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Ding S, Liao H, Huang F, Chen L, Guo W, Feng K, Huang T, Cai YD. Analyzing domain features of small proteins using a machine-learning method. Proteomics 2024; 24:e2300302. [PMID: 38258387 DOI: 10.1002/pmic.202300302] [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: 08/06/2023] [Revised: 01/14/2024] [Accepted: 01/15/2024] [Indexed: 01/24/2024]
Abstract
Small proteins (SPs) are a unique group of proteins that play crucial roles in many important biological processes. Exploring the biological function of SPs is necessary. In this study, the InterPro tool and the maximum correlation method were utilized to analyze functional domains of SPs. The purpose was to identify important functional domains that can indicate the essential differences between small and large protein sequences. First, the small and large proteins were represented by their functional domains via a one-hot scheme. Then, the MaxRel method was adopted to evaluate the relationships between each domain and the target variable, indicating small or large protein. The top 36 domain features were selected for further investigation. Among them, 14 were deemed to be highly related to SPs because they were annotated to SPs more frequently than large proteins. We found the involvement of functional domains, such as ubiquitin-conjugating enzyme/RWD-like, nuclear transport factor 2 domain, and alpha subunit of guanine nucleotide-binding protein (G-protein) in regulating the biological function of SPs. The involvement of these domains has been confirmed by other recent studies. Our findings indicate that protein functional domains may regulate small protein-related functions and predict their biological activity.
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Affiliation(s)
- ShiJian Ding
- School of Life Sciences, Shanghai University, Shanghai, China
| | | | - FeiMing Huang
- School of Life Sciences, Shanghai University, Shanghai, China
| | - Lei Chen
- College of Information Engineering, Shanghai Maritime University, Shanghai, China
| | - Wei Guo
- Key Laboratory of Stem Cell Biology, Shanghai Jiao Tong University School of Medicine (SJTUSM) & Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, China
| | - KaiYan Feng
- Department of Computer Science, Guangdong AIB Polytechnic College, Guangzhou, China
| | - Tao Huang
- Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Yu-Dong Cai
- School of Life Sciences, Shanghai University, Shanghai, China
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2
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Li Y, Zhou H, Chen X, Zheng Y, Kang Q, Hao D, Zhang L, Song T, Luo H, Hao Y, Chen R, Zhang P, He S. SmProt: A Reliable Repository with Comprehensive Annotation of Small Proteins Identified from Ribosome Profiling. GENOMICS PROTEOMICS & BIOINFORMATICS 2021; 19:602-610. [PMID: 34536568 PMCID: PMC9039559 DOI: 10.1016/j.gpb.2021.09.002] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 09/07/2021] [Accepted: 09/08/2021] [Indexed: 12/30/2022]
Abstract
Small proteins specifically refer to proteins consisting of less than 100 amino acids translated from small open reading frames (sORFs), which were usually missed in previous genome annotation. The significance of small proteins has been revealed in current years, along with the discovery of their diverse functions. However, systematic annotation of small proteins is still insufficient. SmProt was specially developed to provide valuable information on small proteins for scientific community. Here we present the update of SmProt, which emphasizes reliability of translated sORFs, genetic variants in translated sORFs, disease-specific sORF translation events or sequences, and remarkably increased data volume. More components such as non-ATG translation initiation, function, and new sources are also included. SmProt incorporated 638,958 unique small proteins curated from 3,165,229 primary records, which were computationally predicted from 419 ribosome profiling (Ribo-seq) datasets or collected from literature and other sources from 370 cell lines or tissues in 8 species (Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Danio rerio, Saccharomyces cerevisiae, Caenorhabditis elegans, and Escherichia coli). In addition, small protein families identified from human microbiomes were also collected. All datasets in SmProt are free to access, and available for browse, search, and bulk downloads at http://bigdata.ibp.ac.cn/SmProt/.
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Affiliation(s)
- Yanyan Li
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Honghong Zhou
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Xiaomin Chen
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yu Zheng
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Quan Kang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Di Hao
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Lili Zhang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingrui Song
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Huaxia Luo
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yajing Hao
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Runsheng Chen
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China; Guangdong Geneway Decoding Bio-Tech Co. Ltd, Foshan 528316, China.
| | - Peng Zhang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
| | - Shunmin He
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China; Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.
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3
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Michel AM, Kiniry SJ, O'Connor PBF, Mullan JP, Baranov PV. GWIPS-viz: 2018 update. Nucleic Acids Res 2019; 46:D823-D830. [PMID: 28977460 PMCID: PMC5753223 DOI: 10.1093/nar/gkx790] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Accepted: 08/29/2017] [Indexed: 12/15/2022] Open
Abstract
The GWIPS-viz browser (http://gwips.ucc.ie/) is an on-line genome browser which is tailored for exploring ribosome profiling (Ribo-seq) data. Since its publication in 2014, GWIPS-viz provides Ribo-seq data for an additional 14 genomes bringing the current total to 23. The integration of new Ribo-seq data has been automated thereby increasing the number of available tracks to 1792, a 10-fold increase in the last three years. The increase is particularly substantial for data derived from human sources. Following user requests, we added the functionality to download these tracks in bigWig format. We also incorporated new types of data (e.g. TCP-seq) as well as auxiliary tracks from other sources that help with the interpretation of Ribo-seq data. Improvements in the visualization of the data have been carried out particularly for bacterial genomes where the Ribo-seq data are now shown in a strand specific manner. For higher eukaryotic datasets, we provide characteristics of individual datasets using the RUST program which includes the triplet periodicity, sequencing biases and relative inferred A-site dwell times. This information can be used for assessing the quality of Ribo-seq datasets. To improve the power of the signal, we aggregate Ribo-seq data from several studies into Global aggregate tracks for each genome.
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Affiliation(s)
- Audrey M Michel
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Stephen J Kiniry
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | | | - James P Mullan
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
| | - Pavel V Baranov
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland
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4
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Irigoyen N, Firth AE, Jones JD, Chung BYW, Siddell SG, Brierley I. High-Resolution Analysis of Coronavirus Gene Expression by RNA Sequencing and Ribosome Profiling. PLoS Pathog 2016; 12:e1005473. [PMID: 26919232 PMCID: PMC4769073 DOI: 10.1371/journal.ppat.1005473] [Citation(s) in RCA: 152] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Accepted: 02/04/2016] [Indexed: 02/07/2023] Open
Abstract
Members of the family Coronaviridae have the largest genomes of all RNA viruses, typically in the region of 30 kilobases. Several coronaviruses, such as Severe acute respiratory syndrome-related coronavirus (SARS-CoV) and Middle East respiratory syndrome-related coronavirus (MERS-CoV), are of medical importance, with high mortality rates and, in the case of SARS-CoV, significant pandemic potential. Other coronaviruses, such as Porcine epidemic diarrhea virus and Avian coronavirus, are important livestock pathogens. Ribosome profiling is a technique which exploits the capacity of the translating ribosome to protect around 30 nucleotides of mRNA from ribonuclease digestion. Ribosome-protected mRNA fragments are purified, subjected to deep sequencing and mapped back to the transcriptome to give a global "snap-shot" of translation. Parallel RNA sequencing allows normalization by transcript abundance. Here we apply ribosome profiling to cells infected with Murine coronavirus, mouse hepatitis virus, strain A59 (MHV-A59), a model coronavirus in the same genus as SARS-CoV and MERS-CoV. The data obtained allowed us to study the kinetics of virus transcription and translation with exquisite precision. We studied the timecourse of positive and negative-sense genomic and subgenomic viral RNA production and the relative translation efficiencies of the different virus ORFs. Virus mRNAs were not found to be translated more efficiently than host mRNAs; rather, virus translation dominates host translation at later time points due to high levels of virus transcripts. Triplet phasing of the profiling data allowed precise determination of translated reading frames and revealed several translated short open reading frames upstream of, or embedded within, known virus protein-coding regions. Ribosome pause sites were identified in the virus replicase polyprotein pp1a ORF and investigated experimentally. Contrary to expectations, ribosomes were not found to pause at the ribosomal frameshift site. To our knowledge this is the first application of ribosome profiling to an RNA virus.
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Affiliation(s)
- Nerea Irigoyen
- Division of Virology, Department of Pathology, University of Cambridge, Cambridge, United Kingdom
| | - Andrew E Firth
- Division of Virology, Department of Pathology, University of Cambridge, Cambridge, United Kingdom
| | - Joshua D Jones
- Division of Virology, Department of Pathology, University of Cambridge, Cambridge, United Kingdom
| | - Betty Y-W Chung
- Department of Plant Sciences, University of Cambridge, Cambridge, United Kingdom
| | - Stuart G Siddell
- Department of Cellular and Molecular Medicine, University of Bristol, Bristol, United Kingdom
| | - Ian Brierley
- Division of Virology, Department of Pathology, University of Cambridge, Cambridge, United Kingdom
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Michel AM, Mullan JPA, Velayudhan V, O'Connor PBF, Donohue CA, Baranov PV. RiboGalaxy: A browser based platform for the alignment, analysis and visualization of ribosome profiling data. RNA Biol 2016; 13:316-9. [PMID: 26821742 PMCID: PMC4829337 DOI: 10.1080/15476286.2016.1141862] [Citation(s) in RCA: 66] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Ribosome profiling (ribo-seq) is a technique that uses high-throughput sequencing to reveal the exact locations and densities of translating ribosomes at the entire transcriptome level. The technique has become very popular since its inception in 2009. Yet experimentalists who generate ribo-seq data often have to rely on bioinformaticians to process and analyze their data. We present RiboGalaxy ( http://ribogalaxy.ucc.ie ), a freely available Galaxy-based web server for processing and analyzing ribosome profiling data with the visualization functionality provided by GWIPS-viz ( http://gwips.ucc.ie ). RiboGalaxy offers researchers a suite of tools specifically tailored for processing ribo-seq and corresponding mRNA-seq data. Researchers can take advantage of the published workflows which reduce the multi-step alignment process to a minimum of inputs from the user. Users can then explore their own aligned data as custom tracks in GWIPS-viz and compare their ribosome profiles to existing ribo-seq tracks from published studies. In addition, users can assess the quality of their ribo-seq data, determine the strength of the triplet periodicity signal, generate meta-gene ribosome profiles as well as analyze the relative impact of mRNA sequence features on local read density. RiboGalaxy is accompanied by extensive documentation and tips for helping users. In addition we provide a forum ( http://gwips.ucc.ie/Forum ) where we encourage users to post their questions and feedback to improve the overall RiboGalaxy service.
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Affiliation(s)
- Audrey M Michel
- a School of Biochemistry & Cell Biology, University College Cork , Cork , Ireland
| | - James P A Mullan
- a School of Biochemistry & Cell Biology, University College Cork , Cork , Ireland
| | | | - Patrick B F O'Connor
- a School of Biochemistry & Cell Biology, University College Cork , Cork , Ireland
| | - Claire A Donohue
- a School of Biochemistry & Cell Biology, University College Cork , Cork , Ireland
| | - Pavel V Baranov
- a School of Biochemistry & Cell Biology, University College Cork , Cork , Ireland
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6
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Chung BY, Hardcastle TJ, Jones JD, Irigoyen N, Firth AE, Baulcombe DC, Brierley I. The use of duplex-specific nuclease in ribosome profiling and a user-friendly software package for Ribo-seq data analysis. RNA (NEW YORK, N.Y.) 2015; 21:1731-45. [PMID: 26286745 PMCID: PMC4574750 DOI: 10.1261/rna.052548.115] [Citation(s) in RCA: 85] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2015] [Accepted: 06/23/2015] [Indexed: 05/19/2023]
Abstract
Ribosome profiling is a technique that permits genome-wide, quantitative analysis of translation and has found broad application in recent years. Here we describe a modified profiling protocol and software package designed to benefit more broadly the translation community in terms of simplicity and utility. The protocol, applicable to diverse organisms, including organelles, is based largely on previously published profiling methodologies, but uses duplex-specific nuclease (DSN) as a convenient, species-independent way to reduce rRNA contamination. We show that DSN-based depletion compares favorably with other commonly used rRNA depletion strategies and introduces little bias. The profiling protocol typically produces high levels of triplet periodicity, facilitating the detection of coding sequences, including upstream, downstream, and overlapping open reading frames (ORFs) and an alternative ribosome conformation evident during termination of protein synthesis. In addition, we provide a software package that presents a set of methods for parsing ribosomal profiling data from multiple samples, aligning reads to coding sequences, inferring alternative ORFs, and plotting average and transcript-specific aspects of the data. Methods are also provided for extracting the data in a form suitable for differential analysis of translation and translational efficiency.
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Affiliation(s)
- Betty Y Chung
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, United Kingdom
| | - Thomas J Hardcastle
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, United Kingdom
| | - Joshua D Jones
- Division of Virology, Department of Pathology, University of Cambridge, Cambridge CB2 1QP, United Kingdom
| | - Nerea Irigoyen
- Division of Virology, Department of Pathology, University of Cambridge, Cambridge CB2 1QP, United Kingdom
| | - Andrew E Firth
- Division of Virology, Department of Pathology, University of Cambridge, Cambridge CB2 1QP, United Kingdom
| | - David C Baulcombe
- Department of Plant Sciences, University of Cambridge, Cambridge CB2 3EA, United Kingdom
| | - Ian Brierley
- Division of Virology, Department of Pathology, University of Cambridge, Cambridge CB2 1QP, United Kingdom
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Michel AM, Fox G, M Kiran A, De Bo C, O'Connor PBF, Heaphy SM, Mullan JPA, Donohue CA, Higgins DG, Baranov PV. GWIPS-viz: development of a ribo-seq genome browser. Nucleic Acids Res 2013; 42:D859-64. [PMID: 24185699 PMCID: PMC3965066 DOI: 10.1093/nar/gkt1035] [Citation(s) in RCA: 189] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023] Open
Abstract
We describe the development of GWIPS-viz (http://gwips.ucc.ie), an online genome browser for viewing ribosome profiling data. Ribosome profiling (ribo-seq) is a recently developed technique that provides genome-wide information on protein synthesis (GWIPS) in vivo. It is based on the deep sequencing of ribosome-protected messenger RNA (mRNA) fragments, which allows the ribosome density along all mRNA transcripts present in the cell to be quantified. Since its inception, ribo-seq has been carried out in a number of eukaryotic and prokaryotic organisms. Owing to the increasing interest in ribo-seq, there is a pertinent demand for a dedicated ribo-seq genome browser. GWIPS-viz is based on The University of California Santa Cruz (UCSC) Genome Browser. Ribo-seq tracks, coupled with mRNA-seq tracks, are currently available for several genomes: human, mouse, zebrafish, nematode, yeast, bacteria (Escherichia coli K12, Bacillus subtilis), human cytomegalovirus and bacteriophage lambda. Our objective is to continue incorporating published ribo-seq data sets so that the wider community can readily view ribosome profiling information from multiple studies without the need to carry out computational processing.
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Affiliation(s)
- Audrey M Michel
- School of Biochemistry and Cell Biology, University College Cork, Cork, Ireland, School of Medicine & Medical Science, Conway Institute, University College Dublin, Dublin 4, Ireland and Howest, University College West Flanders, Rijselstraat 5, 8200 Bruges, Belgium
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RiboTag analysis of actively translated mRNAs in Sertoli and Leydig cells in vivo. PLoS One 2013; 8:e66179. [PMID: 23776628 PMCID: PMC3679032 DOI: 10.1371/journal.pone.0066179] [Citation(s) in RCA: 66] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2013] [Accepted: 05/02/2013] [Indexed: 01/06/2023] Open
Abstract
Male spermatogenesis is a complex biological process that is regulated by hormonal signals from the hypothalamus (GnRH), the pituitary gonadotropins (LH and FSH) and the testis (androgens, inhibin). The two key somatic cell types of the testis, Leydig and Sertoli cells, respond to gonadotropins and androgens and regulate the development and maturation of fertilization competent spermatozoa. Although progress has been made in the identification of specific transcripts that are translated in Sertoli and Leydig cells and their response to hormones, efforts to expand these studies have been restricted by technical hurdles. In order to address this problem we have applied an in vivo ribosome tagging strategy (RiboTag) that allows a detailed and physiologically relevant characterization of the "translatome" (polysome-associated mRNAs) of Leydig or Sertoli cells in vivo. Our analysis identified all previously characterized Leydig and Sertoli cell-specific markers and identified in a comprehensive manner novel markers of Leydig and Sertoli cells; the translational response of these two cell types to gonadotropins or testosterone was also investigated. Modulation of a small subset of Sertoli cell genes occurred after FSH and testosterone stimulation. However, Leydig cells responded robustly to gonadotropin deprivation and LH restoration with acute changes in polysome-associated mRNAs. These studies identified the transcription factors that are induced by LH stimulation, uncovered novel potential regulators of LH signaling and steroidogenesis, and demonstrate the effects of LH on the translational machinery in vivo in the Leydig cell.
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Michel AM, Baranov PV. Ribosome profiling: a Hi-Def monitor for protein synthesis at the genome-wide scale. WILEY INTERDISCIPLINARY REVIEWS-RNA 2013; 4:473-90. [PMID: 23696005 PMCID: PMC3823065 DOI: 10.1002/wrna.1172] [Citation(s) in RCA: 61] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2013] [Revised: 04/21/2013] [Accepted: 04/23/2013] [Indexed: 01/28/2023]
Abstract
Ribosome profiling or ribo-seq is a new technique that provides genome-wide information on protein synthesis (GWIPS) in vivo. It is based on the deep sequencing of ribosome protected mRNA fragments allowing the measurement of ribosome density along all RNA molecules present in the cell. At the same time, the high resolution of this technique allows detailed analysis of ribosome density on individual RNAs. Since its invention, the ribosome profiling technique has been utilized in a range of studies in both prokaryotic and eukaryotic organisms. Several studies have adapted and refined the original ribosome profiling protocol for studying specific aspects of translation. Ribosome profiling of initiating ribosomes has been used to map sites of translation initiation. These studies revealed the surprisingly complex organization of translation initiation sites in eukaryotes. Multiple initiation sites are responsible for the generation of N-terminally extended and truncated isoforms of known proteins as well as for the translation of numerous open reading frames (ORFs), upstream of protein coding ORFs. Ribosome profiling of elongating ribosomes has been used for measuring differential gene expression at the level of translation, the identification of novel protein coding genes and ribosome pausing. It has also provided data for developing quantitative models of translation. Although only a dozen or so ribosome profiling datasets have been published so far, they have already dramatically changed our understanding of translational control and have led to new hypotheses regarding the origin of protein coding genes.
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Zhang YE, Landback P, Vibranovski M, Long M. New genes expressed in human brains: implications for annotating evolving genomes. Bioessays 2012; 34:982-91. [PMID: 23001763 DOI: 10.1002/bies.201200008] [Citation(s) in RCA: 45] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
New genes have frequently formed and spread to fixation in a wide variety of organisms, constituting abundant sets of lineage-specific genes. It was recently reported that an excess of primate-specific and human-specific genes were upregulated in the brains of fetuses and infants, and especially in the prefrontal cortex, which is involved in cognition. These findings reveal the prevalent addition of new genetic components to the transcriptome of the human brain. More generally, these findings suggest that genomes are continually evolving in both sequence and content, eroding the conservation endowed by common ancestry. Despite increasing recognition of the importance of new genes, we highlight here that these genes are still seriously under-characterized in functional studies and that new gene annotation is inconsistent in current practice. We propose an integrative approach to annotate new genes, taking advantage of functional and evolutionary genomic methods. We finally discuss how the refinement of new gene annotation will be important for the detection of evolutionary forces governing new gene origination.
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Affiliation(s)
- Yong E Zhang
- Key Laboratory of the Zoological Systematics and Evolution, Institute of Zoology, Chinese Academy of Sciences, P.R. China
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11
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Positive allosteric feedback regulation of the stringent response enzyme RelA by its product. EMBO Rep 2012; 13:835-9. [PMID: 22814757 DOI: 10.1038/embor.2012.106] [Citation(s) in RCA: 80] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2012] [Revised: 06/23/2012] [Accepted: 07/02/2012] [Indexed: 11/08/2022] Open
Abstract
During the stringent response, Escherichia coli enzyme RelA produces the ppGpp alarmone, which in turn regulates transcription, translation and replication. We show that ppGpp dramatically increases the turnover rate of its own ribosome-dependent synthesis by RelA, resulting in direct positive regulation of an enzyme by its product. Positive allosteric regulation therefore constitutes a new mechanism of enzyme activation. By integrating the output of individual RelA molecules and ppGpp degradation pathways, this regulatory circuit contributes to a fast and coordinated transition to stringency.
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Michel AM, Choudhury KR, Firth AE, Ingolia NT, Atkins JF, Baranov PV. Observation of dually decoded regions of the human genome using ribosome profiling data. Genome Res 2012; 22:2219-29. [PMID: 22593554 PMCID: PMC3483551 DOI: 10.1101/gr.133249.111] [Citation(s) in RCA: 151] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The recently developed ribosome profiling technique (Ribo-Seq) allows mapping of the locations of translating ribosomes on mRNAs with subcodon precision. When ribosome protected fragments (RPFs) are aligned to mRNA, a characteristic triplet periodicity pattern is revealed. We utilized the triplet periodicity of RPFs to develop a computational method for detecting transitions between reading frames that occur during programmed ribosomal frameshifting or in dual coding regions where the same nucleotide sequence codes for multiple proteins in different reading frames. Application of this method to ribosome profiling data obtained for human cells allowed us to detect several human genes where the same genomic segment is translated in more than one reading frame (from different transcripts as well as from the same mRNA) and revealed the translation of hitherto unpredicted coding open reading frames.
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