1
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Aviner R, Lee TT, Masto VB, Li KH, Andino R, Frydman J. Polyglutamine-mediated ribotoxicity disrupts proteostasis and stress responses in Huntington's disease. Nat Cell Biol 2024; 26:892-902. [PMID: 38741019 DOI: 10.1038/s41556-024-01414-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/01/2024] [Indexed: 05/16/2024]
Abstract
Huntington's disease (HD) is a neurodegenerative disorder caused by expansion of a CAG trinucleotide repeat in the Huntingtin (HTT) gene, encoding a homopolymeric polyglutamine (polyQ) tract. Although mutant HTT (mHTT) protein is known to aggregate, the links between aggregation and neurotoxicity remain unclear. Here we show that both translation and aggregation of wild-type HTT and mHTT are regulated by a stress-responsive upstream open reading frame and that polyQ expansions cause abortive translation termination and release of truncated, aggregation-prone mHTT fragments. Notably, we find that mHTT depletes translation elongation factor eIF5A in brains of symptomatic HD mice and cultured HD cells, leading to pervasive ribosome pausing and collisions. Loss of eIF5A disrupts homeostatic controls and impairs recovery from acute stress. Importantly, drugs that inhibit translation initiation reduce premature termination and mitigate this escalating cascade of ribotoxic stress and dysfunction in HD.
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Affiliation(s)
- Ranen Aviner
- Department of Biology and Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
- Chan Zuckerberg Biohub San Francisco, San Francisco, CA, USA
| | - Ting-Ting Lee
- Department of Biology and Department of Genetics, Stanford University, Stanford, CA, USA
| | - Vincent B Masto
- Department of Biology and Department of Genetics, Stanford University, Stanford, CA, USA
| | - Kathy H Li
- Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, USA
| | - Raul Andino
- Department of Microbiology and Immunology, University of California, San Francisco, San Francisco, CA, USA
| | - Judith Frydman
- Department of Biology and Department of Genetics, Stanford University, Stanford, CA, USA.
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2
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Lv D, Li D, Cai Y, Guo J, Chu S, Yu J, Liu K, Jiang T, Ding N, Jin X, Li Y, Xu J. CancerProteome: a resource to functionally decipher the proteome landscape in cancer. Nucleic Acids Res 2024; 52:D1155-D1162. [PMID: 37823596 PMCID: PMC10767844 DOI: 10.1093/nar/gkad824] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Revised: 09/07/2023] [Accepted: 09/20/2023] [Indexed: 10/13/2023] Open
Abstract
Advancements in mass spectrometry (MS)-based proteomics have greatly facilitated the large-scale quantification of proteins and microproteins, thereby revealing altered signalling pathways across many different cancer types. However, specialized and comprehensive resources are lacking for cancer proteomics. Here, we describe CancerProteome (http://bio-bigdata.hrbmu.edu.cn/CancerProteome), which functionally deciphers and visualizes the proteome landscape in cancer. We manually curated and re-analyzed publicly available MS-based quantification and post-translational modification (PTM) proteomes, including 7406 samples from 21 different cancer types, and also examined protein abundances and PTM levels in 31 120 proteins and 4111 microproteins. Six major analytical modules were developed with a view to describe protein contributions to carcinogenesis using proteome analysis, including conventional analyses of quantitative and the PTM proteome, functional enrichment, protein-protein associations by integrating known interactions with co-expression signatures, drug sensitivity and clinical relevance analyses. Moreover, protein abundances, which correlated with corresponding transcript or PTM levels, were evaluated. CancerProteome is convenient as it allows users to access specific proteins/microproteins of interest using quick searches or query options to generate multiple visualization results. In summary, CancerProteome is an important resource, which functionally deciphers the cancer proteome landscape and provides a novel insight for the identification of tumor protein markers in cancer.
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Affiliation(s)
- Dezhong Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Donghao Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Yangyang Cai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Jiyu Guo
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Sen Chu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Jiaxin Yu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Kefan Liu
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Tiantongfei Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Xiyun Jin
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang Province 150000, China
| | - Yongsheng Li
- School of Interdisciplinary Medicine and Engineering, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province 150081, China
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3
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Zheng W, Fong JHC, Wan YK, Chu AHY, Huang Y, Wong ASL, Ho JWK. Discovery of regulatory motifs in 5' untranslated regions using interpretable multi-task learning models. Cell Syst 2023; 14:1103-1112.e6. [PMID: 38016465 DOI: 10.1016/j.cels.2023.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 09/18/2023] [Accepted: 10/31/2023] [Indexed: 11/30/2023]
Abstract
The sequence in the 5' untranslated regions (UTRs) is known to affect mRNA translation rates. However, the underlying regulatory grammar remains elusive. Here, we propose MTtrans, a multi-task translation rate predictor capable of learning common sequence patterns from datasets across various experimental techniques. The core premise is that common motifs are more likely to be genuinely involved in translation control. MTtrans outperforms existing methods in both accuracy and the ability to capture transferable motifs across species, highlighting its strength in identifying evolutionarily conserved sequence motifs. Our independent fluorescence-activated cell sorting coupled with deep sequencing (FACS-seq) experiment validates the impact of most motifs identified by MTtrans. Additionally, we introduce "GRU-rewiring," a technique to interpret the hidden states of the recurrent units. Gated recurrent unit (GRU)-rewiring allows us to identify regulatory element-enriched positions and examine the local effects of 5' UTR mutations. MTtrans is a powerful tool for deciphering the translation regulatory motifs.
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Affiliation(s)
- Weizhong Zheng
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - John H C Fong
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Yuk Kei Wan
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China
| | - Athena H Y Chu
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China
| | - Yuanhua Huang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong SAR, China; Center for Translational Stem Cell Biology, Hong Kong Science and Technology Park, Hong Kong SAR, China
| | - Alan S L Wong
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Centre for Oncology and Immunology, Hong Kong Science Park, Hong Kong SAR, China; Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Joshua W K Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, China; Laboratory of Data Discovery for Health (D24H) Limited, Hong Kong Science Park, Hong Kong SAR, China.
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4
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Kore H, Datta KK, Nagaraj SH, Gowda H. Protein-coding potential of non-canonical open reading frames in human transcriptome. Biochem Biophys Res Commun 2023; 684:149040. [PMID: 37897910 DOI: 10.1016/j.bbrc.2023.09.068] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 09/09/2023] [Accepted: 09/23/2023] [Indexed: 10/30/2023]
Abstract
In recent years, proteogenomics and ribosome profiling studies have identified a large number of proteins encoded by noncoding regions in the human genome. They are encoded by small open reading frames (sORFs) in the untranslated regions (UTRs) of mRNAs and long non-coding RNAs (lncRNAs). These sORF encoded proteins (SEPs) are often <150AA and show poor evolutionary conservation. A subset of them have been functionally characterized and shown to play an important role in fundamental biological processes including cardiac and muscle function, DNA repair, embryonic development and various human diseases. How many novel protein-coding regions exist in the human genome and what fraction of them are functionally important remains a mystery. In this review, we discuss current progress in unraveling SEPs, approaches used for their identification, their limitations and reliability of these identifications. We also discuss functionally characterized SEPs and their involvement in various biological processes and diseases. Lastly, we provide insights into their distinctive features compared to canonical proteins and challenges associated with annotating these in protein reference databases.
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Affiliation(s)
- Hitesh Kore
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, 4059, Australia; Cancer Precision Medicine Group, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Queensland, 4006, Australia; Faculty of Health, Queensland University of Technology, Brisbane, Queensland, 4059, Australia.
| | - Keshava K Datta
- Proteomics and Metabolomics Platform, La Trobe University, Melbourne, VIC, 3083, Australia
| | - Shivashankar H Nagaraj
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, 4059, Australia; Faculty of Health, Queensland University of Technology, Brisbane, Queensland, 4059, Australia
| | - Harsha Gowda
- Centre for Genomics and Personalised Health, Queensland University of Technology, Brisbane, Queensland, 4059, Australia; Cancer Precision Medicine Group, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, Queensland, 4006, Australia; Faculty of Health, Queensland University of Technology, Brisbane, Queensland, 4059, Australia; Faculty of Medicine, The University of Queensland, Queensland, 4072, Australia.
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5
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Yan Y, Tian Y, Wu Z, Zhang K, Yang R. Interchromosomal Colocalization with Parental Genes Is Linked to the Function and Evolution of Mammalian Retrocopies. Mol Biol Evol 2023; 40:msad265. [PMID: 38060983 PMCID: PMC10733166 DOI: 10.1093/molbev/msad265] [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: 07/13/2023] [Revised: 10/25/2023] [Accepted: 11/29/2023] [Indexed: 12/22/2023] Open
Abstract
Retrocopies are gene duplicates arising from reverse transcription of mature mRNA transcripts and their insertion back into the genome. While long being regarded as processed pseudogenes, more and more functional retrocopies have been discovered. How the stripped-down retrocopies recover expression capability and become functional paralogs continually intrigues evolutionary biologists. Here, we investigated the function and evolution of retrocopies in the context of 3D genome organization. By mapping retrocopy-parent pairs onto sequencing-based and imaging-based chromatin contact maps in human and mouse cell lines and onto Hi-C interaction maps in 5 other mammals, we found that retrocopies and their parental genes show a higher-than-expected interchromosomal colocalization frequency. The spatial interactions between retrocopies and parental genes occur frequently at loci in active subcompartments and near nuclear speckles. Accordingly, colocalized retrocopies are more actively transcribed and translated and are more evolutionarily conserved than noncolocalized ones. The active transcription of colocalized retrocopies may result from their permissive epigenetic environment and shared regulatory elements with parental genes. Population genetic analysis of retroposed gene copy number variants in human populations revealed that retrocopy insertions are not entirely random in regard to interchromosomal interactions and that colocalized retroposed gene copy number variants are more likely to reach high frequencies, suggesting that both insertion bias and natural selection contribute to the colocalization of retrocopy-parent pairs. Further dissection implies that reduced selection efficacy, rather than positive selection, contributes to the elevated allele frequency of colocalized retroposed gene copy number variants. Overall, our results hint a role of interchromosomal colocalization in the "resurrection" of initially neutral retrocopies.
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Affiliation(s)
- Yubin Yan
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Yuhan Tian
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Zefeng Wu
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Kunling Zhang
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
| | - Ruolin Yang
- College of Life Sciences, Northwest A&F University, Yangling, Shaanxi, China
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6
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Prensner JR, Abelin JG, Kok LW, Clauser KR, Mudge JM, Ruiz-Orera J, Bassani-Sternberg M, Moritz RL, Deutsch EW, van Heesch S. What Can Ribo-Seq, Immunopeptidomics, and Proteomics Tell Us About the Noncanonical Proteome? Mol Cell Proteomics 2023; 22:100631. [PMID: 37572790 PMCID: PMC10506109 DOI: 10.1016/j.mcpro.2023.100631] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 07/21/2023] [Accepted: 08/08/2023] [Indexed: 08/14/2023] Open
Abstract
Ribosome profiling (Ribo-Seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of noncanonical sites of ribosome translation outside the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7000 noncanonical ORFs are translated, which, at first glance, has the potential to expand the number of human protein CDSs by 30%, from ∼19,500 annotated CDSs to over 26,000 annotated CDSs. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of noncanonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome but searching for guidance on how to proceed. Here, we discuss the current state of noncanonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein coding."
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Affiliation(s)
- John R Prensner
- Division of Pediatric Hematology/Oncology, Department of Pediatrics, University of Michigan Medical School, Ann Arbor, Michigan, USA; Department of Biological Chemistry, University of Michigan Medical School, Ann Arbor, Michigan, USA.
| | | | - Leron W Kok
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
| | - Karl R Clauser
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Jonathan M Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK
| | - Jorge Ruiz-Orera
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, Agora Center Bugnon 25A, University of Lausanne, Lausanne, Switzerland; Department of Oncology, Centre Hospitalier Universitaire Vaudois (CHUV), Lausanne, Switzerland; Agora Cancer Research Centre, Lausanne, Switzerland
| | - Robert L Moritz
- Institute for Systems Biology (ISB), Seattle, Washington, USA
| | - Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington, USA
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7
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Prensner JR, Abelin JG, Kok LW, Clauser KR, Mudge JM, Ruiz-Orera J, Bassani-Sternberg M, Deutsch EW, van Heesch S. What can Ribo-seq and proteomics tell us about the non-canonical proteome? BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.16.541049. [PMID: 37292611 PMCID: PMC10245706 DOI: 10.1101/2023.05.16.541049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Ribosome profiling (Ribo-seq) has proven transformative for our understanding of the human genome and proteome by illuminating thousands of non-canonical sites of ribosome translation outside of the currently annotated coding sequences (CDSs). A conservative estimate suggests that at least 7,000 non-canonical open reading frames (ORFs) are translated, which, at first glance, has the potential to expand the number of human protein-coding sequences by 30%, from ∼19,500 annotated CDSs to over 26,000. Yet, additional scrutiny of these ORFs has raised numerous questions about what fraction of them truly produce a protein product and what fraction of those can be understood as proteins according to conventional understanding of the term. Adding further complication is the fact that published estimates of non-canonical ORFs vary widely by around 30-fold, from several thousand to several hundred thousand. The summation of this research has left the genomics and proteomics communities both excited by the prospect of new coding regions in the human genome, but searching for guidance on how to proceed. Here, we discuss the current state of non-canonical ORF research, databases, and interpretation, focusing on how to assess whether a given ORF can be said to be "protein-coding". In brief The human genome encodes thousands of non-canonical open reading frames (ORFs) in addition to protein-coding genes. As a nascent field, many questions remain regarding non-canonical ORFs. How many exist? Do they encode proteins? What level of evidence is needed for their verification? Central to these debates has been the advent of ribosome profiling (Ribo-seq) as a method to discern genome-wide ribosome occupancy, and immunopeptidomics as a method to detect peptides that are processed and presented by MHC molecules and not observed in traditional proteomics experiments. This article provides a synthesis of the current state of non-canonical ORF research and proposes standards for their future investigation and reporting. Highlights Combined use of Ribo-seq and proteomics-based methods enables optimal confidence in detecting non-canonical ORFs and their protein products.Ribo-seq can provide more sensitive detection of non-canonical ORFs, but data quality and analytical pipelines will impact results.Non-canonical ORF catalogs are diverse and span both high-stringency and low-stringency ORF nominations.A framework for standardized non-canonical ORF evidence will advance the research field.
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Affiliation(s)
- John R. Prensner
- Department of Pediatrics, Division of Pediatric Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | | | - Leron W. Kok
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, the Netherlands
| | - Karl R. Clauser
- Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Jonathan M. Mudge
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Jorge Ruiz-Orera
- Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), 13125 Berlin, Germany
| | - Michal Bassani-Sternberg
- Ludwig Institute for Cancer Research, University of Lausanne, Agora Center Bugnon 25A, 1005 Lausanne, Switzerland
- Department of Oncology, Centre hospitalier universitaire vaudois (CHUV), Rue du Bugnon 46, 1005 Lausanne, Switzerland
- Agora Cancer Research Centre, 1011 Lausanne, Switzerland
| | - Eric W. Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington 98109, USA
| | - Sebastiaan van Heesch
- Princess Máxima Center for Pediatric Oncology, Heidelberglaan 25, 3584 CS, Utrecht, the Netherlands
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8
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Liu Q, Peng X, Shen M, Qian Q, Xing J, Li C, Gregory R. Ribo-uORF: a comprehensive data resource of upstream open reading frames (uORFs) based on ribosome profiling. Nucleic Acids Res 2023; 51:D248-D261. [PMID: 36440758 PMCID: PMC9825487 DOI: 10.1093/nar/gkac1094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 10/27/2022] [Accepted: 11/22/2022] [Indexed: 11/29/2022] Open
Abstract
Upstream open reading frames (uORFs) are typically defined as translation sites located within the 5' untranslated region upstream of the main protein coding sequence (CDS) of messenger RNAs (mRNAs). Although uORFs are prevalent in eukaryotic mRNAs and modulate the translation of downstream CDSs, a comprehensive resource for uORFs is currently lacking. We developed Ribo-uORF (http://rnainformatics.org.cn/RiboUORF) to serve as a comprehensive functional resource for uORF analysis based on ribosome profiling (Ribo-seq) data. Ribo-uORF currently supports six species: human, mouse, rat, zebrafish, fruit fly, and worm. Ribo-uORF includes 501 554 actively translated uORFs and 107 914 upstream translation initiation sites (uTIS), which were identified from 1495 Ribo-seq and 77 quantitative translation initiation sequencing (QTI-seq) datasets, respectively. We also developed mRNAbrowse to visualize items such as uORFs, cis-regulatory elements, genetic variations, eQTLs, GWAS-based associations, RNA modifications, and RNA editing. Ribo-uORF provides a very intuitive web interface for conveniently browsing, searching, and visualizing uORF data. Finally, uORFscan and UTR5var were developed in Ribo-uORF to precisely identify uORFs and analyze the influence of genetic mutations on uORFs using user-uploaded datasets. Ribo-uORF should greatly facilitate studies of uORFs and their roles in mRNA translation and posttranscriptional control of gene expression.
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Affiliation(s)
- Qi Liu
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
- Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Guangzhou 510640, China
| | - Xin Peng
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
- Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Guangzhou 510640, China
| | - Mengyuan Shen
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
- Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Guangzhou 510640, China
| | - Qian Qian
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
- Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Guangzhou 510640, China
| | - Junlian Xing
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
- Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Guangzhou 510640, China
| | - Chen Li
- Rice Research Institute, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New Technology in Rice Breeding, Guangzhou 510640, China
- Guangdong Rice Engineering Laboratory, Guangzhou 510640, China
- Key Laboratory of Genetics and Breeding of High Quality Rice in Southern China (Co-construction by Ministry and Province), Guangzhou 510640, China
| | - 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
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9
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Álvarez-Urdiola R, Borràs E, Valverde F, Matus JT, Sabidó E, Riechmann JL. Peptidomics Methods Applied to the Study of Flower Development. Methods Mol Biol 2023; 2686:509-536. [PMID: 37540375 DOI: 10.1007/978-1-0716-3299-4_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/05/2023]
Abstract
Understanding the global and dynamic nature of plant developmental processes requires not only the study of the transcriptome, but also of the proteome, including its largely uncharacterized peptidome fraction. Recent advances in proteomics and high-throughput analyses of translating RNAs (ribosome profiling) have begun to address this issue, evidencing the existence of novel, uncharacterized, and possibly functional peptides. To validate the accumulation in tissues of sORF-encoded polypeptides (SEPs), the basic setup of proteomic analyses (i.e., LC-MS/MS) can be followed. However, the detection of peptides that are small (up to ~100 aa, 6-7 kDa) and novel (i.e., not annotated in reference databases) presents specific challenges that need to be addressed both experimentally and with computational biology resources. Several methods have been developed in recent years to isolate and identify peptides from plant tissues. In this chapter, we outline two different peptide extraction protocols and the subsequent peptide identification by mass spectrometry using the database search or the de novo identification methods.
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Affiliation(s)
- Raquel Álvarez-Urdiola
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Edifici CRAG, Campus UAB, Cerdanyola del Vallès, Barcelona, Spain
| | - Eva Borràs
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - Federico Valverde
- Institute for Plant Biochemistry and Photosynthesis CSIC - University of Seville, Seville, Spain
| | - José Tomás Matus
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Edifici CRAG, Campus UAB, Cerdanyola del Vallès, Barcelona, Spain
- Institute for Integrative Systems Biology (I2SysBio), Universitat de València-CSIC, Paterna, Valencia, Spain
| | - Eduard Sabidó
- Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
| | - José Luis Riechmann
- Centre for Research in Agricultural Genomics (CRAG) CSIC-IRTA-UAB-UB, Edifici CRAG, Campus UAB, Cerdanyola del Vallès, Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
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10
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Qian SH, Chen L, Xiong YL, Chen ZX. Evolution and function of developmentally dynamic pseudogenes in mammals. Genome Biol 2022; 23:235. [PMID: 36348461 PMCID: PMC9641868 DOI: 10.1186/s13059-022-02802-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 10/23/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Pseudogenes are excellent markers for genome evolution, which are emerging as crucial regulators of development and disease, especially cancer. However, systematic functional characterization and evolution of pseudogenes remain largely unexplored. RESULTS To systematically characterize pseudogenes, we date the origin of human and mouse pseudogenes across vertebrates and observe a burst of pseudogene gain in these two lineages. Based on a hybrid sequencing dataset combining full-length PacBio sequencing, sample-matched Illumina sequencing, and public time-course transcriptome data, we observe that abundant mammalian pseudogenes could be transcribed, which contribute to the establishment of organ identity. Our analyses reveal that developmentally dynamic pseudogenes are evolutionarily conserved and show an increasing weight during development. Besides, they are involved in complex transcriptional and post-transcriptional modulation, exhibiting the signatures of functional enrichment. Coding potential evaluation suggests that 19% of human pseudogenes could be translated, thus serving as a new way for protein innovation. Moreover, pseudogenes carry disease-associated SNPs and conduce to cancer transcriptome perturbation. CONCLUSIONS Our discovery reveals an unexpectedly high abundance of mammalian pseudogenes that can be transcribed and translated, and these pseudogenes represent a novel regulatory layer. Our study also prioritizes developmentally dynamic pseudogenes with signatures of functional enrichment and provides a hybrid sequencing dataset for further unraveling their biological mechanisms in organ development and carcinogenesis in the future.
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Affiliation(s)
- Sheng Hu Qian
- grid.35155.370000 0004 1790 4137Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, 430070 PR China ,grid.35155.370000 0004 1790 4137Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070 PR China
| | - Lu Chen
- grid.35155.370000 0004 1790 4137Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, 430070 PR China ,grid.35155.370000 0004 1790 4137Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070 PR China
| | - Yu-Li Xiong
- grid.35155.370000 0004 1790 4137Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, 430070 PR China ,grid.35155.370000 0004 1790 4137Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070 PR China
| | - Zhen-Xia Chen
- grid.35155.370000 0004 1790 4137Hubei Hongshan Laboratory, College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, 430070 PR China ,grid.35155.370000 0004 1790 4137Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, 430070 PR China ,grid.35155.370000 0004 1790 4137Interdisciplinary Sciences Institute, Huazhong Agricultural University, Wuhan, 430070 PR China ,grid.35155.370000 0004 1790 4137Shenzhen Institute of Nutrition and Health, Huazhong Agricultural University, Shenzhen, 518124 PR China ,grid.488316.00000 0004 4912 1102Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, 518124 PR China
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11
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Cai Y, Lv D, Li D, Yin J, Ma Y, Luo Y, Fu L, Ding N, Li Y, Pan Z, Li X, Xu J. IEAtlas: an atlas of HLA-presented immune epitopes derived from non-coding regions. Nucleic Acids Res 2022; 51:D409-D417. [PMID: 36099422 PMCID: PMC9825419 DOI: 10.1093/nar/gkac776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 08/21/2022] [Accepted: 08/29/2022] [Indexed: 01/29/2023] Open
Abstract
Cancer-related epitopes can engage the immune system against tumor cells, thus exploring epitopes derived from non-coding regions is emerging as a fascinating field in cancer immunotherapies. Here, we described a database, IEAtlas (http://bio-bigdata.hrbmu.edu.cn/IEAtlas), which aims to provide and visualize the comprehensive atlas of human leukocyte antigen (HLA)-presented immunogenic epitopes derived from non-coding regions. IEAtlas reanalyzed publicly available mass spectrometry-based HLA immunopeptidome datasets against our integrated benchmarked non-canonical open reading frame information. The current IEAtlas identified 245 870 non-canonical epitopes binding to HLA-I/II allotypes across 15 cancer types and 30 non-cancerous tissues, greatly expanding the cancer immunopeptidome. IEAtlas further evaluates the immunogenicity via several commonly used immunogenic features, including HLA binding affinity, stability and T-cell receptor recognition. In addition, IEAtlas provides the biochemical properties of epitopes as well as the clinical relevance of corresponding genes across major cancer types and normal tissues. Several flexible tools were also developed to aid retrieval and to analyze the epitopes derived from non-coding regions. Overall, IEAtlas will serve as a valuable resource for investigating the immunogenic capacity of non-canonical epitopes and the potential as therapeutic cancer vaccines.
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Affiliation(s)
| | | | | | - Jiaqi Yin
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yingying Ma
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Ya Luo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Limei Fu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China
| | - Yongsheng Li
- Correspondence may also be addressed to Yongsheng Li.
| | - Zhenwei Pan
- Correspondence may also be addressed to Zhenwei Pan.
| | - Xia Li
- Correspondence may also be addressed to Xia Li.
| | - Juan Xu
- To whom correspondence should be addressed. Tel: +86 13654559904;
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12
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Malekos E, Carpenter S. Short open reading frame genes in innate immunity: from discovery to characterization. Trends Immunol 2022; 43:741-756. [PMID: 35965152 PMCID: PMC10118063 DOI: 10.1016/j.it.2022.07.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/11/2022] [Accepted: 07/13/2022] [Indexed: 12/27/2022]
Abstract
Next-generation sequencing (NGS) technologies have greatly expanded the size of the known transcriptome. Many newly discovered transcripts are classified as long noncoding RNAs (lncRNAs) which are assumed to affect phenotype through sequence and structure and not via translated protein products despite the vast majority of them harboring short open reading frames (sORFs). Recent advances have demonstrated that the noncoding designation is incorrect in many cases and that sORF-encoded peptides (SEPs) translated from these transcripts are important contributors to diverse biological processes. Interest in SEPs is at an early stage and there is evidence for the existence of thousands of SEPs that are yet unstudied. We hope to pique interest in investigating this unexplored proteome by providing a discussion of SEP characterization generally and describing specific discoveries in innate immunity.
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Affiliation(s)
- Eric Malekos
- Department of Biomolecular Engineering, University of California Santa Cruz, Santa Cruz, CA, USA; Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA
| | - Susan Carpenter
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA, USA; Department of Molecular Cell and Developmental Biology, University of California Santa Cruz, Santa Cruz, CA, USA.
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13
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Yang TH, Lin YC, Hsia M, Liao ZY. SSRTool: a web tool for evaluating RNA secondary structure predictions based on species-specific functional interpretability. Comput Struct Biotechnol J 2022; 20:2473-2483. [PMID: 35664227 PMCID: PMC9136272 DOI: 10.1016/j.csbj.2022.05.028] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 05/13/2022] [Accepted: 05/13/2022] [Indexed: 01/02/2023] Open
Abstract
RNA secondary structures can carry out essential cellular functions alone or interact with one another to form the hierarchical tertiary structures. Experimental structure identification approa ches can show the in vitro structures of RNA molecules. However, they usually have limits in the resolution and are costly. In silico structure prediction tools are thus primarily relied on for pre-experiment analysis. Various structure prediction models have been developed over the decades. Since these tools are usually used before knowing the actual RNA structures, evaluating and ranking the pile of secondary structure predictions of a given sequence is essential in computational analysis. In this research, we implemented a web service called SSRTool (RNA Secondary Structure prediction Ranking Tool) to assist in the ranking and evaluation of the generated predicted structures of a given sequence. Based on the computed species-specific interpretability significance in four common RNA structure–function aspects, SSRTool provides three functions along with visualization interfaces: (1) Rank user-generated predictions. (2) Provide an automated streamline of structure prediction and ranking for a given sequence. (3) Infer the functional aspects of a given structure. We demonstrated the applicability of SSRTool via real case studies and reported the similar trends between computed species-specific rankings and the corresponding prediction F1 values. The SSRTool web service is available online at https://cobisHSS0.im.nuk.edu.tw/SSRTool/, http://cosbi3.ee.ncku.edu.tw/SSRTool/, or the redirecting site https://github.com/cobisLab/SSRTool/.
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Affiliation(s)
- Tzu-Hsien Yang
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
- Corresponding author.
| | - Yu-Cian Lin
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
| | - Min Hsia
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
| | - Zhan-Yi Liao
- Department of Information Management, National University of Kaohsiung, Kaohsiung University Rd, 811 Kaohsiung, Taiwan
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14
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Li Y, Zhang Y, Pan T, Zhou P, Zhou W, Gao Y, Zheng S, Xu J. Shedding light on the hidden human proteome expands immunopeptidome in cancer. Brief Bioinform 2022; 23:6533503. [PMID: 35189633 DOI: 10.1093/bib/bbac034] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 01/07/2022] [Accepted: 01/25/2022] [Indexed: 01/04/2023] Open
Abstract
Unrestrained cellular growth and immune escape of a tumor are associated with the incidental errors of the genome and transcriptome. Advances in next-generation sequencing have identified thousands of genomic and transcriptomic aberrations that generate variant peptides that assemble the hidden proteome, further expanding the immunopeptidome. Emerging next-generation sequencing technologies and a number of computational methods estimated the abundance of immune infiltration from bulk transcriptome have advanced our understanding of tumor microenvironments. Here, we will characterize several major types of tumor-specific antigens arising from single-nucleotide variants, insertions and deletions, gene fusion, alternative splicing, RNA editing and non-coding RNAs. Finally, we summarize the current state-of-the-art computational and experimental approaches or resources and provide an integrative pipeline for the identification of candidate tumor antigens. Together, the systematic investigation of the hidden proteome in cancer will help facilitate the development of effective and durable immunotherapy targets for cancer.
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Affiliation(s)
- Yongsheng Li
- College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou 571199, China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Tao Pan
- College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou 571199, China
| | - Ping Zhou
- Department of Radiotherapy, the First Affiliated Hospital of Hainan Medical University, Hainan, China
| | - Weiwei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yueying Gao
- College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou 571199, China
| | - Shaojiang Zheng
- Key Laboratory of Emergency and Trauma of Ministry of Education, Tumor Institute of the First Affiliated Hospital, Hainan Medical University, Haikou, 571199, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
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15
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Liu T, Wu J, Wu Y, Hu W, Fang Z, Wang Z, Jiang C, Li S. LncPep: A Resource of Translational Evidences for lncRNAs. Front Cell Dev Biol 2022; 10:795084. [PMID: 35141219 PMCID: PMC8819059 DOI: 10.3389/fcell.2022.795084] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 01/05/2022] [Indexed: 12/13/2022] Open
Abstract
Long noncoding RNAs (lncRNAs) are a type of transcript that is >200 nucleotides long with no protein-coding capacity. Accumulating studies have suggested that lncRNAs contain open reading frames (ORFs) that encode peptides. Although several noncoding RNA-encoded peptide-related databases have been developed, most of them display only a small number of experimentally validated peptides, and resources focused on lncRNA-encoded peptides are still lacking. We used six types of evidence, coding potential assessment tool (CPAT), coding potential calculator v2.0 (CPC2), N6-methyladenosine modification of RNA sites (m6A), Pfam, ribosome profiling (Ribo-seq), and translation initiation sites (TISs), to evaluate the coding potential of 883,804 lncRNAs across 39 species. We constructed a comprehensive database of lncRNA-encoded peptides, LncPep (http://www.shenglilabs.com/LncPep/). LncPep provides three major functional modules: 1) user-friendly searching/browsing interface, 2) prediction and BLAST modules for exploring novel lncRNAs and peptides, and 3) annotations for lncRNAs, peptides and supporting evidence. Taken together, LncPep is a user-friendly and convenient platform for discovering and investigating peptides encoded by lncRNAs.
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Affiliation(s)
- Teng Liu
- Precision Research Center for Refractory Diseases, Institute for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jingni Wu
- Precision Research Center for Refractory Diseases, Institute for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yangjun Wu
- Department of Gynecological Oncology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wei Hu
- Precision Research Center for Refractory Diseases, Institute for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhixiao Fang
- Precision Research Center for Refractory Diseases, Institute for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zishan Wang
- Department of Genetics and Genomic Sciences, Center for Transformative Disease Modeling, Tisch Cancer Institute, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Chunjie Jiang
- Institute for Diabetes Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, United States
| | - Shengli Li
- Precision Research Center for Refractory Diseases, Institute for Clinical Research, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- *Correspondence: Shengli Li,
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16
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Xie M, Yang L, Chen G, Wang Y, Xie Z, Wang H. RiboChat: a chat-style web interface for analysis and annotation of ribosome profiling data. Brief Bioinform 2022; 23:6511203. [DOI: 10.1093/bib/bbab559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 11/29/2021] [Accepted: 12/08/2021] [Indexed: 11/13/2022] Open
Abstract
Abstract
The increasing volume of ribosome profiling (Ribo-seq) data, computational complexity of its data processing and operational handicap of related analytical procedures present a daunting set of informatics challenges. These impose a substantial barrier to researchers particularly with no or limited bioinformatics expertise in analyzing and decoding translation information from Ribo-seq data, thus driving the need for a new research paradigm for data computation and information extraction. In this knowledge base, we herein present a novel interactive web platform, RiboChat (https://db.cngb.org/ribobench/chat.html), for direct analyzing and annotating Ribo-seq data in the form of a chat conversation. It consists of a user-friendly web interface and a backend cloud-computing service. When typing a data analysis question into the chat window, the object-text detection module will be run to recognize relevant keywords from the input text. Based on the features identified in the input, individual analytics modules are then scored to find the perfect-matching candidate. The corresponding analytics module will be further executed after checking the completion status of the uploading of datasets and configured parameters. Overall, RiboChat represents an important step forward in the emerging direction of next-generation data analytics and will enable the broad research community to conveniently decipher translation information embedded within Ribo-seq data.
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17
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Erady C, Amin K, Onilogbo TOAE, Tomasik J, Jukes-Jones R, Umrania Y, Bahn S, Prabakaran S. Novel open reading frames in human accelerated regions and transposable elements reveal new leads to understand schizophrenia and bipolar disorder. Mol Psychiatry 2022; 27:1455-1468. [PMID: 34937870 PMCID: PMC9095477 DOI: 10.1038/s41380-021-01405-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 11/16/2021] [Accepted: 11/24/2021] [Indexed: 12/13/2022]
Abstract
Schizophrenia (SCZ) and bipolar disorder are debilitating neuropsychiatric disorders arising from a combination of environmental and genetic factors. Novel open reading frames (nORFs) are genomic loci that give rise to previously uncharacterized transcripts and protein products. In our previous work, we have shown that nORFs can be biologically regulated and that they may play a role in cancer and rare diseases. More importantly, we have shown that nORFs may emerge in accelerated regions of the genome giving rise to species-specific functions. We hypothesize that nORFs represent a potentially important group of biological factors that may contribute to SCZ and bipolar disorder pathophysiology. Human accelerated regions (HARs) are genomic features showing human-lineage-specific rapid evolution that may be involved in biological regulation and have additionally been found to associate with SCZ genes. Transposable elements (TEs) are another set of genomic features that have been shown to regulate gene expression. As with HARs, their relevance to SCZ has also been suggested. Here, nORFs are investigated in the context of HARs and TEs. This work shows that nORFs whose expression is disrupted in SCZ and bipolar disorder are in close proximity to HARs and TEs and that some of them are significantly associated with SCZ and bipolar disorder genomic hotspots. We also show that nORF encoded proteins can form structures and potentially constitute novel drug targets.
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Affiliation(s)
- Chaitanya Erady
- grid.5335.00000000121885934Department of Genetics, University of Cambridge, Cambridge, CB2 3EH UK
| | - Krishna Amin
- grid.5335.00000000121885934Department of Genetics, University of Cambridge, Cambridge, CB2 3EH UK
| | - Temiloluwa O. A. E. Onilogbo
- grid.5335.00000000121885934Department of Genetics, University of Cambridge, Cambridge, CB2 3EH UK ,grid.5335.00000000121885934Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Jakub Tomasik
- grid.5335.00000000121885934Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
| | - Rebekah Jukes-Jones
- grid.9918.90000 0004 1936 8411Leicester Cancer Research Centre, RKCSB, University of Leicester, University Road, Leicester, LE1 7RH UK
| | - Yagnesh Umrania
- grid.5335.00000000121885934Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR UK
| | - Sabine Bahn
- grid.5335.00000000121885934Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge, UK
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18
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Identification of Oocyst-Driven Toxoplasma gondii Infections in Humans and Animals through Stage-Specific Serology-Current Status and Future Perspectives. Microorganisms 2021; 9:microorganisms9112346. [PMID: 34835471 PMCID: PMC8618849 DOI: 10.3390/microorganisms9112346] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 11/17/2022] Open
Abstract
The apicomplexan zoonotic parasite Toxoplasma gondii has three infective stages: sporozoites in sporulated oocysts, which are shed in unsporulated form into the environment by infected felids; tissue cysts containing bradyzoites, and fast replicating tachyzoites that are responsible for acute toxoplasmosis. The contribution of oocysts to infections in both humans and animals is understudied despite being highly relevant. Only a few diagnostic antigens have been described to be capable of discriminating which parasite stage has caused an infection. Here we provide an extensive overview of the antigens and serological assays used to detect oocyst-driven infections in humans and animals according to the literature. In addition, we critically discuss the possibility to exploit the increasing knowledge of the T. gondii genome and the various 'omics datasets available, by applying predictive algorithms, for the identification of new oocyst-specific proteins for diagnostic purposes. Finally, we propose a workflow for how such antigens and assays based on them should be evaluated to ensure reproducible and robust results.
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19
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Kim P, Tan H, Liu J, Lee H, Jung H, Kumar H, Zhou X. FusionGDB 2.0: fusion gene annotation updates aided by deep learning. Nucleic Acids Res 2021; 50:D1221-D1230. [PMID: 34755868 PMCID: PMC8728198 DOI: 10.1093/nar/gkab1056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 10/10/2021] [Accepted: 11/03/2021] [Indexed: 01/08/2023] Open
Abstract
A knowledgebase of the systematic functional annotation of fusion genes is critical for understanding genomic breakage context and developing therapeutic strategies. FusionGDB is a unique functional annotation database of human fusion genes and has been widely used for studies with diverse aims. In this study, we report fusion gene annotation updates aided by deep learning (FusionGDB 2.0) available at https://compbio.uth.edu/FusionGDB2/. FusionGDB 2.0 has substantial updates of contents such as up-to-date human fusion genes, fusion gene breakage tendency score with FusionAI deep learning model based on 20 kb DNA sequence around BP, investigation of overlapping between fusion breakpoints with 44 human genomic features across five cellular role's categories, transcribed chimeric sequence and following open reading frame analysis with coding potential based on deep learning approach with Ribo-seq read features, and rigorous investigation of the protein feature retention of individual fusion partner genes in the protein level. Among ∼102k fusion genes, about 15k kept their ORF as In-frames, which is two times compared to the previous version, FusionGDB. FusionGDB 2.0 will be used as the reference knowledgebase of fusion gene annotations. FusionGDB 2.0 provides eight categories of annotations and it will be helpful for diverse human genomic studies.
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Affiliation(s)
- Pora Kim
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Hua Tan
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Jiajia Liu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Haeseung Lee
- Intellectual Information Team, Future Medicine Division, Korea Institute of Oriental Medicine, Daejeon, South Korea
| | - Hyesoo Jung
- Department of Neurology, Asan Medical Center, Seoul, Korea
| | - Himanshu Kumar
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
| | - Xiaobo Zhou
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.,School of Dentistry, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
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20
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Lv D, Chang Z, Cai Y, Li J, Wang L, Jiang Q, Xu K, Ding N, Li X, Xu J, Li Y. TransLnc: a comprehensive resource for translatable lncRNAs extends immunopeptidome. Nucleic Acids Res 2021; 50:D413-D420. [PMID: 34570220 PMCID: PMC8728190 DOI: 10.1093/nar/gkab847] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 09/05/2021] [Accepted: 09/10/2021] [Indexed: 01/10/2023] Open
Abstract
LncRNAs are not only well-known as non-coding elements, but also serve as templates for peptide translation, playing important roles in fundamental cellular processes and diseases. Here, we describe a database, TransLnc (http://bio-bigdata.hrbmu.edu.cn/TransLnc/), which aims to provide comprehensive experimentally supported and predicted lncRNA peptides in multiple species. TransLnc currently documents approximate 583 840 peptides encoded by 33 094 lncRNAs. Six types of direct and indirect evidences supporting the coding potential of lncRNAs were integrated, and 65.28% peptides entries were with at least one type of evidence. Considering the strong tissue-specific expression of lncRNAs, TransLnc allows users to access lncRNA peptides in any of the 34 tissues involved in. In addition, both the unique characteristic and homology relationship were also predicted and provided. Importantly, TransLnc provides computationally predicted tumour neoantigens from peptides encoded by lncRNAs, which would provide novel insights into cancer immunotherapy. There were 220 791 and 237 915 candidate neoantigens binding by major histocompatibility complex (MHC) class I or II molecules, respectively. Several flexible tools were developed to aid retrieve and analyse, particularly lncRNAs tissue expression patterns, clinical relevance across cancer types. TransLnc will serve as a valuable resource for investigating the translation capacity of lncRNAs and greatly extends the cancer immunopeptidome.
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Affiliation(s)
- Dezhong Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China
| | - Zhenghong Chang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China
| | - Yangyang Cai
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China
| | - Junyi Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China
| | - Liping Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China
| | - Qiushuang Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China
| | - Kang Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China
| | - Na Ding
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.,Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China
| | - Juan Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, 150081, China.,Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China
| | - Yongsheng Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan Women and Children's Medical Center, Hainan Medical University, Haikou, 571199, China
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21
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Zhao Y, Lindberg BG, Esfahani SS, Tang X, Piazza S, Engström Y. Stop codon readthrough alters the activity of a POU/Oct transcription factor during Drosophila development. BMC Biol 2021; 19:185. [PMID: 34479564 PMCID: PMC8417969 DOI: 10.1186/s12915-021-01106-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 07/19/2021] [Indexed: 11/24/2022] Open
Abstract
Background A number of cellular processes have evolved in metazoans that increase the proteome repertoire in relation to the genome, such as alternative splicing and translation recoding. Another such process, translational stop codon readthrough (SCR), generates C-terminally extended protein isoforms in many eukaryotes, including yeast, plants, insects, and humans. While comparative genome analyses have predicted the existence of programmed SCR in many species including humans, experimental proof of its functional consequences are scarce. Results We show that SCR of the Drosophila POU/Oct transcription factor Ventral veins lacking/Drifter (Vvl/Dfr) mRNA is prevalent in certain tissues in vivo, reaching a rate of 50% in the larval prothoracic gland. Phylogenetically, the C-terminal extension is conserved and harbors intrinsically disordered regions and amino acid stretches implied in transcriptional activation. Elimination of Vvl/Dfr translational readthrough by CRISPR/Cas9 mutagenesis changed the expression of a large number of downstream genes involved in processes such as chromatin regulation, neurogenesis, development, and immune response. As a proof-of-principle, we demonstrate that the C-terminal extension of Vvl/Dfr is necessary for correct timing of pupariation, by increasing the capacity to regulate its target genes. The extended Vvl/Dfr isoform acts in synergy with the transcription factor Molting defective (Mld) to increase the expression and biosynthesis of the steroid hormone ecdysone, thereby advancing pupariation. Consequently, late-stage larval development was prolonged and metamorphosis delayed in vvl/dfr readthrough mutants. Conclusions We demonstrate that translational recoding of a POU/Oct transcription factor takes place in a highly tissue-specific and temporally controlled manner. This dynamic and regulated recoding is necessary for normal expression of a large number of genes involved in many cellular and developmental processes. Loss of Vvl/Dfr translational readthrough negatively affects steroid hormone biosynthesis and delays larval development and progression into metamorphosis. Thus, this study demonstrates how SCR of a transcription factor can act as a developmental switch in a spatiotemporal manner, feeding into the timing of developmental transitions between different life-cycle stages. Graphical abstract ![]()
Supplementary Information The online version contains supplementary material available at 10.1186/s12915-021-01106-0.
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Affiliation(s)
- Yunpo Zhao
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, SE-106 91, Stockholm, Sweden.,Present address: Department of Molecular Biology, Umeå University, SE-901 87, Umeå, SE, Sweden
| | - Bo Gustav Lindberg
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, SE-106 91, Stockholm, Sweden
| | - Shiva Seyedoleslami Esfahani
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, SE-106 91, Stockholm, Sweden
| | - Xiongzhuo Tang
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, SE-106 91, Stockholm, Sweden.,Present address: Yale Stem Cell Center, Yale University School of Medicine, New Haven, Connecticut, 06520, USA
| | - Stefano Piazza
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, SE-106 91, Stockholm, Sweden.,Present address: Research and Innovation Centre, Fondazione Edmund Mach, via E Mach 1, 38010, San Michele a/Adige, Italy
| | - Ylva Engström
- Department of Molecular Biosciences, The Wenner-Gren Institute, Stockholm University, SE-106 91, Stockholm, Sweden.
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22
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Sinha T, Panigrahi C, Das D, Chandra Panda A. Circular RNA translation, a path to hidden proteome. WILEY INTERDISCIPLINARY REVIEWS-RNA 2021; 13:e1685. [PMID: 34342387 DOI: 10.1002/wrna.1685] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 07/07/2021] [Accepted: 07/08/2021] [Indexed: 11/06/2022]
Abstract
Functional proteins in the cell are translated from the messenger RNA (mRNA) molecules, constituting less than 5% of the cellular transcriptome. The majority of the RNA molecules in the cell are noncoding RNAs, including rRNA, tRNA, snRNA, piRNA, lncRNA, microRNA, and poorly characterized circular RNAs (circRNAs). Recent studies established that circRNAs regulate gene expression by associating with RNA-binding proteins and microRNAs. With the growing understanding of circRNA functions, a subset of circRNAs has been reported to translate into proteins. Interestingly, the presence of Open Reading Frames (ORFs), N6-methyladenosine (m6A) modifications, and internal ribosomal entry sites (IRES) in the circRNA sequences indicate their coding potential through the cap-independent translation initiation mechanism. The purpose of this review is to highlight the mechanism of circRNA translation and the importance of circRNA-encoded proteins (circ-proteins) in cellular physiology and pathology. Here, we discuss the computational and molecular methods currently utilized to systematically identify translatable circRNAs and the functional characterization of the circ-proteins. We foresee that the ongoing and future studies on circRNA translation will uncover the hidden proteome and their therapeutic implications in human health. This article is categorized under: RNA Methods > RNA Analyses in Cells Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs Translation > Mechanisms.
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Affiliation(s)
- Tanvi Sinha
- Institute of Life Sciences, Nalco Square, Bhubaneswar, Odisha, India
| | - Chirag Panigrahi
- Institute of Life Sciences, Nalco Square, Bhubaneswar, Odisha, India
| | - Debojyoti Das
- Institute of Life Sciences, Nalco Square, Bhubaneswar, Odisha, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha, India
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23
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Kiniry SJ, Judge CE, Michel AM, Baranov PV. Trips-Viz: an environment for the analysis of public and user-generated ribosome profiling data. Nucleic Acids Res 2021; 49:W662-W670. [PMID: 33950201 PMCID: PMC8262740 DOI: 10.1093/nar/gkab323] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 04/11/2021] [Accepted: 04/20/2021] [Indexed: 02/07/2023] Open
Abstract
Trips-Viz (https://trips.ucc.ie/) is an interactive platform for the analysis and visualization of ribosome profiling (Ribo-Seq) and shotgun RNA sequencing (RNA-seq) data. This includes publicly available and user generated data, hence Trips-Viz can be classified as a database and as a server. As a database it provides access to many processed Ribo-Seq and RNA-seq data aligned to reference transcriptomes which has been expanded considerably since its inception. Here, we focus on the server functionality of Trips-viz which also has been greatly improved. Trips-viz now enables visualisation of proteomics data from a large number of processed mass spectrometry datasets. It can be used to support translation inferred from Ribo-Seq data. Users are now able to upload a custom reference transcriptome as well as data types other than Ribo-Seq/RNA-Seq. Incorporating custom data has been streamlined with RiboGalaxy (https://ribogalaxy.ucc.ie/) integration. The other new functionality is the rapid detection of translated open reading frames (ORFs) through a simple easy to use interface. The analysis of differential expression has been also improved via integration of DESeq2 and Anota2seq in addition to a number of other improvements of existing Trips-viz features.
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Affiliation(s)
- Stephen J Kiniry
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Ciara E Judge
- 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
- Ribomaps Ltd, Western Gateway Bld, Western Rd, 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
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24
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Choteau SA, Wagner A, Pierre P, Spinelli L, Brun C. MetamORF: a repository of unique short open reading frames identified by both experimental and computational approaches for gene and metagene analyses. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6307706. [PMID: 34156446 PMCID: PMC8218702 DOI: 10.1093/database/baab032] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 04/08/2021] [Accepted: 05/17/2021] [Indexed: 11/12/2022]
Abstract
The development of high-throughput technologies revealed the existence of non-canonical short open reading frames (sORFs) on most eukaryotic ribonucleic acids. They are ubiquitous genetic elements conserved across species and suspected to be involved in numerous cellular processes. MetamORF (https://metamorf.hb.univ-amu.fr/) aims to provide a repository of unique sORFs identified in the human and mouse genomes with both experimental and computational approaches. By gathering publicly available sORF data, normalizing them and summarizing redundant information, we were able to identify a total of 1 162 675 unique sORFs. Despite the usual characterization of ORFs as short, upstream or downstream, there is currently no clear consensus regarding the definition of these categories. Thus, the data have been reprocessed using a normalized nomenclature. MetamORF enables new analyses at locus, gene, transcript and ORF levels, which should offer the possibility to address new questions regarding sORF functions in the future. The repository is available through an user-friendly web interface, allowing easy browsing, visualization, filtering over multiple criteria and export possibilities. sORFs can be searched starting from a gene, a transcript and an ORF ID, looking in a genome area or browsing the whole repository for a species. The database content has also been made available through track hubs at UCSC Genome Browser. Finally, we demonstrated an enrichment of genes harboring upstream ORFs among genes expressed in response to reticular stress. Database URL https://metamorf.hb.univ-amu.fr/.
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Affiliation(s)
- Sebastien A Choteau
- Aix-Marseille University, INSERM, TAGC, Turing Centre for Living Systems, 163 Avenue de Luminy, Marseille 13009, France.,Aix-Marseille University, INSERM, CNRS, CIML, Turing Centre for Living Systems, 163 Avenue de Luminy, Marseille 13009, France
| | - Audrey Wagner
- Aix-Marseille University, INSERM, TAGC, Turing Centre for Living Systems, 163 Avenue de Luminy, Marseille 13009, France
| | - Philippe Pierre
- Aix-Marseille University, INSERM, CNRS, CIML, Turing Centre for Living Systems, 163 Avenue de Luminy, Marseille 13009, France.,Department of Medical Sciences, Institute for Research in Biomedicine (iBiMED) and Ilidio Pinho Foundation, University of Aveiro, Aveiro 3810-193, Portugal.,Shanghai Institute of Immunology, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Lionel Spinelli
- Aix-Marseille University, INSERM, TAGC, Turing Centre for Living Systems, 163 Avenue de Luminy, Marseille 13009, France.,Aix-Marseille University, INSERM, CNRS, CIML, Turing Centre for Living Systems, 163 Avenue de Luminy, Marseille 13009, France
| | - Christine Brun
- Aix-Marseille University, INSERM, TAGC, Turing Centre for Living Systems, 163 Avenue de Luminy, Marseille 13009, France.,CNRS, 31 Chemin Joseph Aiguier, Marseille 13009, France
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25
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Rodrigues DC, Mufteev M, Weatheritt RJ, Djuric U, Ha KCH, Ross PJ, Wei W, Piekna A, Sartori MA, Byres L, Mok RSF, Zaslavsky K, Pasceri P, Diamandis P, Morris Q, Blencowe BJ, Ellis J. Shifts in Ribosome Engagement Impact Key Gene Sets in Neurodevelopment and Ubiquitination in Rett Syndrome. Cell Rep 2021; 30:4179-4196.e11. [PMID: 32209477 DOI: 10.1016/j.celrep.2020.02.107] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 12/30/2019] [Accepted: 02/27/2020] [Indexed: 12/21/2022] Open
Abstract
Regulation of translation during human development is poorly understood, and its dysregulation is associated with Rett syndrome (RTT). To discover shifts in mRNA ribosomal engagement (RE) during human neurodevelopment, we use parallel translating ribosome affinity purification sequencing (TRAP-seq) and RNA sequencing (RNA-seq) on control and RTT human induced pluripotent stem cells, neural progenitor cells, and cortical neurons. We find that 30% of transcribed genes are translationally regulated, including key gene sets (neurodevelopment, transcription and translation factors, and glycolysis). Approximately 35% of abundant intergenic long noncoding RNAs (lncRNAs) are ribosome engaged. Neurons translate mRNAs more efficiently and have longer 3' UTRs, and RE correlates with elements for RNA-binding proteins. RTT neurons have reduced global translation and compromised mTOR signaling, and >2,100 genes are translationally dysregulated. NEDD4L E3-ubiquitin ligase is translationally impaired, ubiquitinated protein levels are reduced, and protein targets accumulate in RTT neurons. Overall, the dynamic translatome in neurodevelopment is disturbed in RTT and provides insight into altered ubiquitination that may have therapeutic implications.
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Affiliation(s)
- Deivid C Rodrigues
- Program in Developmental & Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Marat Mufteev
- Program in Developmental & Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Robert J Weatheritt
- Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - Ugljesa Djuric
- Laboratory Medicine and Pathology Program, University Health Network, Toronto, ON M5G 2C4, Canada
| | - Kevin C H Ha
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Vector Institute, 661 University Avenue, Toronto, ON M5G 1M1, Canada
| | - P Joel Ross
- Program in Developmental & Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Wei Wei
- Program in Developmental & Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Alina Piekna
- Program in Developmental & Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Maria A Sartori
- Program in Developmental & Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Loryn Byres
- Program in Developmental & Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Rebecca S F Mok
- Program in Developmental & Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Kirill Zaslavsky
- Program in Developmental & Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada
| | - Peter Pasceri
- Program in Developmental & Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Phedias Diamandis
- Laboratory Medicine and Pathology Program, University Health Network, Toronto, ON M5G 2C4, Canada; Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, ON M5S 1A1, Canada; Department of Pathology, University Health Network, Toronto, ON M5G 2C4, Canada
| | - Quaid Morris
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada; Vector Institute, 661 University Avenue, Toronto, ON M5G 1M1, Canada
| | - Benjamin J Blencowe
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada; Donnelly Center for Cellular and Biomolecular Research, University of Toronto, Toronto, ON M5S 3E1, Canada
| | - James Ellis
- Program in Developmental & Stem Cell Biology, The Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5S 1A8, Canada.
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26
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Vitorino R, Guedes S, Amado F, Santos M, Akimitsu N. The role of micropeptides in biology. Cell Mol Life Sci 2021; 78:3285-3298. [PMID: 33507325 PMCID: PMC11073438 DOI: 10.1007/s00018-020-03740-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 12/01/2020] [Accepted: 12/11/2020] [Indexed: 12/11/2022]
Abstract
Micropeptides are small polypeptides coded by small open-reading frames. Progress in computational biology and the analyses of large-scale transcriptomes and proteomes have revealed that mammalian genomes produce a large number of transcripts encoding micropeptides. Many of these have been previously annotated as long noncoding RNAs. The role of micropeptides in cellular homeostasis maintenance has been demonstrated. This review discusses different types of micropeptides as well as methods to identify them, such as computational approaches, ribosome profiling, and mass spectrometry.
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Affiliation(s)
- Rui Vitorino
- Departamento de Cirurgia E Fisiologia, Faculdade de Medicina da Universidade Do Porto, UnIC, Porto, Portugal.
- Department of Medical Sciences, iBiMED, University of Aveiro, Aveiro, Portugal.
| | - Sofia Guedes
- Departamento de Química, LAQV-REQUIMTE, Universidade de Aveiro, Aveiro, Portugal
- Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Francisco Amado
- Departamento de Química, LAQV-REQUIMTE, Universidade de Aveiro, Aveiro, Portugal
- Department of Chemistry, University of Aveiro, Aveiro, Portugal
| | - Manuel Santos
- Department of Medical Sciences, iBiMED, University of Aveiro, Aveiro, Portugal
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27
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Li H, Xie M, Wang Y, Yang L, Xie Z, Wang H. riboCIRC: a comprehensive database of translatable circRNAs. Genome Biol 2021; 22:79. [PMID: 33685493 PMCID: PMC7938571 DOI: 10.1186/s13059-021-02300-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 02/18/2021] [Indexed: 01/05/2023] Open
Abstract
riboCIRC is a translatome data-oriented circRNA database specifically designed for hosting, exploring, analyzing, and visualizing translatable circRNAs from multi-species. The database provides a comprehensive repository of computationally predicted ribosome-associated circRNAs; a manually curated collection of experimentally verified translated circRNAs; an evaluation of cross-species conservation of translatable circRNAs; a systematic de novo annotation of putative circRNA-encoded peptides, including sequence, structure, and function; and a genome browser to visualize the context-specific occupant footprints of circRNAs. It represents a valuable resource for the circRNA research community and is publicly available at http://www.ribocirc.com .
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Affiliation(s)
- Huihui Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Mingzhe Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yan Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Ludong Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Zhi Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
| | - Hongwei Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
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28
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Erady C, Boxall A, Puntambekar S, Suhas Jagannathan N, Chauhan R, Chong D, Meena N, Kulkarni A, Kasabe B, Prathivadi Bhayankaram K, Umrania Y, Andreani A, Nel J, Wayland MT, Pina C, Lilley KS, Prabakaran S. Pan-cancer analysis of transcripts encoding novel open-reading frames (nORFs) and their potential biological functions. NPJ Genom Med 2021; 6:4. [PMID: 33495453 PMCID: PMC7835362 DOI: 10.1038/s41525-020-00167-4] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 11/18/2020] [Indexed: 12/13/2022] Open
Abstract
Uncharacterized and unannotated open-reading frames, which we refer to as novel open reading frames (nORFs), may sometimes encode peptides that remain unexplored for novel therapeutic opportunities. To our knowledge, no systematic identification and characterization of transcripts encoding nORFs or their translation products in cancer, or in any other physiological process has been performed. We use our curated nORFs database (nORFs.org), together with RNA-Seq data from The Cancer Genome Atlas (TCGA) and Genotype-Expression (GTEx) consortiums, to identify transcripts containing nORFs that are expressed frequently in cancer or matched normal tissue across 22 cancer types. We show nORFs are subject to extensive dysregulation at the transcript level in cancer tissue and that a small subset of nORFs are associated with overall patient survival, suggesting that nORFs may have prognostic value. We also show that nORF products can form protein-like structures with post-translational modifications. Finally, we perform in silico screening for inhibitors against nORF-encoded proteins that are disrupted in stomach and esophageal cancer, showing that they can potentially be targeted by inhibitors. We hope this work will guide and motivate future studies that perform in-depth characterization of nORF functions in cancer and other diseases.
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Affiliation(s)
- Chaitanya Erady
- Department of Genetics, University of Cambridge, Downing Site, Cambridge, CB2 3EH, UK
| | - Adam Boxall
- Department of Genetics, University of Cambridge, Downing Site, Cambridge, CB2 3EH, UK
| | - Shraddha Puntambekar
- Department of Biology, Indian Institute of Science Education and Research, Pune, Maharashtra, 411008, India
| | - N Suhas Jagannathan
- Cancer and Stem Cell Biology Programme, and Centre for Computational Biology, Duke-NUS Medical School, Singapore, 169857, Singapore
| | - Ruchi Chauhan
- Department of Genetics, University of Cambridge, Downing Site, Cambridge, CB2 3EH, UK
| | - David Chong
- Department of Genetics, University of Cambridge, Downing Site, Cambridge, CB2 3EH, UK
| | - Narendra Meena
- Department of Genetics, University of Cambridge, Downing Site, Cambridge, CB2 3EH, UK
| | - Apurv Kulkarni
- Department of Biology, Indian Institute of Science Education and Research, Pune, Maharashtra, 411008, India
| | - Bhagyashri Kasabe
- Department of Biology, Indian Institute of Science Education and Research, Pune, Maharashtra, 411008, India
| | | | - Yagnesh Umrania
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
| | - Adam Andreani
- Department of Genetics, University of Cambridge, Downing Site, Cambridge, CB2 3EH, UK
| | - Jean Nel
- Department of Genetics, University of Cambridge, Downing Site, Cambridge, CB2 3EH, UK
| | - Matthew T Wayland
- Department of Zoology, University of Cambridge, Downing Street, Cambridge, CB2 3EJ, UK
| | - Cristina Pina
- Department of Haematology, Cambridge Biomedical Campus, Cambridge, CB2 0PT, UK
| | - Kathryn S Lilley
- Cambridge Centre for Proteomics, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QR, UK
| | - Sudhakaran Prabakaran
- Department of Genetics, University of Cambridge, Downing Site, Cambridge, CB2 3EH, UK.
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29
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Huang Y, Wang J, Zhao Y, Wang H, Liu T, Li Y, Cui T, Li W, Feng Y, Luo J, Gong J, Ning L, Zhang Y, Wang D, Zhang Y. cncRNAdb: a manually curated resource of experimentally supported RNAs with both protein-coding and noncoding function. Nucleic Acids Res 2021; 49:D65-D70. [PMID: 33010163 PMCID: PMC7778915 DOI: 10.1093/nar/gkaa791] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 08/30/2020] [Accepted: 09/11/2020] [Indexed: 12/14/2022] Open
Abstract
RNA endowed with both protein-coding and noncoding functions is referred to as 'dual-function RNA', 'binary functional RNA (bifunctional RNA)' or 'cncRNA (coding and noncoding RNA)'. Recently, an increasing number of cncRNAs have been identified, including both translated ncRNAs (ncRNAs with coding functions) and untranslated mRNAs (mRNAs with noncoding functions). However, an appropriate database for storing and organizing cncRNAs is still lacking. Here, we developed cncRNAdb, a manually curated database of experimentally supported cncRNAs, which aims to provide a resource for efficient manipulation, browsing and analysis of cncRNAs. The current version of cncRNAdb documents about 2600 manually curated entries of cncRNA functions with experimental evidence, involving more than 2,000 RNAs (including over 1300 translated ncRNAs and over 600 untranslated mRNAs) across over 20 species. In summary, we believe that cncRNAdb will help elucidate the functions and mechanisms of cncRNAs and develop new prediction methods. The database is available at http://www.rna-society.org/cncrnadb/.
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MESH Headings
- 3' Untranslated Regions
- 5' Untranslated Regions
- Animals
- Databases, Nucleic Acid/organization & administration
- Drosophila melanogaster/genetics
- Humans
- Mice
- MicroRNAs/classification
- MicroRNAs/genetics
- Pan troglodytes/genetics
- RNA, Circular/classification
- RNA, Circular/genetics
- RNA, Long Noncoding/classification
- RNA, Long Noncoding/genetics
- RNA, Messenger/classification
- RNA, Messenger/genetics
- RNA, Ribosomal/classification
- RNA, Ribosomal/genetics
- RNA, Small Interfering/classification
- RNA, Small Interfering/genetics
- RNA, Transfer/classification
- RNA, Transfer/genetics
- Software
- Zebrafish/genetics
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Affiliation(s)
- Yan Huang
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), Foshan 528308, China
| | - Jing Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yue Zhao
- School of Basic Medical Sciences & Forensic Medicine, Hangzhou Medical College, Hangzhou 310053, China
| | - Huafeng Wang
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), Foshan 528308, China
| | - Tianyuan Liu
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yuhe Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Tianyu Cui
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Weiyi Li
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yige Feng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jiaxin Luo
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jiaqi Gong
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Lin Ning
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
| | - Yong Zhang
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), Foshan 528308, China
| | - Dong Wang
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), Foshan 528308, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
| | - Yang Zhang
- Shunde Hospital, Southern Medical University (The First People's Hospital of Shunde Foshan), Foshan 528308, China
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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.
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Affiliation(s)
| | - Zoya Ignatova
- Institute for Biochemistry and Molecular Biology, Department of Chemistry, University of Hamburg, Hamburg, Germany.
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31
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Zhou B, Yang H, Yang C, Bao YL, Yang SM, Liu J, Xiao YF. Translation of noncoding RNAs and cancer. Cancer Lett 2020; 497:89-99. [PMID: 33038492 DOI: 10.1016/j.canlet.2020.10.002] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 09/30/2020] [Accepted: 10/01/2020] [Indexed: 02/07/2023]
Abstract
The human genome contains thousands of noncoding RNAs (ncRNAs), which are thought to lack open reading frames (ORFs) and cannot be translated. Some ncRNAs reportedly have important functions, including epigenetic regulation, chromatin remolding, protein modification, and RNA degradation, but the functions of most ncRNAs remain elusive. Through the application and development of ribosome profiling and sequencing technologies, an increasing number of studies have discovered the translation of ncRNAs. Although ncRNAs were initially defined as noncoding RNAs, a number of ncRNAs actually contain ORFs that are translated into peptides. Here, we summarize the available methods, tools, and databases for identifying and validating ncRNA-encoded peptides/proteins, and the recent findings regarding ncRNA-encoded small peptides/proteins in cancer are compiled and synthesized. Importantly, the role of ncRNA-encoding peptides/proteins has application prospects in cancer research, but some potential challenges remain unresolved. The aim of this review is to provide a theoretical basis that might promote the discovery of more peptides/proteins encoded by ncRNAs and aid the further development of novel diagnostic and prognostic cancer markers and therapeutic targets.
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Affiliation(s)
- Bo Zhou
- Department of Gastroenterology, Xinqiao Hospital, Chongqing, 400037, China
| | - Huan Yang
- Department of Gastroenterology, Xinqiao Hospital, Chongqing, 400037, China
| | - Chuan Yang
- Department of Gastroenterology, Xinqiao Hospital, Chongqing, 400037, China
| | - Yu-Lu Bao
- Department of Gastroenterology, Xinqiao Hospital, Chongqing, 400037, China
| | - Shi-Ming Yang
- Department of Gastroenterology, Xinqiao Hospital, Chongqing, 400037, China
| | - Jiao Liu
- Department of Endoscope, General Hospital of Northern Theater Command, Shenyang, 110016, Liaoning, China.
| | - Yu-Feng Xiao
- Department of Gastroenterology, Xinqiao Hospital, Chongqing, 400037, China.
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32
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Panda A, Yadav A, Yeerna H, Singh A, Biehl M, Lux M, Schulz A, Klecha T, Doniach S, Khiabanian H, Ganesan S, Tamayo P, Bhanot G. Tissue- and development-stage-specific mRNA and heterogeneous CNV signatures of human ribosomal proteins in normal and cancer samples. Nucleic Acids Res 2020; 48:7079-7098. [PMID: 32525984 PMCID: PMC7367157 DOI: 10.1093/nar/gkaa485] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Revised: 05/20/2020] [Accepted: 05/28/2020] [Indexed: 12/26/2022] Open
Abstract
We give results from a detailed analysis of human Ribosomal Protein (RP) levels in normal and cancer samples and cell lines from large mRNA, copy number variation and ribosome profiling datasets. After normalizing total RP mRNA levels per sample, we find highly consistent tissue specific RP mRNA signatures in normal and tumor samples. Multiple RP mRNA-subtypes exist in several cancers, with significant survival and genomic differences. Some RP mRNA variations among subtypes correlate with copy number loss of RP genes. In kidney cancer, RP subtypes map to molecular subtypes related to cell-of-origin. Pan-cancer analysis of TCGA data showed widespread single/double copy loss of RP genes, without significantly affecting survival. In several cancer cell lines, CRISPR-Cas9 knockout of RP genes did not affect cell viability. Matched RP ribosome profiling and mRNA data in humans and rodents stratified by tissue and development stage and were strongly correlated, showing that RP translation rates were proportional to mRNA levels. In a small dataset of human adult and fetal tissues, RP protein levels showed development stage and tissue specific heterogeneity of RP levels. Our results suggest that heterogeneous RP levels play a significant functional role in cellular physiology, in both normal and disease states.
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Affiliation(s)
- Anshuman Panda
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA
| | - Anupama Yadav
- Center for Cancer Systems Biology (CCSB), Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
- Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Huwate Yeerna
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92103, USA
| | - Amartya Singh
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA
| | - Michael Biehl
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Nijenborgh 9, NL-9747 AG Groningen, The Netherlands
| | - Markus Lux
- Cognitive Interaction Technology (CITEC), Bielefeld University, Inspiration 1, D-33619 Bielefeld, Germany
| | - Alexander Schulz
- Cognitive Interaction Technology (CITEC), Bielefeld University, Inspiration 1, D-33619 Bielefeld, Germany
| | - Tyler Klecha
- Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ,08854, USA
| | - Sebastian Doniach
- Department of Applied Physics, Stanford University, Palo Alto, CA 94305, USA
| | | | - Shridar Ganesan
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA
| | - Pablo Tamayo
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92103, USA
- School of Medicine, University of California San Diego, La Jolla, CA 92093, USA
| | - Gyan Bhanot
- Rutgers Cancer Institute of New Jersey, New Brunswick, NJ 08903, USA
- Moores Cancer Center, University of California San Diego, La Jolla, CA 92103, USA
- Department of Molecular Biology and Biochemistry, Rutgers University, Piscataway, NJ,08854, USA
- Department of Physics and Astronomy, Rutgers University, Piscataway, NJ 08854, USA
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33
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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.
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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
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34
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Peeters MKR, Menschaert G. The hunt for sORFs: A multidisciplinary strategy. Exp Cell Res 2020; 391:111923. [PMID: 32135166 DOI: 10.1016/j.yexcr.2020.111923] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Revised: 02/21/2020] [Accepted: 02/23/2020] [Indexed: 11/28/2022]
Abstract
Growing evidence illustrates the shortcomings on the current understanding of the full complexity of the proteome. Previously overlooked small open reading frames (sORFs) and their encoded microproteins have filled important gaps, exerting their function as biologically relevant regulators. The characterization of the full small proteome has potential applications in many fields. Continuous development of techniques and tools led to an improved sORF discovery, where these can originate from bioinformatics analyses, from sequencing routines or proteomics approaches. In this mini review, we discuss the ongoing trends in the three fields and suggest some strategies for further characterization of high potential candidates.
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Affiliation(s)
- Marlies K R Peeters
- BioBix, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 900, Gent, Belgium
| | - Gerben Menschaert
- BioBix, Department of Data Analysis and Mathematical Modelling, Ghent University, Coupure Links 653, 900, Gent, Belgium.
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35
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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.
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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.
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Zhao J, Qin B, Nikolay R, Spahn CMT, Zhang G. Translatomics: The Global View of Translation. Int J Mol Sci 2019; 20:ijms20010212. [PMID: 30626072 PMCID: PMC6337585 DOI: 10.3390/ijms20010212] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 12/19/2018] [Accepted: 01/02/2019] [Indexed: 01/01/2023] Open
Abstract
In all kingdoms of life, proteins are synthesized by ribosomes in a process referred to as translation. The amplitude of translational regulation exceeds the sum of transcription, mRNA degradation and protein degradation. Therefore, it is essential to investigate translation in a global scale. Like the other “omics”-methods, translatomics investigates the totality of the components in the translation process, including but not limited to translating mRNAs, ribosomes, tRNAs, regulatory RNAs and nascent polypeptide chains. Technical advances in recent years have brought breakthroughs in the investigation of these components at global scale, both for their composition and dynamics. These methods have been applied in a rapidly increasing number of studies to reveal multifaceted aspects of translation control. The process of translation is not restricted to the conversion of mRNA coding sequences into polypeptide chains, it also controls the composition of the proteome in a delicate and responsive way. Therefore, translatomics has extended its unique and innovative power to many fields including proteomics, cancer research, bacterial stress response, biological rhythmicity and plant biology. Rational design in translation can enhance recombinant protein production for thousands of times. This brief review summarizes the main state-of-the-art methods of translatomics, highlights recent discoveries made in this field and introduces applications of translatomics on basic biological and biomedical research.
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Affiliation(s)
- Jing Zhao
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China.
| | - Bo Qin
- Institut für Medizinische Physik und Biophysik, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
| | - Rainer Nikolay
- Institut für Medizinische Physik und Biophysik, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
| | - Christian M T Spahn
- Institut für Medizinische Physik und Biophysik, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.
| | - Gong Zhang
- Key Laboratory of Functional Protein Research of Guangdong Higher Education Institutes, Institute of Life and Health Engineering, College of Life Science and Technology, Jinan University, Guangzhou 510632, China.
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