, Carlin AF, Van Nostrand EL, Leibel SL, Yeo GW. Discovery and functional interrogation of SARS-CoV-2 protein-RNA interactions. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2022:2022.02.21.481223. [PMID: 35233578 PMCID: PMC8887137 DOI: 10.1101/2022.02.21.481223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The COVID-19 pandemic is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The betacoronvirus has a positive sense RNA genome which encodes for several RNA binding proteins. Here, we use enhanced crosslinking and immunoprecipitation to investigate SARS-CoV-2 protein interactions with viral and host RNAs in authentic virus-infected cells. SARS-CoV-2 proteins, NSP8, NSP12, and nucleocapsid display distinct preferences to specific regions in the RNA viral genome, providing evidence for their shared and separate roles in replication, transcription, and viral packaging. SARS-CoV-2 proteins expressed in human lung epithelial cells bind to 4773 unique host coding RNAs. Nine SARS-CoV-2 proteins upregulate target gene expression, including NSP12 and ORF9c, whose RNA substrates are associated with pathways in protein N-linked glycosylation ER processing and mitochondrial processes. Furthermore, siRNA knockdown of host genes targeted by viral proteins in human lung organoid cells identify potential antiviral host targets across different SARS-CoV-2 variants. Conversely, NSP9 inhibits host gene expression by blocking mRNA export and dampens cytokine productions, including interleukin-1α/β. Our viral protein-RNA interactome provides a catalog of potential therapeutic targets and offers insight into the etiology of COVID-19 as a safeguard against future pandemics.
Collapse
Affiliation(s)
- Joy S. Xiang
- Institute of Molecular and Cellular Biology, A*STAR, Singapore
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Jasmine R. Mueller
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - En-Ching Luo
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Brian A. Yee
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Danielle Schafer
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Jonathan C. Schmok
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Frederick E. Tan
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Katherine Rothamel
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Rachael N. McVicar
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Elizabeth M. Kwong
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA 92037, USA
| | - Krysten L. Jones
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Hsuan-Lin Her
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Chun-Yuan Chen
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Anthony Q. Vu
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Wenhao Jin
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Samuel S. Park
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Phuong Le
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Kristopher W. Brannan
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Eric R. Kofman
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Yanhua Li
- Department of Integrative Structural and Computational Biology, The Scripps Research Institute, La Jolla, CA 92037, USA
| | - Alexandra T. Tankka
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Kevin D. Dong
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Yan Song
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| | - Aaron F. Carlin
- Department of Medicine, University of California, San Diego, La Jolla, CA 92037, USA
| | - Eric L. Van Nostrand
- Verna & Marrs McLean Department of Biochemistry & Molecular Biology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sandra L. Leibel
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA 92037, USA
| | - Gene W. Yeo
- Department of Cellular and Molecular Medicine, Institute for Genomic Medicine, UCSD Stem Cell Program, University of California, San Diego, La Jolla, CA 92037, USA
| |
Collapse
|
11
|
Christopher JA, Geladaki A, Dawson CS, Vennard OL, Lilley KS. SUBCELLULAR TRANSCRIPTOMICS & PROTEOMICS: A COMPARATIVE METHODS REVIEW. Mol Cell Proteomics 2021; 21:100186. [PMID: 34922010 PMCID: PMC8864473 DOI: 10.1016/j.mcpro.2021.100186] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 11/16/2021] [Accepted: 12/13/2021] [Indexed: 12/23/2022] Open
Abstract
The internal environment of cells is molecularly crowded, which requires spatial organization via subcellular compartmentalization. These compartments harbor specific conditions for molecules to perform their biological functions, such as coordination of the cell cycle, cell survival, and growth. This compartmentalization is also not static, with molecules trafficking between these subcellular neighborhoods to carry out their functions. For example, some biomolecules are multifunctional, requiring an environment with differing conditions or interacting partners, and others traffic to export such molecules. Aberrant localization of proteins or RNA species has been linked to many pathological conditions, such as neurological, cancer, and pulmonary diseases. Differential expression studies in transcriptomics and proteomics are relatively common, but the majority have overlooked the importance of subcellular information. In addition, subcellular transcriptomics and proteomics data do not always colocate because of the biochemical processes that occur during and after translation, highlighting the complementary nature of these fields. In this review, we discuss and directly compare the current methods in spatial proteomics and transcriptomics, which include sequencing- and imaging-based strategies, to give the reader an overview of the current tools available. We also discuss current limitations of these strategies as well as future developments in the field of spatial -omics. Subcellular information of protein and RNA give insights into molecular function. This review discusses strategies available to measure subcellular information. Hybridization of methods shows promise for exploring the composition of organelles. Advances are aiding understanding of the organisation and dynamics of cells.
Collapse
Affiliation(s)
- Josie A Christopher
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK
| | - Aikaterini Geladaki
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK; Department of Genetics, University of Cambridge, 20 Downing Place, Cambridge, CB2 3EJ, UK
| | - Charlotte S Dawson
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK
| | - Owen L Vennard
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK
| | - Kathryn S Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, 80 Tennis Court Road, Cambridge, CB2 1GA, UK; Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK.
| |
Collapse
|
12
|
Dai X, Li Y, Liu W, Pan X, Guo C, Zhao X, Lv J, Lei H, Zhang L. Application of RNA subcellular fraction estimation method to explore RNA localization regulation. G3-GENES GENOMES GENETICS 2021; 12:6427545. [PMID: 34791188 PMCID: PMC8727992 DOI: 10.1093/g3journal/jkab371] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 10/18/2021] [Indexed: 12/15/2022]
Abstract
RNA localization is involved in multiple biological processes. Recent advances in subcellular fractionation based sequencing approaches uncovered localization pattern on a global scale. Most of existing methods adopt relative localization ratios (such as ratios of separately normalized TPMs of different subcellular fractions without considering the difference in total RNA abundances in different fractions), however, absolute ratios may yield different results on the preference to different cellular compartment. Experimentally, adding external Spike-in RNAs to different fractionation can be used to obtain absolute ratios. In addition, a spike-in independent computational approach based on multiple linear regression model can also be used. However, currently no custom tool is available. To solve this problem, we developed a method called Subcellular Fraction Abundance Estimator (SFAE) to correctly estimate relative RNA abundances of different subcellular fractionations. The ratios estimated by our method were consistent with existing reports. By applying the estimated ratios for different fractions, we explored the RNA localization pattern in cell lines and also predicted RBP motifs that were associated with different localization patterns. In addition, we showed that different isoforms of same genes could exhibit distinct localization patterns. To conclude, we believed our tool will facilitate future subcellular fractionation related sequencing study to explore the function of RNA localization in various biological problems.
Collapse
Affiliation(s)
- Xiaomin Dai
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.,CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yangmengjie Li
- Institute of Cancer Stem Cell, Cancer Center, Dalian Medical University, 9 West Section, Lvshun South Rd, Dalian, P.R. China 116044
| | - Weizhen Liu
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xiuqi Pan
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Chenyue Guo
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Xiaojing Zhao
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Jingwen Lv
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.,CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, Shanghai 200031, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haixin Lei
- Institute of Cancer Stem Cell, Cancer Center, Dalian Medical University, 9 West Section, Lvshun South Rd, Dalian, P.R. China 116044
| | - Liye Zhang
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| |
Collapse
|
13
|
Markmiller S, Sathe S, Server KL, Nguyen TB, Fulzele A, Cody N, Javaherian A, Broski S, Finkbeiner S, Bennett EJ, Lécuyer E, Yeo GW. Persistent mRNA localization defects and cell death in ALS neurons caused by transient cellular stress. Cell Rep 2021; 36:109685. [PMID: 34496257 DOI: 10.1016/j.celrep.2021.109685] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 07/19/2021] [Accepted: 08/17/2021] [Indexed: 12/15/2022] Open
Abstract
Persistent cytoplasmic aggregates containing RNA binding proteins (RBPs) are central to the pathogenesis of late-onset neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS). These aggregates share components, molecular mechanisms, and cellular protein quality control pathways with stress-induced RNA granules (SGs). Here, we assess the impact of stress on the global mRNA localization landscape of human pluripotent stem cell-derived motor neurons (PSC-MNs) using subcellular fractionation with RNA sequencing and proteomics. Transient stress disrupts subcellular RNA and protein distributions, alters the RNA binding profile of SG- and ALS-relevant RBPs and recapitulates disease-associated molecular changes such as aberrant splicing of STMN2. Although neurotypical PSC-MNs re-establish a normal subcellular localization landscape upon recovery from stress, cells harboring ALS-linked mutations are intransigent and display a delayed-onset increase in neuronal cell death. Our results highlight subcellular molecular distributions as predictive features and underscore the utility of cellular stress as a paradigm to study ALS-relevant mechanisms.
Collapse
Affiliation(s)
- Sebastian Markmiller
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA 92093, USA; Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, 92039, USA
| | - Shashank Sathe
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA 92093, USA; Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, 92039, USA
| | - Kari L Server
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA 92093, USA; Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, 92039, USA
| | - Thai B Nguyen
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA 92093, USA; Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, 92039, USA
| | - Amit Fulzele
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Neal Cody
- Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada
| | - Ashkan Javaherian
- Center for Systems and Therapeutics, Gladstone Institutes, San Francisco, CA 94158, USA; Taube/Koret Center for Neurodegenerative Disease Research, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Sara Broski
- Center for Systems and Therapeutics, Gladstone Institutes, San Francisco, CA 94158, USA; Taube/Koret Center for Neurodegenerative Disease Research, Gladstone Institutes, San Francisco, CA 94158, USA
| | - Steven Finkbeiner
- Center for Systems and Therapeutics, Gladstone Institutes, San Francisco, CA 94158, USA; Taube/Koret Center for Neurodegenerative Disease Research, Gladstone Institutes, San Francisco, CA 94158, USA; Departments of Neurology and Physiology, University of California-San Francisco, San Francisco, CA 94158, USA
| | - Eric J Bennett
- Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093, USA
| | - Eric Lécuyer
- Institut de Recherches Cliniques de Montréal, Montréal, QC H2W 1R7, Canada; Département de Biochimie et Médecine Moléculaire, Université de Montréal, Montréal, QC H3C 3J7, Canada; Division of Experimental Medicine, McGill University, Montréal, QC H3A 1A3, Canada
| | - Gene W Yeo
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA; Stem Cell Program, University of California, San Diego, La Jolla, CA 92093, USA; Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, 92039, USA.
| |
Collapse
|
14
|
Huang Y, Qiao Y, Zhao Y, Li Y, Yuan J, Zhou J, Sun H, Wang H. Large scale RNA-binding proteins/LncRNAs interaction analysis to uncover lncRNA nuclear localization mechanisms. Brief Bioinform 2021; 22:6287336. [PMID: 34056657 DOI: 10.1093/bib/bbab195] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 04/28/2021] [Accepted: 04/29/2021] [Indexed: 12/25/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) are key regulators of major biological processes and their functional modes are dictated by their subcellular localization. Relative nuclear enrichment of lncRNAs compared to mRNAs is a prevalent phenomenon but the molecular mechanisms governing their nuclear retention in cells remain largely unknown. Here in this study, we harness the recently released eCLIP data for a large number of RNA-binding proteins (RBPs) in K562 and HepG2 cells and utilize multiple bioinformatics methods to comprehensively survey the roles of RBPs in lncRNA nuclear retention. We identify an array of splicing RBPs that bind to nuclear-enriched lincRNAs (large intergenic non-coding RNAs) thus may act as trans-factors regulating their nuclear retention. Further analyses reveal that these RBPs may bind with distinct core motifs, flanking sequence compositions, or secondary structures to drive lincRNA nuclear retention. Moreover, network analyses uncover potential co-regulatory RBP clusters and the physical interaction between HNRNPU and SAFB2 proteins in K562 cells is further experimentally verified. Altogether, our analyses reveal previously unknown factors and mechanisms that govern lincRNA nuclear localization in cells.
Collapse
Affiliation(s)
- Yile Huang
- Department of Chemical Pathology, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Yulong Qiao
- Department of Chemical Pathology, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Yu Zhao
- Department of Orthaepedics and Traumatology, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.,Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.,School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yuying Li
- Department of Chemical Pathology, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Jie Yuan
- Department of Chemical Pathology, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Jiajian Zhou
- Department of Chemical Pathology, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.,Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Hao Sun
- Department of Chemical Pathology, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.,Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Huating Wang
- Department of Orthaepedics and Traumatology, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.,Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| |
Collapse
|
15
|
Christopher JA, Stadler C, Martin CE, Morgenstern M, Pan Y, Betsinger CN, Rattray DG, Mahdessian D, Gingras AC, Warscheid B, Lehtiö J, Cristea IM, Foster LJ, Emili A, Lilley KS. Subcellular proteomics. NATURE REVIEWS. METHODS PRIMERS 2021; 1:32. [PMID: 34549195 PMCID: PMC8451152 DOI: 10.1038/s43586-021-00029-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/15/2021] [Indexed: 12/11/2022]
Abstract
The eukaryotic cell is compartmentalized into subcellular niches, including membrane-bound and membrane-less organelles. Proteins localize to these niches to fulfil their function, enabling discreet biological processes to occur in synchrony. Dynamic movement of proteins between niches is essential for cellular processes such as signalling, growth, proliferation, motility and programmed cell death, and mutations causing aberrant protein localization are associated with a wide range of diseases. Determining the location of proteins in different cell states and cell types and how proteins relocalize following perturbation is important for understanding their functions, related cellular processes and pathologies associated with their mislocalization. In this Primer, we cover the major spatial proteomics methods for determining the location, distribution and abundance of proteins within subcellular structures. These technologies include fluorescent imaging, protein proximity labelling, organelle purification and cell-wide biochemical fractionation. We describe their workflows, data outputs and applications in exploring different cell biological scenarios, and discuss their main limitations. Finally, we describe emerging technologies and identify areas that require technological innovation to allow better characterization of the spatial proteome.
Collapse
Affiliation(s)
- Josie A. Christopher
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK
| | - Charlotte Stadler
- Department of Protein Sciences, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Claire E. Martin
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
| | - Marcel Morgenstern
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, Freiburg, Germany
| | - Yanbo Pan
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Cora N. Betsinger
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - David G. Rattray
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Diana Mahdessian
- Department of Protein Sciences, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Anne-Claude Gingras
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Bettina Warscheid
- Institute of Biology II, Biochemistry and Functional Proteomics, Faculty of Biology, University of Freiburg, Freiburg, Germany
- BIOSS and CIBSS Signaling Research Centers, University of Freiburg, Freiburg, Germany
| | - Janne Lehtiö
- Department of Oncology and Pathology, Karolinska Institutet, Science for Life Laboratory, Solna, Sweden
| | - Ileana M. Cristea
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Leonard J. Foster
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, University of British Columbia, Vancouver, British Columbia, Canada
| | - Andrew Emili
- Center for Network Systems Biology, Boston University, Boston, MA, USA
| | - Kathryn S. Lilley
- Department of Biochemistry, University of Cambridge, Cambridge, UK
- Milner Therapeutics Institute, Jeffrey Cheah Biomedical Centre, Cambridge, UK
| |
Collapse
|
16
|
Bergalet J, Patel D, Legendre F, Lapointe C, Benoit Bouvrette LP, Chin A, Blanchette M, Kwon E, Lécuyer E. Inter-dependent Centrosomal Co-localization of the cen and ik2 cis-Natural Antisense mRNAs in Drosophila. Cell Rep 2021; 30:3339-3352.e6. [PMID: 32160541 DOI: 10.1016/j.celrep.2020.02.047] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 12/24/2019] [Accepted: 02/10/2020] [Indexed: 11/30/2022] Open
Abstract
Overlapping genes are prevalent in most genomes, but the extent to which this organization influences regulatory events operating at the post-transcriptional level remains unclear. Studying the cen and ik2 genes of Drosophila melanogaster, which are convergently transcribed as cis-natural antisense transcripts (cis-NATs) with overlapping 3' UTRs, we found that their encoded mRNAs strikingly co-localize to centrosomes. These transcripts physically interact in a 3' UTR-dependent manner, and the targeting of ik2 requires its 3' UTR sequence and the presence of cen mRNA, which serves as the main driver of centrosomal co-localization. The cen transcript undergoes localized translation in proximity to centrosomes, and its localization is perturbed by polysome-disrupting drugs. By interrogating global fractionation-sequencing datasets generated from Drosophila and human cellular models, we find that RNAs expressed as cis-NATs tend to co-localize to specific subcellular fractions. This work suggests that post-transcriptional interactions between RNAs with complementary sequences can dictate their localization fate in the cytoplasm.
Collapse
Affiliation(s)
- Julie Bergalet
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada
| | - Dhara Patel
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada; Département de Biochimie et Médecine Moléculaire and Programme de Biologie Moléculaire, Université de Montréal, Montréal, QC, Canada
| | - Félix Legendre
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada; Département de Biochimie et Médecine Moléculaire and Programme de Biologie Moléculaire, Université de Montréal, Montréal, QC, Canada
| | - Catherine Lapointe
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada
| | - Louis Philip Benoit Bouvrette
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada; Département de Biochimie et Médecine Moléculaire and Programme de Biologie Moléculaire, Université de Montréal, Montréal, QC, Canada
| | - Ashley Chin
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada; Division of Experimental Medicine, McGill University, Montréal, QC, Canada
| | | | - Eunjeong Kwon
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada
| | - Eric Lécuyer
- Institut de Recherches Cliniques de Montréal (IRCM), Montréal, QC, Canada; Département de Biochimie et Médecine Moléculaire and Programme de Biologie Moléculaire, Université de Montréal, Montréal, QC, Canada; Division of Experimental Medicine, McGill University, Montréal, QC, Canada.
| |
Collapse
|
17
|
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: 15] [Impact Index Per Article: 3.8] [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.
Collapse
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:
| |
Collapse
|
18
|
Yan Z, Lécuyer E, Blanchette M. Prediction of mRNA subcellular localization using deep recurrent neural networks. Bioinformatics 2020; 35:i333-i342. [PMID: 31510698 PMCID: PMC6612824 DOI: 10.1093/bioinformatics/btz337] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023] Open
Abstract
MOTIVATION Messenger RNA subcellular localization mechanisms play a crucial role in post-transcriptional gene regulation. This trafficking is mediated by trans-acting RNA-binding proteins interacting with cis-regulatory elements called zipcodes. While new sequencing-based technologies allow the high-throughput identification of RNAs localized to specific subcellular compartments, the precise mechanisms at play, and their dependency on specific sequence elements, remain poorly understood. RESULTS We introduce RNATracker, a novel deep neural network built to predict, from their sequence alone, the distributions of mRNA transcripts over a predefined set of subcellular compartments. RNATracker integrates several state-of-the-art deep learning techniques (e.g. CNN, LSTM and attention layers) and can make use of both sequence and secondary structure information. We report on a variety of evaluations showing RNATracker's strong predictive power, which is significantly superior to a variety of baseline predictors. Despite its complexity, several aspects of the model can be isolated to yield valuable, testable mechanistic hypotheses, and to locate candidate zipcode sequences within transcripts. AVAILABILITY AND IMPLEMENTATION Code and data can be accessed at https://www.github.com/HarveyYan/RNATracker. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Zichao Yan
- School of Computer Science, McGill University, Montreal, QC, Canada
| | - Eric Lécuyer
- Department of Biochemistry, University of Montreal, Montreal, QC, Canada.,Institut de Recherches Clinique de Montréal (IRCM), Montreal, QC, Canada.,Division of Experimental Medicine, McGill University, Montreal, QC, Canada
| | | |
Collapse
|
19
|
Chaudhuri A, Das S, Das B. Localization elements and zip codes in the intracellular transport and localization of messenger RNAs in Saccharomyces cerevisiae. WILEY INTERDISCIPLINARY REVIEWS-RNA 2020; 11:e1591. [PMID: 32101377 DOI: 10.1002/wrna.1591] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 02/05/2020] [Accepted: 02/07/2020] [Indexed: 12/13/2022]
Abstract
Intracellular trafficking and localization of mRNAs provide a mechanism of regulation of expression of genes with excellent spatial control. mRNA localization followed by localized translation appears to be a mechanism of targeted protein sorting to a specific cell-compartment, which is linked to the establishment of cell polarity, cell asymmetry, embryonic axis determination, and neuronal plasticity in metazoans. However, the complexity of the mechanism and the components of mRNA localization in higher organisms prompted the use of the unicellular organism Saccharomyces cerevisiae as a simplified model organism to study this vital process. Current knowledge indicates that a variety of mRNAs are asymmetrically and selectively localized to the tip of the bud of the daughter cells, to the vicinity of endoplasmic reticulum, mitochondria, and nucleus in this organism, which are connected to diverse cellular processes. Interestingly, specific cis-acting RNA localization elements (LEs) or RNA zip codes play a crucial role in the localization and trafficking of these localized mRNAs by providing critical binding sites for the specific RNA-binding proteins (RBPs). In this review, we present a comprehensive account of mRNA localization in S. cerevisiae, various types of localization elements influencing the mRNA localization, and the RBPs, which bind to these LEs to implement a number of vital physiological processes. Finally, we emphasize the significance of this process by highlighting their connection to several neuropathological disorders and cancers. This article is categorized under: RNA Export and Localization > RNA Localization.
Collapse
Affiliation(s)
- Anusha Chaudhuri
- Department of Life Science and Biotechnology, Jadavpur University, Kolkata, India
| | - Subhadeep Das
- Department of Life Science and Biotechnology, Jadavpur University, Kolkata, India
| | - Biswadip Das
- Department of Life Science and Biotechnology, Jadavpur University, Kolkata, India
| |
Collapse
|
20
|
Bioinformatics Approaches to Gain Insights into cis-Regulatory Motifs Involved in mRNA Localization. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2019; 1203:165-194. [PMID: 31811635 DOI: 10.1007/978-3-030-31434-7_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Messenger RNA (mRNA) is a fundamental intermediate in the expression of proteins. As an integral part of this important process, protein production can be localized by the targeting of mRNA to a specific subcellular compartment. The subcellular destination of mRNA is suggested to be governed by a region of its primary sequence or secondary structure, which consequently dictates the recruitment of trans-acting factors, such as RNA-binding proteins or regulatory RNAs, to form a messenger ribonucleoprotein particle. This molecular ensemble is requisite for precise and spatiotemporal control of gene expression. In the context of RNA localization, the description of the binding preferences of an RNA-binding protein defines a motif, and one, or more, instance of a given motif is defined as a localization element (zip code). In this chapter, we first discuss the cis-regulatory motifs previously identified as mRNA localization elements. We then describe motif representation in terms of entropy and information content and offer an overview of motif databases and search algorithms. Finally, we provide an outline of the motif topology of asymmetrically localized mRNA molecules.
Collapse
|
21
|
Benoit Bouvrette LP, Cody NAL, Bergalet J, Lefebvre FA, Diot C, Wang X, Blanchette M, Lécuyer E. CeFra-seq reveals broad asymmetric mRNA and noncoding RNA distribution profiles in Drosophila and human cells. RNA (NEW YORK, N.Y.) 2018; 24:98-113. [PMID: 29079635 PMCID: PMC5733575 DOI: 10.1261/rna.063172.117] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2017] [Accepted: 10/13/2017] [Indexed: 05/26/2023]
Abstract
Cells are highly asymmetrical, a feature that relies on the sorting of molecular constituents, including proteins, lipids, and nucleic acids, to distinct subcellular locales. The localization of RNA molecules is an important layer of gene regulation required to modulate localized cellular activities, although its global prevalence remains unclear. We combine biochemical cell fractionation with RNA-sequencing (CeFra-seq) analysis to assess the prevalence and conservation of RNA asymmetric distribution on a transcriptome-wide scale in Drosophila and human cells. This approach reveals that the majority (∼80%) of cellular RNA species are asymmetrically distributed, whether considering coding or noncoding transcript populations, in patterns that are broadly conserved evolutionarily. Notably, a large number of Drosophila and human long noncoding RNAs and circular RNAs display enriched levels within specific cytoplasmic compartments, suggesting that these RNAs fulfill extra-nuclear functions. Moreover, fraction-specific mRNA populations exhibit distinctive sequence characteristics. Comparative analysis of mRNA fractionation profiles with that of their encoded proteins reveals a general lack of correlation in subcellular distribution, marked by strong cases of asymmetry. However, coincident distribution profiles are observed for mRNA/protein pairs related to a variety of functional protein modules, suggesting complex regulatory inputs of RNA localization to cellular organization.
Collapse
Affiliation(s)
- Louis Philip Benoit Bouvrette
- Institut de Recherches Clinique de Montréal (IRCM), Montréal H2W 1R7, Canada
- Département de Biochimie, Université de Montréal, Montréal H3C 3J7, Canada
| | - Neal A L Cody
- Institut de Recherches Clinique de Montréal (IRCM), Montréal H2W 1R7, Canada
| | - Julie Bergalet
- Institut de Recherches Clinique de Montréal (IRCM), Montréal H2W 1R7, Canada
| | - Fabio Alexis Lefebvre
- Institut de Recherches Clinique de Montréal (IRCM), Montréal H2W 1R7, Canada
- Département de Biochimie, Université de Montréal, Montréal H3C 3J7, Canada
| | - Cédric Diot
- Institut de Recherches Clinique de Montréal (IRCM), Montréal H2W 1R7, Canada
- Département de Biochimie, Université de Montréal, Montréal H3C 3J7, Canada
| | - Xiaofeng Wang
- Institut de Recherches Clinique de Montréal (IRCM), Montréal H2W 1R7, Canada
| | - Mathieu Blanchette
- McGill School of Computer Science, McGill University, Montréal H3A 0E9, Canada
| | - Eric Lécuyer
- Institut de Recherches Clinique de Montréal (IRCM), Montréal H2W 1R7, Canada
- Département de Biochimie, Université de Montréal, Montréal H3C 3J7, Canada
- Division of Experimental Medicine, McGill University, Montréal H4A 3J1, Canada
| |
Collapse
|
22
|
Optimized FISH methods for visualizing RNA localization properties in Drosophila and human tissues and cultured cells. Methods 2017; 126:156-165. [DOI: 10.1016/j.ymeth.2017.06.027] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2017] [Revised: 06/20/2017] [Accepted: 06/24/2017] [Indexed: 11/24/2022] Open
|
23
|
Lipshitz HD, Claycomb JM, Smibert CA. Post-transcriptional regulation of gene expression. Methods 2017; 126:1-2. [DOI: 10.1016/j.ymeth.2017.08.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
|