1
|
Iosub IA, Wilkins OG, Ule J. Riboseq-flow: A streamlined, reliable pipeline for ribosome profiling data analysis and quality control. Wellcome Open Res 2024; 9:179. [PMID: 38846930 PMCID: PMC11153996 DOI: 10.12688/wellcomeopenres.21000.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/12/2024] [Indexed: 06/09/2024] Open
Abstract
Ribosome profiling is a powerful technique to study translation at a transcriptome-wide level. However, ensuring good data quality is paramount for accurate interpretation, as is ensuring that the analyses are reproducible. We introduce a new Nextflow DSL2 pipeline, riboseq-flow, designed for processing and comprehensive quality control of ribosome profiling experiments. Riboseq-flow is user-friendly, versatile and upholds high standards in reproducibility, scalability, portability, version control and continuous integration. It enables users to efficiently analyse multiple samples in parallel and helps them evaluate the quality and utility of their data based on the detailed metrics and visualisations that are automatically generated. Riboseq-flow is available at https://github.com/iraiosub/riboseq-flow.
Collapse
Affiliation(s)
- Ira A. Iosub
- The Francis Crick Institute, London, England, UK
- UK Dementia Research Institute at King's College London, London, UK
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Oscar G. Wilkins
- The Francis Crick Institute, London, England, UK
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Jernej Ule
- The Francis Crick Institute, London, England, UK
- UK Dementia Research Institute at King's College London, London, UK
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| |
Collapse
|
2
|
Shao B, Yan J, Zhang J, Liu L, Chen Y, Buskirk AR. Riboformer: a deep learning framework for predicting context-dependent translation dynamics. Nat Commun 2024; 15:2011. [PMID: 38443396 PMCID: PMC10915169 DOI: 10.1038/s41467-024-46241-8] [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: 05/10/2023] [Accepted: 02/18/2024] [Indexed: 03/07/2024] Open
Abstract
Translation elongation is essential for maintaining cellular proteostasis, and alterations in the translational landscape are associated with a range of diseases. Ribosome profiling allows detailed measurements of translation at the genome scale. However, it remains unclear how to disentangle biological variations from technical artifacts in these data and identify sequence determinants of translation dysregulation. Here we present Riboformer, a deep learning-based framework for modeling context-dependent changes in translation dynamics. Riboformer leverages the transformer architecture to accurately predict ribosome densities at codon resolution. When trained on an unbiased dataset, Riboformer corrects experimental artifacts in previously unseen datasets, which reveals subtle differences in synonymous codon translation and uncovers a bottleneck in translation elongation. Further, we show that Riboformer can be combined with in silico mutagenesis to identify sequence motifs that contribute to ribosome stalling across various biological contexts, including aging and viral infection. Our tool offers a context-aware and interpretable approach for standardizing ribosome profiling datasets and elucidating the regulatory basis of translation kinetics.
Collapse
Affiliation(s)
- Bin Shao
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Jiawei Yan
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Jing Zhang
- Biological Design Center, Boston University, Boston, MA, USA
| | - Lili Liu
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Ye Chen
- Key Laboratory of Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Allen R Buskirk
- Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| |
Collapse
|
3
|
Weber M, Sogues A, Yus E, Burgos R, Gallo C, Martínez S, Lluch‐Senar M, Serrano L. Comprehensive quantitative modeling of translation efficiency in a genome-reduced bacterium. Mol Syst Biol 2023; 19:e11301. [PMID: 37642167 PMCID: PMC10568206 DOI: 10.15252/msb.202211301] [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: 08/16/2022] [Revised: 07/17/2023] [Accepted: 07/24/2023] [Indexed: 08/31/2023] Open
Abstract
Translation efficiency has been mainly studied by ribosome profiling, which only provides an incomplete picture of translation kinetics. Here, we integrated the absolute quantifications of tRNAs, mRNAs, RNA half-lives, proteins, and protein half-lives with ribosome densities and derived the initiation and elongation rates for 475 genes (67% of all genes), 73 with high precision, in the bacterium Mycoplasma pneumoniae (Mpn). We found that, although the initiation rate varied over 160-fold among genes, most of the known factors had little impact on translation efficiency. Local codon elongation rates could not be fully explained by the adaptation to tRNA abundances, which varied over 100-fold among tRNA isoacceptors. We provide a comprehensive quantitative view of translation efficiency, which suggests the existence of unidentified mechanisms of translational regulation in Mpn.
Collapse
Affiliation(s)
- Marc Weber
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Adrià Sogues
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Eva Yus
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Raul Burgos
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Carolina Gallo
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Sira Martínez
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Maria Lluch‐Senar
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
| | - Luis Serrano
- Centre for Genomic Regulation (CRG)The Barcelona Institute of Science and TechnologyBarcelonaSpain
- Universitat Pompeu Fabra (UPF)BarcelonaSpain
- ICREABarcelonaSpain
| |
Collapse
|
4
|
Pu T, Peddle A, Zhu J, Tejpar S, Verbandt S. Neoantigen identification: Technological advances and challenges. Methods Cell Biol 2023; 183:265-302. [PMID: 38548414 DOI: 10.1016/bs.mcb.2023.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.
Collapse
Affiliation(s)
- Ting Pu
- Digestive Oncology Unit, KULeuven, Leuven, Belgium
| | | | - Jingjing Zhu
- de Duve Institute, Université catholique de Louvain, Brussels, Belgium
| | | | | |
Collapse
|
5
|
Shao B, Yan J, Zhang J, Buskirk AR. Riboformer: A Deep Learning Framework for Predicting Context-Dependent Translation Dynamics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.24.538053. [PMID: 37163112 PMCID: PMC10168224 DOI: 10.1101/2023.04.24.538053] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Translation elongation is essential for maintaining cellular proteostasis, and alterations in the translational landscape are associated with a range of diseases. Ribosome profiling allows detailed measurement of translation at genome scale. However, it remains unclear how to disentangle biological variations from technical artifacts and identify sequence determinant of translation dysregulation. Here we present Riboformer, a deep learning-based framework for modeling context-dependent changes in translation dynamics. Riboformer leverages the transformer architecture to accurately predict ribosome densities at codon resolution. It corrects experimental artifacts in previously unseen datasets, reveals subtle differences in synonymous codon translation and uncovers a bottleneck in protein synthesis. Further, we show that Riboformer can be combined with in silico mutagenesis analysis to identify sequence motifs that contribute to ribosome stalling across various biological contexts, including aging and viral infection. Our tool offers a context-aware and interpretable approach for standardizing ribosome profiling datasets and elucidating the regulatory basis of translation kinetics.
Collapse
Affiliation(s)
- Bin Shao
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
- Present address: Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jiawei Yan
- Department of Chemistry, Stanford University, Stanford, CA, USA
| | - Jing Zhang
- Biological Design Center, Boston University, Boston, MA, USA
| | - Allen R. Buskirk
- Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, USA
| |
Collapse
|
6
|
Shiraishi C, Matsumoto A, Ichihara K, Yamamoto T, Yokoyama T, Mizoo T, Hatano A, Matsumoto M, Tanaka Y, Matsuura-Suzuki E, Iwasaki S, Matsushima S, Tsutsui H, Nakayama KI. RPL3L-containing ribosomes determine translation elongation dynamics required for cardiac function. Nat Commun 2023; 14:2131. [PMID: 37080962 PMCID: PMC10119107 DOI: 10.1038/s41467-023-37838-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 04/03/2023] [Indexed: 04/22/2023] Open
Abstract
Although several ribosomal protein paralogs are expressed in a tissue-specific manner, how these proteins affect translation and why they are required only in certain tissues have remained unclear. Here we show that RPL3L, a paralog of RPL3 specifically expressed in heart and skeletal muscle, influences translation elongation dynamics. Deficiency of RPL3L-containing ribosomes in RPL3L knockout male mice resulted in impaired cardiac contractility. Ribosome occupancy at mRNA codons was found to be altered in the RPL3L-deficient heart, and the changes were negatively correlated with those observed in myoblasts overexpressing RPL3L. RPL3L-containing ribosomes were less prone to collisions compared with RPL3-containing canonical ribosomes. Although the loss of RPL3L-containing ribosomes altered translation elongation dynamics for the entire transcriptome, its effects were most pronounced for transcripts related to cardiac muscle contraction and dilated cardiomyopathy, with the abundance of the encoded proteins being correspondingly decreased. Our results provide further insight into the mechanisms and physiological relevance of tissue-specific translational regulation.
Collapse
Affiliation(s)
- Chisa Shiraishi
- Division of Cell Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Fukuoka, 812-8582, Japan
| | - Akinobu Matsumoto
- Division of Cell Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Fukuoka, 812-8582, Japan.
| | - Kazuya Ichihara
- Division of Cell Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Fukuoka, 812-8582, Japan
| | - Taishi Yamamoto
- Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, 812-8582, Japan
| | - Takeshi Yokoyama
- Graduate School of Life Sciences, Tohoku University, Sendai, Miyagi, 980-8577, Japan
| | - Taisuke Mizoo
- Division of Cell Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Fukuoka, 812-8582, Japan
| | - Atsushi Hatano
- Department of Omics and Systems Biology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Niigata, 951-8510, Japan
| | - Masaki Matsumoto
- Department of Omics and Systems Biology, Graduate School of Medical and Dental Sciences, Niigata University, Niigata, Niigata, 951-8510, Japan
| | - Yoshikazu Tanaka
- Graduate School of Life Sciences, Tohoku University, Sendai, Miyagi, 980-8577, Japan
| | - Eriko Matsuura-Suzuki
- RNA Systems Biochemistry Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama, 351-0198, Japan
| | - Shintaro Iwasaki
- RNA Systems Biochemistry Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama, 351-0198, Japan
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, 277-8561, Japan
| | - Shouji Matsushima
- Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, 812-8582, Japan
| | - Hiroyuki Tsutsui
- Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University, Fukuoka, Fukuoka, 812-8582, Japan
| | - Keiichi I Nakayama
- Division of Cell Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Fukuoka, 812-8582, Japan.
| |
Collapse
|
7
|
Mok A, Tunney R, Benegas G, Wallace EWJ, Lareau LF. choros: correction of sequence-based biases for accurate quantification of ribosome profiling data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.21.529452. [PMID: 36865295 PMCID: PMC9980091 DOI: 10.1101/2023.02.21.529452] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
Abstract
Ribosome profiling quantifies translation genome-wide by sequencing ribosome-protected fragments, or footprints. Its single-codon resolution allows identification of translation regulation, such as ribosome stalls or pauses, on individual genes. However, enzyme preferences during library preparation lead to pervasive sequence artifacts that obscure translation dynamics. Widespread over- and under-representation of ribosome footprints can dominate local footprint densities and skew estimates of elongation rates by up to five fold. To address these biases and uncover true patterns of translation, we present choros, a computational method that models ribosome footprint distributions to provide bias-corrected footprint counts. choros uses negative binomial regression to accurately estimate two sets of parameters: (i) biological contributions from codon-specific translation elongation rates; and (ii) technical contributions from nuclease digestion and ligation efficiencies. We use these parameter estimates to generate bias correction factors that eliminate sequence artifacts. Applying choros to multiple ribosome profiling datasets, we are able to accurately quantify and attenuate ligation biases to provide more faithful measurements of ribosome distribution. We show that a pattern interpreted as pervasive ribosome pausing near the beginning of coding regions is likely to arise from technical biases. Incorporating choros into standard analysis pipelines will improve biological discovery from measurements of translation.
Collapse
Affiliation(s)
- Amanda Mok
- Center for Computational Biology, University of California, Berkeley
| | - Robert Tunney
- Center for Computational Biology, University of California, Berkeley
| | - Gonzalo Benegas
- Center for Computational Biology, University of California, Berkeley
| | | | - Liana F. Lareau
- Center for Computational Biology, University of California, Berkeley
- Department of Bioengineering, University of California, Berkeley
| |
Collapse
|
8
|
Hedayioglu F, Mead EJ, O'Connor PBF, Skiotys M, Sansom OJ, Mallucci GR, Willis AE, Baranov PV, Smales CM, von der Haar T. Evaluating data integrity in ribosome footprinting datasets through modelled polysome profiles. Nucleic Acids Res 2022; 50:e112. [PMID: 35979952 PMCID: PMC9638929 DOI: 10.1093/nar/gkac705] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 07/23/2022] [Accepted: 08/07/2022] [Indexed: 11/30/2022] Open
Abstract
The assessment of transcriptome-wide ribosome binding to mRNAs is useful for studying the dynamic regulation of protein synthesis. Two methods frequently applied in eukaryotic cells that operate at different levels of resolution are polysome profiling, which reveals the distribution of ribosome loads across the transcriptome, and ribosome footprinting (also termed ribosome profiling or Ribo-Seq), which when combined with appropriate data on mRNA expression can reveal ribosome densities on individual transcripts. In this study we develop methods for relating the information content of these two methods to one another, by reconstructing theoretical polysome profiles from ribosome footprinting data. Our results validate both approaches as experimental tools. Although we show that both methods can yield highly consistent data, some published ribosome footprinting datasets give rise to reconstructed polysome profiles with non-physiological features. We trace these aberrant features to inconsistencies in RNA and Ribo-Seq data when compared to datasets yielding physiological polysome profiles, thereby demonstrating that modelled polysomes are useful for assessing global dataset properties such as its quality in a simple, visual approach. Aside from using polysome profile reconstructions on published datasets, we propose that this also provides a useful tool for validating new ribosome footprinting datasets in early stages of analyses.
Collapse
Affiliation(s)
- Fabio Hedayioglu
- Kent Fungal Group, School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury CT2 7NJ, UK
| | - Emma J Mead
- Industrial Biotechnology Centre, School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury CT2 7NJ, UK
| | | | - Matas Skiotys
- Kent Fungal Group, School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury CT2 7NJ, UK
| | - Owen J Sansom
- Cancer Research UK Beatson Institute, Garscube Estate, Switchback Road, Glasgow G61 1BD, UK
- Institute of Cancer Sciences, University of Glasgow, Garscube Estate, Switchback Road, Glasgow G61 1QH, UK
| | - Giovanna R Mallucci
- UK Dementia Research Institute at the University of Cambridge and Department of Clinical Neurosciences, Island Research Building, Cambridge Biomedical Campus, Cambridge CB2 0XY, UK
| | - Anne E Willis
- MRC Toxciology Unit, University of Cambridge, Tennis Court Rd, Cambridge CB2 1QR, UK
| | - Pavel V Baranov
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - C Mark Smales
- Industrial Biotechnology Centre, School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury CT2 7NJ, UK
| | - Tobias von der Haar
- Kent Fungal Group, School of Biosciences, Division of Natural Sciences, University of Kent, Canterbury CT2 7NJ, UK
| |
Collapse
|
9
|
Komarova ES, Dontsova OA, Pyshnyi DV, Kabilov MR, Sergiev PV. Flow-Seq Method: Features and Application in Bacterial Translation Studies. Acta Naturae 2022; 14:20-37. [PMID: 36694903 PMCID: PMC9844084 DOI: 10.32607/actanaturae.11820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/11/2022] [Indexed: 01/22/2023] Open
Abstract
The Flow-seq method is based on using reporter construct libraries, where a certain element regulating the gene expression of fluorescent reporter proteins is represented in many thousands of variants. Reporter construct libraries are introduced into cells, sorted according to their fluorescence level, and then subjected to next-generation sequencing. Therefore, it turns out to be possible to identify patterns that determine the expression efficiency, based on tens and hundreds of thousands of reporter constructs in one experiment. This method has become common in evaluating the efficiency of protein synthesis simultaneously by multiple mRNA variants. However, its potential is not confined to this area. In the presented review, a comparative analysis of the Flow-seq method and other alternative approaches used for translation efficiency evaluation of mRNA was carried out; the features of its application and the results obtained by Flow-seq were also considered.
Collapse
Affiliation(s)
- E. S. Komarova
- Institute of Functional Genomics, Lomonosov Moscow State University, Moscow, 119234 Russia
| | - O. A. Dontsova
- Department of Chemistry, Lomonosov Moscow State University, Moscow, 119234 Russia
- Skolkovo Institute of Science and Technology, Moscow, 121205 Russia
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119234 Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Moscow 117437 Russia
| | - D. V. Pyshnyi
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090 Russia
| | - M. R. Kabilov
- Institute of Chemical Biology and Fundamental Medicine, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, 630090 Russia
| | - P. V. Sergiev
- Institute of Functional Genomics, Lomonosov Moscow State University, Moscow, 119234 Russia
- Department of Chemistry, Lomonosov Moscow State University, Moscow, 119234 Russia
- Skolkovo Institute of Science and Technology, Moscow, 121205 Russia
- Belozersky Institute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, 119234 Russia
| |
Collapse
|
10
|
Bagheri A, Astafev A, Al-Hashimy T, Jiang P. Tracing Translational Footprint by Ribo-Seq: Principle, Workflow, and Applications to Understand the Mechanism of Human Diseases. Cells 2022; 11:cells11192966. [PMID: 36230928 PMCID: PMC9562884 DOI: 10.3390/cells11192966] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 09/02/2022] [Accepted: 09/19/2022] [Indexed: 11/30/2022] Open
Abstract
RNA-seq has been widely used as a high-throughput method to characterize transcript dynamic changes in a broad context, such as development and diseases. However, whether RNA-seq-estimated transcriptional dynamics can be translated into protein level changes is largely unknown. Ribo-seq (Ribosome profiling) is an emerging technology that allows for the investigation of the translational footprint via profiling ribosome-bounded mRNA fragments. Ribo-seq coupled with RNA-seq will allow us to understand the transcriptional and translational control of the fundamental biological process and human diseases. This review focuses on discussing the principle, workflow, and applications of Ribo-seq to study human diseases.
Collapse
Affiliation(s)
- Atefeh Bagheri
- Department of Biological, Geological and Environmental Sciences (BGES), Cleveland State University, Cleveland, OH 44115, USA
- Center for Gene Regulation in Health and Disease (GRHD), Cleveland State University, Cleveland, OH 44115, USA
| | - Artem Astafev
- Department of Biological, Geological and Environmental Sciences (BGES), Cleveland State University, Cleveland, OH 44115, USA
- Center for Gene Regulation in Health and Disease (GRHD), Cleveland State University, Cleveland, OH 44115, USA
| | - Tara Al-Hashimy
- Department of Biological, Geological and Environmental Sciences (BGES), Cleveland State University, Cleveland, OH 44115, USA
| | - Peng Jiang
- Department of Biological, Geological and Environmental Sciences (BGES), Cleveland State University, Cleveland, OH 44115, USA
- Center for Gene Regulation in Health and Disease (GRHD), Cleveland State University, Cleveland, OH 44115, USA
- Center for Applied Data Analysis and Modeling (ADAM), Cleveland State University, Cleveland, OH 44115, USA
- Center for RNA Science and Therapeutics, School of Medicine, Case Western Reserve University, Cleveland, OH 44106, USA
- Correspondence: ; Tel.: +1-(216)-687-3917
| |
Collapse
|
11
|
Veltri AJ, D'Orazio KN, Lessen LN, Loll-Krippleber R, Brown GW, Green R. Distinct elongation stalls during translation are linked with distinct pathways for mRNA degradation. eLife 2022; 11:e76038. [PMID: 35894211 PMCID: PMC9352352 DOI: 10.7554/elife.76038] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 07/25/2022] [Indexed: 11/13/2022] Open
Abstract
Key protein adapters couple translation to mRNA decay on specific classes of problematic mRNAs in eukaryotes. Slow decoding on non-optimal codons leads to codon-optimality-mediated decay (COMD) and prolonged arrest at stall sites leads to no-go decay (NGD). The identities of the decay factors underlying these processes and the mechanisms by which they respond to translational distress remain open areas of investigation. We use carefully designed reporter mRNAs to perform genetic screens and functional assays in Saccharomyces cerevisiae. We characterize the roles of Hel2, Syh1, and Smy2 in coordinating translational repression and mRNA decay on NGD reporter mRNAs, finding that Syh1 and, to a lesser extent its paralog Smy2, act in a distinct pathway from Hel2. This Syh1/Smy2-mediated pathway acts as a redundant, compensatory pathway to elicit NGD when Hel2-dependent NGD is impaired. Importantly, we observe that these NGD factors are not involved in the degradation of mRNAs enriched in non-optimal codons. Further, we establish that a key factor previously implicated in COMD, Not5, contributes modestly to the degradation of an NGD-targeted mRNA. Finally, we use ribosome profiling to reveal distinct ribosomal states associated with each reporter mRNA that readily rationalize the contributions of NGD and COMD factors to degradation of these reporters. Taken together, these results provide new insight into the role of Syh1 and Smy2 in NGD and into the ribosomal states that correlate with the activation of distinct pathways targeting mRNAs for degradation in yeast.
Collapse
Affiliation(s)
- Anthony J Veltri
- Department of Molecular Biology and Genetics, Howard Hughes Medical Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
| | - Karole N D'Orazio
- Department of Molecular Biology and Genetics, Howard Hughes Medical Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
| | - Laura N Lessen
- Department of Molecular Biology and Genetics, Howard Hughes Medical Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
| | | | - Grant W Brown
- Department of Biochemistry and Donnelly Centre, University of TorontoTorontoCanada
| | - Rachel Green
- Department of Molecular Biology and Genetics, Howard Hughes Medical Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
| |
Collapse
|
12
|
Krokowski D, Jobava R, Szkop KJ, Chen CW, Fu X, Venus S, Guan BJ, Wu J, Gao Z, Banaszuk W, Tchorzewski M, Mu T, Ropelewski P, Merrick WC, Mao Y, Sevval AI, Miranda H, Qian SB, Manifava M, Ktistakis NT, Vourekas A, Jankowsky E, Topisirovic I, Larsson O, Hatzoglou M. Stress-induced perturbations in intracellular amino acids reprogram mRNA translation in osmoadaptation independently of the ISR. Cell Rep 2022; 40:111092. [PMID: 35858571 PMCID: PMC9491157 DOI: 10.1016/j.celrep.2022.111092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 04/26/2022] [Accepted: 06/22/2022] [Indexed: 12/23/2022] Open
Abstract
The integrated stress response (ISR) plays a pivotal role in adaptation of translation machinery to cellular stress. Here, we demonstrate an ISR-independent osmoadaptation mechanism involving reprogramming of translation via coordinated but independent actions of mTOR and plasma membrane amino acid transporter SNAT2. This biphasic response entails reduced global protein synthesis and mTOR signaling followed by translation of SNAT2. Induction of SNAT2 leads to accumulation of amino acids and reactivation of mTOR and global protein synthesis, paralleled by partial reversal of the early-phase, stress-induced translatome. We propose SNAT2 functions as a molecular switch between inhibition of protein synthesis and establishment of an osmoadaptive translation program involving the formation of cytoplasmic condensates of SNAT2-regulated RNA-binding proteins DDX3X and FUS. In summary, we define key roles of SNAT2 in osmotolerance.
Collapse
Affiliation(s)
- Dawid Krokowski
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA; Department of Molecular Biology, Institute of Biological Sciences, Maria Curie-Skłodowska University, Lublin, Poland.
| | - Raul Jobava
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA; Department of Biochemistry, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Krzysztof J Szkop
- Department of Oncology-Pathology, Science for Life Laboratories, Karolinska Institute, Stockholm, Sweden
| | - Chien-Wen Chen
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Xu Fu
- Department of Physiology and Biophysics, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Sarah Venus
- Department of Biochemistry, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Bo-Jhih Guan
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Jing Wu
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Zhaofeng Gao
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Wioleta Banaszuk
- Department of Molecular Biology, Institute of Biological Sciences, Maria Curie-Skłodowska University, Lublin, Poland
| | - Marek Tchorzewski
- Department of Molecular Biology, Institute of Biological Sciences, Maria Curie-Skłodowska University, Lublin, Poland; EcoTech-Complex Centre, Maria Curie-Skłodowska University, Lublin, Poland
| | - Tingwei Mu
- Department of Physiology and Biophysics, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Phil Ropelewski
- Department of Physiology and Biophysics, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - William C Merrick
- Department of Biochemistry, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Yuanhui Mao
- Division of Nutritional Sciences, Cornell University, Ithaca, NY 14853, USA
| | - Aksoylu Inci Sevval
- Department of Oncology-Pathology, Science for Life Laboratories, Karolinska Institute, Stockholm, Sweden
| | - Helen Miranda
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Shu-Bing Qian
- Division of Nutritional Sciences, Cornell University, Ithaca, NY 14853, USA
| | | | | | - Anastasios Vourekas
- Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA
| | - Eckhard Jankowsky
- Department of Biochemistry, School of Medicine, Case Western Reserve University, Cleveland, OH, USA
| | - Ivan Topisirovic
- The Lady Davis Institute, Jewish General Hospital, Montréal, QC, Canada; Gerald Bronfman Department of Oncology, McGill University, Montréal, QC, Canada; Department of Biochemistry and Division of Experimental Medicine, McGill University, Montréal, QC, Canada.
| | - Ola Larsson
- Department of Oncology-Pathology, Science for Life Laboratories, Karolinska Institute, Stockholm, Sweden.
| | - Maria Hatzoglou
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
| |
Collapse
|
13
|
Fujita T, Yokoyama T, Shirouzu M, Taguchi H, Ito T, Iwasaki S. The landscape of translational stall sites in bacteria revealed by monosome and disome profiling. RNA (NEW YORK, N.Y.) 2022; 28:290-302. [PMID: 34906996 PMCID: PMC8848927 DOI: 10.1261/rna.078188.120] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 11/24/2021] [Indexed: 05/29/2023]
Abstract
Ribosome pauses are associated with various cotranslational events and determine the fate of mRNAs and proteins. Thus, the identification of precise pause sites across the transcriptome is desirable; however, the landscape of ribosome pauses in bacteria remains ambiguous. Here, we harness monosome and disome (or collided ribosome) profiling strategies to survey ribosome pause sites in Escherichia coli Compared to eukaryotes, ribosome collisions in bacteria showed remarkable differences: a low frequency of disomes at stop codons, collisions occurring immediately after 70S assembly on start codons, and shorter queues of ribosomes trailing upstream. The pause sites corresponded with the biochemical validation by integrated nascent chain profiling (iNP) to detect polypeptidyl-tRNA, an elongation intermediate. Moreover, the subset of those sites showed puromycin resistance, presenting slow peptidyl transfer. Among the identified sites, the ribosome pause at Asn586 of ycbZ was validated by biochemical reporter assay, tRNA sequencing (tRNA-seq), and cryo-electron microscopy (cryo-EM) experiments. Our results provide a useful resource for ribosome stalling sites in bacteria.
Collapse
Affiliation(s)
- Tomoya Fujita
- RNA Systems Biochemistry Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198 Japan
- School of Life Science and Technology, Tokyo Institute of Technology, Midori-ku, Yokohama 226-8503, Japan
| | - Takeshi Yokoyama
- Laboratory for Protein Functional and Structural Biology, RIKEN Center for Biosystems Dynamics Research, Tsurumi-ku, Yokohama 230-0045, Japan
- Graduate School of Life Sciences, Tohoku University, Aoba-ku, Sendai 980-8577, Japan
| | - Mikako Shirouzu
- Laboratory for Protein Functional and Structural Biology, RIKEN Center for Biosystems Dynamics Research, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Hideki Taguchi
- School of Life Science and Technology, Tokyo Institute of Technology, Midori-ku, Yokohama 226-8503, Japan
- Cell Biology Center, Institute of Innovative Research, Tokyo Institute of Technology, Yokohama, Midori-ku, Yokohama 226-8503, Japan
| | - Takuhiro Ito
- Laboratory for Translation Structural Biology, RIKEN Center for Biosystems Dynamics Research, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Shintaro Iwasaki
- RNA Systems Biochemistry Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198 Japan
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
| |
Collapse
|
14
|
Li Q, Yang H, Stroup EK, Wang H, Ji Z. Low-input RNase footprinting for simultaneous quantification of cytosolic and mitochondrial translation. Genome Res 2022; 32:545-557. [PMID: 35193938 PMCID: PMC8896460 DOI: 10.1101/gr.276139.121] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Accepted: 01/25/2022] [Indexed: 12/02/2022]
Abstract
We describe a low-input RNase footprinting approach for the rapid quantification of ribosome-protected fragments with as few as 1000 cultured cells. The assay uses a simplified procedure to selectively capture ribosome footprints based on optimized RNase digestion. It simultaneously maps cytosolic and mitochondrial translation with single-nucleotide resolution. We applied it to reveal selective functions of the elongation factor TUFM in mitochondrial translation, as well as synchronized repression of cytosolic translation after TUFM perturbation. We show the assay is applicable to small amounts of primary tissue samples with low protein synthesis rates, including snap-frozen tissues and immune cells from an individual's blood draw. We showed its feasibility to characterize the personalized immuno-translatome. Our analyses revealed that thousands of genes show lower translation efficiency in monocytes compared with lymphocytes, and identified thousands of translated noncanonical open reading frames (ORFs). Altogether, our RNase footprinting approach opens an avenue to assay transcriptome-wide translation using low-input samples from a wide range of physiological conditions.
Collapse
Affiliation(s)
- Qianru Li
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, USA
| | - Haiwang Yang
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, USA
| | - Emily K Stroup
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, USA
| | - Hongbin Wang
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, USA
| | - Zhe Ji
- Department of Pharmacology, Feinberg School of Medicine, Northwestern University, Chicago, Illinois 60611, USA.,Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, Illinois 60628, USA
| |
Collapse
|
15
|
Ristau J, Watt K, Oertlin C, Larsson O. Polysome Fractionation for Transcriptome-Wide Studies of mRNA Translation. Methods Mol Biol 2022; 2418:223-241. [PMID: 35119669 DOI: 10.1007/978-1-0716-1920-9_14] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Protein synthesis and degradation determine the relationship between mRNA and corresponding protein amounts. This relationship can change in a dynamic and selective fashion when translational efficiencies of transcript subsets are altered downstream of, for example, translation factors and/or RNA binding proteins. Notably, even transcription factors such as estrogen receptor alpha (ERα) can modulate mRNA translation in a transcript-selective manner. Yet, despite ample evidence suggesting a key role for mRNA translation in shaping the proteome in health and disease, it remains largely unexplored. Here, we present a guide for the extraction of mRNA engaged in translation using polysome fractionation with linear and optimized sucrose gradients. The isolated polysome-associated RNA is then quantified, in parallel with total mRNA from the same conditions, using methods such as RNA sequencing; and the resulting data set is analyzed to derive transcriptome-wide insights into how mRNA translation is modulated. The methods we describe are applicable to cultured cells, small numbers of FACS-isolated primary cells, and small tissue samples from biobanks or animal studies. Accordingly, this approach can be applied to study in detail how ERα and other factors control gene expression by selectively modulating mRNA translation both in vitro and in vivo.
Collapse
Affiliation(s)
- Johannes Ristau
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Kathleen Watt
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Christian Oertlin
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden
| | - Ola Larsson
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institute, Stockholm, Sweden.
| |
Collapse
|
16
|
A critical period of translational control during brain development at codon resolution. Nat Struct Mol Biol 2022; 29:1277-1290. [PMID: 36482253 PMCID: PMC9758057 DOI: 10.1038/s41594-022-00882-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 10/19/2022] [Indexed: 12/13/2022]
Abstract
Translation modulates the timing and amplification of gene expression after transcription. Brain development requires uniquely complex gene expression patterns, but large-scale measurements of translation directly in the prenatal brain are lacking. We measure the reactants, synthesis and products of mRNA translation spanning mouse neocortex neurogenesis, and discover a transient window of dynamic regulation at mid-gestation. Timed translation upregulation of chromatin-binding proteins like Satb2, which is essential for neuronal subtype differentiation, restricts protein expression in neuronal lineages despite broad transcriptional priming in progenitors. In contrast, translation downregulation of ribosomal proteins sharply decreases ribosome biogenesis, coinciding with a major shift in protein synthesis dynamics at mid-gestation. Changing activity of eIF4EBP1, a direct inhibitor of ribosome biogenesis, is concurrent with ribosome downregulation and affects neurogenesis of the Satb2 lineage. Thus, the molecular logic of brain development includes the refinement of transcriptional programs by translation. Modeling of the developmental neocortex translatome is provided as an open-source searchable resource at https://shiny.mdc-berlin.de/cortexomics .
Collapse
|
17
|
Shirokikh NE. Translation complex stabilization on messenger RNA and footprint profiling to study the RNA responses and dynamics of protein biosynthesis in the cells. Crit Rev Biochem Mol Biol 2021; 57:261-304. [PMID: 34852690 DOI: 10.1080/10409238.2021.2006599] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
During protein biosynthesis, ribosomes bind to messenger (m)RNA, locate its protein-coding information, and translate the nucleotide triplets sequentially as codons into the corresponding sequence of amino acids, forming proteins. Non-coding mRNA features, such as 5' and 3' untranslated regions (UTRs), start sites or stop codons of different efficiency, stretches of slower or faster code and nascent polypeptide interactions can alter the translation rates transcript-wise. Most of the homeostatic and signal response pathways of the cells converge on individual mRNA control, as well as alter the global translation output. Among the multitude of approaches to study translational control, one of the most powerful is to infer the locations of translational complexes on mRNA based on the mRNA fragments protected by these complexes from endonucleolytic hydrolysis, or footprints. Translation complex profiling by high-throughput sequencing of the footprints allows to quantify the transcript-wise, as well as global, alterations of translation, and uncover the underlying control mechanisms by attributing footprint locations and sizes to different configurations of the translational complexes. The accuracy of all footprint profiling approaches critically depends on the fidelity of footprint generation and many methods have emerged to preserve certain or multiple configurations of the translational complexes, often in challenging biological material. In this review, a systematic summary of approaches to stabilize translational complexes on mRNA for footprinting is presented and major findings are discussed. Future directions of translation footprint profiling are outlined, focusing on the fidelity and accuracy of inference of the native in vivo translation complex distribution on mRNA.
Collapse
Affiliation(s)
- Nikolay E Shirokikh
- Division of Genome Sciences and Cancer, The John Curtin School of Medical Research, The Australian National University, Canberra, Australia
| |
Collapse
|
18
|
Gobet C, Naef F. Ribo-DT: An automated pipeline for inferring codon dwell times from ribosome profiling data. Methods 2021; 203:10-16. [PMID: 34673173 DOI: 10.1016/j.ymeth.2021.10.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 10/08/2021] [Accepted: 10/11/2021] [Indexed: 11/16/2022] Open
Abstract
Protein synthesis is an energy consuming process characterised as a pivotal and highly regulated step in gene expression. The net protein output is dictated by a combination of translation initiation, elongation and termination rates that have remained difficult to measure. Recently, the development of ribosome profiling has enabled the inference of translation parameters through modelling, as this method informs on the ribosome position along the mRNA. Here, we present an automated, reproducible and portable computational pipeline to infer relative single-codon and codon-pair dwell times as well as gene flux from raw ribosome profiling sequencing data. As a case study, we applied our workflow to a publicly available yeast ribosome profiling dataset consisting of 57 independent gene knockouts related to RNA and tRNA modifications. We uncovered the effects of those modifications on translation elongation and codon selection during decoding. In particular, knocking out mod5 and trm7 increases codon-specific dwell times which indicates their potential tRNA targets, and highlights effects of nucleotide modifications on ribosome decoding rate.
Collapse
Affiliation(s)
- Cédric Gobet
- Institute of Bioengineering (IBI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
| | - Félix Naef
- Institute of Bioengineering (IBI), Ecole Polytechnique Fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
| |
Collapse
|
19
|
Zaheed O, Kiniry SJ, Baranov PV, Dean K. Exploring Evidence of Non-coding RNA Translation With Trips-Viz and GWIPS-Viz Browsers. Front Cell Dev Biol 2021; 9:703374. [PMID: 34490252 PMCID: PMC8416628 DOI: 10.3389/fcell.2021.703374] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Accepted: 07/12/2021] [Indexed: 11/21/2022] Open
Abstract
Detection of translation in so-called non-coding RNA provides an opportunity for identification of novel bioactive peptides and microproteins. The main methods used for these purposes are ribosome profiling and mass spectrometry. A number of publicly available datasets already exist for a substantial number of different cell types grown under various conditions, and public data mining is an attractive strategy for identification of translation in non-coding RNAs. Since the analysis of publicly available data requires intensive data processing, several data resources have been created recently for exploring processed publicly available data, such as OpenProt, GWIPS-viz, and Trips-Viz. In this work we provide a detailed demonstration of how to use the latter two tools for exploring experimental evidence for translation of RNAs hitherto classified as non-coding. For this purpose, we use a set of transcripts with substantially different patterns of ribosome footprint distributions. We discuss how certain features of these patterns can be used as evidence for or against genuine translation. During our analysis we concluded that the MTLN mRNA, previously misannotated as lncRNA LINC00116, likely encodes only a short proteoform expressed from shorter RNA transcript variants.
Collapse
Affiliation(s)
- Oza Zaheed
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Stephen J Kiniry
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Pavel V Baranov
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland.,Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, RAS, Moscow, Russia
| | - Kellie Dean
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| |
Collapse
|
20
|
Pavlov MY, Ullman G, Ignatova Z, Ehrenberg M. Estimation of peptide elongation times from ribosome profiling spectra. Nucleic Acids Res 2021; 49:5124-5142. [PMID: 33885812 PMCID: PMC8136808 DOI: 10.1093/nar/gkab260] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 03/25/2021] [Accepted: 04/15/2021] [Indexed: 11/13/2022] Open
Abstract
Ribosome profiling spectra bear rich information on translation control and dynamics. Yet, due to technical biases in library generation, extracting quantitative measures of discrete translation events has remained elusive. Using maximum likelihood statistics and data set from Escherichia coli we develop a robust method for neutralizing technical biases (e.g. base specific RNase preferences in ribosome-protected mRNA fragments (RPF) generation), which allows for correct estimation of translation times at single codon resolution. Furthermore, we validated the method with available datasets from E. coli treated with antibiotic to inhibit isoleucyl-tRNA synthetase, and two datasets from Saccharomyces cerevisiae treated with two RNases with distinct cleavage signatures. We demonstrate that our approach accounts for RNase cleavage preferences and provides bias-corrected translation times estimates. Our approach provides a solution to the long-standing problem of extracting reliable information about peptide elongation times from highly noisy and technically biased ribosome profiling spectra.
Collapse
Affiliation(s)
- Michael Y Pavlov
- Department of Cell and Molecular Biology, Biomedical Center, University of Uppsala, 75237 Uppsala, Sweden
| | - Gustaf Ullman
- Department of Cell and Molecular Biology, Biomedical Center, University of Uppsala, 75237 Uppsala, Sweden
| | - Zoya Ignatova
- Institute for Biochemistry & Molecular Biology, University of Hamburg, 20146 Hamburg, Germany
| | - Måns Ehrenberg
- Department of Cell and Molecular Biology, Biomedical Center, University of Uppsala, 75237 Uppsala, Sweden
| |
Collapse
|
21
|
Yadav V, Ullah Irshad I, Kumar H, Sharma AK. Quantitative Modeling of Protein Synthesis Using Ribosome Profiling Data. Front Mol Biosci 2021; 8:688700. [PMID: 34262940 PMCID: PMC8274658 DOI: 10.3389/fmolb.2021.688700] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 05/25/2021] [Indexed: 12/12/2022] Open
Abstract
Quantitative prediction on protein synthesis requires accurate translation initiation and codon translation rates. Ribosome profiling data, which provide steady-state distribution of relative ribosome occupancies along a transcript, can be used to extract these rate parameters. Various methods have been developed in the past few years to measure translation-initiation and codon translation rates from ribosome profiling data. In the review, we provide a detailed analysis of the key methods employed to extract the translation rate parameters from ribosome profiling data. We further discuss how these approaches were used to decipher the role of various structural and sequence-based features of mRNA molecules in the regulation of gene expression. The utilization of these accurate rate parameters in computational modeling of protein synthesis may provide new insights into the kinetic control of the process of gene expression.
Collapse
Affiliation(s)
- Vandana Yadav
- Department of Physics, Indian Institute of Technology Madras, Chennai, India
| | | | - Hemant Kumar
- School of Basic Sciences, Indian Institute of Technology Bhubaneswar, Bhubaneswar, India
| | - Ajeet K Sharma
- Department of Physics, Indian Institute of Technology Jammu, Jammu, India
| |
Collapse
|
22
|
Frye M, Bornelöv S. CONCUR: quick and robust calculation of codon usage from ribosome profiling data. Bioinformatics 2021; 37:717-719. [PMID: 32866237 PMCID: PMC8097682 DOI: 10.1093/bioinformatics/btaa733] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 07/09/2020] [Accepted: 08/21/2020] [Indexed: 11/20/2022] Open
Abstract
SUMMARY CONCUR is a standalone tool for codon usage analysis in ribosome profiling experiments. CONCUR uses the aligned reads in BAM format to estimate codon counts at the ribosome E-, P- and A-sites and at flanking positions. AVAILABILITY AND IMPLEMENTATION CONCUR is written in Perl and is freely available at https://github.com/susbo/concur. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Michaela Frye
- Cell Biology and Tumor Biology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Susanne Bornelöv
- Wellcome – MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge CB2 0AW, UK
| |
Collapse
|
23
|
Tian T, Li S, Lang P, Zhao D, Zeng J. Full-length ribosome density prediction by a multi-input and multi-output model. PLoS Comput Biol 2021; 17:e1008842. [PMID: 33770074 PMCID: PMC8026034 DOI: 10.1371/journal.pcbi.1008842] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2020] [Revised: 04/07/2021] [Accepted: 03/01/2021] [Indexed: 11/29/2022] Open
Abstract
Translation elongation is regulated by a series of complicated mechanisms in both prokaryotes and eukaryotes. Although recent advance in ribosome profiling techniques has enabled one to capture the genome-wide ribosome footprints along transcripts at codon resolution, the regulatory codes of elongation dynamics are still not fully understood. Most of the existing computational approaches for modeling translation elongation from ribosome profiling data mainly focus on local contextual patterns, while ignoring the continuity of the elongation process and relations between ribosome densities of remote codons. Modeling the translation elongation process in full-length coding sequence (CDS) level has not been studied to the best of our knowledge. In this paper, we developed a deep learning based approach with a multi-input and multi-output framework, named RiboMIMO, for modeling the ribosome density distributions of full-length mRNA CDS regions. Through considering the underlying correlations in translation efficiency among neighboring and remote codons and extracting hidden features from the input full-length coding sequence, RiboMIMO can greatly outperform the state-of-the-art baseline approaches and accurately predict the ribosome density distributions along the whole mRNA CDS regions. In addition, RiboMIMO explores the contributions of individual input codons to the predictions of output ribosome densities, which thus can help reveal important biological factors influencing the translation elongation process. The analyses, based on our interpretable metric named codon impact score, not only identified several patterns consistent with the previously-published literatures, but also for the first time (to the best of our knowledge) revealed that the codons located at a long distance from the ribosomal A site may also have an association on the translation elongation rate. This finding of long-range impact on translation elongation velocity may shed new light on the regulatory mechanisms of protein synthesis. Overall, these results indicated that RiboMIMO can provide a useful tool for studying the regulation of translation elongation in the range of full-length CDS. Translation elongation is a process in which amino acids are linked into proteins by ribosomes in cells. Translation elongation rates along the mRNAs are not constant, and are regulated by a series of mechanisms, such as codon rarity and mRNA stability. In this study, we modeled the translation elongation process at a full-length coding sequence level and developed a deep learning based approach to predict the translation elongation rates from mRNA sequences, through extracting the regulatory codes of elongation rates from the contextual sequences. The analyses, based on our interpretable metric named codon impact score, for the first time (to the best of our knowledge), revealed that in addition to the neighboring codons of the ribosomal A sites, the remote codons may also have an important impact on the translation elongation rates. This new finding may stimulate additional experiments and shed light on the regulatory mechanisms of protein synthesis.
Collapse
Affiliation(s)
- Tingzhong Tian
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Shuya Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Peng Lang
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Dan Zhao
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
- * E-mail: (DZ); (JZ)
| | - Jianyang Zeng
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
- MOE Key Laboratory of Bioinformatics, Tsinghua University, Beijing, China
- * E-mail: (DZ); (JZ)
| |
Collapse
|
24
|
do Couto Bordignon P, Pechmann S. Inferring translational heterogeneity from Saccharomyces cerevisiae ribosome profiling. FEBS J 2021; 288:4541-4559. [PMID: 33539640 DOI: 10.1111/febs.15748] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 01/27/2021] [Accepted: 02/02/2021] [Indexed: 11/30/2022]
Abstract
Translation of mRNAs into proteins by the ribosome is the most important step of protein biosynthesis. Accordingly, translation is tightly controlled and heavily regulated to maintain cellular homeostasis. Ribosome profiling (Ribo-seq) has revolutionized the study of translation by revealing many of its underlying mechanisms. However, equally many aspects of translation remain mysterious, in part also due to persisting challenges in the interpretation of data obtained from Ribo-seq experiments. Here, we show that some of the variability observed in Ribo-seq data has biological origins and reflects programmed heterogeneity of translation. Through a comparative analysis of Ribo-seq data from Saccharomyces cerevisiae, we systematically identify short 3-codon sequences that are differentially translated (DT) across mRNAs, that is, identical sequences that are translated sometimes fast and sometimes slowly beyond what can be attributed to variability between experiments. Remarkably, the thus identified DT sequences link to mechanisms known to regulate translation elongation and are enriched in genes important for protein and organelle biosynthesis. Our results thus highlight examples of translational heterogeneity that are encoded in the genomic sequences and tuned to optimizing cellular homeostasis. More generally, our work highlights the power of Ribo-seq to understand the complexities of translation regulation.
Collapse
|
25
|
Rauscher R, Bampi GB, Guevara-Ferrer M, Santos LA, Joshi D, Mark D, Strug LJ, Rommens JM, Ballmann M, Sorscher EJ, Oliver KE, Ignatova Z. Positive epistasis between disease-causing missense mutations and silent polymorphism with effect on mRNA translation velocity. Proc Natl Acad Sci U S A 2021; 118:e2010612118. [PMID: 33468668 PMCID: PMC7848603 DOI: 10.1073/pnas.2010612118] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
Epistasis refers to the dependence of a mutation on other mutation(s) and the genetic context in general. In the context of human disorders, epistasis complicates the spectrum of disease symptoms and has been proposed as a major contributor to variations in disease outcome. The nonadditive relationship between mutations and the lack of complete understanding of the underlying physiological effects limit our ability to predict phenotypic outcome. Here, we report positive epistasis between intragenic mutations in the cystic fibrosis transmembrane conductance regulator (CFTR)-the gene responsible for cystic fibrosis (CF) pathology. We identified a synonymous single-nucleotide polymorphism (sSNP) that is invariant for the CFTR amino acid sequence but inverts translation speed at the affected codon. This sSNP in cis exhibits positive epistatic effects on some CF disease-causing missense mutations. Individually, both mutations alter CFTR structure and function, yet when combined, they lead to enhanced protein expression and activity. The most robust effect was observed when the sSNP was present in combination with missense mutations that, along with the primary amino acid change, also alter the speed of translation at the affected codon. Functional studies revealed that synergistic alteration in ribosomal velocity is the underlying mechanism; alteration of translation speed likely increases the time window for establishing crucial domain-domain interactions that are otherwise perturbed by each individual mutation.
Collapse
Affiliation(s)
- Robert Rauscher
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Giovana B Bampi
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Marta Guevara-Ferrer
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Leonardo A Santos
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Disha Joshi
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322
- Children's Healthcare of Atlanta, Atlanta, GA 30322
| | - David Mark
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany
| | - Lisa J Strug
- Program in Genetics & Genome Biology, The Hospital for Sick Children, Toronto M5G 0A4, Canada
- Department of Statistical Sciences, Computer Science and Division of Biostatistics, University of Toronto, Toronto M5G 0A4, Canada
| | - Johanna M Rommens
- Program in Genetics & Genome Biology, The Hospital for Sick Children, Toronto M5G 0A4, Canada
| | | | - Eric J Sorscher
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322
- Children's Healthcare of Atlanta, Atlanta, GA 30322
| | - Kathryn E Oliver
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322
- Children's Healthcare of Atlanta, Atlanta, GA 30322
| | - Zoya Ignatova
- Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, 20146 Hamburg, Germany;
| |
Collapse
|
26
|
Hu H, Liu X, Xiao A, Li Y, Zhang C, Jiang T, Zhao D, Song S, Zeng J. Riboexp: an interpretable reinforcement learning framework for ribosome density modeling. Brief Bioinform 2021; 22:6105941. [PMID: 33479731 DOI: 10.1093/bib/bbaa412] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 12/11/2020] [Indexed: 11/13/2022] Open
Abstract
Translation elongation is a crucial phase during protein biosynthesis. In this study, we develop a novel deep reinforcement learning-based framework, named Riboexp, to model the determinants of the uneven distribution of ribosomes on mRNA transcripts during translation elongation. In particular, our model employs a policy network to perform a context-dependent feature selection in the setting of ribosome density prediction. Our extensive tests demonstrated that Riboexp can significantly outperform the state-of-the-art methods in predicting ribosome density by up to 5.9% in terms of per-gene Pearson correlation coefficient on the datasets from three species. In addition, Riboexp can indicate more informative sequence features for the prediction task than other commonly used attribution methods in deep learning. In-depth analyses also revealed the meaningful biological insights generated by the Riboexp framework. Moreover, the application of Riboexp in codon optimization resulted in an increase of protein production by around 31% over the previous state-of-the-art method that models ribosome density. These results have established Riboexp as a powerful and useful computational tool in the studies of translation dynamics and protein synthesis. Availability: The data and code of this study are available on GitHub: https://github.com/Liuxg16/Riboexp. Contact: zengjy321@tsinghua.edu.cn; songsen@tsinghua.edu.cn.
Collapse
Affiliation(s)
- Hailin Hu
- School of Medicine, Tsinghua University, Beijing, 100084, China
| | - Xianggen Liu
- Laboratory for Brain and Intelligence and Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.,Beijing Innovation Center for Future Chip, Tsinghua University, Beijing, 100084, China
| | - An Xiao
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
| | - YangYang Li
- Comprehensive AIDS Research Center, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, School of Life Sciences, and School of Medicine, Tsinghua University, Beijing, 100084, China
| | | | - Tao Jiang
- Department of Computer Science and Engineering, University of California, Riverside, CA 92521, USA.,Bioinformatics Division, BNRIST/Department of Computer Science and Technology, Tsinghua University, Beijing, 100084, China.,Institute of Integrative Genome Biology, University of California, Riverside, CA 92521, USA
| | - Dan Zhao
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, 100084, China
| | - Sen Song
- Laboratory for Brain and Intelligence and Department of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.,Beijing Innovation Center for Future Chip, Tsinghua University, Beijing, 100084, China
| | - Jianyang Zeng
- School of Medicine, Tsinghua University, Beijing, 100084, China
| |
Collapse
|
27
|
Abstract
Translation is a central biological process in living cells. Ribosome profiling approach enables assessing translation on a global, cell-wide level. Extracting versatile information from the ribosome profiling data usually requires specialized expertise for handling the sequencing data that is not available to the broad community of experimentalists. Here, we provide an easy-to-use and modifiable workflow that uses a small set of commands and enables full data analysis in a standardized way, including precise positioning of the ribosome-protected fragments, for determining codon-specific translation features. The workflow is complemented with simple step-by-step explanations and is accessible to scientists with no computational background.
Collapse
Affiliation(s)
| | - Zoya Ignatova
- Institute for Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, Hamburg, Germany.
| |
Collapse
|
28
|
Li K, Hope CM, Wang XA, Wang JP. RiboDiPA: a novel tool for differential pattern analysis in Ribo-seq data. Nucleic Acids Res 2020; 48:12016-12029. [PMID: 33211868 PMCID: PMC7708064 DOI: 10.1093/nar/gkaa1049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Revised: 10/14/2020] [Accepted: 10/20/2020] [Indexed: 12/18/2022] Open
Abstract
Ribosome profiling, also known as Ribo-seq, has become a popular approach to investigate regulatory mechanisms of translation in a wide variety of biological contexts. Ribo-seq not only provides a measurement of translation efficiency based on the relative abundance of ribosomes bound to transcripts, but also has the capacity to reveal dynamic and local regulation at different stages of translation based on positional information of footprints across individual transcripts. While many computational tools exist for the analysis of Ribo-seq data, no method is currently available for rigorous testing of the pattern differences in ribosome footprints. In this work, we develop a novel approach together with an R package, RiboDiPA, for Differential Pattern Analysis of Ribo-seq data. RiboDiPA allows for quick identification of genes with statistically significant differences in ribosome occupancy patterns for model organisms ranging from yeast to mammals. We show that differential pattern analysis reveals information that is distinct and complimentary to existing methods that focus on translational efficiency analysis. Using both simulated Ribo-seq footprint data and three benchmark data sets, we illustrate that RiboDiPA can uncover meaningful pattern differences across multiple biological conditions on a global scale, and pinpoint characteristic ribosome occupancy patterns at single codon resolution.
Collapse
Affiliation(s)
- Keren Li
- Department of Statistics, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA.,NSF-Simons Center for Quantitative Biology, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA
| | - C Matthew Hope
- NSF-Simons Center for Quantitative Biology, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA.,Department of Molecular Biosciences, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA
| | - Xiaozhong A Wang
- NSF-Simons Center for Quantitative Biology, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA.,Department of Molecular Biosciences, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA
| | - Ji-Ping Wang
- Department of Statistics, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA.,NSF-Simons Center for Quantitative Biology, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA
| |
Collapse
|
29
|
Neelagandan N, Lamberti I, Carvalho HJF, Gobet C, Naef F. What determines eukaryotic translation elongation: recent molecular and quantitative analyses of protein synthesis. Open Biol 2020; 10:200292. [PMID: 33292102 PMCID: PMC7776565 DOI: 10.1098/rsob.200292] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 11/10/2020] [Indexed: 12/14/2022] Open
Abstract
Protein synthesis from mRNA is an energy-intensive and tightly controlled cellular process. Translation elongation is a well-coordinated, multifactorial step in translation that undergoes dynamic regulation owing to cellular state and environmental determinants. Recent studies involving genome-wide approaches have uncovered some crucial aspects of translation elongation including the mRNA itself and the nascent polypeptide chain. Additionally, these studies have fuelled quantitative and mathematical modelling of translation elongation. In this review, we provide a comprehensive overview of the key determinants of translation elongation. We discuss consequences of ribosome stalling or collision, and how the cells regulate translation in case of such events. Next, we review theoretical approaches and widely used mathematical models that have become an essential ingredient to interpret complex molecular datasets and study translation dynamics quantitatively. Finally, we review recent advances in live-cell reporter and related analysis techniques, to monitor the translation dynamics of single cells and single-mRNA molecules in real time.
Collapse
Affiliation(s)
| | | | | | | | - Felix Naef
- Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne CH-1015, Switzerland
| |
Collapse
|
30
|
Liu Q, Shvarts T, Sliz P, Gregory RI. RiboToolkit: an integrated platform for analysis and annotation of ribosome profiling data to decode mRNA translation at codon resolution. Nucleic Acids Res 2020; 48:W218-W229. [PMID: 32427338 PMCID: PMC7319539 DOI: 10.1093/nar/gkaa395] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 04/23/2020] [Accepted: 05/15/2020] [Indexed: 12/31/2022] Open
Abstract
Ribosome profiling (Ribo-seq) is a powerful technology for globally monitoring RNA translation; ranging from codon occupancy profiling, identification of actively translated open reading frames (ORFs), to the quantification of translational efficiency under various physiological or experimental conditions. However, analyzing and decoding translation information from Ribo-seq data is not trivial. Although there are many existing tools to analyze Ribo-seq data, most of these tools are designed for specific or limited functionalities and an easy-to-use integrated tool to analyze Ribo-seq data is lacking. Fortunately, the small size (26–34 nt) of ribosome protected fragments (RPFs) in Ribo-seq and the relatively small amount of sequencing data greatly facilitates the development of such a web platform, which is easy to manipulate for users with or without bioinformatic expertise. Thus, we developed RiboToolkit (http://rnabioinfor.tch.harvard.edu/RiboToolkit), a convenient, freely available, web-based service to centralize Ribo-seq data analyses, including data cleaning and quality evaluation, expression analysis based on RPFs, codon occupancy, translation efficiency analysis, differential translation analysis, functional annotation, translation metagene analysis, and identification of actively translated ORFs. Besides, easy-to-use web interfaces were developed to facilitate data analysis and intuitively visualize results. Thus, RiboToolkit will greatly facilitate the study of mRNA translation based on ribosome profiling.
Collapse
Affiliation(s)
- Qi Liu
- Stem Cell Program, Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA
| | - Tanya Shvarts
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02115, USA
| | - Piotr Sliz
- Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.,Computational Health Informatics Program, Boston Children's Hospital, Boston, MA 02115, USA
| | - Richard I Gregory
- Stem Cell Program, Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA 02115, USA.,Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA 02115, USA.,Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.,Harvard Initiative for RNA Medicine, Boston, MA 02115, USA.,Harvard Stem Cell Institute, Cambridge, MA 02138, USA
| |
Collapse
|
31
|
Espah Borujeni A, Zhang J, Doosthosseini H, Nielsen AAK, Voigt CA. Genetic circuit characterization by inferring RNA polymerase movement and ribosome usage. Nat Commun 2020; 11:5001. [PMID: 33020480 PMCID: PMC7536230 DOI: 10.1038/s41467-020-18630-2] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 09/02/2020] [Indexed: 02/06/2023] Open
Abstract
To perform their computational function, genetic circuits change states through a symphony of genetic parts that turn regulator expression on and off. Debugging is frustrated by an inability to characterize parts in the context of the circuit and identify the origins of failures. Here, we take snapshots of a large genetic circuit in different states: RNA-seq is used to visualize circuit function as a changing pattern of RNA polymerase (RNAP) flux along the DNA. Together with ribosome profiling, all 54 genetic parts (promoters, ribozymes, RBSs, terminators) are parameterized and used to inform a mathematical model that can predict circuit performance, dynamics, and robustness. The circuit behaves as designed; however, it is riddled with genetic errors, including cryptic sense/antisense promoters and translation, attenuation, incorrect start codons, and a failed gate. While not impacting the expected Boolean logic, they reduce the prediction accuracy and could lead to failures when the parts are used in other designs. Finally, the cellular power (RNAP and ribosome usage) required to maintain a circuit state is calculated. This work demonstrates the use of a small number of measurements to fully parameterize a regulatory circuit and quantify its impact on host.
Collapse
Affiliation(s)
- Amin Espah Borujeni
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jing Zhang
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Hamid Doosthosseini
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Alec A K Nielsen
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Christopher A Voigt
- Synthetic Biology Center, Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| |
Collapse
|
32
|
Choudhary S, Li W, D Smith A. Accurate detection of short and long active ORFs using Ribo-seq data. Bioinformatics 2020; 36:2053-2059. [PMID: 31750902 DOI: 10.1093/bioinformatics/btz878] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 11/04/2019] [Accepted: 11/20/2019] [Indexed: 12/27/2022] Open
Abstract
MOTIVATION Ribo-seq, a technique for deep-sequencing ribosome-protected mRNA fragments, has enabled transcriptome-wide monitoring of translation in vivo. It has opened avenues for re-evaluating the coding potential of open reading frames (ORFs), including many short ORFs that were previously presumed to be non-translating. However, the detection of translating ORFs, specifically short ORFs, from Ribo-seq data, remains challenging due to its high heterogeneity and noise. RESULTS We present ribotricer, a method for detecting actively translating ORFs by directly leveraging the three-nucleotide periodicity of Ribo-seq data. Ribotricer demonstrates higher accuracy and robustness compared with other methods at detecting actively translating ORFs including short ORFs on multiple published datasets across species inclusive of Arabidopsis, Caenorhabditis elegans, Drosophila, human, mouse, rat, yeast and zebrafish. AVAILABILITY AND IMPLEMENTATION Ribotricer is available at https://github.com/smithlabcode/ribotricer. All analysis scripts and results are available at https://github.com/smithlabcode/ribotricer-results. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Saket Choudhary
- Computational Biology and Bioinformatics, University of Southern California, Los Angeles, CA 90089, USA
| | - Wenzheng Li
- Computational Biology and Bioinformatics, University of Southern California, Los Angeles, CA 90089, USA
| | - Andrew D Smith
- Computational Biology and Bioinformatics, University of Southern California, Los Angeles, CA 90089, USA
| |
Collapse
|
33
|
Arpat AB, Liechti A, De Matos M, Dreos R, Janich P, Gatfield D. Transcriptome-wide sites of collided ribosomes reveal principles of translational pausing. Genome Res 2020; 30:985-999. [PMID: 32703885 PMCID: PMC7397865 DOI: 10.1101/gr.257741.119] [Citation(s) in RCA: 57] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2019] [Accepted: 06/29/2020] [Indexed: 01/28/2023]
Abstract
Translation initiation is the major regulatory step defining the rate of protein production from an mRNA. Meanwhile, the impact of nonuniform ribosomal elongation rates is largely unknown. Using a modified ribosome profiling protocol based on footprints from two closely packed ribosomes (disomes), we have mapped ribosomal collisions transcriptome-wide in mouse liver. We uncover that the stacking of an elongating onto a paused ribosome occurs frequently and scales with translation rate, trapping ∼10% of translating ribosomes in the disome state. A distinct class of pause sites is indicative of deterministic pausing signals. Pause site association with specific amino acids, peptide motifs, and nascent polypeptide structure is suggestive of programmed pausing as a widespread mechanism associated with protein folding. Evolutionary conservation at disome sites indicates functional relevance of translational pausing. Collectively, our disome profiling approach allows unique insights into gene regulation occurring at the step of translation elongation.
Collapse
Affiliation(s)
- Alaaddin Bulak Arpat
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland.,Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Angélica Liechti
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| | - Mara De Matos
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| | - René Dreos
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland.,Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Peggy Janich
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| | - David Gatfield
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| |
Collapse
|
34
|
Translation initiation downstream from annotated start codons in human mRNAs coevolves with the Kozak context. Genome Res 2020; 30:974-984. [PMID: 32669370 PMCID: PMC7397870 DOI: 10.1101/gr.257352.119] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 06/25/2020] [Indexed: 12/13/2022]
Abstract
Eukaryotic translation initiation involves preinitiation ribosomal complex 5′-to-3′ directional probing of mRNA for codons suitable for starting protein synthesis. The recognition of codons as starts depends on the codon identity and on its immediate nucleotide context known as Kozak context. When the context is weak (i.e., nonoptimal), leaky scanning takes place during which a fraction of ribosomes continues the mRNA probing. We explored the relationship between the context of AUG codons annotated as starts of protein-coding sequences and the next AUG codon occurrence. We found that AUG codons downstream from weak starts occur in the same frame more frequently than downstream from strong starts. We suggest that evolutionary selection on in-frame AUGs downstream from weak start codons is driven by the advantage of the reduction of wasteful out-of-frame product synthesis and also by the advantage of producing multiple proteoforms from certain mRNAs. We confirmed translation initiation downstream from weak start codons using ribosome profiling data. We also tested translation of alternative start codons in 10 specific human genes using reporter constructs. In all tested cases, initiation at downstream start codons was more productive than at the annotated ones. In most cases, optimization of Kozak context did not completely abolish downstream initiation, and in the specific example of CMPK1 mRNA, the optimized start remained unproductive. Collectively, our work reveals previously uncharacterized forces shaping the evolution of protein-coding genes and points to the plurality of translation initiation and the existence of sequence features influencing start codon selection, other than Kozak context.
Collapse
|
35
|
Wright G, Rodriguez A, Li J, Clark PL, Milenković T, Emrich SJ. Analysis of computational codon usage models and their association with translationally slow codons. PLoS One 2020; 15:e0232003. [PMID: 32352987 PMCID: PMC7192439 DOI: 10.1371/journal.pone.0232003] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Accepted: 04/05/2020] [Indexed: 11/19/2022] Open
Abstract
Improved computational modeling of protein translation rates, including better prediction of where translational slowdowns along an mRNA sequence may occur, is critical for understanding co-translational folding. Because codons within a synonymous codon group are translated at different rates, many computational translation models rely on analyzing synonymous codons. Some models rely on genome-wide codon usage bias (CUB), believing that globally rare and common codons are the most informative of slow and fast translation, respectively. Others use the CUB observed only in highly expressed genes, which should be under selective pressure to be translated efficiently (and whose CUB may therefore be more indicative of translation rates). No prior work has analyzed these models for their ability to predict translational slowdowns. Here, we evaluate five models for their association with slowly translated positions as denoted by two independent ribosome footprint (RFP) count experiments from S. cerevisiae, because RFP data is often considered as a “ground truth” for translation rates across mRNA sequences. We show that all five considered models strongly associate with the RFP data and therefore have potential for estimating translational slowdowns. However, we also show that there is a weak correlation between RFP counts for the same genes originating from independent experiments, even when their experimental conditions are similar. This raises concerns about the efficacy of using current RFP experimental data for estimating translation rates and highlights a potential advantage of using computational models to understand translation rates instead.
Collapse
Affiliation(s)
- Gabriel Wright
- Department of Computer Science & Engineering, University of Notre Dame, Notre Dame, IN, United States of America
- * E-mail:
| | - Anabel Rodriguez
- Department of Chemistry & Biochemistry, University of Notre Dame, Notre Dame, IN, United States of America
| | - Jun Li
- Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, United States of America
| | - Patricia L. Clark
- Department of Chemistry & Biochemistry, University of Notre Dame, Notre Dame, IN, United States of America
| | - Tijana Milenković
- Department of Computer Science & Engineering, University of Notre Dame, Notre Dame, IN, United States of America
| | - Scott J. Emrich
- Department of Electrical Engineering & Computer Science, University of Tennessee, Knoxville, TN, United States of America
| |
Collapse
|
36
|
Kiniry SJ, Michel AM, Baranov PV. Computational methods for ribosome profiling data analysis. WILEY INTERDISCIPLINARY REVIEWS. RNA 2020; 11:e1577. [PMID: 31760685 DOI: 10.1002/wrna.1577] [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: 07/30/2019] [Revised: 10/12/2019] [Accepted: 10/16/2019] [Indexed: 12/15/2022]
Abstract
Since the introduction of the ribosome profiling technique in 2009 its popularity has greatly increased. It is widely used for the comprehensive assessment of gene expression and for studying the mechanisms of regulation at the translational level. As the number of ribosome profiling datasets being produced continues to grow, so too does the need for reliable software that can provide answers to the biological questions it can address. This review describes the computational methods and tools that have been developed to analyze ribosome profiling data at the different stages of the process. It starts with initial routine processing of raw data and follows with more specific tasks such as the identification of translated open reading frames, differential gene expression analysis, or evaluation of local or global codon decoding rates. The review pinpoints challenges associated with each step and explains the ways in which they are currently addressed. In addition it provides a comprehensive, albeit incomplete, list of publicly available software applicable to each step, which may be a beneficial starting point to those unexposed to ribosome profiling analysis. The outline of current challenges in ribosome profiling data analysis may inspire computational biologists to search for novel, potentially superior, solutions that will improve and expand the bioinformatician's toolbox for ribosome profiling data analysis. This article is characterized under: Translation > Ribosome Structure/Function RNA Evolution and Genomics > Computational Analyses of RNA Translation > Translation Mechanisms Translation > Translation Regulation.
Collapse
Affiliation(s)
- Stephen J Kiniry
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Audrey M Michel
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Pavel V Baranov
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, RAS, Moscow, Russia
| |
Collapse
|
37
|
Gobet C, Weger BD, Marquis J, Martin E, Neelagandan N, Gachon F, Naef F. Robust landscapes of ribosome dwell times and aminoacyl-tRNAs in response to nutrient stress in liver. Proc Natl Acad Sci U S A 2020; 117:9630-9641. [PMID: 32295881 PMCID: PMC7196831 DOI: 10.1073/pnas.1918145117] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Translation depends on messenger RNA (mRNA)-specific initiation, elongation, and termination rates. While translation elongation is well studied in bacteria and yeast, less is known in higher eukaryotes. Here we combined ribosome and transfer RNA (tRNA) profiling to investigate the relations between translation elongation rates, (aminoacyl-) tRNA levels, and codon usage in mammals. We modeled codon-specific ribosome dwell times from ribosome profiling, considering codon pair interactions between ribosome sites. In mouse liver, the model revealed site- and codon-specific dwell times that differed from those in yeast, as well as pairs of adjacent codons in the P and A site that markedly slow down or speed up elongation. While translation efficiencies vary across diurnal time and feeding regimen, codon dwell times were highly stable and conserved in human. Measured tRNA levels correlated with codon usage and several tRNAs showed reduced aminoacylation, which was conserved in fasted mice. Finally, we uncovered that the longest codon dwell times could be explained by aminoacylation levels or high codon usage relative to tRNA abundance.
Collapse
Affiliation(s)
- Cédric Gobet
- Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Nestlé Research, CH-1015 Lausanne, Switzerland
| | - Benjamin Dieter Weger
- Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
- Nestlé Research, CH-1015 Lausanne, Switzerland
| | | | - Eva Martin
- Nestlé Research, CH-1015 Lausanne, Switzerland
| | - Nagammal Neelagandan
- Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland
| | | | - Felix Naef
- Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne, Lausanne CH-1015, Switzerland;
| |
Collapse
|
38
|
Collart MA, Weiss B. Ribosome pausing, a dangerous necessity for co-translational events. Nucleic Acids Res 2020; 48:1043-1055. [PMID: 31598688 PMCID: PMC7026645 DOI: 10.1093/nar/gkz763] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 08/13/2019] [Accepted: 08/21/2019] [Indexed: 12/31/2022] Open
Abstract
In recent years translation elongation has emerged as an important contributor to the regulation of gene expression. There are multiple quality control checkpoints along the way of producing mature proteins and targeting them to the right cellular compartment, or associating them correctly with their partners. Ribosomes pause to allow co-translational protein folding, protein targeting or protein interactions, and the pausing is dictated by a combination of the mRNA sequence and structure, the tRNA availability and the nascent peptide. However, ribosome pausing can also lead to ribosome collisions and co-translational degradation of both mRNA and nascent chain. Understanding how the translating ribosome tunes the different maturation steps that nascent proteins must undergo, what the timing of these maturation events is, and how degradation can be avoided when pausing is needed, is now possible by the emergence of methods to follow ribosome dynamics in vivo. This review summarizes some of the recent studies that have advanced our knowledge about co-translational events using the power of ribosome profiling, and some of the questions that have emerged from these studies.
Collapse
Affiliation(s)
- Martine A Collart
- Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, Institute of Genetics and Genomics, Geneva, 1 rue Michel Servet, 1211 Genève 4, Switzerland
| | - Benjamin Weiss
- Department of Microbiology and Molecular Medicine, Faculty of Medicine, University of Geneva, Institute of Genetics and Genomics, Geneva, 1 rue Michel Servet, 1211 Genève 4, Switzerland
| |
Collapse
|
39
|
Oertlin C, Lorent J, Murie C, Furic L, Topisirovic I, Larsson O. Generally applicable transcriptome-wide analysis of translation using anota2seq. Nucleic Acids Res 2020; 47:e70. [PMID: 30926999 PMCID: PMC6614820 DOI: 10.1093/nar/gkz223] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Revised: 03/18/2019] [Accepted: 03/28/2019] [Indexed: 12/28/2022] Open
Abstract
mRNA translation plays an evolutionarily conserved role in homeostasis and when dysregulated contributes to various disorders including metabolic and neurological diseases and cancer. Notwithstanding that optimal and universally applicable methods are critical for understanding the complex role of translational control under physiological and pathological conditions, approaches to analyze translatomes are largely underdeveloped. To address this, we developed the anota2seq algorithm which outperforms current methods for statistical identification of changes in translation. Notably, in contrast to available analytical methods, anota2seq also allows specific identification of an underappreciated mode of gene expression regulation whereby translation acts as a buffering mechanism which maintains protein levels despite fluctuations in corresponding mRNA abundance (‘translational buffering’). Thus, the universal anota2seq algorithm allows efficient and hitherto unprecedented interrogation of translatomes which is anticipated to advance knowledge regarding the role of translation in homeostasis and disease.
Collapse
Affiliation(s)
- Christian Oertlin
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Julie Lorent
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Carl Murie
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| | - Luc Furic
- Cancer Program, Biomedicine Discovery Institute and Department of Anatomy & Developmental Biology, Monash University, VIC, Australia.,Prostate Cancer Translational Research Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, VIC, Australia
| | - Ivan Topisirovic
- Lady Davis Institute, SMBD Jewish General Hospital, Gerald Bronfman Department of Oncology, and Departments of Experimental Medicine, and Biochemistry McGill University, Montreal, Canada
| | - Ola Larsson
- Department of Oncology-Pathology, Science for Life Laboratory, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
40
|
XPRESSyourself: Enhancing, standardizing, and automating ribosome profiling computational analyses yields improved insight into data. PLoS Comput Biol 2020; 16:e1007625. [PMID: 32004313 PMCID: PMC7015430 DOI: 10.1371/journal.pcbi.1007625] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Revised: 02/12/2020] [Accepted: 12/20/2019] [Indexed: 11/19/2022] Open
Abstract
Ribosome profiling, an application of nucleic acid sequencing for monitoring ribosome activity, has revolutionized our understanding of protein translation dynamics. This technique has been available for a decade, yet the current state and standardization of publicly available computational tools for these data is bleak. We introduce XPRESSyourself, an analytical toolkit that eliminates barriers and bottlenecks associated with this specialized data type by filling gaps in the computational toolset for both experts and non-experts of ribosome profiling. XPRESSyourself automates and standardizes analysis procedures, decreasing time-to-discovery and increasing reproducibility. This toolkit acts as a reference implementation of current best practices in ribosome profiling analysis. We demonstrate this toolkit’s performance on publicly available ribosome profiling data by rapidly identifying hypothetical mechanisms related to neurodegenerative phenotypes and neuroprotective mechanisms of the small-molecule ISRIB during acute cellular stress. XPRESSyourself brings robust, rapid analysis of ribosome-profiling data to a broad and ever-expanding audience and will lead to more reproducible and accessible measurements of translation regulation. XPRESSyourself software is perpetually open-source under the GPL-3.0 license and is hosted at https://github.com/XPRESSyourself, where users can access additional documentation and report software issues.
Collapse
|
41
|
Neelagandan N, Gonnella G, Dang S, Janiesch PC, Miller KK, Küchler K, Marques RF, Indenbirken D, Alawi M, Grundhoff A, Kurtz S, Duncan KE. TDP-43 enhances translation of specific mRNAs linked to neurodegenerative disease. Nucleic Acids Res 2019; 47:341-361. [PMID: 30357366 PMCID: PMC6326785 DOI: 10.1093/nar/gky972] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2018] [Accepted: 10/08/2018] [Indexed: 12/12/2022] Open
Abstract
The RNA-binding protein TDP-43 is heavily implicated in neurodegenerative disease. Numerous patient mutations in TARDBP, the gene encoding TDP-43, combined with data from animal and cell-based models, imply that altered RNA regulation by TDP-43 causes Amyotrophic Lateral Sclerosis and Frontotemporal Dementia. However, underlying mechanisms remain unresolved. Increased cytoplasmic TDP-43 levels in diseased neurons suggest a possible role in this cellular compartment. Here, we examined the impact on translation of overexpressing human TDP-43 and the TDP-43A315T patient mutant protein in motor neuron-like cells and primary cultures of cortical neurons. In motor-neuron like cells, TDP-43 associates with ribosomes without significantly affecting global translation. However, ribosome profiling and additional assays revealed enhanced translation and direct binding of Camta1, Mig12, and Dennd4a mRNAs. Overexpressing either wild-type TDP-43 or TDP-43A315T stimulated translation of Camta1 and Mig12 mRNAs via their 5'UTRs and increased CAMTA1 and MIG12 protein levels. In contrast, translational enhancement of Dennd4a mRNA required a specific 3'UTR region and was specifically observed with the TDP-43A315T patient mutant allele. Our data reveal that TDP-43 can function as an mRNA-specific translational enhancer. Moreover, since CAMTA1 and DENND4A are linked to neurodegeneration, they suggest that this function could contribute to disease.
Collapse
Affiliation(s)
- Nagammal Neelagandan
- Neuronal Translational Control Research Group, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg 20251, Germany
| | - Giorgio Gonnella
- Universität Hamburg, MIN-Fakultät, ZBH-Center for Bioinformatics, Hamburg 20146, Germany
| | - Stefan Dang
- Universität Hamburg, MIN-Fakultät, ZBH-Center for Bioinformatics, Hamburg 20146, Germany
| | - Philipp C Janiesch
- Neuronal Translational Control Research Group, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg 20251, Germany
| | - Katharine K Miller
- Neuronal Translational Control Research Group, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg 20251, Germany
| | - Katrin Küchler
- Neuronal Translational Control Research Group, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg 20251, Germany
| | - Rita F Marques
- Neuronal Translational Control Research Group, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg 20251, Germany
| | - Daniela Indenbirken
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg 20251, Germany
| | - Malik Alawi
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg 20251, Germany.,Bioinformatics Core, University Medical Center Hamburg-Eppendorf (UKE), Hamburg 20251, Germany
| | - Adam Grundhoff
- Heinrich Pette Institute, Leibniz Institute for Experimental Virology, Hamburg 20251, Germany
| | - Stefan Kurtz
- Universität Hamburg, MIN-Fakultät, ZBH-Center for Bioinformatics, Hamburg 20146, Germany
| | - Kent E Duncan
- Neuronal Translational Control Research Group, Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf (UKE), Hamburg 20251, Germany
| |
Collapse
|
42
|
Michel AM, Kiniry SJ, O'Connor PBF, Mullan JP, Baranov PV. GWIPS-viz: 2018 update. Nucleic Acids Res 2019; 46:D823-D830. [PMID: 28977460 PMCID: PMC5753223 DOI: 10.1093/nar/gkx790] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 08/29/2017] [Indexed: 12/15/2022] Open
Abstract
The GWIPS-viz browser (http://gwips.ucc.ie/) is an on-line genome browser which is tailored for exploring ribosome profiling (Ribo-seq) data. Since its publication in 2014, GWIPS-viz provides Ribo-seq data for an additional 14 genomes bringing the current total to 23. The integration of new Ribo-seq data has been automated thereby increasing the number of available tracks to 1792, a 10-fold increase in the last three years. The increase is particularly substantial for data derived from human sources. Following user requests, we added the functionality to download these tracks in bigWig format. We also incorporated new types of data (e.g. TCP-seq) as well as auxiliary tracks from other sources that help with the interpretation of Ribo-seq data. Improvements in the visualization of the data have been carried out particularly for bacterial genomes where the Ribo-seq data are now shown in a strand specific manner. For higher eukaryotic datasets, we provide characteristics of individual datasets using the RUST program which includes the triplet periodicity, sequencing biases and relative inferred A-site dwell times. This information can be used for assessing the quality of Ribo-seq datasets. To improve the power of the signal, we aggregate Ribo-seq data from several studies into Global aggregate tracks for each genome.
Collapse
Affiliation(s)
- Audrey M Michel
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Stephen J Kiniry
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | | | - James P Mullan
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Pavel V Baranov
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| |
Collapse
|
43
|
Verbruggen S, Ndah E, Van Criekinge W, Gessulat S, Kuster B, Wilhelm M, Van Damme P, Menschaert G. PROTEOFORMER 2.0: Further Developments in the Ribosome Profiling-assisted Proteogenomic Hunt for New Proteoforms. Mol Cell Proteomics 2019; 18:S126-S140. [PMID: 31040227 PMCID: PMC6692777 DOI: 10.1074/mcp.ra118.001218] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2018] [Revised: 04/30/2019] [Indexed: 12/20/2022] Open
Abstract
PROTEOFORMER is a pipeline that enables the automated processing of data derived from ribosome profiling (RIBO-seq, i.e. the sequencing of ribosome-protected mRNA fragments). As such, genome-wide ribosome occupancies lead to the delineation of data-specific translation product candidates and these can improve the mass spectrometry-based identification. Since its first publication, different upgrades, new features and extensions have been added to the PROTEOFORMER pipeline. Some of the most important upgrades include P-site offset calculation during mapping, comprehensive data pre-exploration, the introduction of two alternative proteoform calling strategies and extended pipeline output features. These novelties are illustrated by analyzing ribosome profiling data of human HCT116 and Jurkat data. The different proteoform calling strategies are used alongside one another and in the end combined together with reference sequences from UniProt. Matching mass spectrometry data are searched against this extended search space with MaxQuant. Overall, besides annotated proteoforms, this pipeline leads to the identification and validation of different categories of new proteoforms, including translation products of up- and downstream open reading frames, 5' and 3' extended and truncated proteoforms, single amino acid variants, splice variants and translation products of so-called noncoding regions. Further, proof-of-concept is reported for the improvement of spectrum matching by including Prosit, a deep neural network strategy that adds extra fragmentation spectrum intensity features to the analysis. In the light of ribosome profiling-driven proteogenomics, it is shown that this allows validating the spectrum matches of newly identified proteoforms with elevated stringency. These updates and novel conclusions provide new insights and lessons for the ribosome profiling-based proteogenomic research field. More practical information on the pipeline, raw code, the user manual (README) and explanations on the different modes of availability can be found at the GitHub repository of PROTEOFORMER: https://github.com/Biobix/proteoformer.
Collapse
Affiliation(s)
- Steven Verbruggen
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.
| | - Elvis Ndah
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium; VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
| | - Wim Van Criekinge
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium
| | - Siegfried Gessulat
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Munich, Germany; SAP SE, Potsdam, Germany
| | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Munich, Germany
| | - Mathias Wilhelm
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Munich, Germany
| | - Petra Van Damme
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium; Department of Biochemistry and Microbiology, Faculty of Sciences, Ghent University, Ghent, Belgium
| | - Gerben Menschaert
- BioBix, Lab of Bioinformatics and Computational Genomics, Department of Mathematical Modeling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, Ghent, Belgium.
| |
Collapse
|
44
|
Wang H, Wang Y, Xie Z. Computational resources for ribosome profiling: from database to Web server and software. Brief Bioinform 2019; 20:144-155. [PMID: 28968766 DOI: 10.1093/bib/bbx093] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2017] [Indexed: 01/04/2023] Open
Abstract
Ribosome profiling is emerging as a powerful technique that enables genome-wide investigation of in vivo translation at sub-codon resolution. The increasing application of ribosome profiling in recent years has achieved remarkable progress toward understanding the composition, regulation and mechanism of translation. This benefits from not only the awesome power of ribosome profiling but also an extensive range of computational resources available for ribosome profiling. At present, however, a comprehensive review on these resources is still lacking. Here, we survey the recent computational advances guided by ribosome profiling, with a focus on databases, Web servers and software tools for storing, visualizing and analyzing ribosome profiling data. This review is intended to provide experimental and computational biologists with a reference to make appropriate choices among existing resources for the question at hand.
Collapse
Affiliation(s)
- Hongwei Wang
- Zhongshan Ophthalmic Center, Sun Yat-sen University
| | - Yan Wang
- Zhongshan Ophthalmic Center, Sun Yat-sen University
| | - Zhi Xie
- Zhongshan Ophthalmic Center, Sun Yat-sen University
| |
Collapse
|
45
|
Perkins P, Mazzoni-Putman S, Stepanova A, Alonso J, Heber S. RiboStreamR: a web application for quality control, analysis, and visualization of Ribo-seq data. BMC Genomics 2019; 20:422. [PMID: 31167636 PMCID: PMC6551240 DOI: 10.1186/s12864-019-5700-7] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Ribo-seq is a popular technique for studying translation and its regulation. A Ribo-seq experiment produces a snap-shot of the location and abundance of actively translating ribosomes within a cell's transcriptome. In practice, Ribo-seq data analysis can be sensitive to quality issues such as read length variation, low read periodicities, and contaminations with ribosomal and transfer RNA. Various software tools for data preprocessing, quality assessment, analysis, and visualization of Ribo-seq data have been developed. However, many of these tools require considerable practical knowledge of software applications, and often multiple different tools have to be used in combination with each other. RESULTS We present riboStreamR, a comprehensive Ribo-seq quality control (QC) platform in the form of an R Shiny web application. RiboStreamR provides visualization and analysis tools for various Ribo-seq QC metrics, including read length distribution, read periodicity, and translational efficiency. Our platform is focused on providing a user-friendly experience, and includes various options for graphical customization, report generation, and anomaly detection within Ribo-seq datasets. CONCLUSIONS RiboStreamR takes advantage of the vast resources provided by the R and Bioconductor environments, and utilizes the Shiny R package to ensure a high level of usability. Our goal is to develop a tool which facilitates in-depth quality assessment of Ribo-seq data by providing reference datasets and automatically highlighting quality issues and anomalies within datasets.
Collapse
Affiliation(s)
- Patrick Perkins
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27607, USA
| | - Serina Mazzoni-Putman
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, 27607, USA
| | - Anna Stepanova
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, 27607, USA
| | - Jose Alonso
- Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, 27607, USA
| | - Steffen Heber
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC, 27607, USA. .,Computer Science Department, North Carolina State University, Campus Box 8206, Raleigh, NC, 27695-8206, USA.
| |
Collapse
|
46
|
Ingolia NT, Hussmann JA, Weissman JS. Ribosome Profiling: Global Views of Translation. Cold Spring Harb Perspect Biol 2019; 11:cshperspect.a032698. [PMID: 30037969 DOI: 10.1101/cshperspect.a032698] [Citation(s) in RCA: 159] [Impact Index Per Article: 31.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The translation of messenger RNA (mRNA) into protein and the folding of the resulting protein into an active form are prerequisites for virtually every cellular process and represent the single largest investment of energy by cells. Ribosome profiling-based approaches have revolutionized our ability to monitor every step of protein synthesis in vivo, allowing one to measure the rate of protein synthesis across the proteome, annotate the protein coding capacity of genomes, monitor localized protein synthesis, and explore cotranslational folding and targeting. The rich and quantitative nature of ribosome profiling data provides an unprecedented opportunity to explore and model complex cellular processes. New analytical techniques and improved experimental protocols will provide a deeper understanding of the factors controlling translation speed and its impact on protein function and cell physiology as well as the role of ribosomal RNA and mRNA modifications in regulating translation.
Collapse
Affiliation(s)
- Nicholas T Ingolia
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720
| | - Jeffrey A Hussmann
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California 94158.,Howard Hughes Medical Institute, San Francisco, California 94158
| | - Jonathan S Weissman
- Department of Cellular and Molecular Pharmacology, University of California, San Francisco, California 94158.,Howard Hughes Medical Institute, San Francisco, California 94158
| |
Collapse
|
47
|
Liu B, Molinaro G, Shu H, Stackpole EE, Huber KM, Richter JD. Optimization of ribosome profiling using low-input brain tissue from fragile X syndrome model mice. Nucleic Acids Res 2019; 47:e25. [PMID: 30590705 PMCID: PMC6411937 DOI: 10.1093/nar/gky1292] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2018] [Revised: 11/23/2018] [Accepted: 12/15/2018] [Indexed: 01/23/2023] Open
Abstract
Dysregulated protein synthesis is a major underlying cause of many neurodevelopmental diseases including fragile X syndrome. In order to capture subtle but biologically significant differences in translation in these disorders, a robust technique is required. One powerful tool to study translational control is ribosome profiling, which is based on deep sequencing of mRNA fragments protected from ribonuclease (RNase) digestion by ribosomes. However, this approach has been mainly applied to rapidly dividing cells where translation is active and large amounts of starting material are readily available. The application of ribosome profiling to low-input brain tissue where translation is modest and gene expression changes between genotypes are expected to be small has not been carefully evaluated. Using hippocampal tissue from wide type and fragile X mental retardation 1 (Fmr1) knockout mice, we show that variable RNase digestion can lead to significant sample batch effects. We also establish GC content and ribosome footprint length as quality control metrics for RNase digestion. We performed RNase titration experiments for low-input samples to identify optimal conditions for this critical step that is often improperly conducted. Our data reveal that optimal RNase digestion is essential to ensure high quality and reproducibility of ribosome profiling for low-input brain tissue.
Collapse
Affiliation(s)
- Botao Liu
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Gemma Molinaro
- Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Huan Shu
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Emily E Stackpole
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01605, USA
| | - Kimberly M Huber
- Department of Neuroscience, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Joel D Richter
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01605, USA
| |
Collapse
|
48
|
Eraslan B, Wang D, Gusic M, Prokisch H, Hallström BM, Uhlén M, Asplund A, Pontén F, Wieland T, Hopf T, Hahne H, Kuster B, Gagneur J. Quantification and discovery of sequence determinants of protein-per-mRNA amount in 29 human tissues. Mol Syst Biol 2019; 15:e8513. [PMID: 30777893 PMCID: PMC6379048 DOI: 10.15252/msb.20188513] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2018] [Revised: 01/22/2019] [Accepted: 01/23/2019] [Indexed: 12/15/2022] Open
Abstract
Despite their importance in determining protein abundance, a comprehensive catalogue of sequence features controlling protein-to-mRNA (PTR) ratios and a quantification of their effects are still lacking. Here, we quantified PTR ratios for 11,575 proteins across 29 human tissues using matched transcriptomes and proteomes. We estimated by regression the contribution of known sequence determinants of protein synthesis and degradation in addition to 45 mRNA and 3 protein sequence motifs that we found by association testing. While PTR ratios span more than 2 orders of magnitude, our integrative model predicts PTR ratios at a median precision of 3.2-fold. A reporter assay provided functional support for two novel UTR motifs, and an immobilized mRNA affinity competition-binding assay identified motif-specific bound proteins for one motif. Moreover, our integrative model led to a new metric of codon optimality that captures the effects of codon frequency on protein synthesis and degradation. Altogether, this study shows that a large fraction of PTR ratio variation in human tissues can be predicted from sequence, and it identifies many new candidate post-transcriptional regulatory elements.
Collapse
Affiliation(s)
- Basak Eraslan
- Computational Biology, Department of Informatics, Technical University of Munich, Garching Munich, Germany
- Graduate School of Quantitative Biosciences (QBM), Ludwig-Maximilians-Universität München, Munich, Germany
| | - Dongxue Wang
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Mirjana Gusic
- Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Holger Prokisch
- Institute of Human Genetics, Technical University of Munich, Munich, Germany
- Institute of Human Genetics, Helmholtz Zentrum München, Neuherberg, Germany
| | - Björn M Hallström
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlén
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Anna Asplund
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Frederik Pontén
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Thomas Wieland
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | - Thomas Hopf
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
| | | | - Bernhard Kuster
- Chair of Proteomics and Bioanalytics, Technical University of Munich, Freising, Germany
- Center For Integrated Protein Science Munich (CIPSM), Munich, Germany
| | - Julien Gagneur
- Computational Biology, Department of Informatics, Technical University of Munich, Garching Munich, Germany
| |
Collapse
|
49
|
Mohammad F, Green R, Buskirk AR. A systematically-revised ribosome profiling method for bacteria reveals pauses at single-codon resolution. eLife 2019; 8:e42591. [PMID: 30724162 PMCID: PMC6377232 DOI: 10.7554/elife.42591] [Citation(s) in RCA: 110] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2018] [Accepted: 02/05/2019] [Indexed: 12/17/2022] Open
Abstract
In eukaryotes, ribosome profiling provides insight into the mechanism of protein synthesis at the codon level. In bacteria, however, the method has been more problematic and no consensus has emerged for how to best prepare profiling samples. Here, we identify the sources of these problems and describe new solutions for arresting translation and harvesting cells in order to overcome them. These improvements remove confounding artifacts and improve the resolution to allow analyses of ribosome behavior at the codon level. With a clearer view of the translational landscape in vivo, we observe that filtering cultures leads to translational pauses at serine and glycine codons through the reduction of tRNA aminoacylation levels. This observation illustrates how bacterial ribosome profiling studies can yield insight into the mechanism of protein synthesis at the codon level and how these mechanisms are regulated in response to changes in the physiology of the cell.
Collapse
Affiliation(s)
- Fuad Mohammad
- Department of Molecular Biology and GeneticsJohns Hopkins University School of MedicineBaltimoreUnited States
| | - Rachel Green
- Department of Molecular Biology and GeneticsJohns Hopkins University School of MedicineBaltimoreUnited States
- Howard Hughes Medical Institute, Johns Hopkins University School of MedicineBaltimoreUnited States
| | - Allen R Buskirk
- Department of Molecular Biology and GeneticsJohns Hopkins University School of MedicineBaltimoreUnited States
| |
Collapse
|
50
|
Zhao D, Baez WD, Fredrick K, Bundschuh R. RiboProP: a probabilistic ribosome positioning algorithm for ribosome profiling. Bioinformatics 2018; 35:1486-1493. [DOI: 10.1093/bioinformatics/bty854] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2018] [Revised: 09/03/2018] [Accepted: 10/09/2018] [Indexed: 11/14/2022] Open
Abstract
Abstract
Motivation
Ribosome profiling has been widely used to study translation in a genome-wide fashion. It requires deep sequencing of ribosome protected mRNA fragments followed by mapping of fragments to the reference genome. For applications such as identification of ribosome pausing sites, it is not enough to map a fragment to a given gene, but the exact position of the ribosome represented by the fragment must be identified for each mRNA fragment. The assignment of the correct ribosome position is complicated by the broad length distribution of the ribosome protected fragments caused by the known sequence bias of micrococcal nuclease (MNase), the most widely used nuclease for digesting mRNAs in bacteria. Available mapping algorithms suffer from either MNase bias or low accuracy in characterizing the ribosome pausing kinetics.
Results
In this paper, we introduce a new computational method for mapping the ribosome protected fragments to ribosome locations. We first develop a mathematical model of the interplay between MNase digestion and ribosome protection of the mRNAs. We then use the model to reconstruct the ribosome occupancy profile on a per gene level. We demonstrate that our method has the capability of mitigating the sequence bias introduced by MNase and accurately locating ribosome pausing sites at codon resolution. We believe that our method can be broadly applied to ribosome profiling studies on bacteria where codon resolution is necessary.
Availability and implementation
Source code implementing our approach can be downloaded under GPL3 license at http://bioserv.mps.ohio-state.edu/RiboProP.
Supplementary information
Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Dengke Zhao
- Interdisciplinary Biophysics Graduate Program, Division of Hematology, The Ohio State University, Columbus, OH, USA
| | - William D Baez
- Department of Physics, Division of Hematology, The Ohio State University, Columbus, OH, USA
| | - Kurt Fredrick
- Department of Microbiology, Division of Hematology, The Ohio State University, Columbus, OH, USA
- Center for RNA Biology, Division of Hematology, The Ohio State University, Columbus, OH, USA
| | - Ralf Bundschuh
- Interdisciplinary Biophysics Graduate Program, Division of Hematology, The Ohio State University, Columbus, OH, USA
- Department of Physics, Division of Hematology, The Ohio State University, Columbus, OH, USA
- Center for RNA Biology, Division of Hematology, The Ohio State University, Columbus, OH, USA
- Department of Chemistry & Biochemistry, Division of Hematology, The Ohio State University, Columbus, OH, USA
- Department of Internal Medicine, Division of Hematology, The Ohio State University, Columbus, OH, USA
| |
Collapse
|