1
|
Li G, Wu J, Wang X. Predicting functional UTR variants by integrating region-specific features. Brief Bioinform 2024; 25:bbae248. [PMID: 38783704 PMCID: PMC11116830 DOI: 10.1093/bib/bbae248] [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: 01/15/2024] [Revised: 03/30/2024] [Accepted: 05/08/2024] [Indexed: 05/25/2024] Open
Abstract
The untranslated region (UTR) of messenger ribonucleic acid (mRNA), including the 5'UTR and 3'UTR, plays a critical role in regulating gene expression and translation. Variants within the UTR can lead to changes associated with human traits and diseases; however, computational prediction of UTR variant effect is challenging. Current noncoding variant prediction mainly focuses on the promoters and enhancers, neglecting the unique sequence of the UTR and thereby limiting their predictive accuracy. In this study, using consolidated datasets of UTR variants from disease databases and large-scale experimental data, we systematically analyzed more than 50 region-specific features of UTR, including functional elements, secondary structure, sequence composition and site conservation. Our analysis reveals that certain features, such as C/G-related sequence composition in 5'UTR and A/T-related sequence composition in 3'UTR, effectively differentiate between nonfunctional and functional variant sets, unveiling potential sequence determinants of functional UTR variants. Leveraging these insights, we developed two classification models to predict functional UTR variants using machine learning, achieving an area under the curve (AUC) value of 0.94 for 5'UTR and 0.85 for 3'UTR, outperforming all existing methods. Our models will be valuable for enhancing clinical interpretation of genetic variants, facilitating the prediction and management of disease risk.
Collapse
Affiliation(s)
- Guangyu Li
- State Key Laboratory of Common Mechanism Research for Major Diseases; Center for bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1 Shuai Fu Yuan, Dongcheng District, Beijing 100005, China
| | - Jiayu Wu
- State Key Laboratory of Common Mechanism Research for Major Diseases; Center for bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1 Shuai Fu Yuan, Dongcheng District, Beijing 100005, China
| | - Xiaoyue Wang
- State Key Laboratory of Common Mechanism Research for Major Diseases; Center for bioinformatics, National Infrastructures for Translational Medicine, Institute of Clinical Medicine and Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, 1 Shuai Fu Yuan, Dongcheng District, Beijing 100005, China
| |
Collapse
|
2
|
Rennie S. Deep Learning for Elucidating Modifications to RNA-Status and Challenges Ahead. Genes (Basel) 2024; 15:629. [PMID: 38790258 PMCID: PMC11121098 DOI: 10.3390/genes15050629] [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: 04/15/2024] [Revised: 05/11/2024] [Accepted: 05/11/2024] [Indexed: 05/26/2024] Open
Abstract
RNA-binding proteins and chemical modifications to RNA play vital roles in the co- and post-transcriptional regulation of genes. In order to fully decipher their biological roles, it is an essential task to catalogue their precise target locations along with their preferred contexts and sequence-based determinants. Recently, deep learning approaches have significantly advanced in this field. These methods can predict the presence or absence of modification at specific genomic regions based on diverse features, particularly sequence and secondary structure, allowing us to decipher the highly non-linear sequence patterns and structures that underlie site preferences. This article provides an overview of how deep learning is being applied to this area, with a particular focus on the problem of mRNA-RBP binding, while also considering other types of chemical modification to RNA. It discusses how different types of model can handle sequence-based and/or secondary-structure-based inputs, the process of model training, including choice of negative regions and separating sets for testing and training, and offers recommendations for developing biologically relevant models. Finally, it highlights four key areas that are crucial for advancing the field.
Collapse
Affiliation(s)
- Sarah Rennie
- Section for Computational and RNA Biology, Department of Biology, University of Copenhagen, 2200 Copenhagen, Denmark
| |
Collapse
|
3
|
Saintomé C, Monfret O, Doisneau G, Guianvarc'h D. Oligonucleotide-Based Photoaffinity Probes: Chemical Tools and Applications for Protein Labeling. Chembiochem 2024:e202400097. [PMID: 38703401 DOI: 10.1002/cbic.202400097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/06/2024]
Abstract
A variety of proteins interact with DNA and RNA, including polymerases, histones, ribosomes, transcription factors, and repair enzymes. However, the transient non-covalent nature of these interactions poses challenges for analysis. Introducing a covalent bond between proteins and DNA via photochemical activation of a photosensitive functional group introduced onto nucleic acids offers a means to stabilize these often weak interactions without significantly altering the binding interface. Consequently, photoactivatable oligonucleotides are powerful tools for investigating nucleic acid-protein interactions involved in numerous biological and pathological processes. In this review, we provide a comprehensive overview of the chemical tools developed so far and the different strategies used for incorporating the most commonly used photoreactive reagents into oligonucleotide probes or nucleic acids. Furthermore, we illustrate their application with several examples including protein binding site mapping, identification of protein binding partners, and in cell studies.
Collapse
Affiliation(s)
- Carole Saintomé
- Sorbonne Université, UFR 927, MNHN CNRS UMR 7196, INSERM U1154, 43 rue Cuvier, 75005, Paris, France
| | - Océane Monfret
- Université Paris-Saclay, CNRS, Institut de Chimie Moléculaire et des Matériaux d'Orsay, UMR CNRS 8182, 91405, Orsay, France
| | - Gilles Doisneau
- Université Paris-Saclay, CNRS, Institut de Chimie Moléculaire et des Matériaux d'Orsay, UMR CNRS 8182, 91405, Orsay, France
| | - Dominique Guianvarc'h
- Université Paris-Saclay, CNRS, Institut de Chimie Moléculaire et des Matériaux d'Orsay, UMR CNRS 8182, 91405, Orsay, France
| |
Collapse
|
4
|
Schwarzl T, Sahadevan S, Lang B, Miladi M, Backofen R, Huber W, Hentze MW, Tartaglia GG. Improved discovery of RNA-binding protein binding sites in eCLIP data using DEWSeq. Nucleic Acids Res 2024; 52:e1. [PMID: 37962298 PMCID: PMC10783507 DOI: 10.1093/nar/gkad998] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 09/04/2023] [Accepted: 10/18/2023] [Indexed: 11/15/2023] Open
Abstract
Enhanced crosslinking and immunoprecipitation (eCLIP) sequencing is a method for transcriptome-wide detection of binding sites of RNA-binding proteins (RBPs). However, identified crosslink sites can deviate from experimentally established functional elements of even well-studied RBPs. Current peak-calling strategies result in low replication and high false positive rates. Here, we present the R/Bioconductor package DEWSeq that makes use of replicate information and size-matched input controls. We benchmarked DEWSeq on 107 RBPs for which both eCLIP data and RNA sequence motifs are available and were able to more than double the number of motif-containing binding regions relative to standard eCLIP processing. The improvement not only relates to the number of binding sites (3.1-fold with known motifs for RBFOX2), but also their subcellular localization (1.9-fold of mitochondrial genes for FASTKD2) and structural targets (2.2-fold increase of stem-loop regions for SLBP. On several orthogonal CLIP-seq datasets, DEWSeq recovers a larger number of motif-containing binding sites (3.3-fold). DEWSeq is a well-documented R/Bioconductor package, scalable to adequate numbers of replicates, and tends to substantially increase the proportion and total number of RBP binding sites containing biologically relevant features.
Collapse
Affiliation(s)
- Thomas Schwarzl
- European Molecular Biology Laboratory (EMBL), Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Sudeep Sahadevan
- European Molecular Biology Laboratory (EMBL), Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Benjamin Lang
- Department of Structural Biology and Center of Excellence for Data-Driven Discovery, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA
| | - Milad Miladi
- Bioinformatics Group, Department of Computer Science, University of Freiburg, 79098 Freiburg im Breisgau, Germany
| | - Rolf Backofen
- Bioinformatics Group, Department of Computer Science, University of Freiburg, 79098 Freiburg im Breisgau, Germany
| | - Wolfgang Huber
- European Molecular Biology Laboratory (EMBL), Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Matthias W Hentze
- European Molecular Biology Laboratory (EMBL), Meyerhofstraße 1, 69117 Heidelberg, Germany
| | - Gian Gaetano Tartaglia
- Center for Life Nano & Neuroscience, Italian Institute of Technology, 00161 Rome, Italy and Department of Biology, Sapienza University of Rome, 00185 Rome, Italy
| |
Collapse
|
5
|
Horlacher M, Cantini G, Hesse J, Schinke P, Goedert N, Londhe S, Moyon L, Marsico A. A systematic benchmark of machine learning methods for protein-RNA interaction prediction. Brief Bioinform 2023; 24:bbad307. [PMID: 37635383 PMCID: PMC10516373 DOI: 10.1093/bib/bbad307] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/15/2023] [Accepted: 07/18/2023] [Indexed: 08/29/2023] Open
Abstract
RNA-binding proteins (RBPs) are central actors of RNA post-transcriptional regulation. Experiments to profile-binding sites of RBPs in vivo are limited to transcripts expressed in the experimental cell type, creating the need for computational methods to infer missing binding information. While numerous machine-learning based methods have been developed for this task, their use of heterogeneous training and evaluation datasets across different sets of RBPs and CLIP-seq protocols makes a direct comparison of their performance difficult. Here, we compile a set of 37 machine learning (primarily deep learning) methods for in vivo RBP-RNA interaction prediction and systematically benchmark a subset of 11 representative methods across hundreds of CLIP-seq datasets and RBPs. Using homogenized sample pre-processing and two negative-class sample generation strategies, we evaluate methods in terms of predictive performance and assess the impact of neural network architectures and input modalities on model performance. We believe that this study will not only enable researchers to choose the optimal prediction method for their tasks at hand, but also aid method developers in developing novel, high-performing methods by introducing a standardized framework for their evaluation.
Collapse
Affiliation(s)
- Marc Horlacher
- Computational Health Center, Helmholtz Center Munich, Germany
- School of Computation, Information and Technology, Technical University Munich (TUM), Germany
| | - Giulia Cantini
- Computational Health Center, Helmholtz Center Munich, Germany
| | - Julian Hesse
- Computational Health Center, Helmholtz Center Munich, Germany
| | - Patrick Schinke
- Computational Health Center, Helmholtz Center Munich, Germany
| | - Nicolas Goedert
- Computational Health Center, Helmholtz Center Munich, Germany
| | | | - Lambert Moyon
- Computational Health Center, Helmholtz Center Munich, Germany
| | | |
Collapse
|
6
|
Chakrabarti AM, Capitanchik C, Ule J, Luscombe NM. clipplotr-a comparative visualization and analysis tool for CLIP data. RNA (NEW YORK, N.Y.) 2023; 29:715-723. [PMID: 36894192 PMCID: PMC10187674 DOI: 10.1261/rna.079326.122] [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: 09/12/2022] [Accepted: 02/09/2023] [Indexed: 05/18/2023]
Abstract
CLIP technologies are now widely used to study RNA-protein interactions and many data sets are now publicly available. An important first step in CLIP data exploration is the visual inspection and assessment of processed genomic data on selected genes or regions and performing comparisons: either across conditions within a particular project, or incorporating publicly available data. However, the output files produced by data processing pipelines or preprocessed files available to download from data repositories are often not suitable for direct comparison and usually need further processing. Furthermore, to derive biological insight it is usually necessary to visualize a CLIP signal alongside other data such as annotations, or orthogonal functional genomic data (e.g., RNA-seq). We have developed a simple, but powerful, command-line tool: clipplotr, which facilitates these visual comparative and integrative analyses with normalization and smoothing options for CLIP data and the ability to show these alongside reference annotation tracks and functional genomic data. These data can be supplied as input to clipplotr in a range of file formats, which will output a publication quality figure. It is written in R and can both run on a laptop computer independently or be integrated into computational workflows on a high-performance cluster. Releases, source code, and documentation are freely available at https://github.com/ulelab/clipplotr.
Collapse
Affiliation(s)
| | | | - Jernej Ule
- The Francis Crick Institute, London, NW1 4AT, United Kingdom
- UK Dementia Research Institute at King's College London, Institute of Psychiatry, Psychology and Neuroscience, London, SE5 9RX, United Kingdom
| | - Nicholas M Luscombe
- The Francis Crick Institute, London, NW1 4AT, United Kingdom
- Okinawa Institute of Science and Technology Graduate University, Onna-son, Okinawa 904-0495, Japan
| |
Collapse
|
7
|
Horlacher M, Oleshko S, Hu Y, Ghanbari M, Cantini G, Schinke P, Vergara EE, Bittner F, Mueller NS, Ohler U, Moyon L, Marsico A. A computational map of the human-SARS-CoV-2 protein-RNA interactome predicted at single-nucleotide resolution. NAR Genom Bioinform 2023; 5:lqad010. [PMID: 36814457 PMCID: PMC9940458 DOI: 10.1093/nargab/lqad010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 01/10/2023] [Accepted: 02/14/2023] [Indexed: 02/22/2023] Open
Abstract
RNA-binding proteins (RBPs) are critical host factors for viral infection, however, large scale experimental investigation of the binding landscape of human RBPs to viral RNAs is costly and further complicated due to sequence variation between viral strains. To fill this gap, we investigated the role of RBPs in the context of SARS-CoV-2 by constructing the first in silico map of human RBP-viral RNA interactions at nucleotide-resolution using two deep learning methods (pysster and DeepRiPe) trained on data from CLIP-seq experiments on more than 100 human RBPs. We evaluated conservation of RBP binding between six other human pathogenic coronaviruses and identified sites of conserved and differential binding in the UTRs of SARS-CoV-1, SARS-CoV-2 and MERS. We scored the impact of mutations from 11 variants of concern on protein-RNA interaction, identifying a set of gain- and loss-of-binding events, as well as predicted the regulatory impact of putative future mutations. Lastly, we linked RBPs to functional, OMICs and COVID-19 patient data from other studies, and identified MBNL1, FTO and FXR2 RBPs as potential clinical biomarkers. Our results contribute towards a deeper understanding of how viruses hijack host cellular pathways and open new avenues for therapeutic intervention.
Collapse
Affiliation(s)
- Marc Horlacher
- Computational Health Center, Helmholtz Center Munich, Munich, Germany
| | - Svitlana Oleshko
- Computational Health Center, Helmholtz Center Munich, Munich, Germany
| | - Yue Hu
- Computational Health Center, Helmholtz Center Munich, Munich, Germany,Informatics 12 Chair of Bioinformatics, Technical University Munich, Garching, Germany
| | - Mahsa Ghanbari
- Institutes of Biology and Computer Science, Humboldt University, Berlin, Germany,Max Delbruck Center, Computational Regulatory Genomics, Berlin, Germany
| | - Giulia Cantini
- Computational Health Center, Helmholtz Center Munich, Munich, Germany
| | - Patrick Schinke
- Computational Health Center, Helmholtz Center Munich, Munich, Germany
| | | | | | | | - Uwe Ohler
- Institutes of Biology and Computer Science, Humboldt University, Berlin, Germany,Max Delbruck Center, Computational Regulatory Genomics, Berlin, Germany
| | - Lambert Moyon
- To whom correspondence should be addressed. Tel: +49 89318749193;
| | - Annalisa Marsico
- Correspondence may also be addressed to Annalisa Marsico. Tel: +49 89318743073;
| |
Collapse
|
8
|
Agarwal V, Kelley DR. The genetic and biochemical determinants of mRNA degradation rates in mammals. Genome Biol 2022; 23:245. [PMID: 36419176 PMCID: PMC9684954 DOI: 10.1186/s13059-022-02811-x] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2022] [Accepted: 11/02/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Degradation rate is a fundamental aspect of mRNA metabolism, and the factors governing it remain poorly characterized. Understanding the genetic and biochemical determinants of mRNA half-life would enable more precise identification of variants that perturb gene expression through post-transcriptional gene regulatory mechanisms. RESULTS We establish a compendium of 39 human and 27 mouse transcriptome-wide mRNA decay rate datasets. A meta-analysis of these data identified a prevalence of technical noise and measurement bias, induced partially by the underlying experimental strategy. Correcting for these biases allowed us to derive more precise, consensus measurements of half-life which exhibit enhanced consistency between species. We trained substantially improved statistical models based upon genetic and biochemical features to better predict half-life and characterize the factors molding it. Our state-of-the-art model, Saluki, is a hybrid convolutional and recurrent deep neural network which relies only upon an mRNA sequence annotated with coding frame and splice sites to predict half-life (r=0.77). The key novel principle learned by Saluki is that the spatial positioning of splice sites, codons, and RNA-binding motifs within an mRNA is strongly associated with mRNA half-life. Saluki predicts the impact of RNA sequences and genetic mutations therein on mRNA stability, in agreement with functional measurements derived from massively parallel reporter assays. CONCLUSIONS Our work produces a more robust ground truth for transcriptome-wide mRNA half-lives in mammalian cells. Using these revised measurements, we trained Saluki, a model that is over 50% more accurate in predicting half-life from sequence than existing models. Saluki succinctly captures many of the known determinants of mRNA half-life and can be rapidly deployed to predict the functional consequences of arbitrary mutations in the transcriptome.
Collapse
Affiliation(s)
- Vikram Agarwal
- grid.497059.6Calico Life Sciences LLC, South San Francisco, CA 94080 USA ,grid.417555.70000 0000 8814 392XPresent Address: mRNA Center of Excellence, Sanofi Pasteur Inc., Waltham, MA 02451 USA
| | - David R. Kelley
- grid.497059.6Calico Life Sciences LLC, South San Francisco, CA 94080 USA
| |
Collapse
|
9
|
Benoit Bouvrette LP, Wang X, Boulais J, Kong J, Syed E, Blue S, Zhan L, Olson S, Stanton R, Wei X, Yee B, Van Nostrand EL, Fu XD, Burge CB, Graveley B, Yeo G, Lécuyer E. RBP Image Database: A resource for the systematic characterization of the subcellular distribution properties of human RNA binding proteins. Nucleic Acids Res 2022; 51:D1549-D1557. [PMID: 36321651 PMCID: PMC9825414 DOI: 10.1093/nar/gkac971] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/04/2022] [Accepted: 10/31/2022] [Indexed: 11/07/2022] Open
Abstract
RNA binding proteins (RBPs) are central regulators of gene expression implicated in all facets of RNA metabolism. As such, they play key roles in cellular physiology and disease etiology. Since different steps of post-transcriptional gene expression tend to occur in specific regions of the cell, including nuclear or cytoplasmic locations, defining the subcellular distribution properties of RBPs is an important step in assessing their potential functions. Here, we present the RBP Image Database, a resource that details the subcellular localization features of 301 RBPs in the human HepG2 and HeLa cell lines, based on the results of systematic immuno-fluorescence studies conducted using a highly validated collection of RBP antibodies and a panel of 12 markers for specific organelles and subcellular structures. The unique features of the RBP Image Database include: (i) hosting of comprehensive representative images for each RBP-marker pair, with ∼250,000 microscopy images; (ii) a manually curated controlled vocabulary of annotation terms detailing the localization features of each factor; and (iii) a user-friendly interface allowing the rapid querying of the data by target or annotation. The RBP Image Database is freely available at https://rnabiology.ircm.qc.ca/RBPImage/.
Collapse
Affiliation(s)
| | | | - Jonathan Boulais
- Institut de Recherches Cliniques de Montréal (IRCM) Montréal, Québec, Canada
| | - Jian Kong
- Institut de Recherches Cliniques de Montréal (IRCM) Montréal, Québec, Canada
| | - Easin Uddin Syed
- Institut de Recherches Cliniques de Montréal (IRCM) Montréal, Québec, Canada,Département de Biochimie et Médecine Moléculaire, Université de Montréal, Montréal, Québec, Canada
| | - Steven M Blue
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Lijun Zhan
- Department of Genetics and Genome Sciences, UConn Health Center, Farmington, CT, USA
| | - Sara Olson
- Department of Genetics and Genome Sciences, UConn Health Center, Farmington, CT, USA
| | - Rebecca Stanton
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Xintao Wei
- Department of Genetics and Genome Sciences, UConn Health Center, Farmington, CT, USA
| | - Brian Yee
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Eric L Van Nostrand
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA,Verna & Marrs McLean Department of Biochemistry & Molecular Biology and Therapeutic Innovation Center, Baylor College of Medicine, Houston, TX, USA
| | - Xiang-Dong Fu
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | - Christopher B Burge
- Program of Computational and Systems Biology, Department of Biology, MIT, Cambridge, MA, USA
| | - Brenton R Graveley
- Department of Genetics and Genome Sciences, UConn Health Center, Farmington, CT, USA
| | - Gene W Yeo
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA
| | | |
Collapse
|
10
|
Smirnov A. How global RNA-binding proteins coordinate the behaviour of RNA regulons: an information approach. Comput Struct Biotechnol J 2022; 20:6317-6338. [DOI: 10.1016/j.csbj.2022.11.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 11/07/2022] [Accepted: 11/07/2022] [Indexed: 11/13/2022] Open
|
11
|
Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes. Nat Commun 2022; 13:5332. [PMID: 36088354 PMCID: PMC9464252 DOI: 10.1038/s41467-022-32864-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 08/22/2022] [Indexed: 12/05/2022] Open
Abstract
Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene-based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for missense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a computationally efficient combination of the likelihood-ratio and score tests that found 36% more associations than the score test alone while also controlling the type-1 error. Kernel-based tests identified 13% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.
Collapse
|
12
|
Song J, Liu C, Li B, Liu L, Zeng L, Ye Z, Mao T, Wu W, Hu B. Tunable Cellular Localization and Extensive Cytoskeleton-Interplay of Reflectins. Front Cell Dev Biol 2022; 10:862011. [PMID: 35813206 PMCID: PMC9259870 DOI: 10.3389/fcell.2022.862011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 05/09/2022] [Indexed: 11/13/2022] Open
Abstract
Reflectin proteins are natural copolymers consisting of repeated canonical domains. They are located in a biophotonic system called Bragg lamellae and manipulate the dynamic structural coloration of iridocytes. Their biological functions are intriguing, but the underlying mechanism is not fully understood. Reflectin A1, A2, B1, and C were found to present distinguished cyto-/nucleoplasmic localization preferences in the work. Comparable intracellular localization was reproduced by truncated reflectin variants, suggesting a conceivable evolutionary order among reflectin proteins. The size-dependent access of reflectin variants into the nucleus demonstrated a potential model of how reflectins get into Bragg lamellae. Moreover, RfA1 was found to extensively interact with the cytoskeleton, including its binding to actin and enrichment at the microtubule organizing center. This implied that the cytoskeleton system plays a fundamental role during the organization and transportation of reflectin proteins. The findings presented here provide evidence to get an in-depth insight into the evolutionary processes and working mechanisms of reflectins, as well as novel molecular tools to achieve tunable intracellular transportation.
Collapse
Affiliation(s)
- Junyi Song
- College of Liberal Arts Science, National University of Defense Technology, Changsha, China
- *Correspondence: Junyi Song, ; Biru Hu,
| | - Chuanyang Liu
- College of Liberal Arts Science, National University of Defense Technology, Changsha, China
| | - Baoshan Li
- College of Liberal Arts Science, National University of Defense Technology, Changsha, China
| | - Liangcheng Liu
- College of Liberal Arts Science, National University of Defense Technology, Changsha, China
| | - Ling Zeng
- College of Liberal Arts Science, National University of Defense Technology, Changsha, China
| | - Zonghuang Ye
- College of Liberal Arts Science, National University of Defense Technology, Changsha, China
| | - Ting Mao
- Logistics Center, National University of Defense Technology, Changsha, China
| | - Wenjian Wu
- College of Liberal Arts Science, National University of Defense Technology, Changsha, China
| | - Biru Hu
- College of Liberal Arts Science, National University of Defense Technology, Changsha, China
- *Correspondence: Junyi Song, ; Biru Hu,
| |
Collapse
|
13
|
The potential of long noncoding RNA therapies. Trends Pharmacol Sci 2022; 43:269-280. [DOI: 10.1016/j.tips.2022.01.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 01/17/2022] [Accepted: 01/20/2022] [Indexed: 02/06/2023]
|
14
|
Rosenblum SL, Garner AL. RiPCA: An Assay for the Detection of RNA-Protein Interactions in Live Cells. Curr Protoc 2022; 2:e358. [PMID: 35113480 PMCID: PMC8852372 DOI: 10.1002/cpz1.358] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Increasing interest in studying and modulating the interactions between RNAs and their RNA-binding proteins has indicated the need for enabling technologies. Existing means of detecting RNA-protein interactions (RPIs) are often limited to biochemical or post-lysis methods or cell-based methods that require the addition of an RNA-based affinity tag, such as the MS2 hairpin, precluding them from use in detecting small or highly processed RNAs. Taking advantage of bioorthogonal chemistry- and split-luciferase-based technologies, we developed an assay for the detection of RPIs in live cells. This article details the protocol and design considerations for RiPCA, or RNA interaction with Protein-mediated Complementation Assay. © 2022 Wiley Periodicals LLC.
Collapse
Affiliation(s)
| | - Amanda L. Garner
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI,Department of Medicinal Chemistry, College of Pharmacy, University of Michigan, Ann Arbor, MI
| |
Collapse
|
15
|
Xu W, Biswas J, Singer RH, Rosbash M. Targeted RNA editing: novel tools to study post-transcriptional regulation. Mol Cell 2022; 82:389-403. [PMID: 34739873 PMCID: PMC8792254 DOI: 10.1016/j.molcel.2021.10.010] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 10/06/2021] [Accepted: 10/11/2021] [Indexed: 01/22/2023]
Abstract
RNA binding proteins (RBPs) regulate nearly all post-transcriptional processes within cells. To fully understand RBP function, it is essential to identify their in vivo targets. Standard techniques for profiling RBP targets, such as crosslinking immunoprecipitation (CLIP) and its variants, are limited or suboptimal in some situations, e.g. when compatible antibodies are not available and when dealing with small cell populations such as neuronal subtypes and primary stem cells. This review summarizes and compares several genetic approaches recently designed to identify RBP targets in such circumstances. TRIBE (targets of RNA binding proteins identified by editing), RNA tagging, and STAMP (surveying targets by APOBEC-mediated profiling) are new genetic tools useful for the study of post-transcriptional regulation and RBP identification. We describe the underlying RNA base editing technology, recent applications, and therapeutic implications.
Collapse
Affiliation(s)
- Weijin Xu
- Howard Hughes Medical Institute, Department of Biology, Brandeis University, Waltham, MA 02451, USA
| | - Jeetayu Biswas
- Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Robert H Singer
- Department of Anatomy and Structural Biology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Michael Rosbash
- Howard Hughes Medical Institute, Department of Biology, Brandeis University, Waltham, MA 02451, USA.
| |
Collapse
|
16
|
Louis JM, Agarwal A, Mondal S, Talukdar I. A global analysis on the differential regulation of RNA binding proteins (RBPs) by TNF–α as potential modulators of metabolic syndromes. BBA ADVANCES 2022; 2:100037. [PMID: 37082594 PMCID: PMC10074950 DOI: 10.1016/j.bbadva.2021.100037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Revised: 12/12/2021] [Accepted: 12/15/2021] [Indexed: 11/25/2022] Open
Abstract
Metabolic syndrome (MetS) is associated with a group of conditions, which enhances the risk of diabetes, heart diseases and stroke in the affected individuals. Earlier reports from our lab have shown that Tumor necrosis factor-α (TNF-α) significantly modulates the expression of 56 genes at the alternative splicing level which are involved in various signaling and metabolic pathways (MetS genes) connected to MetS. These MetS genes were predicted to interact with various RNA-binding proteins (RBPs) when exposed to TNF-α, resulting changes in their alternative splicing patterns. Here we are presenting data of an RNA-Seq analysis, which identified 1218 unique, and significantly regulated genes by TNF-α, 15% of which are RBPs . Among the 1218 genes, 204 genes have been identified as MetS genes by the ingenuity pathway analysis, and 10% of the MetS genes are found as RBPs. Our results also show that TNF-α changes the phosphorylation status of certain RBPs such as SR proteins, crucial players in alternative splicing, possibly via changing the activation status of certain upstream signaling molecules which also act as upstream kinases for these proteins. Taken together, these findings suggest that TNF-α influences the regulation of the RBPs at the various levels for their expression, which may lead to the alteration of the splicing pattern of the MetS genes. MetS genes acting as RBPs and are modulated by TNF-α, predict the existence of highly interconnected mechanisms which require further analysis to understand their dual roles on the onset of these diseases.
Collapse
|
17
|
De Silva D, Ferguson L, Chin GH, Smith BE, Apathy RA, Roth TL, Blaeschke F, Kudla M, Marson A, Ingolia NT, Cate JHD. Robust T cell activation requires an eIF3-driven burst in T cell receptor translation. eLife 2021; 10:e74272. [PMID: 34970966 PMCID: PMC8758144 DOI: 10.7554/elife.74272] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/30/2021] [Indexed: 11/13/2022] Open
Abstract
Activation of T cells requires a rapid surge in cellular protein synthesis. However, the role of translation initiation in the early induction of specific genes remains unclear. Here, we show human translation initiation factor eIF3 interacts with select immune system related mRNAs including those encoding the T cell receptor (TCR) subunits TCRA and TCRB. Binding of eIF3 to the TCRA and TCRB mRNA 3'-untranslated regions (3'-UTRs) depends on CD28 coreceptor signaling and regulates a burst in TCR translation required for robust T cell activation. Use of the TCRA or TCRB 3'-UTRs to control expression of an anti-CD19 chimeric antigen receptor (CAR) improves the ability of CAR-T cells to kill tumor cells in vitro. These results identify a new mechanism of eIF3-mediated translation control that can aid T cell engineering for immunotherapy applications.
Collapse
Affiliation(s)
- Dasmanthie De Silva
- Department of Molecular and Cell Biology, University of California-BerkeleyBerkeleyUnited States
- The J. David Gladstone InstitutesSan FranciscoUnited States
| | - Lucas Ferguson
- Department of Molecular and Cell Biology, University of California-BerkeleyBerkeleyUnited States
| | - Grant H Chin
- Department of Molecular and Cell Biology, University of California-BerkeleyBerkeleyUnited States
| | - Benjamin E Smith
- School of Optometry, University of California, BerkeleyBerkeleyUnited States
| | - Ryan A Apathy
- Department of Microbiology and Immunology, University of California, San FranciscoSan FranciscoUnited States
| | - Theodore L Roth
- Department of Microbiology and Immunology, University of California, San FranciscoSan FranciscoUnited States
| | | | - Marek Kudla
- Department of Molecular and Cell Biology, University of California-BerkeleyBerkeleyUnited States
| | - Alexander Marson
- Department of Microbiology and Immunology, University of California, San FranciscoSan FranciscoUnited States
- Gladstone-UCSF Institute of Genomic ImmunologySan FranciscoUnited States
- Diabetes Center, University of California, San FranciscoSan FranciscoUnited States
- Chan Zuckerberg BiohubSan FranciscoUnited States
- Department of Medicine, University of California, San FranciscoSan FranciscoUnited States
- Parker Institute for Cancer ImmunotherapySan FranciscoUnited States
- Innovative Genomics Institute, University of California, BerkeleyBerkeleyUnited States
| | - Nicholas T Ingolia
- Department of Molecular and Cell Biology, University of California-BerkeleyBerkeleyUnited States
- California Institute for Quantitative Biosciences, University of California, BerkeleyBerkeleyUnited States
| | - Jamie HD Cate
- Department of Molecular and Cell Biology, University of California-BerkeleyBerkeleyUnited States
- The J. David Gladstone InstitutesSan FranciscoUnited States
- Innovative Genomics Institute, University of California, BerkeleyBerkeleyUnited States
- California Institute for Quantitative Biosciences, University of California, BerkeleyBerkeleyUnited States
- Department of Chemistry, University of California-BerkeleyBerkeleyUnited States
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National LaboratoryBerkeleyUnited States
| |
Collapse
|
18
|
RBPSpot: Learning on appropriate contextual information for RBP binding sites discovery. iScience 2021; 24:103381. [PMID: 34841226 PMCID: PMC8605353 DOI: 10.1016/j.isci.2021.103381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 09/01/2021] [Accepted: 10/27/2021] [Indexed: 11/29/2022] Open
Abstract
Identifying the factors determining the RBP-RNA interactions remains a big challenge. It involves sparse binding motifs and a suitable sequence context for binding. The present work describes an approach to detect RBP binding sites in RNAs using an ultra-fast inexact k-mers search for statistically significant seeds. The seeds work as an anchor to evaluate the context and binding potential using flanking region information while leveraging from Deep Feed-forward Neural Network. The developed models also received support from MD-simulation studies. The implemented software, RBPSpot, scored consistently high for all the performance metrics including average accuracy of ∼90% across a large number of validated datasets. It outperformed the compared tools, including some with much complex deep-learning models, during a comprehensive benchmarking process. RBPSpot can identify RBP binding sites in the human system and can also be used to develop new models, making it a valuable resource in the area of regulatory system studies. Efficient motif anchoring helps to get good quality contextual information on binding Realistic and high granularity datasets ensure better performance of the classifiers DNN models on the contextual features outperform more complex deep learning tools RBPSpot algorithm may be used to develop RBP binding models for other species also
Collapse
|
19
|
Zhao S, Hamada M. Multi-resBind: a residual network-based multi-label classifier for in vivo RNA binding prediction and preference visualization. BMC Bioinformatics 2021; 22:554. [PMID: 34781902 PMCID: PMC8594109 DOI: 10.1186/s12859-021-04430-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 10/06/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Protein-RNA interactions play key roles in many processes regulating gene expression. To understand the underlying binding preference, ultraviolet cross-linking and immunoprecipitation (CLIP)-based methods have been used to identify the binding sites for hundreds of RNA-binding proteins (RBPs) in vivo. Using these large-scale experimental data to infer RNA binding preference and predict missing binding sites has become a great challenge. Some existing deep-learning models have demonstrated high prediction accuracy for individual RBPs. However, it remains difficult to avoid significant bias due to the experimental protocol. The DeepRiPe method was recently developed to solve this problem via introducing multi-task or multi-label learning into this field. However, this method has not reached an ideal level of prediction power due to the weak neural network architecture. RESULTS Compared to the DeepRiPe approach, our Multi-resBind method demonstrated substantial improvements using the same large-scale PAR-CLIP dataset with respect to an increase in the area under the receiver operating characteristic curve and average precision. We conducted extensive experiments to evaluate the impact of various types of input data on the final prediction accuracy. The same approach was used to evaluate the effect of loss functions. Finally, a modified integrated gradient was employed to generate attribution maps. The patterns disentangled from relative contributions according to context offer biological insights into the underlying mechanism of protein-RNA interactions. CONCLUSIONS Here, we propose Multi-resBind as a new multi-label deep-learning approach to infer protein-RNA binding preferences and predict novel interactions. The results clearly demonstrate that Multi-resBind is a promising tool to predict unknown binding sites in vivo and gain biology insights into why the neural network makes a given prediction.
Collapse
Affiliation(s)
- Shitao Zhao
- Waseda Research Institute for Science and Engineering, Waseda University, 3-4-1 Okubo Shinjuku-ku, Tokyo, 169-8555, Japan.
| | - Michiaki Hamada
- Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 3-4-1 Okubo Shinjuku-ku, Tokyo, 169-8555, Japan. .,Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL), National Institute of Advanced Industrial Science and Technology, 3-4-1 Okubo Shinjuku-ku, Tokyo, 169-8555, Japan. .,Graduate School of Medicine, Nippon Medical School, 1-1-5 Sendagi, Bunkyo-ku, Tokyo, 113-8602, Japan.
| |
Collapse
|
20
|
Griesemer D, Xue JR, Reilly SK, Ulirsch JC, Kukreja K, Davis JR, Kanai M, Yang DK, Butts JC, Guney MH, Luban J, Montgomery SB, Finucane HK, Novina CD, Tewhey R, Sabeti PC. Genome-wide functional screen of 3'UTR variants uncovers causal variants for human disease and evolution. Cell 2021; 184:5247-5260.e19. [PMID: 34534445 PMCID: PMC8487971 DOI: 10.1016/j.cell.2021.08.025] [Citation(s) in RCA: 72] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 05/25/2021] [Accepted: 08/19/2021] [Indexed: 12/11/2022]
Abstract
3' untranslated region (3'UTR) variants are strongly associated with human traits and diseases, yet few have been causally identified. We developed the massively parallel reporter assay for 3'UTRs (MPRAu) to sensitively assay 12,173 3'UTR variants. We applied MPRAu to six human cell lines, focusing on genetic variants associated with genome-wide association studies (GWAS) and human evolutionary adaptation. MPRAu expands our understanding of 3'UTR function, suggesting that simple sequences predominately explain 3'UTR regulatory activity. We adapt MPRAu to uncover diverse molecular mechanisms at base pair resolution, including an adenylate-uridylate (AU)-rich element of LEPR linked to potential metabolic evolutionary adaptations in East Asians. We nominate hundreds of 3'UTR causal variants with genetically fine-mapped phenotype associations. Using endogenous allelic replacements, we characterize one variant that disrupts a miRNA site regulating the viral defense gene TRIM14 and one that alters PILRB abundance, nominating a causal variant underlying transcriptional changes in age-related macular degeneration.
Collapse
Affiliation(s)
- Dustin Griesemer
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA 02115, USA; Department of Anesthesiology, Perioperative, and Pain Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - James R Xue
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02143, USA.
| | - Steven K Reilly
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02143, USA
| | - Jacob C Ulirsch
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Kalki Kukreja
- Department of Molecular and Cell Biology, Harvard University, Cambridge, MA 02138, USA
| | - Joe R Davis
- BigHat Biosciences, San Carlos, CA 94070, USA
| | - Masahiro Kanai
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Program in Bioinformatics and Integrative Genomics, Harvard Medical School, Boston, MA 02115, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - David K Yang
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA
| | - John C Butts
- The Jackson Laboratory, Bar Harbor, ME 04609, USA; Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME 04469, USA
| | - Mehmet H Guney
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Jeremy Luban
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01655, USA; Department of Biochemistry and Molecular Pharmacology, University of Massachusetts Medical School, Worcester, MA 01655, USA
| | - Stephen B Montgomery
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA; Department of Genetics, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Hilary K Finucane
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Carl D Novina
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA 02115, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Ryan Tewhey
- The Jackson Laboratory, Bar Harbor, ME 04609, USA; Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME 04469, USA; Tufts University School of Medicine, Boston, MA 02111, USA
| | - Pardis C Sabeti
- Broad Institute of MIT and Harvard, Cambridge, MA 02143, USA; Department Of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02143, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| |
Collapse
|
21
|
Lang B, Yang JS, Garriga-Canut M, Speroni S, Aschern M, Gili M, Hoffmann T, Tartaglia GG, Maurer SP. Matrix-screening reveals a vast potential for direct protein-protein interactions among RNA binding proteins. Nucleic Acids Res 2021; 49:6702-6721. [PMID: 34133714 PMCID: PMC8266617 DOI: 10.1093/nar/gkab490] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Revised: 04/23/2021] [Accepted: 05/20/2021] [Indexed: 01/02/2023] Open
Abstract
RNA-binding proteins (RBPs) are crucial factors of post-transcriptional gene regulation and their modes of action are intensely investigated. At the center of attention are RNA motifs that guide where RBPs bind. However, sequence motifs are often poor predictors of RBP-RNA interactions in vivo. It is hence believed that many RBPs recognize RNAs as complexes, to increase specificity and regulatory possibilities. To probe the potential for complex formation among RBPs, we assembled a library of 978 mammalian RBPs and used rec-Y2H matrix screening to detect direct interactions between RBPs, sampling > 600 K interactions. We discovered 1994 new interactions and demonstrate that interacting RBPs bind RNAs adjacently in vivo. We further find that the mRNA binding region and motif preferences of RBPs deviate, depending on their adjacently binding interaction partners. Finally, we reveal novel RBP interaction networks among major RNA processing steps and show that splicing impairing RBP mutations observed in cancer rewire spliceosomal interaction networks. The dataset we provide will be a valuable resource for understanding the combinatorial interactions of RBPs with RNAs and the resulting regulatory outcomes.
Collapse
Affiliation(s)
- Benjamin Lang
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Doctor Aiguader 88, Barcelona 08003, Spain.,Department of Structural Biology and Center of Excellence for Data-Driven Discovery, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, USA
| | - Jae-Seong Yang
- Centre de Recerca en Agrigenòmica, Consortium CSIC-IRTA-UAB-UB (CRAG), Cerdanyola del Vallès, 08193 Barcelona, Spain
| | - Mireia Garriga-Canut
- Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi 129188, UAE
| | - Silvia Speroni
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Doctor Aiguader 88, Barcelona 08003, Spain
| | - Moritz Aschern
- Centre de Recerca en Agrigenòmica, Consortium CSIC-IRTA-UAB-UB (CRAG), Cerdanyola del Vallès, 08193 Barcelona, Spain
| | - Maria Gili
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Doctor Aiguader 88, Barcelona 08003, Spain
| | - Tobias Hoffmann
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Doctor Aiguader 88, Barcelona 08003, Spain
| | - Gian Gaetano Tartaglia
- Center for Human Technologies, Istituto Italiano di Tecnologia, Via Enrico Melen 83, 16152, Genoa, Italy.,Biology and Biotechnology Department "Charles Darwin", Sapienza University of Rome, P.le A. Moro 5, Rome 00185, Italy
| | - Sebastian P Maurer
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology (BIST), Doctor Aiguader 88, Barcelona 08003, Spain.,Universitat Pompeu Fabra (UPF), Department of Experimental and Health Sciences, Barcelona, Spain
| |
Collapse
|
22
|
Sohrabi-Jahromi S, Söding J. Thermodynamic modeling reveals widespread multivalent binding by RNA-binding proteins. Bioinformatics 2021; 37:i308-i316. [PMID: 34252974 PMCID: PMC8275352 DOI: 10.1093/bioinformatics/btab300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Understanding how proteins recognize their RNA targets is essential to elucidate regulatory processes in the cell. Many RNA-binding proteins (RBPs) form complexes or have multiple domains that allow them to bind to RNA in a multivalent, cooperative manner. They can thereby achieve higher specificity and affinity than proteins with a single RNA-binding domain. However, current approaches to de novo discovery of RNA binding motifs do not take multivalent binding into account. RESULTS We present Bipartite Motif Finder (BMF), which is based on a thermodynamic model of RBPs with two cooperatively binding RNA-binding domains. We show that bivalent binding is a common strategy among RBPs, yielding higher affinity and sequence specificity. We furthermore illustrate that the spatial geometry between the binding sites can be learned from bound RNA sequences. These discovered bipartite motifs are consistent with previously known motifs and binding behaviors. Our results demonstrate the importance of multivalent binding for RNA-binding proteins and highlight the value of bipartite motif models in representing the multivalency of protein-RNA interactions. AVAILABILITY AND IMPLEMENTATION BMF source code is available at https://github.com/soedinglab/bipartite_motif_finder under a GPL license. The BMF web server is accessible at https://bmf.soedinglab.org. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Salma Sohrabi-Jahromi
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen 37077, Germany
| | - Johannes Söding
- Quantitative and Computational Biology, Max Planck Institute for Biophysical Chemistry, Göttingen 37077, Germany.,Campus-Institut Data Science (CIDAS), Göttingen 37077, Germany
| |
Collapse
|
23
|
Calviello L, Venkataramanan S, Rogowski KJ, Wyler E, Wilkins K, Tejura M, Thai B, Krol J, Filipowicz W, Landthaler M, Floor SN. DDX3 depletion represses translation of mRNAs with complex 5' UTRs. Nucleic Acids Res 2021; 49:5336-5350. [PMID: 33905506 PMCID: PMC8136831 DOI: 10.1093/nar/gkab287] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 04/02/2021] [Accepted: 04/08/2021] [Indexed: 12/13/2022] Open
Abstract
DDX3 is an RNA chaperone of the DEAD-box family that regulates translation. Ded1, the yeast ortholog of DDX3, is a global regulator of translation, whereas DDX3 is thought to preferentially affect a subset of mRNAs. However, the set of mRNAs that are regulated by DDX3 are unknown, along with the relationship between DDX3 binding and activity. Here, we use ribosome profiling, RNA-seq, and PAR-CLIP to define the set of mRNAs that are regulated by DDX3 in human cells. We find that while DDX3 binds highly expressed mRNAs, depletion of DDX3 particularly affects the translation of a small subset of the transcriptome. We further find that DDX3 binds a site on helix 16 of the human ribosomal rRNA, placing it immediately adjacent to the mRNA entry channel. Translation changes caused by depleting DDX3 levels or expressing an inactive point mutation are different, consistent with different association of these genetic variant types with disease. Taken together, this work defines the subset of the transcriptome that is responsive to DDX3 inhibition, with relevance for basic biology and disease states where DDX3 is altered.
Collapse
Affiliation(s)
- Lorenzo Calviello
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Srivats Venkataramanan
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Karol J Rogowski
- Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany
| | - Emanuel Wyler
- Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany
| | - Kevin Wilkins
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Malvika Tejura
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Bao Thai
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Jacek Krol
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
| | - Witold Filipowicz
- Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
| | - Markus Landthaler
- Berlin Institute for Medical Systems Biology, Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, 13125 Berlin, Germany.,IRI Life Sciences, Institut für Biologie, Humboldt Universität zu Berlin, Philippstraße 13, 10115 Berlin, Germany
| | - Stephen N Floor
- Department of Cell and Tissue Biology, University of California, San Francisco, San Francisco, CA 94143, USA.,Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA 94143, USA
| |
Collapse
|
24
|
Prieto C, Nguyen DTT, Liu Z, Wheat J, Perez A, Gourkanti S, Chou T, Barin E, Velleca A, Rohwetter T, Chow A, Taggart J, Savino AM, Hoskova K, Dhodapkar M, Schurer A, Barlowe TS, Vu LP, Leslie C, Steidl U, Rabadan R, Kharas MG. Transcriptional control of CBX5 by the RNA binding proteins RBMX and RBMXL1 maintains chromatin state in myeloid leukemia. NATURE CANCER 2021; 2:741-757. [PMID: 34458856 PMCID: PMC8388313 DOI: 10.1038/s43018-021-00220-w] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 05/11/2021] [Indexed: 01/08/2023]
Abstract
RNA binding proteins (RBPs) are key arbiters of post-transcriptional regulation and are found to be found dysregulated in hematological malignancies. Here, we identify the RBP RBMX and its retrogene RBMXL1 to be required for murine and human myeloid leukemogenesis. RBMX/L1 are overexpressed in acute myeloid leukemia (AML) primary patients compared to healthy individuals, and RBMX/L1 loss delayed leukemia development. RBMX/L1 loss lead to significant changes in chromatin accessibility, as well as chromosomal breaks and gaps. We found that RBMX/L1 directly bind to mRNAs, affect transcription of multiple loci, including CBX5 (HP1α), and control the nascent transcription of the CBX5 locus. Forced CBX5 expression rescued the RBMX/L1 depletion effects on cell growth and apoptosis. Overall, we determine that RBMX/L1 control leukemia cell survival by regulating chromatin state through their downstream target CBX5. These findings identify a mechanism for RBPs directly promoting transcription and suggest RBMX/L1, as well as CBX5, as potential therapeutic targets in myeloid malignancies.
Collapse
Affiliation(s)
- Camila Prieto
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Diu T T Nguyen
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Zhaoqi Liu
- Program for Mathematical Genomics, Department of Systems Biology, Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, China National Center for Bioinformation, Beijing 100101, China
| | - Justin Wheat
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY USA
| | - Alexendar Perez
- Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Saroj Gourkanti
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Timothy Chou
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ersilia Barin
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anthony Velleca
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Thomas Rohwetter
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Arthur Chow
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - James Taggart
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Angela M Savino
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Katerina Hoskova
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meera Dhodapkar
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Alexandra Schurer
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Trevor S Barlowe
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ly P Vu
- Terry Fox Laboratory, British Columbia Cancer Research Centre, Vancouver, BC, Canada; Molecular Biology and Biochemistry, Simon Fraser University, Vancouver, BC, Canada
| | - Christina Leslie
- Computational Biology Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ulrich Steidl
- Department of Cell Biology, Albert Einstein College of Medicine, Bronx, NY USA
| | - Raul Rabadan
- Program for Mathematical Genomics, Department of Systems Biology, Department of Biomedical Informatics, Columbia University Medical Center, New York, NY, USA
| | - Michael G Kharas
- Molecular Pharmacology Program and Center for Cell Engineering, Center for Stem Cell Biology, Center for Experimental Therapeutics, Center for Hematologic Malignancies, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| |
Collapse
|
25
|
Zooming in on protein-RNA interactions: a multi-level workflow to identify interaction partners. Biochem Soc Trans 2021; 48:1529-1543. [PMID: 32820806 PMCID: PMC7458403 DOI: 10.1042/bst20191059] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 07/17/2020] [Accepted: 07/20/2020] [Indexed: 02/01/2023]
Abstract
Interactions between proteins and RNA are at the base of numerous cellular regulatory and functional phenomena. The investigation of the biological relevance of non-coding RNAs has led to the identification of numerous novel RNA-binding proteins (RBPs). However, defining the RNA sequences and structures that are selectively recognised by an RBP remains challenging, since these interactions can be transient and highly dynamic, and may be mediated by unstructured regions in the protein, as in the case of many non-canonical RBPs. Numerous experimental and computational methodologies have been developed to predict, identify and verify the binding between a given RBP and potential RNA partners, but navigating across the vast ocean of data can be frustrating and misleading. In this mini-review, we propose a workflow for the identification of the RNA binding partners of putative, newly identified RBPs. The large pool of potential binders selected by in-cell experiments can be enriched by in silico tools such as catRAPID, which is able to predict the RNA sequences more likely to interact with specific RBP regions with high accuracy. The RNA candidates with the highest potential can then be analysed in vitro to determine the binding strength and to precisely identify the binding sites. The results thus obtained can furthermore validate the computational predictions, offering an all-round solution to the issue of finding the most likely RNA binding partners for a newly identified potential RBP.
Collapse
|
26
|
Fu R, Wellman K, Baldwin A, Rege J, Walters K, Hirsekorn A, Riemondy K, Rainey WE, Mukherjee N. RNA-binding proteins regulate aldosterone homeostasis in human steroidogenic cells. RNA (NEW YORK, N.Y.) 2021; 27:rna.078727.121. [PMID: 34074709 PMCID: PMC8284322 DOI: 10.1261/rna.078727.121] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/20/2021] [Indexed: 06/12/2023]
Abstract
Angiotensin II (AngII) stimulates adrenocortical cells to produce aldosterone, a master regulator of blood pressure. Despite extensive characterization of the transcriptional and enzymatic control of adrenocortical steroidogenesis, there are still major gaps in the precise regulation of AII-induced gene expression kinetics. Specifically, we do not know the regulatory contribution of RNA-binding proteins (RBPs) and RNA decay, which can control the timing of stimulus-induced gene expression. To investigate this question, we performed a high-resolution RNA-seq time course of the AngII stimulation response and 4-thiouridine pulse labeling in a steroidogenic human cell line (H295R). We identified twelve temporally distinct gene expression responses that contained mRNA encoding proteins known to be important for various steps of aldosterone production, such as cAMP signaling components and steroidogenic enzymes. AngII response kinetics for many of these mRNAs revealed a coordinated increase in both synthesis and decay. These findings were validated in primary human adrenocortical cells stimulated ex vivo with AngII. Using a candidate screen, we identified a subset of RNA-binding protein and RNA decay factors that activate or repress AngII-stimulated aldosterone production. Among the repressors of aldosterone were BTG2, which promotes deadenylation and global RNA decay. BTG2 was induced in response to AngII stimulation and promoted the repression of mRNAs encoding pro-steroidogenic factors indicating the existence of an incoherent feedforward loop controlling aldosterone homeostasis. These data support a model in which coordinated increases in transcription and decay facilitate the major transcriptomic changes required to implement a pro-steroidogenic expression program that actively resolved to prevent aldosterone overproduction.
Collapse
Affiliation(s)
- Rui Fu
- University of Colorado Denver School of Medicine
| | | | | | | | | | | | | | | | | |
Collapse
|
27
|
Yin YW, Liu KL, Lu BS, Li W, Niu YL, Zhao CM, Yang Z, Guo PY, Qi JC. RBM24 exacerbates bladder cancer progression by forming a Runx1t1/TCF4/miR-625-5p feedback loop. Exp Mol Med 2021; 53:933-946. [PMID: 34021255 PMCID: PMC8178337 DOI: 10.1038/s12276-021-00623-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 01/31/2021] [Accepted: 02/23/2021] [Indexed: 12/02/2022] Open
Abstract
RNA–binding motif protein 24 (RBM24) acts as a multifunctional determinant of cell fate, proliferation, apoptosis, and differentiation during development by regulating premRNA splicing and mRNA stability. It is also implicated in carcinogenesis, but the functions of RBM24 in bladder cancer (BC) remain unclear. In the present study, we revealed that RBM24 was upregulated in BC tissues. Importantly, we found that a higher level of RBM24 was correlated with poor prognosis in BC patients. Overexpression of RBM24 promoted BC cell proliferation, while depletion of RBM24 inhibited BC cell proliferation in vivo and in vitro. Mechanistically, RBM24 positively regulated Runx1t1 expression in BC cells by binding to and enhancing Runx1t1 mRNA stability. Furthermore, Runx1t1 in turn promoted RBM24 expression by interacting with the transcription factor TCF4 and suppressing the transcription of miR-625-5p, which directly targets RBM24 and suppresses RBM24 expression. RBM24-regulated BC cell proliferation was moderated via the Runx1t1/TCF4/miR-625-5p feedback loop. These results indicate that the RBM24/Runx1t1/TCF4/miR-625-5p positive feedback loop participates in BC progression. Disruption of this pathway may be a potential therapeutic strategy for BC treatment. A protein called RBM24 promotes progression of bladder cancer (BC) by forming a positive feedback loop with a specific transcription factor, driving cancer cell proliferation. Survival rates for BC are low, and the current imperfect understanding of the underlying mechanisms makes it difficult to treat. Ping-Ying Guo at Hebei Medical University in Shijiazhuang, China, and co-workers investigated the role of RBM24, known to be involved in other cancers, and found increased levels in BC tissues. Higher levels were associated with a poor prognosis. Further investigation revealed that RBM24 boosts levels of the transcription factor, which suppresses a molecule that in turn suppresses RBM24, forming a positive feedback loop promoting BC cell proliferation. Interrupting the feedback loop decreased tumor size in a mouse model. These results may help identify better treatments for BC.
Collapse
Affiliation(s)
- Yue-Wei Yin
- Department of Urology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, P.R. China.,Hebei Institute of Urology, Shijiazhuang, 050000, P.R. China
| | - Kai-Long Liu
- Department of Urology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, P.R. China.,Hebei Institute of Urology, Shijiazhuang, 050000, P.R. China
| | - Bao-Sai Lu
- Department of Urology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, P.R. China.,Hebei Institute of Urology, Shijiazhuang, 050000, P.R. China
| | - Wei Li
- Department of Urology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, P.R. China.,Hebei Institute of Urology, Shijiazhuang, 050000, P.R. China
| | - Ya-Lin Niu
- Department of Urology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, P.R. China
| | - Chen-Ming Zhao
- Department of Urology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, P.R. China
| | - Zhan Yang
- Department of Urology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, P.R. China.,Hebei Institute of Urology, Shijiazhuang, 050000, P.R. China
| | - Ping-Ying Guo
- Department of Urology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, P.R. China. .,Hebei Institute of Urology, Shijiazhuang, 050000, P.R. China.
| | - Jin-Chun Qi
- Department of Urology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, P.R. China. .,Hebei Institute of Urology, Shijiazhuang, 050000, P.R. China.
| |
Collapse
|
28
|
Rothamel K, Arcos S, Kim B, Reasoner C, Lisy S, Mukherjee N, Ascano M. ELAVL1 primarily couples mRNA stability with the 3' UTRs of interferon-stimulated genes. Cell Rep 2021; 35:109178. [PMID: 34038724 PMCID: PMC8225249 DOI: 10.1016/j.celrep.2021.109178] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 03/13/2021] [Accepted: 05/04/2021] [Indexed: 12/17/2022] Open
Abstract
Upon pathogen detection, the innate immune system triggers signaling events leading to upregulation of pro-inflammatory and anti-microbial mRNA transcripts. RNA-binding proteins (RBPs) interact with these critical mRNAs and regulate their fates at the post-transcriptional level. One such RBP is ELAVL1. Although significant progress has been made in understanding how embryonic lethal vision-like protein 1 (ELAVL1) regulates mRNAs, its target repertoire and binding distribution within an immunological context remain poorly understood. We overlap four high-throughput approaches to define its context-dependent targets and determine its regulatory impact during immune activation. ELAVL1 transitions from binding overwhelmingly intronic sites to 3′ UTR sites upon immune stimulation of cells, binding previously and newly expressed mRNAs. We find that ELAVL1 mediates the RNA stability of genes that regulate pathways essential to pathogen sensing and cytokine production. Our findings reveal the importance of examining RBP regulatory impact under dynamic transcriptomic events to understand their post-transcriptional regulatory roles within specific biological circuitries. Rothamel et al. show that upon immune activation, the RNA-binding protein ELAVL1 accumulates in the cytoplasm and redistributes from introns to mRNA 3′ UTRs. 3′ UTR binding confers enrichment and transcript stability. Many top-ranking transcripts are interferon-stimulated genes (ISGs), indicating that ELAVL1 is a positive regulator of an innate immune response.
Collapse
Affiliation(s)
- Katherine Rothamel
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Sarah Arcos
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Byungil Kim
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Clara Reasoner
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Samantha Lisy
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Neelanjan Mukherjee
- Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Manuel Ascano
- Department of Biochemistry, Vanderbilt University School of Medicine, Nashville, TN 37232, USA.
| |
Collapse
|
29
|
Lee S, Wei L, Zhang B, Goering R, Majumdar S, Wen J, Taliaferro JM, Lai EC. ELAV/Hu RNA binding proteins determine multiple programs of neural alternative splicing. PLoS Genet 2021; 17:e1009439. [PMID: 33826609 PMCID: PMC8055025 DOI: 10.1371/journal.pgen.1009439] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 04/19/2021] [Accepted: 02/22/2021] [Indexed: 12/15/2022] Open
Abstract
ELAV/Hu factors are conserved RNA binding proteins (RBPs) that play diverse roles in mRNA processing and regulation. The founding member, Drosophila Elav, was recognized as a vital neural factor 35 years ago. Nevertheless, little was known about its impacts on the transcriptome, and potential functional overlap with its paralogs. Building on our recent findings that neural-specific lengthened 3' UTR isoforms are co-determined by ELAV/Hu factors, we address their impacts on splicing. While only a few splicing targets of Drosophila are known, ectopic expression of each of the three family members (Elav, Fne and Rbp9) alters hundreds of cassette exon and alternative last exon (ALE) splicing choices. Reciprocally, double mutants of elav/fne, but not elav alone, exhibit opposite effects on both classes of regulated mRNA processing events in larval CNS. While manipulation of Drosophila ELAV/Hu RBPs induces both exon skipping and inclusion, characteristic ELAV/Hu motifs are enriched only within introns flanking exons that are suppressed by ELAV/Hu factors. Moreover, the roles of ELAV/Hu factors in global promotion of distal ALE splicing are mechanistically linked to terminal 3' UTR extensions in neurons, since both processes involve bypass of proximal polyadenylation signals linked to ELAV/Hu motifs downstream of cleavage sites. We corroborate the direct action of Elav in diverse modes of mRNA processing using RRM-dependent Elav-CLIP data from S2 cells. Finally, we provide evidence for conservation in mammalian neurons, which undergo broad programs of distal ALE and APA lengthening, linked to ELAV/Hu motifs downstream of regulated polyadenylation sites. Overall, ELAV/Hu RBPs orchestrate multiple broad programs of neuronal mRNA processing and isoform diversification in Drosophila and mammalian neurons.
Collapse
Affiliation(s)
- Seungjae Lee
- Developmental Biology Program, Sloan Kettering Institute, New York City, New York, United States of America
| | - Lu Wei
- Developmental Biology Program, Sloan Kettering Institute, New York City, New York, United States of America
| | - Binglong Zhang
- Developmental Biology Program, Sloan Kettering Institute, New York City, New York, United States of America
| | - Raeann Goering
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
- RNA Bioscience Initiative University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Sonali Majumdar
- Developmental Biology Program, Sloan Kettering Institute, New York City, New York, United States of America
| | - Jiayu Wen
- Department of Genome Sciences, The John Curtin School of Medical Research, The Australian National University, Canberra, Australia
| | - J. Matthew Taliaferro
- Department of Biochemistry and Molecular Genetics, University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
- RNA Bioscience Initiative University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Eric C. Lai
- Developmental Biology Program, Sloan Kettering Institute, New York City, New York, United States of America
| |
Collapse
|
30
|
Ho JJD, Man JHS, Schatz JH, Marsden PA. Translational remodeling by RNA-binding proteins and noncoding RNAs. WILEY INTERDISCIPLINARY REVIEWS-RNA 2021; 12:e1647. [PMID: 33694288 DOI: 10.1002/wrna.1647] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 02/09/2021] [Accepted: 02/10/2021] [Indexed: 12/14/2022]
Abstract
Responsible for generating the proteome that controls phenotype, translation is the ultimate convergence point for myriad upstream signals that influence gene expression. System-wide adaptive translational reprogramming has recently emerged as a pillar of cellular adaptation. As classic regulators of mRNA stability and translation efficiency, foundational studies established the concept of collaboration and competition between RNA-binding proteins (RBPs) and noncoding RNAs (ncRNAs) on individual mRNAs. Fresh conceptual innovations now highlight stress-activated, evolutionarily conserved RBP networks and ncRNAs that increase the translation efficiency of populations of transcripts encoding proteins that participate in a common cellular process. The discovery of post-transcriptional functions for long noncoding RNAs (lncRNAs) was particularly intriguing given their cell-type-specificity and historical definition as nuclear-functioning epigenetic regulators. The convergence of RBPs, lncRNAs, and microRNAs on functionally related mRNAs to enable adaptive protein synthesis is a newer biological paradigm that highlights their role as "translatome (protein output) remodelers" and reinvigorates the paradigm of "RNA operons." Together, these concepts modernize our understanding of cellular stress adaptation and strategies for therapeutic development. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications Translation > Translation Regulation Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.
Collapse
Affiliation(s)
- J J David Ho
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, Florida, USA.,Division of Hematology, Department of Medicine, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Jeffrey H S Man
- Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.,Department of Medicine, University of Toronto, Toronto, Ontario, Canada.,Department of Respirology, University Health Network, Latner Thoracic Research Laboratories, University of Toronto, Toronto, Ontario, Canada
| | - Jonathan H Schatz
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, Florida, USA.,Division of Hematology, Department of Medicine, Miller School of Medicine, University of Miami, Miami, Florida, USA
| | - Philip A Marsden
- Keenan Research Centre, Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.,Department of Medicine, University of Toronto, Toronto, Ontario, Canada
| |
Collapse
|
31
|
Hafner M, Katsantoni M, Köster T, Marks J, Mukherjee J, Staiger D, Ule J, Zavolan M. CLIP and complementary methods. ACTA ACUST UNITED AC 2021. [DOI: 10.1038/s43586-021-00018-1] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
|
32
|
Louis JM, Agarwal A, Aduri R, Talukdar I. Global analysis of RNA-protein interactions in TNF-α induced alternative splicing in metabolic disorders. FEBS Lett 2021; 595:476-490. [PMID: 33417721 DOI: 10.1002/1873-3468.14029] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2020] [Revised: 11/26/2020] [Accepted: 12/10/2020] [Indexed: 12/27/2022]
Abstract
In this report, using the database of RNA-binding protein specificities (RBPDB) and our previously published RNA-seq data, we analyzed the interactions between RNA and RNA-binding proteins to decipher the role of alternative splicing in metabolic disorders induced by TNF-α. We identified 13 395 unique RNA-RBP interactions, including 385 unique RNA motifs and 35 RBPs, some of which (including MBNL-1 and 3, ZFP36, ZRANB2, and SNRPA) are transcriptionally regulated by TNF-α. In addition to some previously reported RBPs, such as RBMX and HuR/ELAVL1, we found a few novel RBPs, such as ZRANB2 and SNRPA, to be involved in the regulation of metabolic syndrome-associated genes that contain an enrichment of tetrameric RNA sequences (AUUU). Taken together, this study paves the way for novel RNA-protein interaction-based therapeutics for treating metabolic syndromes.
Collapse
Affiliation(s)
- Jiss Maria Louis
- Department of Biological Sciences, BITS Pilani, Zuarinagar, India
| | - Arjun Agarwal
- Department of Computer Science, BITS Pilani, Zuarinagar, India
| | - Raviprasad Aduri
- Department of Biological Sciences, BITS Pilani, Zuarinagar, India
| | - Indrani Talukdar
- Department of Biological Sciences, BITS Pilani, Zuarinagar, India
| |
Collapse
|
33
|
Andrzejewska A, Zawadzka M, Pachulska-Wieczorek K. On the Way to Understanding the Interplay between the RNA Structure and Functions in Cells: A Genome-Wide Perspective. Int J Mol Sci 2020; 21:E6770. [PMID: 32942713 PMCID: PMC7554983 DOI: 10.3390/ijms21186770] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 09/04/2020] [Accepted: 09/11/2020] [Indexed: 12/22/2022] Open
Abstract
RNAs adopt specific structures in order to perform their biological activities. The structure of RNA is an important layer of gene expression regulation, and can impact a plethora of cellular processes, starting with transcription, RNA processing, and translation, and ending with RNA turnover. The development of high-throughput technologies has enabled a deeper insight into the sophisticated interplay between the structure of the cellular transcriptome and the living cells environment. In this review, we present the current view on the RNA structure in vivo resulting from the most recent transcriptome-wide studies in different organisms, including mammalians, yeast, plants, and bacteria. We focus on the relationship between the mRNA structure and translation, mRNA stability and degradation, protein binding, and RNA posttranscriptional modifications.
Collapse
Affiliation(s)
| | | | - Katarzyna Pachulska-Wieczorek
- Institute of Bioorganic Chemistry, Polish Academy of Sciences, Department of Structure and Function of Retrotransposons, Noskowskiego 12/14, 61-704 Poznan, Poland; (A.A.); (M.Z.)
| |
Collapse
|
34
|
Sternburg EL, Karginov FV. Global Approaches in Studying RNA-Binding Protein Interaction Networks. Trends Biochem Sci 2020; 45:593-603. [DOI: 10.1016/j.tibs.2020.03.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 02/28/2020] [Accepted: 03/09/2020] [Indexed: 12/24/2022]
|
35
|
Ye X, Jankowsky E. High throughput approaches to study RNA-protein interactions in vitro. Methods 2020; 178:3-10. [PMID: 31494245 PMCID: PMC7071787 DOI: 10.1016/j.ymeth.2019.09.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 06/07/2019] [Accepted: 09/01/2019] [Indexed: 02/08/2023] Open
Abstract
To understand the regulation of gene expression it is critical to determine how proteins interact with and discriminate between different RNAs. In this review, we discuss experimental techniques that utilize high throughput approaches to characterize the interactions of proteins with large numbers of RNAs in vitro. We describe the underlying principles for the main methods, briefly discuss their scope and limitations, and outline how insight from the techniques contributes to our understanding of specificity for RNA-protein interactions.
Collapse
Affiliation(s)
- Xuan Ye
- Center for RNA Science and Therapeutics, School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Eckhard Jankowsky
- Center for RNA Science and Therapeutics, School of Medicine, Case Western Reserve University, Cleveland, OH, United States.
| |
Collapse
|
36
|
Characterization of RNP Networks of PUM1 and PUM2 Post-Transcriptional Regulators in TCam-2 Cells, a Human Male Germ Cell Model. Cells 2020; 9:cells9040984. [PMID: 32316190 PMCID: PMC7226987 DOI: 10.3390/cells9040984] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 04/09/2020] [Accepted: 04/13/2020] [Indexed: 12/28/2022] Open
Abstract
Mammalian Pumilio (PUM) proteins are sequence-specific, RNA-binding proteins (RBPs) with wide-ranging roles. They are involved in germ cell development, which has functional implications in development and fertility. Although human PUM1 and PUM2 are closely related to each other and recognize the same RNA binding motif, there is some evidence for functional diversity. To address that problem, first we used RIP-Seq and RNA-Seq approaches, and identified mRNA pools regulated by PUM1 and PUM2 proteins in the TCam-2 cell line, a human male germ cell model. Second, applying global mass spectrometry-based profiling, we identified distinct PUM1- and PUM2-interacting putative protein cofactors, most of them involved in RNA processing. Third, combinatorial analysis of RIP and RNA-Seq, mass spectrometry, and RNA motif enrichment analysis revealed that PUM1 and PUM2 form partially varied RNP-regulatory networks (RNA regulons), which indicate different roles in human reproduction and testicular tumorigenesis. Altogether, this work proposes that protein paralogues with very similar and evolutionary highly conserved functional domains may play divergent roles in the cell by combining with different sets of protein cofactors. Our findings highlight the versatility of PUM paralogue-based post-transcriptional regulation, offering insight into the mechanisms underlying their diverse biological roles and diseases resulting from their dysfunction.
Collapse
|
37
|
Ghanbari M, Ohler U. Deep neural networks for interpreting RNA-binding protein target preferences. Genome Res 2020; 30:214-226. [PMID: 31992613 PMCID: PMC7050519 DOI: 10.1101/gr.247494.118] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 01/07/2020] [Indexed: 11/29/2022]
Abstract
Deep learning has become a powerful paradigm to analyze the binding sites of regulatory factors including RNA-binding proteins (RBPs), owing to its strength to learn complex features from possibly multiple sources of raw data. However, the interpretability of these models, which is crucial to improve our understanding of RBP binding preferences and functions, has not yet been investigated in significant detail. We have designed a multitask and multimodal deep neural network for characterizing in vivo RBP targets. The model incorporates not only the sequence but also the region type of the binding sites as input, which helps the model to boost the prediction performance. To interpret the model, we quantified the contribution of the input features to the predictive score of each RBP. Learning across multiple RBPs at once, we are able to avoid experimental biases and to identify the RNA sequence motifs and transcript context patterns that are the most important for the predictions of each individual RBP. Our findings are consistent with known motifs and binding behaviors and can provide new insights about the regulatory functions of RBPs.
Collapse
Affiliation(s)
- Mahsa Ghanbari
- The Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, 10115 Berlin, Germany
| | - Uwe Ohler
- The Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine, 10115 Berlin, Germany.,Department of Biology, Humboldt Universität zu Berlin, 10117 Berlin, Germany.,Department of Computer Science, Humboldt Universität zu Berlin, 10117 Berlin, Germany
| |
Collapse
|
38
|
Meyer C, Garzia A, Morozov P, Molina H, Tuschl T. The G3BP1-Family-USP10 Deubiquitinase Complex Rescues Ubiquitinated 40S Subunits of Ribosomes Stalled in Translation from Lysosomal Degradation. Mol Cell 2020; 77:1193-1205.e5. [PMID: 31981475 DOI: 10.1016/j.molcel.2019.12.024] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 11/11/2019] [Accepted: 12/20/2019] [Indexed: 02/05/2023]
Abstract
Ribosome-associated quality control (RQC) purges aberrant mRNAs and nascent polypeptides in a multi-step molecular process initiated by the E3 ligase ZNF598 through sensing of ribosomes collided at aberrant mRNAs and monoubiquitination of distinct small ribosomal subunit proteins. We show that G3BP1-family-USP10 complexes are required for deubiquitination of RPS2, RPS3, and RPS10 to rescue modified 40S subunits from programmed degradation. Knockout of USP10 or G3BP1 family proteins increased lysosomal ribosomal degradation and perturbed ribosomal subunit stoichiometry, both of which were rescued by a single K214R substitution of RPS3. While the majority of RPS2 and RPS3 monoubiquitination resulted from ZNF598-dependent sensing of ribosome collisions initiating RQC, another minor pathway contributed to their monoubiquitination. G3BP1 family proteins have long been considered RNA-binding proteins, however, our results identified 40S subunits and associated mRNAs as their predominant targets, a feature shared by stress granules to which G3BP1 family proteins localize under stress.
Collapse
Affiliation(s)
- Cindy Meyer
- Laboratory for RNA Molecular Biology, The Rockefeller University, 1230 York Ave, Box 186, New York, NY 10065, USA
| | - Aitor Garzia
- Laboratory for RNA Molecular Biology, The Rockefeller University, 1230 York Ave, Box 186, New York, NY 10065, USA
| | - Pavel Morozov
- Laboratory for RNA Molecular Biology, The Rockefeller University, 1230 York Ave, Box 186, New York, NY 10065, USA
| | - Henrik Molina
- Proteomics Resource Center, The Rockefeller University, 1230 York Ave, Box 105, New York, NY 10065, USA
| | - Thomas Tuschl
- Laboratory for RNA Molecular Biology, The Rockefeller University, 1230 York Ave, Box 186, New York, NY 10065, USA.
| |
Collapse
|
39
|
Licatalosi DD, Ye X, Jankowsky E. Approaches for measuring the dynamics of RNA-protein interactions. WILEY INTERDISCIPLINARY REVIEWS. RNA 2020; 11:e1565. [PMID: 31429211 PMCID: PMC7006490 DOI: 10.1002/wrna.1565] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 07/20/2019] [Accepted: 07/25/2019] [Indexed: 12/17/2022]
Abstract
RNA-protein interactions are pivotal for the regulation of gene expression from bacteria to human. RNA-protein interactions are dynamic; they change over biologically relevant timescales. Understanding the regulation of gene expression at the RNA level therefore requires knowledge of the dynamics of RNA-protein interactions. Here, we discuss the main experimental approaches to measure dynamic aspects of RNA-protein interactions. We cover techniques that assess dynamics of cellular RNA-protein interactions that accompany biological processes over timescales of hours or longer and techniques measuring the kinetic dynamics of RNA-protein interactions in vitro. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes RNA Evolution and Genomics > Ribonomics.
Collapse
Affiliation(s)
- Donny D Licatalosi
- Center for RNA Science and Therapeutics, School of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Xuan Ye
- Center for RNA Science and Therapeutics, School of Medicine, Case Western Reserve University, Cleveland, Ohio
| | - Eckhard Jankowsky
- Center for RNA Science and Therapeutics, School of Medicine, Case Western Reserve University, Cleveland, Ohio
| |
Collapse
|
40
|
Quattrone A, Dassi E. The Architecture of the Human RNA-Binding Protein Regulatory Network. iScience 2019; 21:706-719. [PMID: 31733516 PMCID: PMC6864347 DOI: 10.1016/j.isci.2019.10.058] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2019] [Revised: 10/01/2019] [Accepted: 10/28/2019] [Indexed: 12/22/2022] Open
Abstract
RNA-binding proteins (RBPs) are key players of post-transcriptional regulation of gene expression, relying on competitive and cooperative interactions to fine-tune their action. Several studies have described individual interactions of RBPs with RBP mRNAs. Here we present a systematic network investigation of fifty thousand interactions between RBPs and the UTRs of RBP mRNAs. We identified two structural features in this network. RBP clusters are groups of densely interconnected RBPs co-binding their targets, suggesting a tight control of cooperative and competitive behaviors. RBP chains are hierarchical structures connecting RBP clusters and driven by evolutionarily ancient RBPs. These features suggest that RBP chains may coordinate the different cell programs controlled by RBP clusters. Under this model, the regulatory signal flows through chains from one cluster to another, implementing elaborate regulatory plans. This work thus suggests RBP-RBP interactions as a backbone driving post-transcriptional regulation of gene expression to control RBPs action on their targets. The RBP-RBP network is a robust and efficient hierarchical structure The RBP-RBP network is formed by RBP clusters and chains RBP-RBP interactions cluster to cooperate and compete on common target mRNAs RBP chains are master regulatory units of the cell led by ancient RBPs
Collapse
Affiliation(s)
- Alessandro Quattrone
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, TN 38123, Italy
| | - Erik Dassi
- Department of Cellular, Computational and Integrative Biology (CIBIO), University of Trento, Trento, TN 38123, Italy.
| |
Collapse
|