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Wacholder A, Parikh SB, Coelho NC, Acar O, Houghton C, Chou L, Carvunis AR. A vast evolutionarily transient translatome contributes to phenotype and fitness. Cell Syst 2023; 14:363-381.e8. [PMID: 37164009 PMCID: PMC10348077 DOI: 10.1016/j.cels.2023.04.002] [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: 08/18/2022] [Revised: 01/30/2023] [Accepted: 04/06/2023] [Indexed: 05/12/2023]
Abstract
Translation is the process by which ribosomes synthesize proteins. Ribosome profiling recently revealed that many short sequences previously thought to be noncoding are pervasively translated. To identify protein-coding genes in this noncanonical translatome, we combine an integrative framework for extremely sensitive ribosome profiling analysis, iRibo, with high-powered selection inferences tailored for short sequences. We construct a reference translatome for Saccharomyces cerevisiae comprising 5,400 canonical and almost 19,000 noncanonical translated elements. Only 14 noncanonical elements were evolving under detectable purifying selection. A representative subset of translated elements lacking signatures of selection demonstrated involvement in processes including DNA repair, stress response, and post-transcriptional regulation. Our results suggest that most translated elements are not conserved protein-coding genes and contribute to genotype-phenotype relationships through fast-evolving molecular mechanisms.
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Affiliation(s)
- Aaron Wacholder
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Center for Evolutionary Biology and Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Saurin Bipin Parikh
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Center for Evolutionary Biology and Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Integrative Systems Biology Program, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Nelson Castilho Coelho
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Center for Evolutionary Biology and Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Omer Acar
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Center for Evolutionary Biology and Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Joint CMU-Pitt PhD Program in Computational Biology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Carly Houghton
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Center for Evolutionary Biology and Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Joint CMU-Pitt PhD Program in Computational Biology, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Lin Chou
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Center for Evolutionary Biology and Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Integrative Systems Biology Program, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Anne-Ruxandra Carvunis
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA; Pittsburgh Center for Evolutionary Biology and Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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2
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Angrand G, Quillévéré A, Loaëc N, Dinh VT, Le Sénéchal R, Chennoufi R, Duchambon P, Keruzoré M, Martins R, Teulade-Fichou MP, Fåhraeus R, Blondel M. Type I arginine methyltransferases are intervention points to unveil the oncogenic Epstein-Barr virus to the immune system. Nucleic Acids Res 2022; 50:11799-11819. [PMID: 36350639 PMCID: PMC9723642 DOI: 10.1093/nar/gkac915] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 10/06/2022] [Indexed: 11/10/2022] Open
Abstract
The oncogenic Epstein-Barr virus (EBV) evades the immune system but has an Achilles heel: its genome maintenance protein EBNA1. Indeed, EBNA1 is essential for viral genome maintenance but is also highly antigenic. Hence, EBV seemingly evolved a system in which the glycine-alanine repeat (GAr) of EBNA1 limits the translation of its own mRNA to the minimal level to ensure its essential function, thereby, at the same time, minimizing immune recognition. Therefore, defining intervention points at which to interfere with GAr-based inhibition of translation is an important step to trigger an immune response against EBV-carrying cancers. The host protein nucleolin (NCL) plays a critical role in this process via a direct interaction with G-quadruplexes (G4) formed in the GAr-encoding sequence of the viral EBNA1 mRNA. Here we show that the C-terminal arginine-glycine-rich (RGG) motif of NCL is crucial for its role in GAr-based inhibition of translation by mediating interaction of NCL with G4 of EBNA1 mRNA. We also show that this interaction depends on the type I arginine methyltransferase family, notably PRMT1 and PRMT3: drugs or small interfering RNA that target these enzymes prevent efficient binding of NCL on G4 of EBNA1 mRNA and relieve GAr-based inhibition of translation and of antigen presentation. Hence, this work defines type I arginine methyltransferases as therapeutic targets to interfere with EBNA1 and EBV immune evasion.
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Affiliation(s)
| | | | | | - Van-Trang Dinh
- Institut National de la Santé et de la Recherche Médicale UMR1078; Université de Bretagne Occidentale, Faculté de Médecine et des Sciences de la Santé; Etablissement Français du Sang (EFS) Bretagne; CHRU Brest, Hôpital Morvan, Laboratoire de Génétique Moléculaire, 22 avenue Camille Desmoulins, F-29200 Brest, France
| | - Ronan Le Sénéchal
- Institut National de la Santé et de la Recherche Médicale UMR1078; Université de Bretagne Occidentale, Faculté de Médecine et des Sciences de la Santé; Etablissement Français du Sang (EFS) Bretagne; CHRU Brest, Hôpital Morvan, Laboratoire de Génétique Moléculaire, 22 avenue Camille Desmoulins, F-29200 Brest, France
| | - Rahima Chennoufi
- Chemistry and Modelling for the Biology of Cancer, CNRS UMR9187 - Inserm U1196, Institut Curie, Université Paris-Saclay, Orsay, Campus universitaire, Bat. 110, F-91405, France
| | - Patricia Duchambon
- Chemistry and Modelling for the Biology of Cancer, CNRS UMR9187 - Inserm U1196, Institut Curie, Université Paris-Saclay, Orsay, Campus universitaire, Bat. 110, F-91405, France
| | - Marc Keruzoré
- Institut National de la Santé et de la Recherche Médicale UMR1078; Université de Bretagne Occidentale, Faculté de Médecine et des Sciences de la Santé; Etablissement Français du Sang (EFS) Bretagne; CHRU Brest, Hôpital Morvan, Laboratoire de Génétique Moléculaire, 22 avenue Camille Desmoulins, F-29200 Brest, France
| | | | - Marie-Paule Teulade-Fichou
- Chemistry and Modelling for the Biology of Cancer, CNRS UMR9187 - Inserm U1196, Institut Curie, Université Paris-Saclay, Orsay, Campus universitaire, Bat. 110, F-91405, France
| | - Robin Fåhraeus
- Cibles Thérapeutiques, Institut National de la Santé et de la Recherche Médicale UMR1162, Institut de Génétique Moléculaire, Université Paris 7, Hôpital St. Louis, 27 rue Juliette Dodu, F-75010 Paris, France,RECAMO, Masaryk Memorial Cancer Institute, Zluty kopec 7, 65653 Brno, Czech Republic
| | - Marc Blondel
- To whom correspondence should be addressed. Tel: +33 2 98 01 83 88;
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3
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Parikh SB, Houghton C, Van Oss SB, Wacholder A, Carvunis A. Origins, evolution, and physiological implications of de novo genes in yeast. Yeast 2022; 39:471-481. [PMID: 35959631 PMCID: PMC9544372 DOI: 10.1002/yea.3810] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 12/03/2022] Open
Abstract
De novo gene birth is the process by which new genes emerge in sequences that were previously noncoding. Over the past decade, researchers have taken advantage of the power of yeast as a model and a tool to study the evolutionary mechanisms and physiological implications of de novo gene birth. We summarize the mechanisms that have been proposed to explicate how noncoding sequences can become protein-coding genes, highlighting the discovery of pervasive translation of the yeast transcriptome and its presumed impact on evolutionary innovation. We summarize current best practices for the identification and characterization of de novo genes. Crucially, we explain that the field is still in its nascency, with the physiological roles of most young yeast de novo genes identified thus far still utterly unknown. We hope this review inspires researchers to investigate the true contribution of de novo gene birth to cellular physiology and phenotypic diversity across yeast strains and species.
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Affiliation(s)
- Saurin B. Parikh
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Carly Houghton
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - S. Branden Van Oss
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Aaron Wacholder
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Anne‐Ruxandra Carvunis
- Department of Computational and Systems Biology, School of Medicine, Pittsburgh Center for Evolutionary Biology and EvolutionUniversity of PittsburghPittsburghPennsylvaniaUSA
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4
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Sun Y, Huang J, Wang Z, Pan N, Wan C. Identification of Microproteins in Saccharomyces cerevisiae under Different Stress Conditions. J Proteome Res 2022; 21:1939-1947. [PMID: 35838590 DOI: 10.1021/acs.jproteome.2c00212] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Small open reading frame-encoded peptides (SEPs) are microproteins with a length of 100 amino acids or less, which may play a critical role in maintaining cell homeostasis under stress. Therefore, we used mass spectrometry-based proteomics to explore microproteins potentially involved in cellular stress responses in Saccharomyces cerevisiae. A total of 225 microproteins with 1920 unique peptides were identified under six culture conditions: normal, oxidation, starvation, ultraviolet radiation, heat shock, and heat shock with starvation. Among these microproteins, we found 70 SEPs with 75 unique peptides. The annotated microproteins are involved in stress-related processes, such as cell redox reactions, cell wall modification, protein folding and degradation, and DNA damage repair. It suggests that SEPs may also play similar functions under stress conditions. For example, SEP IP_008057, translated from a short coding sequence of YJL159W, may play a role in heat shock. This study identified stress-responsive SEPs in S. cerevisiae and provided valuable information to determine the functions of these proteins, which enrich the genome and proteome of S. cerevisiae and show clues to improving the stress tolerance of S. cerevisiae.
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Affiliation(s)
- Yan Sun
- School of Life Sciences and Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
| | - Jiangmei Huang
- School of Life Sciences and Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
| | - Zhiwei Wang
- School of Life Sciences and Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
| | - Ni Pan
- School of Life Sciences and Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
| | - Cuihong Wan
- School of Life Sciences and Hubei Key Laboratory of Genetic Regulation and Integrative Biology, Central China Normal University, Wuhan, Hubei 430079, People's Republic of China
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5
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Engel SR, Wong ED, Nash RS, Aleksander S, Alexander M, Douglass E, Karra K, Miyasato SR, Simison M, Skrzypek MS, Weng S, Cherry JM. New data and collaborations at the Saccharomyces Genome Database: updated reference genome, alleles, and the Alliance of Genome Resources. Genetics 2022; 220:iyab224. [PMID: 34897464 PMCID: PMC9209811 DOI: 10.1093/genetics/iyab224] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 11/11/2021] [Indexed: 02/03/2023] Open
Abstract
Saccharomyces cerevisiae is used to provide fundamental understanding of eukaryotic genetics, gene product function, and cellular biological processes. Saccharomyces Genome Database (SGD) has been supporting the yeast research community since 1993, serving as its de facto hub. Over the years, SGD has maintained the genetic nomenclature, chromosome maps, and functional annotation, and developed various tools and methods for analysis and curation of a variety of emerging data types. More recently, SGD and six other model organism focused knowledgebases have come together to create the Alliance of Genome Resources to develop sustainable genome information resources that promote and support the use of various model organisms to understand the genetic and genomic bases of human biology and disease. Here we describe recent activities at SGD, including the latest reference genome annotation update, the development of a curation system for mutant alleles, and new pages addressing homology across model organisms as well as the use of yeast to study human disease.
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Affiliation(s)
- Stacia R Engel
- Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - Edith D Wong
- Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - Robert S Nash
- Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - Suzi Aleksander
- Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - Micheal Alexander
- Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - Eric Douglass
- Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - Kalpana Karra
- Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - Stuart R Miyasato
- Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - Matt Simison
- Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - Marek S Skrzypek
- Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - Shuai Weng
- Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
| | - J Michael Cherry
- Department of Genetics, Stanford University, Stanford, CA 94305-5120, USA
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6
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Tian F, Shi J, Li Y, Gao H, Chang L, Zhang Y, Gao L, Xu P, Tang S. Proteogenomics Study of Blastobotrys adeninivorans TMCC 70007-A Dominant Yeast in the Fermentation Process of Pu-erh Tea. J Proteome Res 2021; 20:3290-3304. [PMID: 34008989 DOI: 10.1021/acs.jproteome.1c00205] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Blastobotrys adeninivorans plays an essential role in pile-fermenting of Pu-erh tea. Its ability to assimilate various carbon and nitrogen sources makes it available for application in a wide range of industry sectors. The genome of B. adeninivorans TMCC 70007 isolated from pile-fermented Pu-erh tea was sequenced and assembled. Proteomics analysis indicated that 4900 proteins in TMCC 70007 were expressed under various culture conditions. Proteogenomics mapping revealed 48 previously unknown genes and corrected 118 gene models predicted by GeneMark-ES. Ortho-proteogenomics analysis identified 17 previously unidentified genes in B. adeninivorans LS3, the first strain with a sequenced genome among the genus Blastobotrys as well. More importantly, five species specific genes were identified from TMCC 70007, which could serve as a barcode for strain typing and were applicable for fermentation process protection of this industrial species. The datasets generated from tea aqueous extract culture not only increased the proteome coverage and accuracy but also contributed to the identification of proteins related to polyphenols and caffeine, which were considered to change greatly during the microbial fermentation of Pu-erh tea. This study provides a proteome perspective on TMCC 70007, which was considered to be an important strain in the production of Pu-erh tea. The systematic proteogenomics analysis not only made a better annotation on the genome of B. adeninivorans TMCC 70007 as previous proteogenomics study but also provided solution for fermentation process protection on valuable industrial species with species specific genes uniquely identified from proteogenomics study.
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Affiliation(s)
- Fei Tian
- Key Laboratory of Microbial Diversity in Southwest China, Ministry of Education, and Laboratory for Conservation and Utilization of Bio-resources, Yunnan Institute of Microbiology, School of Life Sciences, Yunnan University, Kunming 650091, China.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, Research Unit of Proteomics & Research and Development of New Drug, Chinese Academy of Medical Sciences, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Jiahui Shi
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Research Unit of Proteomics & Research and Development of New Drug, Chinese Academy of Medical Sciences, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.,Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding 071002, China
| | - Yanchang Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Research Unit of Proteomics & Research and Development of New Drug, Chinese Academy of Medical Sciences, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Huiying Gao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Research Unit of Proteomics & Research and Development of New Drug, Chinese Academy of Medical Sciences, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Lei Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Research Unit of Proteomics & Research and Development of New Drug, Chinese Academy of Medical Sciences, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yao Zhang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Research Unit of Proteomics & Research and Development of New Drug, Chinese Academy of Medical Sciences, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Linrui Gao
- Yunnan Pu-erh Tea Fermentation Engineering Research Center, Yunnan TAETEA Microbial Technology Co., Ltd., Kunming 650217, China
| | - Ping Xu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, Research Unit of Proteomics & Research and Development of New Drug, Chinese Academy of Medical Sciences, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.,Hebei Province Key Lab of Research and Application on Microbial Diversity, College of Life Sciences, Hebei University, Baoding 071002, China
| | - Shukun Tang
- Key Laboratory of Microbial Diversity in Southwest China, Ministry of Education, and Laboratory for Conservation and Utilization of Bio-resources, Yunnan Institute of Microbiology, School of Life Sciences, Yunnan University, Kunming 650091, China.,Yunnan Pu-erh Tea Fermentation Engineering Research Center, Yunnan TAETEA Microbial Technology Co., Ltd., Kunming 650217, China
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7
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Yang M, Zhu Z, Zhuang Z, Bai Y, Wang S, Ge F. Proteogenomic Characterization of the Pathogenic Fungus Aspergillus flavus Reveals Novel Genes Involved in Aflatoxin Production. Mol Cell Proteomics 2020; 20:100013. [PMID: 33568340 PMCID: PMC7950108 DOI: 10.1074/mcp.ra120.002144] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2020] [Revised: 10/06/2020] [Accepted: 11/24/2020] [Indexed: 12/20/2022] Open
Abstract
Aspergillus flavus (A. flavus), a pathogenic fungus, can produce carcinogenic and toxic aflatoxins that are a serious agricultural and medical threat worldwide. Attempts to decipher the aflatoxin biosynthetic pathway have been hampered by the lack of a high-quality genome annotation for A. flavus. To address this gap, we performed a comprehensive proteogenomic analysis using high-accuracy mass spectrometry data for this pathogen. The resulting high-quality data set confirmed the translation of 8724 previously predicted genes and identified 732 novel proteins, 269 splice variants, 447 single amino acid variants, 188 revised genes. A subset of novel proteins was experimentally validated by RT-PCR and synthetic peptides. Further functional annotation suggested that a number of the identified novel proteins may play roles in aflatoxin biosynthesis and stress responses in A. flavus. This comprehensive strategy also identified a wide range of posttranslational modifications (PTMs), including 3461 modification sites from 1765 proteins. Functional analysis suggested the involvement of these modified proteins in the regulation of cellular metabolic and aflatoxin biosynthetic pathways. Together, we provided a high-quality annotation of A. flavus genome and revealed novel insights into the mechanisms of aflatoxin production and pathogenicity in this pathogen.
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Affiliation(s)
- Mingkun Yang
- School of Life Sciences, and Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Fujian Agriculture and Forestry University, Fuzhou, China; State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China
| | - Zhuo Zhu
- School of Life Sciences, and Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Zhenhong Zhuang
- School of Life Sciences, and Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Youhuang Bai
- School of Life Sciences, and Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Fujian Agriculture and Forestry University, Fuzhou, China
| | - Shihua Wang
- School of Life Sciences, and Key Laboratory of Pathogenic Fungi and Mycotoxins of Fujian Province, Fujian Agriculture and Forestry University, Fuzhou, China.
| | - Feng Ge
- State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, China.
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8
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Tay AP, Liang A, Hamey JJ, Hart‐Smith G, Wilkins MR. MS2‐Deisotoper: A Tool for Deisotoping High‐Resolution MS/MS Spectra in Normal and Heavy Isotope‐Labelled Samples. Proteomics 2019; 19:e1800444. [DOI: 10.1002/pmic.201800444] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 07/05/2019] [Indexed: 01/09/2023]
Affiliation(s)
- Aidan P. Tay
- Systems Biology InitiativeSchool of Biotechnology and Biomolecular SciencesThe University of New South Wales Sydney New South Wales 2052 Australia
| | - Angelita Liang
- Systems Biology InitiativeSchool of Biotechnology and Biomolecular SciencesThe University of New South Wales Sydney New South Wales 2052 Australia
| | - Joshua J. Hamey
- Systems Biology InitiativeSchool of Biotechnology and Biomolecular SciencesThe University of New South Wales Sydney New South Wales 2052 Australia
| | - Gene Hart‐Smith
- Systems Biology InitiativeSchool of Biotechnology and Biomolecular SciencesThe University of New South Wales Sydney New South Wales 2052 Australia
| | - Marc R. Wilkins
- Systems Biology InitiativeSchool of Biotechnology and Biomolecular SciencesThe University of New South Wales Sydney New South Wales 2052 Australia
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9
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Li S, Cha SW, Heffner K, Hizal DB, Bowen MA, Chaerkady R, Cole RN, Tejwani V, Kaushik P, Henry M, Meleady P, Sharfstein ST, Betenbaugh MJ, Bafna V, Lewis NE. Proteogenomic Annotation of Chinese Hamsters Reveals Extensive Novel Translation Events and Endogenous Retroviral Elements. J Proteome Res 2019; 18:2433-2445. [PMID: 31020842 DOI: 10.1021/acs.jproteome.8b00935] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
A high-quality genome annotation greatly facilitates successful cell line engineering. Standard draft genome annotation pipelines are based largely on de novo gene prediction, homology, and RNA-Seq data. However, draft annotations can suffer from incorrect predictions of translated sequence, inaccurate splice isoforms, and missing genes. Here, we generated a draft annotation for the newly assembled Chinese hamster genome and used RNA-Seq, proteomics, and Ribo-Seq to experimentally annotate the genome. We identified 3529 new proteins compared to the hamster RefSeq protein annotation and 2256 novel translational events (e.g., alternative splices, mutations, and novel splices). Finally, we used this pipeline to identify the source of translated retroviruses contaminating recombinant products from Chinese hamster ovary (CHO) cell lines, including 119 type-C retroviruses, thus enabling future efforts to eliminate retroviruses to reduce the costs incurred with retroviral particle clearance. In summary, the improved annotation provides a more accurate resource for CHO cell line engineering, by facilitating the interpretation of omics data, defining of cellular pathways, and engineering of complex phenotypes.
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Affiliation(s)
| | | | | | - Deniz Baycin Hizal
- Antibody Discovery and Protein Engineering , AstraZeneca , Gaithersburg , Maryland , United States
| | - Michael A Bowen
- Antibody Discovery and Protein Engineering , AstraZeneca , Gaithersburg , Maryland , United States
| | - Raghothama Chaerkady
- Antibody Discovery and Protein Engineering , AstraZeneca , Gaithersburg , Maryland , United States
| | | | - Vijay Tejwani
- Colleges of Nanoscale Science and Engineering , SUNY Polytechnic Institute , Albany , New York 12203 , United States
| | - Prashant Kaushik
- National Institute for Cellular Biotechnology , Dublin City University , Dublin 9, Ireland
| | - Michael Henry
- National Institute for Cellular Biotechnology , Dublin City University , Dublin 9, Ireland
| | - Paula Meleady
- National Institute for Cellular Biotechnology , Dublin City University , Dublin 9, Ireland
| | - Susan T Sharfstein
- Colleges of Nanoscale Science and Engineering , SUNY Polytechnic Institute , Albany , New York 12203 , United States
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10
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Hurtado Silva M, Berry IJ, Strange N, Djordjevic SP, Padula MP. Terminomics Methodologies and the Completeness of Reductive Dimethylation: A Meta-Analysis of Publicly Available Datasets. Proteomes 2019; 7:proteomes7020011. [PMID: 30934878 PMCID: PMC6631386 DOI: 10.3390/proteomes7020011] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Revised: 03/22/2019] [Accepted: 03/25/2019] [Indexed: 12/30/2022] Open
Abstract
Methods for analyzing the terminal sequences of proteins have been refined over the previous decade; however, few studies have evaluated the quality of the data that have been produced from those methodologies. While performing global N-terminal labelling on bacteria, we observed that the labelling was not complete and investigated whether this was a common occurrence. We assessed the completeness of labelling in a selection of existing, publicly available N-terminomics datasets and empirically determined that amine-based labelling chemistry does not achieve complete labelling and potentially has issues with labelling amine groups at sequence-specific residues. This finding led us to conduct a thorough review of the historical literature that showed that this is not an unexpected finding, with numerous publications reporting incomplete labelling. These findings have implications for the quantitation of N-terminal peptides and the biological interpretations of these data.
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Affiliation(s)
- Mariella Hurtado Silva
- Proteomics Core Facility and School of Life Sciences, Faculty of Science, University of Technology Sydney, Broadway NSW 2007, Australia.
| | - Iain J Berry
- Proteomics Core Facility and School of Life Sciences, Faculty of Science, University of Technology Sydney, Broadway NSW 2007, Australia.
- The ithree Institute, Faculty of Science, University of Technology Sydney, Broadway NSW 2007, Australia.
| | - Natalie Strange
- Proteomics Core Facility and School of Life Sciences, Faculty of Science, University of Technology Sydney, Broadway NSW 2007, Australia.
| | - Steven P Djordjevic
- The ithree Institute, Faculty of Science, University of Technology Sydney, Broadway NSW 2007, Australia.
| | - Matthew P Padula
- Proteomics Core Facility and School of Life Sciences, Faculty of Science, University of Technology Sydney, Broadway NSW 2007, Australia.
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11
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Cassidy L, Kaulich PT, Tholey A. Depletion of High-Molecular-Mass Proteins for the Identification of Small Proteins and Short Open Reading Frame Encoded Peptides in Cellular Proteomes. J Proteome Res 2019; 18:1725-1734. [DOI: 10.1021/acs.jproteome.8b00948] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Affiliation(s)
- Liam Cassidy
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
| | - Philipp T. Kaulich
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
| | - Andreas Tholey
- Systematic Proteome Research & Bioanalytics, Institute for Experimental Medicine, Christian-Albrechts-Universität zu Kiel, 24105 Kiel, Germany
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12
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Low TY, Mohtar MA, Ang MY, Jamal R. Connecting Proteomics to Next‐Generation Sequencing: Proteogenomics and Its Current Applications in Biology. Proteomics 2018; 19:e1800235. [DOI: 10.1002/pmic.201800235] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 10/09/2018] [Indexed: 12/17/2022]
Affiliation(s)
- Teck Yew Low
- UKM Medical Molecular Biology Institute (UMBI)Universiti Kebangsaan Malaysia 56000 Kuala Lumpur Malaysia
| | - M. Aiman Mohtar
- UKM Medical Molecular Biology Institute (UMBI)Universiti Kebangsaan Malaysia 56000 Kuala Lumpur Malaysia
| | - Mia Yang Ang
- UKM Medical Molecular Biology Institute (UMBI)Universiti Kebangsaan Malaysia 56000 Kuala Lumpur Malaysia
| | - Rahman Jamal
- UKM Medical Molecular Biology Institute (UMBI)Universiti Kebangsaan Malaysia 56000 Kuala Lumpur Malaysia
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13
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Chia SZ, Lai YW, Yagoub D, Lev S, Hamey JJ, Pang CNI, Desmarini D, Chen Z, Djordjevic JT, Erce MA, Hart-Smith G, Wilkins MR. Knockout of the Hmt1p Arginine Methyltransferase in Saccharomyces cerevisiae Leads to the Dysregulation of Phosphate-associated Genes and Processes. Mol Cell Proteomics 2018; 17:2462-2479. [PMID: 30206180 PMCID: PMC6283299 DOI: 10.1074/mcp.ra117.000214] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 08/14/2018] [Indexed: 11/06/2022] Open
Abstract
Hmt1p is the predominant arginine methyltransferase in Saccharomyces cerevisiae Its substrate proteins are involved in transcription, transcriptional regulation, nucleocytoplasmic transport and RNA splicing. Hmt1p-catalyzed methylation can also modulate protein-protein interactions. Hmt1p is conserved from unicellular eukaryotes through to mammals where its ortholog, PRMT1, is lethal upon knockout. In yeast, however, the effect of knockout on the transcriptome and proteome has not been described. Transcriptome analysis revealed downregulation of phosphate-responsive genes in hmt1Δ, including acid phosphatases PHO5, PHO11, and PHO12, phosphate transporters PHO84 and PHO89 and the vacuolar transporter chaperone VTC3 Analysis of the hmt1Δ proteome revealed decreased abundance of phosphate-associated proteins including phosphate transporter Pho84p, vacuolar alkaline phosphatase Pho8p, acid phosphatase Pho3p and subunits of the vacuolar transporter chaperone complex Vtc1p, Vtc3p and Vtc4p. Consistent with this, phosphate homeostasis was dysregulated in hmt1Δ cells, showing decreased extracellular phosphatase levels and decreased total Pi in phosphate-depleted medium. In vitro, we showed that transcription factor Pho4p can be methylated at Arg-241, which could explain phosphate dysregulation in hmt1Δ if interplay exists with phosphorylation at Ser-242 or Ser-243, or if Arg-241 methylation affects the capacity of Pho4p to homodimerize or interact with Pho2p. However, the Arg-241 methylation site was not validated in vivo and the localization of a Pho4p-GFP fusion in hmt1Δ was not different from wild type. To our knowledge, this is the first study to reveal an association between Hmt1p and phosphate homeostasis and one which suggests a regulatory link between S-adenosyl methionine and intracellular phosphate.
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Affiliation(s)
- Samantha Z Chia
- Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Yu-Wen Lai
- Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Daniel Yagoub
- Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Sophie Lev
- Centre for Infectious Diseases and Microbiology, Westmead Millennium Institute and Sydney Medical School, University of Sydney at Westmead Hospital, Westmead, New South Wales, Australia
| | - Joshua J Hamey
- Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Chi Nam Ignatius Pang
- Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Desmarini Desmarini
- Centre for Infectious Diseases and Microbiology, Westmead Millennium Institute and Sydney Medical School, University of Sydney at Westmead Hospital, Westmead, New South Wales, Australia
| | - Zhiliang Chen
- Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Julianne T Djordjevic
- Centre for Infectious Diseases and Microbiology, Westmead Millennium Institute and Sydney Medical School, University of Sydney at Westmead Hospital, Westmead, New South Wales, Australia
| | - Melissa A Erce
- Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Gene Hart-Smith
- Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia
| | - Marc R Wilkins
- Systems Biology Initiative, School of Biotechnology and Biomolecular Sciences, University of New South Wales, Sydney, NSW 2052, Australia.
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14
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Olexiouk V, Van Criekinge W, Menschaert G. An update on sORFs.org: a repository of small ORFs identified by ribosome profiling. Nucleic Acids Res 2018; 46:D497-D502. [PMID: 29140531 PMCID: PMC5753181 DOI: 10.1093/nar/gkx1130] [Citation(s) in RCA: 133] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 10/25/2017] [Accepted: 10/26/2017] [Indexed: 12/13/2022] Open
Abstract
sORFs.org (http://www.sorfs.org) is a public repository of small open reading frames (sORFs) identified by ribosome profiling (RIBO-seq). This update elaborates on the major improvements implemented since its initial release. sORFs.org now additionally supports three more species (zebrafish, rat and Caenorhabditis elegans) and currently includes 78 RIBO-seq datasets, a vast increase compared to the three that were processed in the initial release. Therefore, a novel pipeline was constructed that also enables sORF detection in RIBO-seq datasets comprising solely elongating RIBO-seq data while previously, matching initiating RIBO-seq data was necessary to delineate the sORFs. Furthermore, a novel noise filtering algorithm was designed, able to distinguish sORFs with true ribosomal activity from simulated noise, consequently reducing the false positive identification rate. The inclusion of other species also led to the development of an inner BLAST pipeline, assessing sequence similarity between sORFs in the repository. Building on the proof of concept model in the initial release of sORFs.org, a full PRIDE-ReSpin pipeline was now released, reprocessing publicly available MS-based proteomics PRIDE datasets, reporting on true translation events. Next to reporting those identified peptides, sORFs.org allows visual inspection of the annotated spectra within the Lorikeet MS/MS viewer, thus enabling detailed manual inspection and interpretation.
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Affiliation(s)
- Volodimir Olexiouk
- Lab of Bioinformatics and Computational Genomics (BioBix), Department of Mathematical Modelling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium
| | - Wim Van Criekinge
- Lab of Bioinformatics and Computational Genomics (BioBix), Department of Mathematical Modelling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium
| | - Gerben Menschaert
- Lab of Bioinformatics and Computational Genomics (BioBix), Department of Mathematical Modelling, Statistics and Bioinformatics, Faculty of Bioscience Engineering, Ghent University, 9000 Ghent, Belgium
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15
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Heunis T, Dippenaar A, Warren RM, van Helden PD, van der Merwe RG, Gey van Pittius NC, Pain A, Sampson SL, Tabb DL. Proteogenomic Investigation of Strain Variation in Clinical Mycobacterium tuberculosis Isolates. J Proteome Res 2017; 16:3841-3851. [PMID: 28820946 DOI: 10.1021/acs.jproteome.7b00483] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Mycobacterium tuberculosis consists of a large number of different strains that display unique virulence characteristics. Whole-genome sequencing has revealed substantial genetic diversity among clinical M. tuberculosis isolates, and elucidating the phenotypic variation encoded by this genetic diversity will be of the utmost importance to fully understand M. tuberculosis biology and pathogenicity. In this study, we integrated whole-genome sequencing and mass spectrometry (GeLC-MS/MS) to reveal strain-specific characteristics in the proteomes of two clinical M. tuberculosis Latin American-Mediterranean isolates. Using this approach, we identified 59 peptides containing single amino acid variants, which covered ∼9% of all coding nonsynonymous single nucleotide variants detected by whole-genome sequencing. Furthermore, we identified 29 distinct peptides that mapped to a hypothetical protein not present in the M. tuberculosis H37Rv reference proteome. Here, we provide evidence for the expression of this protein in the clinical M. tuberculosis SAWC3651 isolate. The strain-specific databases enabled confirmation of genomic differences (i.e., large genomic regions of difference and nonsynonymous single nucleotide variants) in these two clinical M. tuberculosis isolates and allowed strain differentiation at the proteome level. Our results contribute to the growing field of clinical microbial proteogenomics and can improve our understanding of phenotypic variation in clinical M. tuberculosis isolates.
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Affiliation(s)
- Tiaan Heunis
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - Anzaan Dippenaar
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - Robin M Warren
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - Paul D van Helden
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - Ruben G van der Merwe
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - Nicolaas C Gey van Pittius
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - Arnab Pain
- Pathogen Genomics Laboratory, BESE Division, King Abdullah University of Science and Technology , Thuwal 23955, Saudi Arabia
| | - Samantha L Sampson
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
| | - David L Tabb
- DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, SAMRC Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town 7505, South Africa
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16
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Zhu Y, Engström PG, Tellgren-Roth C, Baudo CD, Kennell JC, Sun S, Billmyre RB, Schröder MS, Andersson A, Holm T, Sigurgeirsson B, Wu G, Sankaranarayanan SR, Siddharthan R, Sanyal K, Lundeberg J, Nystedt B, Boekhout T, Dawson TL, Heitman J, Scheynius A, Lehtiö J. Proteogenomics produces comprehensive and highly accurate protein-coding gene annotation in a complete genome assembly of Malassezia sympodialis. Nucleic Acids Res 2017; 45:2629-2643. [PMID: 28100699 PMCID: PMC5389616 DOI: 10.1093/nar/gkx006] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 01/16/2017] [Indexed: 11/23/2022] Open
Abstract
Complete and accurate genome assembly and annotation is a crucial foundation for comparative and functional genomics. Despite this, few complete eukaryotic genomes are available, and genome annotation remains a major challenge. Here, we present a complete genome assembly of the skin commensal yeast Malassezia sympodialis and demonstrate how proteogenomics can substantially improve gene annotation. Through long-read DNA sequencing, we obtained a gap-free genome assembly for M. sympodialis (ATCC 42132), comprising eight nuclear and one mitochondrial chromosome. We also sequenced and assembled four M. sympodialis clinical isolates, and showed their value for understanding Malassezia reproduction by confirming four alternative allele combinations at the two mating-type loci. Importantly, we demonstrated how proteomics data could be readily integrated with transcriptomics data in standard annotation tools. This increased the number of annotated protein-coding genes by 14% (from 3612 to 4113), compared to using transcriptomics evidence alone. Manual curation further increased the number of protein-coding genes by 9% (to 4493). All of these genes have RNA-seq evidence and 87% were confirmed by proteomics. The M. sympodialis genome assembly and annotation presented here is at a quality yet achieved only for a few eukaryotic organisms, and constitutes an important reference for future host-microbe interaction studies.
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Affiliation(s)
- Yafeng Zhu
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, 17121 Solna, Sweden
| | - Pär G Engström
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, 17121 Solna, Sweden
| | - Christian Tellgren-Roth
- National Genomics Infrastructure, Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, 75108 Uppsala, Sweden
| | - Charles D Baudo
- Department of Biology, Saint Louis University, St. Louis, MO 63103, USA
| | - John C Kennell
- Department of Biology, Saint Louis University, St. Louis, MO 63103, USA
| | - Sheng Sun
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC 27710, USA
| | - R Blake Billmyre
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Markus S Schröder
- School of Biomedical and Biomolecular Science, Conway Institute, University College Dublin, Belfield, Dublin 4, Ireland
| | - Anna Andersson
- Department of Medicine Solna, Translational Immunology Unit, Karolinska Institutet and University Hospital, 17177 Stockholm, Sweden
| | - Tina Holm
- Department of Medicine Solna, Translational Immunology Unit, Karolinska Institutet and University Hospital, 17177 Stockholm, Sweden
| | - Benjamin Sigurgeirsson
- Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology, 17121 Solna, Sweden
| | - Guangxi Wu
- Computational and Systems Biology, Genome Institute of Singapore, Agency for Science, Technology and Research (A*STAR), 138672, Singapore
| | - Sundar Ram Sankaranarayanan
- Molecular Mycology Laboratory, Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bangalore 560 064, India
| | - Rahul Siddharthan
- The Institute of Mathematical Sciences/HBNI, Taramani, Chennai 600 113, India
| | - Kaustuv Sanyal
- Molecular Mycology Laboratory, Molecular Biology and Genetics Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Jakkur, Bangalore 560 064, India
| | - Joakim Lundeberg
- Science for Life Laboratory, School of Biotechnology, Royal Institute of Technology, 17121 Solna, Sweden
| | - Björn Nystedt
- Science for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University, 75123 Uppsala, Sweden
| | - Teun Boekhout
- CBS-Fungal Biodiversity Centre, Utrecht, 3508, The Netherlands and Institute for Biodiversity and ecosystem Dynamics (IBED), University of Amsterdam, 1012 WX Amsterdam, The Netherlands
| | - Thomas L Dawson
- Institute of Medical Biology, Agency for Science, Technology and Research (A*STAR), 138648, Singapore
| | - Joseph Heitman
- Department of Molecular Genetics and Microbiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Annika Scheynius
- Science for Life Laboratory, Department of Clinical Science and Education, Karolinska Institutet, and Sachs' Children and Youth Hospital, Södersjukhuset, SE-118 83 Stockholm, Sweden
| | - Janne Lehtiö
- Science for Life Laboratory, Department of Oncology-Pathology, Karolinska Institutet, 17121 Solna, Sweden
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17
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Yan R, Zhang J, Zellmer L, Chen L, Wu D, Liu S, Xu N, Liao JD. Probably less than one-tenth of the genes produce only the wild type protein without at least one additional protein isoform in some human cancer cell lines. Oncotarget 2017; 8:82714-82727. [PMID: 29137297 PMCID: PMC5669923 DOI: 10.18632/oncotarget.20015] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2017] [Accepted: 06/30/2017] [Indexed: 11/25/2022] Open
Abstract
To estimate how many genes produce multiple protein isoforms, we electrophoresed proteins from MCF7 and MDA-MB231 (MB231) human breast cancer cells in SDS-PAGE and excised narrow stripes of the gel at the 48kD, 55kD and 72kD. Proteins in these stripes were identified using liquid chromatography and tandem mass spectrometry. A total of 765, 750 and 679 proteins from MB231 cells, as well as 470, 390 and 490 proteins from MCF7 cells, were identified from the 48kD, 55kD and 72kD stripes, respectively. We arbitrarily allowed a 10% technical variation from the proteins' theoretical molecular mass (TMM) and considered those proteins with their TMMs within the 43-53 kD, 49-61 kD and 65-79 kD ranges as the wild type (WT) expected from the corresponding stripe, whereas those with a TMM above or below this range as a smaller- or larger-group, respectively. Only 263 (34.4%), 269 (35.9%) and 151 (22.2%) proteins from MB231 cells and 117 (24.9%), 135 (34.6%) and 130 (26.5%) proteins from MCF7 cells from the 48kD, 55kD and 72kD stripes, respectively, belonged to the WT, while the remaining majority belonged to the smaller- or larger-groups. Only about 3-16%, on average about 10% regardless of the stripe and cell line, of the proteins appeared in only one stripe and within the WT range, while the remaining preponderance appeared also in additional stripe(s) or had a larger or smaller TMM. We conclude that few (fewer than 10%) of the human genes produce only the WT protein without additional isoform(s).
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Affiliation(s)
- Rui Yan
- Nephrology Department, Guizhou Medical University Hospital, Guiyang, P.R. China
| | - Ju Zhang
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, P.R. China
| | - Lucas Zellmer
- Hormel Institute, University of Minnesota, Austin, Minnesota, USA
| | - Lichan Chen
- Hormel Institute, University of Minnesota, Austin, Minnesota, USA
| | - Di Wu
- Beijing Protein Innovation Co., Ltd, Beijing, P.R. China
| | - Siqi Liu
- CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, P.R. China
| | - Ningzhi Xu
- Laboratory of Cell and Molecular Biology & State Key Laboratory of Molecular Oncology, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, P.R. China
| | - Joshua D Liao
- Department of Pathology, Guizhou Medical University Hospital, Guiyang, P.R. China
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18
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Zheng J, Chen L, Liu L, Li H, Liu B, Zheng D, Liu T, Dong J, Sun L, Zhu Y, Yang J, Zhang X, Jin Q. Proteogenomic Analysis and Discovery of Immune Antigens in Mycobacterium vaccae. Mol Cell Proteomics 2017; 16:1578-1590. [PMID: 28733429 DOI: 10.1074/mcp.m116.065813] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2016] [Revised: 07/05/2017] [Indexed: 11/06/2022] Open
Abstract
Tuberculosis (TB) is one of the leading causes of death worldwide, especially in developing countries. Neonatal BCG vaccination occurs in various regions, but the level of protection varies in different populations. Recently, Mycobacterium vaccae is found to be an immunomodulating therapeutic agent that could confer a significant level of protection against TB. It is the only vaccine in a phase III trial from WHO to assess its efficacy and safety in preventing TB disease in people with latent TB infection. However, the mechanism of immunotherapy of M. vaccae remains poorly understood. In this study, the full genome of M. vaccae was obtained by next-generation sequencing technology, and a proteogenomic approach was successfully applied to further perform genome annotation using high resolution and high accuracy MS data. A total of 3,387 proteins (22,508 unique peptides) were identified, and 581 proteins annotated as hypothetical proteins in the genome database were confirmed. Furthermore, 38 novel protein products not annotated at the genome level were detected and validated. Additionally, the translational start sites of 445 proteins were confirmed, and 98 proteins were validated through extension of their translational start sites based on N terminus-derived peptides. The physicochemical characteristics of the identified proteins were determined. Thirty-five immunogenic proteins of M. vaccae were identified by immunoproteomic analysis, and 20 of them were selected to be expressed and validated by Western blot for immunoreactivity to serum from patients infected with M. tuberculosis The results revealed that eight of them showed strong specific reactive signals on the immunoblots. Furthermore, cellular immune response was further examined and one protein displayed a higher cellular immune level in pulmonary TB patients. Twelve identified immunogenic proteins have orthologous in H37Rv and BCG. This is the first study to obtain the full genome and annotation of M. vaccae using a proteogenomic approach, and some immunogenic proteins that were validated by immunoproteomic analysis could contribute to the understanding of the mechanism of M. vaccae immunotherapy.
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Affiliation(s)
- Jianhua Zheng
- ‡From the MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Centre for Tuberculosis, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lihong Chen
- ‡From the MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Centre for Tuberculosis, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Liguo Liu
- ‡From the MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Centre for Tuberculosis, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Haifeng Li
- ‡From the MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Centre for Tuberculosis, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bo Liu
- ‡From the MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Centre for Tuberculosis, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Dandan Zheng
- ‡From the MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Centre for Tuberculosis, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Tao Liu
- ‡From the MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Centre for Tuberculosis, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jie Dong
- ‡From the MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Centre for Tuberculosis, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Lilian Sun
- ‡From the MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Centre for Tuberculosis, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Yafang Zhu
- ‡From the MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Centre for Tuberculosis, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jian Yang
- ‡From the MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Centre for Tuberculosis, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Xiaobing Zhang
- ‡From the MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Centre for Tuberculosis, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Qi Jin
- ‡From the MOH Key Laboratory of Systems Biology of Pathogens, Institute of Pathogen Biology, and Centre for Tuberculosis, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
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19
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Chu Q, Rathore A, Diedrich JK, Donaldson CJ, Yates JR, Saghatelian A. Identification of Microprotein-Protein Interactions via APEX Tagging. Biochemistry 2017; 56:3299-3306. [PMID: 28589727 PMCID: PMC5499098 DOI: 10.1021/acs.biochem.7b00265] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Revised: 05/17/2017] [Indexed: 02/08/2023]
Abstract
Microproteins are peptides and small proteins encoded by small open reading frames (smORFs). Newer technologies have led to the recent discovery of hundreds to thousands of new microproteins. The biological functions of a few microproteins have been elucidated, and these microproteins have fundamental roles in biology ranging from limb development to muscle function, highlighting the value of characterizing these molecules. The identification of microprotein-protein interactions (MPIs) has proven to be a successful approach to the functional characterization of these genes; however, traditional immunoprecipitation methods result in the enrichment of nonspecific interactions for microproteins. Here, we test and apply an in situ proximity tagging method that relies on an engineered ascorbate peroxidase 2 (APEX) to elucidate MPIs. The results demonstrate that APEX tagging is superior to traditional immunoprecipitation methods for microproteins. Furthermore, the application of APEX tagging to an uncharacterized microprotein called C11orf98 revealed that this microprotein interacts with nucleolar proteins nucleophosmin and nucleolin, demonstrating the ability of this approach to identify novel hypothesis-generating MPIs.
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Affiliation(s)
- Qian Chu
- Clayton
Foundation Laboratories for Peptide Biology, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Annie Rathore
- Clayton
Foundation Laboratories for Peptide Biology, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California 92037, United States
- Division
of Biological Sciences, University of California,
San Diego, 9500 Gilman
Drive, La Jolla, California 92093, United States
| | - Jolene K. Diedrich
- Clayton
Foundation Laboratories for Peptide Biology, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California 92037, United States
- Department
of Chemical Physiology, The Scripps Research
Institute, 10550 North
Torrey Pines Road, La Jolla, California 92037, United States
| | - Cynthia J. Donaldson
- Clayton
Foundation Laboratories for Peptide Biology, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California 92037, United States
| | - John R. Yates
- Department
of Chemical Physiology, The Scripps Research
Institute, 10550 North
Torrey Pines Road, La Jolla, California 92037, United States
| | - Alan Saghatelian
- Clayton
Foundation Laboratories for Peptide Biology, The Salk Institute for Biological Studies, 10010 North Torrey Pines Road, La Jolla, California 92037, United States
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20
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Prasad TSK, Mohanty AK, Kumar M, Sreenivasamurthy SK, Dey G, Nirujogi RS, Pinto SM, Madugundu AK, Patil AH, Advani J, Manda SS, Gupta MK, Dwivedi SB, Kelkar DS, Hall B, Jiang X, Peery A, Rajagopalan P, Yelamanchi SD, Solanki HS, Raja R, Sathe GJ, Chavan S, Verma R, Patel KM, Jain AP, Syed N, Datta KK, Khan AA, Dammalli M, Jayaram S, Radhakrishnan A, Mitchell CJ, Na CH, Kumar N, Sinnis P, Sharakhov IV, Wang C, Gowda H, Tu Z, Kumar A, Pandey A. Integrating transcriptomic and proteomic data for accurate assembly and annotation of genomes. Genome Res 2016; 27:133-144. [PMID: 28003436 PMCID: PMC5204337 DOI: 10.1101/gr.201368.115] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2015] [Accepted: 11/10/2016] [Indexed: 01/05/2023]
Abstract
Complementing genome sequence with deep transcriptome and proteome data could enable more accurate assembly and annotation of newly sequenced genomes. Here, we provide a proof-of-concept of an integrated approach for analysis of the genome and proteome of Anopheles stephensi, which is one of the most important vectors of the malaria parasite. To achieve broad coverage of genes, we carried out transcriptome sequencing and deep proteome profiling of multiple anatomically distinct sites. Based on transcriptomic data alone, we identified and corrected 535 events of incomplete genome assembly involving 1196 scaffolds and 868 protein-coding gene models. This proteogenomic approach enabled us to add 365 genes that were missed during genome annotation and identify 917 gene correction events through discovery of 151 novel exons, 297 protein extensions, 231 exon extensions, 192 novel protein start sites, 19 novel translational frames, 28 events of joining of exons, and 76 events of joining of adjacent genes as a single gene. Incorporation of proteomic evidence allowed us to change the designation of more than 87 predicted “noncoding RNAs” to conventional mRNAs coded by protein-coding genes. Importantly, extension of the newly corrected genome assemblies and gene models to 15 other newly assembled Anopheline genomes led to the discovery of a large number of apparent discrepancies in assembly and annotation of these genomes. Our data provide a framework for how future genome sequencing efforts should incorporate transcriptomic and proteomic analysis in combination with simultaneous manual curation to achieve near complete assembly and accurate annotation of genomes.
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Affiliation(s)
- T S Keshava Prasad
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore 575018, India.,NIMHANS-IOB Proteomics and Bioinformatics Laboratory, Neurobiology Research Centre, National Institute of Mental Health and Neuro Sciences, Bangalore, Karnataka 560029, India
| | - Ajeet Kumar Mohanty
- National Institute of Malaria Research, Field Station, Goa 403001, India.,Department of Zoology, Goa University, Taleigao Plateau, Goa 403206, India
| | - Manish Kumar
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Sreelakshmi K Sreenivasamurthy
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Gourav Dey
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Raja Sekhar Nirujogi
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Centre for Bioinformatics, Pondicherry University, Puducherry 605014, India
| | - Sneha M Pinto
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore 575018, India
| | - Anil K Madugundu
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Centre for Bioinformatics, Pondicherry University, Puducherry 605014, India
| | - Arun H Patil
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Jayshree Advani
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Srikanth S Manda
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Centre for Bioinformatics, Pondicherry University, Puducherry 605014, India
| | - Manoj Kumar Gupta
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Sutopa B Dwivedi
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India
| | - Dhanashree S Kelkar
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India
| | - Brantley Hall
- Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Xiaofang Jiang
- Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Ashley Peery
- Department of Entomology, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Pavithra Rajagopalan
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Soujanya D Yelamanchi
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Hitendra S Solanki
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Remya Raja
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India
| | - Gajanan J Sathe
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Sandip Chavan
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Renu Verma
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Krishna M Patel
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India
| | - Ankit P Jain
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Nazia Syed
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Department of Biochemistry and Molecular Biology, Pondicherry University, Puducherry 605014, India
| | - Keshava K Datta
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Aafaque Ahmed Khan
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,School of Biotechnology, KIIT University, Bhubaneswar, Odisha 751024, India
| | - Manjunath Dammalli
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Department of Biotechnology, Siddaganga Institute of Technology, Tumkur, Karnataka 572103, India
| | - Savita Jayaram
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Manipal University, Madhav Nagar, Manipal, Karnataka 576104, India
| | - Aneesha Radhakrishnan
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,Department of Biochemistry and Molecular Biology, Pondicherry University, Puducherry 605014, India
| | - Christopher J Mitchell
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
| | - Chan-Hyun Na
- Department of Neurology, Johns Hopkins University, Baltimore, Maryland 21205, USA
| | - Nirbhay Kumar
- Department of Tropical Medicine, Tulane University School of Public Health and Tropical Medicine, New Orleans, Louisiana 70112, USA
| | - Photini Sinnis
- Malaria Research Institute, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA
| | - Igor V Sharakhov
- Department of Entomology, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Charles Wang
- Center for Genomics and Department of Basic Sciences, School of Medicine, Loma Linda University, Loma Linda, California 92350, USA
| | - Harsha Gowda
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,YU-IOB Center for Systems Biology and Molecular Medicine, Yenepoya University, Mangalore 575018, India
| | - Zhijian Tu
- Department of Biochemistry, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, USA
| | - Ashwani Kumar
- National Institute of Malaria Research, Field Station, Goa 403001, India
| | - Akhilesh Pandey
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka 560066, India.,McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.,Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA
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21
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Olexiouk V, Menschaert G. Identification of Small Novel Coding Sequences, a Proteogenomics Endeavor. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2016; 926:49-64. [PMID: 27686805 DOI: 10.1007/978-3-319-42316-6_4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The identification of small proteins and peptides has consistently proven to be challenging. However, technological advances as well as multi-omics endeavors facilitate the identification of novel small coding sequences, leading to new insights. Specifically, the application of next generation sequencing technologies (NGS), providing accurate and sample specific transcriptome / translatome information, into the proteomics field led to more comprehensive results and new discoveries. This book chapter focuses on the inclusion of RNA-Seq and RIBO-Seq also known as ribosome profiling, an RNA-Seq based technique sequencing the +/- 30 bp long fragments captured by translating ribosomes. We emphasize the identification of micropeptides and neo-antigens, two distinct classes of small translation products, triggering our current understanding of biology. RNA-Seq is capable of capturing sample specific genomic variations, enabling focused neo-antigen identification. RIBO-Seq can identify translation events in small open reading frames which are considered to be non-coding, leading to the discovery of micropeptides. The identification of small translation products requires the integration of multi-omics data, stressing the importance of proteogenomics in this novel research area.
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Affiliation(s)
- Volodimir Olexiouk
- Lab of Bioinformatics and Computational Genomics (BioBix), Faculty of Bioscience Engineering, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, Building A, Ghent, 9000, Belgium.
| | - Gerben Menschaert
- Lab of Bioinformatics and Computational Genomics (BioBix), Faculty of Bioscience Engineering, Department of Mathematical Modelling, Statistics and Bioinformatics, Ghent University, Coupure Links 653, Building A, Ghent, 9000, Belgium
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