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Walzer M, Jeong K, Tabb DL, Vizcaíno JA. TopDownApp: An open and modular platform for analysis and visualisation of top-down proteomics data. Proteomics 2024; 24:e2200403. [PMID: 37787899 DOI: 10.1002/pmic.202200403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 09/13/2023] [Accepted: 09/13/2023] [Indexed: 10/04/2023]
Abstract
Although Top-down (TD) proteomics techniques, aimed at the analysis of intact proteins and proteoforms, are becoming increasingly popular, efforts are needed at different levels to generalise their adoption. In this context, there are numerous improvements that are possible in the area of open science practices, including a greater application of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. These include, for example, increased data sharing practices and readily available open data standards. Additionally, the field would benefit from the development of open data analysis workflows that can enable data reuse of public datasets, something that is increasingly common in other proteomics fields.
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Affiliation(s)
- Mathias Walzer
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
| | - Kyowon Jeong
- Applied Bioinformatics, Computer Science Department, University of Tübingen, Tübingen, Germany
| | - David L Tabb
- Institut Pasteur, Université Paris Cité, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris, France
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
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2
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Potgieter MG, Nel AJM, Fortuin S, Garnett S, Wendoh JM, Tabb DL, Mulder NJ, Blackburn JM. MetaNovo: An open-source pipeline for probabilistic peptide discovery in complex metaproteomic datasets. PLoS Comput Biol 2023; 19:e1011163. [PMID: 37327214 DOI: 10.1371/journal.pcbi.1011163] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 05/08/2023] [Indexed: 06/18/2023] Open
Abstract
BACKGROUND Microbiome research is providing important new insights into the metabolic interactions of complex microbial ecosystems involved in fields as diverse as the pathogenesis of human diseases, agriculture and climate change. Poor correlations typically observed between RNA and protein expression datasets make it hard to accurately infer microbial protein synthesis from metagenomic data. Additionally, mass spectrometry-based metaproteomic analyses typically rely on focused search sequence databases based on prior knowledge for protein identification that may not represent all the proteins present in a set of samples. Metagenomic 16S rRNA sequencing only targets the bacterial component, while whole genome sequencing is at best an indirect measure of expressed proteomes. Here we describe a novel approach, MetaNovo, that combines existing open-source software tools to perform scalable de novo sequence tag matching with a novel algorithm for probabilistic optimization of the entire UniProt knowledgebase to create tailored sequence databases for target-decoy searches directly at the proteome level, enabling metaproteomic analyses without prior expectation of sample composition or metagenomic data generation and compatible with standard downstream analysis pipelines. RESULTS We compared MetaNovo to published results from the MetaPro-IQ pipeline on 8 human mucosal-luminal interface samples, with comparable numbers of peptide and protein identifications, many shared peptide sequences and a similar bacterial taxonomic distribution compared to that found using a matched metagenome sequence database-but simultaneously identified many more non-bacterial peptides than the previous approaches. MetaNovo was also benchmarked on samples of known microbial composition against matched metagenomic and whole genomic sequence database workflows, yielding many more MS/MS identifications for the expected taxa, with improved taxonomic representation, while also highlighting previously described genome sequencing quality concerns for one of the organisms, and identifying an experimental sample contaminant without prior expectation. CONCLUSIONS By estimating taxonomic and peptide level information directly on microbiome samples from tandem mass spectrometry data, MetaNovo enables the simultaneous identification of peptides from all domains of life in metaproteome samples, bypassing the need for curated sequence databases to search. We show that the MetaNovo approach to mass spectrometry metaproteomics is more accurate than current gold standard approaches of tailored or matched genomic sequence database searches, can identify sample contaminants without prior expectation and yields insights into previously unidentified metaproteomic signals, building on the potential for complex mass spectrometry metaproteomic data to speak for itself.
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Affiliation(s)
- Matthys G Potgieter
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Andrew J M Nel
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Suereta Fortuin
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Shaun Garnett
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
| | - Jerome M Wendoh
- Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - David L Tabb
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences; African Microbiome Institute; South African Tuberculosis Bioinformatics Initiative; Stellenbosch University, Cape Town, South Africa
| | - Nicola J Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
- Institute of Infectious Disease & Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Jonathan M Blackburn
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, South Africa
- Institute of Infectious Disease & Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
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3
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Tabb DL, Jeong K, Druart K, Gant MS, Brown KA, Nicora C, Zhou M, Couvillion S, Nakayasu E, Williams JE, Peterson HK, McGuire MK, McGuire MA, Metz TO, Chamot-Rooke J. Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection. J Proteome Res 2023. [PMID: 37235544 DOI: 10.1021/acs.jproteome.2c00673] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrap-class and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations. Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.
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Affiliation(s)
- David L Tabb
- Université Paris Cité, Institut Pasteur, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris 75015, France
| | - Kyowon Jeong
- Applied Bioinformatics, Computer Science Department, University of Tübingen, Tübingen 72076, Germany
| | - Karen Druart
- Université Paris Cité, Institut Pasteur, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris 75015, France
| | - Megan S Gant
- Université Paris Cité, Institut Pasteur, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris 75015, France
| | - Kyle A Brown
- School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin 53705, United States
| | - Carrie Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Mowei Zhou
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Sneha Couvillion
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ernesto Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Janet E Williams
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - Haley K Peterson
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - Michelle K McGuire
- Margaret Ritchie School of Family and Consumer Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - Mark A McGuire
- Department of Animal, Veterinary, and Food Sciences, University of Idaho, Moscow, Idaho 83844, United States
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Julia Chamot-Rooke
- Université Paris Cité, Institut Pasteur, CNRS UAR 2024, Mass Spectrometry for Biology Unit, Paris 75015, France
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4
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Deutsch EW, Vizcaíno JA, Jones AR, Binz PA, Lam H, Klein J, Bittremieux W, Perez-Riverol Y, Tabb DL, Walzer M, Ricard-Blum S, Hermjakob H, Neumann S, Mak TD, Kawano S, Mendoza L, Van Den Bossche T, Gabriels R, Bandeira N, Carver J, Pullman B, Sun Z, Hoffmann N, Shofstahl J, Zhu Y, Licata L, Quaglia F, Tosatto SCE, Orchard SE. Proteomics Standards Initiative at Twenty Years: Current Activities and Future Work. J Proteome Res 2023; 22:287-301. [PMID: 36626722 PMCID: PMC9903322 DOI: 10.1021/acs.jproteome.2c00637] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Indexed: 01/11/2023]
Abstract
The Human Proteome Organization (HUPO) Proteomics Standards Initiative (PSI) has been successfully developing guidelines, data formats, and controlled vocabularies (CVs) for the proteomics community and other fields supported by mass spectrometry since its inception 20 years ago. Here we describe the general operation of the PSI, including its leadership, working groups, yearly workshops, and the document process by which proposals are thoroughly and publicly reviewed in order to be ratified as PSI standards. We briefly describe the current state of the many existing PSI standards, some of which remain the same as when originally developed, some of which have undergone subsequent revisions, and some of which have become obsolete. Then the set of proposals currently being developed are described, with an open call to the community for participation in the forging of the next generation of standards. Finally, we describe some synergies and collaborations with other organizations and look to the future in how the PSI will continue to promote the open sharing of data and thus accelerate the progress of the field of proteomics.
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Affiliation(s)
- Eric W. Deutsch
- Institute
for Systems Biology, Seattle, Washington 98109, United States
| | - Juan Antonio Vizcaíno
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Andrew R. Jones
- Institute
of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Pierre-Alain Binz
- Clinical
Chemistry Service, Lausanne University Hospital, 1011 976 Lausanne, Switzerland
| | - Henry Lam
- Department
of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong 999077, P. R. China.
| | - Joshua Klein
- Program for
Bioinformatics, Boston University, Boston, Massachusetts 02215, United States
| | - Wout Bittremieux
- Skaggs
School
of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
- Department
of Computer Science, University of Antwerp, 2020 Antwerpen, Belgium
| | - Yasset Perez-Riverol
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - David L. Tabb
- SA MRC
Centre for TB Research, DST/NRF Centre of Excellence for Biomedical
TB Research, Division of Molecular Biology and Human Genetics, Faculty
of Medicine and Health Sciences, Stellenbosch
University, Cape Town 7602, South Africa
| | - Mathias Walzer
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Sylvie Ricard-Blum
- Univ.
Lyon, Université Lyon 1, ICBMS, UMR 5246, 69622 Villeurbanne, France
| | - Henning Hermjakob
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Steffen Neumann
- Bioinformatics
and Scientific Data, Leibniz Institute of
Plant Biochemistry, 06120 Halle, Germany
- German
Centre for Integrative Biodiversity Research (iDiv), 04103 Halle-Jena-Leipzig, Germany
| | - Tytus D. Mak
- Mass Spectrometry
Data Center, National Institute of Standards
and Technology, 100 Bureau Drive, Gaithersburg, Maryland 20899, United
States
| | - Shin Kawano
- Database
Center for Life Science, Joint Support Center for Data Science Research, Research Organization of Information and Systems, Chiba 277-0871, Japan
- Faculty
of Contemporary Society, Toyama University
of International Studies, Toyama 930-1292, Japan
- School
of Frontier Engineering, Kitasato University, Sagamihara 252-0373, Japan
| | - Luis Mendoza
- Institute
for Systems Biology, Seattle, Washington 98109, United States
| | - Tim Van Den Bossche
- VIB-UGent
Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department
of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9052 Ghent, Belgium
| | - Ralf Gabriels
- VIB-UGent
Center for Medical Biotechnology, VIB, 9052 Ghent, Belgium
- Department
of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9052 Ghent, Belgium
| | - Nuno Bandeira
- Skaggs
School
of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California 92093, United States
- Center
for Computational Mass Spectrometry, Department of Computer Science
and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego 92093-0404, United States
| | - Jeremy Carver
- Center
for Computational Mass Spectrometry, Department of Computer Science
and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego 92093-0404, United States
| | - Benjamin Pullman
- Center
for Computational Mass Spectrometry, Department of Computer Science
and Engineering, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, San Diego 92093-0404, United States
| | - Zhi Sun
- Institute
for Systems Biology, Seattle, Washington 98109, United States
| | - Nils Hoffmann
- Institute
for Bio- and Geosciences (IBG-5), Forschungszentrum
Jülich GmbH, 52428 Jülich, Germany
| | - Jim Shofstahl
- Thermo
Fisher Scientific, 355 River Oaks Parkway, San Jose, California 95134, United States
| | - Yunping Zhu
- National
Center for Protein Sciences (Beijing), Beijing
Institute of Lifeomics, #38, Life Science Park, Changping District, Beijing 102206, China
| | - Luana Licata
- Fondazione
Human Technopole, 20157 Milan, Italy
- Department
of Biology, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Federica Quaglia
- Institute
of Biomembranes, Bioenergetics and Molecular Biotechnologies, National Research Council (CNR-IBIOM), 70126 Bari, Italy
- Department
of Biomedical Sciences, University of Padova, 35131 Padova, Italy
| | | | - Sandra E. Orchard
- European
Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
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5
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Ali AEE, Husselmann LH, Tabb DL, Ludidi N. Comparative Proteomics Analysis between Maize and Sorghum Uncovers Important Proteins and Metabolic Pathways Mediating Drought Tolerance. Life (Basel) 2023; 13:life13010170. [PMID: 36676117 PMCID: PMC9863747 DOI: 10.3390/life13010170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 12/31/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
Drought severely affects crop yield and yield stability. Maize and sorghum are major crops in Africa and globally, and both are negatively impacted by drought. However, sorghum has a better ability to withstand drought than maize. Consequently, this study identifies differences between maize and sorghum grown in water deficit conditions, and identifies proteins associated with drought tolerance in these plant species. Leaf relative water content and proline content were measured, and label-free proteomics analysis was carried out to identify differences in protein expression in the two species in response to water deficit. Water deficit enhanced the proline accumulation in sorghum roots to a higher degree than in maize, and this higher accumulation was associated with enhanced water retention in sorghum. Proteomic analyses identified proteins with differing expression patterns between the two species, revealing key metabolic pathways that explain the better drought tolerance of sorghum than maize. These proteins include phenylalanine/tyrosine ammonia-lyases, indole-3-acetaldehyde oxidase, sucrose synthase and phenol/catechol oxidase. This study highlights the importance of phenylpropanoids, sucrose, melanin-related metabolites and indole acetic acid (auxin) as determinants of the differences in drought stress tolerance between maize and sorghum. The selection of maize and sorghum genotypes with enhanced expression of the genes encoding these differentially expressed proteins, or genetically engineering maize and sorghum to increase the expression of such genes, can be used as strategies for the production of maize and sorghum varieties with improved drought tolerance.
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Affiliation(s)
- Ali Elnaeim Elbasheir Ali
- Department of Biotechnology, University of the Western Cape, Robert Sobukwe Road, Bellville 7530, South Africa
| | - Lizex Hollenbach Husselmann
- Department of Biotechnology, University of the Western Cape, Robert Sobukwe Road, Bellville 7530, South Africa
| | - David L. Tabb
- Department of Biotechnology, University of the Western Cape, Robert Sobukwe Road, Bellville 7530, South Africa
- Centre for Bioinformatics and Computational Biology, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7500, South Africa
| | - Ndiko Ludidi
- Department of Biotechnology, University of the Western Cape, Robert Sobukwe Road, Bellville 7530, South Africa
- DSI-NRF Centre of Excellence in Food Security, University of the Western Cape, Robert Sobukwe Road, Bellville 7530, South Africa
- Correspondence:
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6
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Fischer F, Vorontsov E, Turlin E, Malosse C, Garcia C, Tabb DL, Chamot-Rooke J, Percudani R, Vinella D, De Reuse H. Expansion of nickel binding- and histidine-rich proteins during gastric adaptation of Helicobacter species. Metallomics 2022; 14:6674772. [PMID: 36002005 DOI: 10.1093/mtomcs/mfac060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 08/17/2022] [Indexed: 11/14/2022]
Abstract
Acquisition and homeostasis of essential metals during host colonization by bacterial pathogens rely on metal uptake, trafficking and storage proteins. How these factors have evolved within bacterial pathogens is poorly defined. Urease, a nickel enzyme, is essential for Helicobacter pylori to colonize the acidic stomach. Our previous data suggest that acquisition of nickel transporters and a Histidine-rich protein (HRP) involved in nickel storage in H. pylori and gastric Helicobacter spp. have been essential evolutionary events for gastric colonization. Using bioinformatics, proteomics and phylogenetics, we extended this analysis to determine how evolution has framed the repertoire of HRPs among 39 Epsilonproteobacteria; 18 gastric and 11 non-gastric enterohepatic (EH) Helicobacter spp., as well as 10 other Epsilonproteobacteria. We identified a total of 213 HRPs distributed in 22 protein families named orthologous groups (OG) with His-rich domains, including 15 newly described OGs. Gastric Helicobacter spp. are enriched in HRPs (7.7 ± 1.9 HRPs/strain) as compared to EH Helicobacter spp. (1.9 ± 1.0 HRPs/strain) with a particular prevalence of HRPs with C-terminal Histidine-rich domains in gastric species. The expression and nickel-binding capacity of several HRPs was validated in five gastric Helicobacter spp. We established the evolutionary history of new HRP families, such as the periplasmic HP0721-like proteins and the HugZ-type heme-oxygenases. The expansion of Histidine-rich extensions in gastric Helicobacter spp. proteins is intriguing but can tentatively be associated with the presence of the urease nickel-enzyme. We conclude that this HRP expansion is associated with unique properties of organisms that rely on large intracellular nickel amounts for their survival.
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Affiliation(s)
- Frédéric Fischer
- Institut Pasteur, Département de Microbiologie, Unité Pathogenèse de Helicobacter, UMR CNRS 6047, 28 rue du Dr Roux 75724 PARIS Cedex 15 FRANCE.,Génétique Moléculaire, Génomique, Microbiologie, UMR 7156, Université de Strasbourg, Institut de Physiologie et Chimie Biologiques, 4 allée Konrad Roentgen, 67084 Strasbourg, FRANCE
| | - Egor Vorontsov
- Institut Pasteur, Department of Structural Biology and Chemistry, Université Paris Cité, CNRS UAR 2024, Mass Spectrometry for Biology Unit, 28 rue du Dr Roux 75724 PARIS Cedex 15 FRANCE.,Proteomics Core Facility, Sahlgrenska Academy, University of Gothenburg, Box 413, 40530 Gothenburg, SWEDEN
| | - Evelyne Turlin
- Institut Pasteur, Département de Microbiologie, Unité Pathogenèse de Helicobacter, UMR CNRS 6047, 28 rue du Dr Roux 75724 PARIS Cedex 15 FRANCE
| | - Christian Malosse
- Institut Pasteur, Department of Structural Biology and Chemistry, Université Paris Cité, CNRS UAR 2024, Mass Spectrometry for Biology Unit, 28 rue du Dr Roux 75724 PARIS Cedex 15 FRANCE
| | - Camille Garcia
- Institut Pasteur, Department of Structural Biology and Chemistry, Université Paris Cité, CNRS UAR 2024, Mass Spectrometry for Biology Unit, 28 rue du Dr Roux 75724 PARIS Cedex 15 FRANCE
| | - David L Tabb
- Institut Pasteur, Department of Structural Biology and Chemistry, Université Paris Cité, CNRS UAR 2024, Mass Spectrometry for Biology Unit, 28 rue du Dr Roux 75724 PARIS Cedex 15 FRANCE
| | - Julia Chamot-Rooke
- Institut Pasteur, Department of Structural Biology and Chemistry, Université Paris Cité, CNRS UAR 2024, Mass Spectrometry for Biology Unit, 28 rue du Dr Roux 75724 PARIS Cedex 15 FRANCE
| | - Riccardo Percudani
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124, Parma, ITALY
| | - Daniel Vinella
- Institut Pasteur, Département de Microbiologie, Unité Pathogenèse de Helicobacter, UMR CNRS 6047, 28 rue du Dr Roux 75724 PARIS Cedex 15 FRANCE
| | - Hilde De Reuse
- Institut Pasteur, Département de Microbiologie, Unité Pathogenèse de Helicobacter, UMR CNRS 6047, 28 rue du Dr Roux 75724 PARIS Cedex 15 FRANCE
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7
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Alisoltani A, Manhanzva MT, Potgieter M, Balle C, Bell L, Ross E, Iranzadeh A, du Plessis M, Radzey N, McDonald Z, Calder B, Allali I, Mulder N, Dabee S, Barnabas S, Gamieldien H, Godzik A, Blackburn JM, Tabb DL, Bekker LG, Jaspan HB, Passmore JAS, Masson L. Correction to: Microbial function and genital inflammation in young South African women at high risk of HIV infection. Microbiome 2022; 10:42. [PMID: 35264249 PMCID: PMC8905787 DOI: 10.1186/s40168-022-01245-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Affiliation(s)
- Arghavan Alisoltani
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
- Division of Biomedical Sciences, University of California Riverside School of Medicine, Riverside, CA, 92521, USA
| | - Monalisa T Manhanzva
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
| | - Matthys Potgieter
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
| | - Christina Balle
- Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
| | - Liam Bell
- Centre for Proteomic and Genomic Research, Cape Town, 7925, South Africa
| | - Elizabeth Ross
- Centre for Proteomic and Genomic Research, Cape Town, 7925, South Africa
| | - Arash Iranzadeh
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
| | | | - Nina Radzey
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
| | - Zac McDonald
- Centre for Proteomic and Genomic Research, Cape Town, 7925, South Africa
| | - Bridget Calder
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
| | - Imane Allali
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
- Laboratory of Human Pathologies Biology, Department of Biology and Genomic Center of Human Pathologies, Mohammed V University, Rabat, Morocco
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa
- Centre for Infectious Diseases Research (CIDRI) in Africa Wellcome Trust Centre, University of Cape Town, Cape Town, 7925, South Africa
| | - Smritee Dabee
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
- Seattle Children's Research Institute, University of Washington, Seattle, WA, 98101, USA
| | - Shaun Barnabas
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
| | - Hoyam Gamieldien
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
| | - Adam Godzik
- Division of Biomedical Sciences, University of California Riverside School of Medicine, Riverside, CA, 92521, USA
| | - Jonathan M Blackburn
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa
| | - David L Tabb
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa
- Bioinformatics Unit, South African Tuberculosis Bioinformatics Initiative, Stellenbosch University, Stellenbosch, 7602, South Africa
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, Stellenbosch University, Stellenbosch, 7602, South Africa
| | - Linda-Gail Bekker
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa
- Desmond Tutu HIV Centre, University of Cape Town, Cape Town, 7925, South Africa
| | - Heather B Jaspan
- Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa
- Seattle Children's Research Institute, University of Washington, Seattle, WA, 98101, USA
| | - Jo-Ann S Passmore
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa
- Centre for the AIDS Programme of Research in South Africa, Durban, 4013, South Africa
- National Health Laboratory Service, Cape Town, 7925, South Africa
| | - Lindi Masson
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa.
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa.
- Centre for the AIDS Programme of Research in South Africa, Durban, 4013, South Africa.
- Disease Elimination Program, Life Sciences Discipline, Burnet Institute, 85 Commercial Road, Melbourne, Victoria, 3004, Australia.
- Central Clinical School, Monash University, Melbourne, 3004, Australia.
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8
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Premsagar P, Aldous C, Esterhuizen TM, Gomes BJ, Gaskell JW, Tabb DL. Comparing conventional statistical models and machine learning in a small cohort of South African cardiac patients. Informatics in Medicine Unlocked 2022. [DOI: 10.1016/j.imu.2022.101103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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9
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Klein A, Husselmann LHH, Williams A, Bell L, Cooper B, Ragar B, Tabb DL. Proteomic Identification and Meta-Analysis in Salvia hispanica RNA-Seq de novo Assemblies. Plants (Basel) 2021; 10:765. [PMID: 33919777 PMCID: PMC8070742 DOI: 10.3390/plants10040765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 03/26/2021] [Accepted: 03/28/2021] [Indexed: 11/24/2022]
Abstract
While proteomics has demonstrated its value for model organisms and for organisms with mature genome sequence annotations, proteomics has been of less value in nonmodel organisms that are unaccompanied by genome sequence annotations. This project sought to determine the value of RNA-Seq experiments as a basis for establishing a set of protein sequences to represent a nonmodel organism, in this case, the pseudocereal chia. Assembling four publicly available chia RNA-Seq datasets produced transcript sequence sets with a high BUSCO completeness, though the number of transcript sequences and Trinity "genes" varied considerably among them. After six-frame translation, ProteinOrtho detected substantial numbers of orthologs among other species within the taxonomic order Lamiales. These protein sequence databases demonstrated a good identification efficiency for three different LC-MS/MS proteomics experiments, though a seed proteome showed considerable variability in the identification of peptides based on seed protein sequence inclusion. If a proteomics experiment emphasizes a particular tissue, an RNA-Seq experiment incorporating that same tissue is more likely to support a database search identification of that proteome.
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Affiliation(s)
- Ashwil Klein
- Department of Biotechnology, University of the Western Cape, Bellville 7535, South Africa; (A.K.); (L.H.H.H.); (A.W.)
| | - Lizex H. H. Husselmann
- Department of Biotechnology, University of the Western Cape, Bellville 7535, South Africa; (A.K.); (L.H.H.H.); (A.W.)
| | - Achmat Williams
- Department of Biotechnology, University of the Western Cape, Bellville 7535, South Africa; (A.K.); (L.H.H.H.); (A.W.)
| | - Liam Bell
- Centre for Proteomic and Genomic Research, Cape Town 7925, South Africa;
| | - Bret Cooper
- USDA Agricultural Research Service, Beltsville, MD 20705, USA;
| | - Brent Ragar
- Departments of Internal Medicine and Pediatrics, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02150, USA;
| | - David L. Tabb
- Department of Biotechnology, University of the Western Cape, Bellville 7535, South Africa; (A.K.); (L.H.H.H.); (A.W.)
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town 7500, South Africa
- Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch 7602, South Africa
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10
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Boodhoo K, Vlok M, Tabb DL, Myburgh KH, van de Vyver M. Dysregulated healing responses in diabetic wounds occur in the early stages postinjury. J Mol Endocrinol 2021; 66:141-155. [PMID: 33350981 DOI: 10.1530/jme-20-0256] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 12/16/2020] [Indexed: 11/08/2022]
Abstract
Chronic wounds are a serious and debilitating complication of diabetes. A better understanding of the dysregulated healing responses following injury will provide insight into the optimal time frame for therapeutic intervention. In this study, a direct comparison was done between the healing dynamics and the proteome of acute and obese diabetic wounds on days 2 and 7 following injury. Full thickness excisional wounds were induced on obese diabetic (B6.Cg-lepob/J, ob/ob, n = 14) (blood glucose 423.25 ± 127.92 mg/dL) and WT control (C57BL/6J, n = 14) (blood glucose 186.67 ± 24.5 mg/dL) mice. Histological analysis showed no signs of healing in obese DM wounds whereas complete wound closure/re-epithelisation, the formation of granulation tissue and signs of re-vascularisation, was evident in acute wounds on day 7. In obese DM wounds, substance P deficiency and increased MMP-9 activity on day 2 coincided with increased cytokine/chemokine levels within wound fluid. LC-MS/MS identified 906 proteins, of which 23 (Actn3, Itga6, Epb41, Sncg, Nefm, Rsp18, Rsp19, Rpl22, Macroh2a1, Rpn1, Ppib, Snrnp70, Ddx5, Eif3g, Tpt1, FABP5, Cavin1, Stfa1, Stfa3, Cycs, Tkt, Mb, Chmp2a) were differentially expressed in wounded tissue on day 2 (P < 0.05; more than two-fold) and 6 (Cfd, Ptms, Hp, Hmga1, Cbx3, Syap1) (P < 0.05; more than two-fold) on day 7. A large number of dysregulated proteins on day 2 was associated with an inability to progress into the proliferative stage of healing and suggest that early intervention might be pivotal for effective healing outcomes. The proteomic approach highlighted the complexity of obese DM wounds in which the dysregulation involves multiple regulatory pathways and biological processes.
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Affiliation(s)
- Kiara Boodhoo
- Department of Medicine, Faculty of Medicine & Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Mare Vlok
- Central Analytical Facility, Proteomics Unit, Stellenbosch University, Cape Town, South Africa
| | - David L Tabb
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
- Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, South Africa
- Bioinformatics Unit, South African Tuberculosis Bioinformatics Initiative, Stellenbosch University, Cape Town, South Africa
| | - Kathryn H Myburgh
- Department of Physiological Sciences, Faculty of Science, Stellenbosch University, Stellenbosch, South Africa
| | - Mari van de Vyver
- Department of Medicine, Faculty of Medicine & Health Sciences, Stellenbosch University, Cape Town, South Africa
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11
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Alisoltani A, Manhanzva MT, Potgieter M, Balle C, Bell L, Ross E, Iranzadeh A, du Plessis M, Radzey N, McDonald Z, Calder B, Allali I, Mulder N, Dabee S, Barnabas S, Gamieldien H, Godzik A, Blackburn JM, Tabb DL, Bekker LG, Jaspan HB, Passmore JAS, Masson L. Microbial function and genital inflammation in young South African women at high risk of HIV infection. Microbiome 2020; 8:165. [PMID: 33220709 PMCID: PMC7679981 DOI: 10.1186/s40168-020-00932-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Accepted: 10/06/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Female genital tract (FGT) inflammation is an important risk factor for HIV acquisition. The FGT microbiome is closely associated with inflammatory profile; however, the relative importance of microbial activities has not been established. Since proteins are key elements representing actual microbial functions, this study utilized metaproteomics to evaluate the relationship between FGT microbial function and inflammation in 113 young and adolescent South African women at high risk of HIV infection. Women were grouped as having low, medium, or high FGT inflammation by K-means clustering according to pro-inflammatory cytokine concentrations. RESULTS A total of 3186 microbial and human proteins were identified in lateral vaginal wall swabs using liquid chromatography-tandem mass spectrometry, while 94 microbial taxa were included in the taxonomic analysis. Both metaproteomics and 16S rRNA gene sequencing analyses showed increased non-optimal bacteria and decreased lactobacilli in women with FGT inflammatory profiles. However, differences in the predicted relative abundance of most bacteria were observed between 16S rRNA gene sequencing and metaproteomics analyses. Bacterial protein functional annotations (gene ontology) predicted inflammatory cytokine profiles more accurately than bacterial relative abundance determined by 16S rRNA gene sequence analysis, as well as functional predictions based on 16S rRNA gene sequence data (p < 0.0001). The majority of microbial biological processes were underrepresented in women with high inflammation compared to those with low inflammation, including a Lactobacillus-associated signature of reduced cell wall organization and peptidoglycan biosynthesis. This signature remained associated with high FGT inflammation in a subset of 74 women 9 weeks later, was upheld after adjusting for Lactobacillus relative abundance, and was associated with in vitro inflammatory cytokine responses to Lactobacillus isolates from the same women. Reduced cell wall organization and peptidoglycan biosynthesis were also associated with high FGT inflammation in an independent sample of ten women. CONCLUSIONS Both the presence of specific microbial taxa in the FGT and their properties and activities are critical determinants of FGT inflammation. Our findings support those of previous studies suggesting that peptidoglycan is directly immunosuppressive, and identify a possible avenue for biotherapeutic development to reduce inflammation in the FGT. To facilitate further investigations of microbial activities, we have developed the FGT-DB application that is available at http://fgtdb.org/ . Video Abstract.
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Affiliation(s)
- Arghavan Alisoltani
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
- Division of Biomedical Sciences, University of California Riverside School of Medicine, Riverside, CA, 92521, USA
| | - Monalisa T Manhanzva
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
| | - Matthys Potgieter
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
| | - Christina Balle
- Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
| | - Liam Bell
- Centre for Proteomic and Genomic Research, Cape Town, 7925, South Africa
| | - Elizabeth Ross
- Centre for Proteomic and Genomic Research, Cape Town, 7925, South Africa
| | - Arash Iranzadeh
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
| | | | - Nina Radzey
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
| | - Zac McDonald
- Centre for Proteomic and Genomic Research, Cape Town, 7925, South Africa
| | - Bridget Calder
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
| | - Imane Allali
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
- Laboratory of Human Pathologies Biology, Department of Biology and Genomic Center of Human Pathologies, Mohammed V University, Rabat, Morocco
| | - Nicola Mulder
- Computational Biology Division, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa
- Centre for Infectious Diseases Research (CIDRI) in Africa Wellcome Trust Centre, University of Cape Town, Cape Town, 7925, South Africa
| | - Smritee Dabee
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
- Seattle Children's Research Institute, University of Washington, Seattle, WA, 98101, USA
| | - Shaun Barnabas
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
| | - Hoyam Gamieldien
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
| | - Adam Godzik
- Division of Biomedical Sciences, University of California Riverside School of Medicine, Riverside, CA, 92521, USA
| | - Jonathan M Blackburn
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, University of Cape Town, Cape Town, 7925, South Africa
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa
| | - David L Tabb
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa
- Bioinformatics Unit, South African Tuberculosis Bioinformatics Initiative, Stellenbosch University, Stellenbosch, 7602, South Africa
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, Stellenbosch University, Stellenbosch, 7602, South Africa
| | - Linda-Gail Bekker
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa
- Desmond Tutu HIV Centre, University of Cape Town, Cape Town, 7925, South Africa
| | - Heather B Jaspan
- Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa
- Seattle Children's Research Institute, University of Washington, Seattle, WA, 98101, USA
| | - Jo-Ann S Passmore
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa
- Centre for the AIDS Programme of Research in South Africa, Durban, 4013, South Africa
- National Health Laboratory Service, Cape Town, 7925, South Africa
| | - Lindi Masson
- Division of Medical Virology, Department of Pathology, University of Cape Town, Cape Town, 7925, South Africa.
- Institute of Infectious Disease and Molecular Medicine (IDM), University of Cape Town, Cape Town, 7925, South Africa.
- Centre for the AIDS Programme of Research in South Africa, Durban, 4013, South Africa.
- Disease Elimination Program, Life Sciences Discipline, Burnet Institute, 85 Commercial Road, Melbourne, Victoria, 3004, Australia.
- Central Clinical School, Monash University, Melbourne, 3004, Australia.
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12
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Kriek M, Monyai K, Magcwebeba TU, Du Plessis N, Stoychev SH, Tabb DL. Interrogating Fractionation and Other Sources of Variability in Shotgun Proteomes Using Quality Metrics. Proteomics 2020; 20:e1900382. [PMID: 32415754 DOI: 10.1002/pmic.201900382] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2020] [Revised: 04/04/2020] [Indexed: 12/14/2022]
Abstract
The increasing amount of publicly available proteomics data creates opportunities for data scientists to investigate quality metrics in novel ways. QuaMeter IDFree is used to generate quality metrics from 665 RAW files and 97 WIFF files representing publicly available "shotgun" mass spectrometry datasets. These experiments are selected to represent Mycobacterium tuberculosis lysates, mouse MDSCs, and exosomes derived from human cell lines. Machine learning techniques are demonstrated to detect outliers within experiments and it is shown that quality metrics may be used to distinguish sources of variability among these experiments. In particular, the findings demonstrate that according to nested ANOVA performed on an SDS-PAGE shotgun principal component analysis, runs of fractions from the same gel regions cluster together rather than technical replicates, close temporal proximity, or even biological samples. This indicates that the individual fraction may have had a higher impact on the quality metrics than other factors. In addition, sample type, instrument type, mass analyzer, fragmentation technique, and digestion enzyme are identified as sources of variability. From a quality control perspective, the importance of study design and in particular, the run order, is illustrated in seeking ways to limit the impact of technical variability.
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Affiliation(s)
- Marina Kriek
- SATBBI (South African Tuberculosis Bioinformatics Initiative), Centre for Bioinformatics and Computational Biology, Stellenbosch University, Cape Town, 7505, South Africa.,DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, Cape Town, 7505, South Africa.,South African Medical Research Council Centre for Tuberculosis Research, Cape Town, 7505, South Africa.,Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7505, South Africa
| | - Koena Monyai
- Council for Scientific and Industrial Research, Pretoria, 0001, South Africa
| | - Tandeka U Magcwebeba
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, Cape Town, 7505, South Africa.,South African Medical Research Council Centre for Tuberculosis Research, Cape Town, 7505, South Africa.,Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7505, South Africa
| | - Nelita Du Plessis
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, Cape Town, 7505, South Africa.,South African Medical Research Council Centre for Tuberculosis Research, Cape Town, 7505, South Africa.,Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7505, South Africa
| | - Stoyan H Stoychev
- Council for Scientific and Industrial Research, Pretoria, 0001, South Africa
| | - David L Tabb
- SATBBI (South African Tuberculosis Bioinformatics Initiative), Centre for Bioinformatics and Computational Biology, Stellenbosch University, Cape Town, 7505, South Africa.,DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, Cape Town, 7505, South Africa.,South African Medical Research Council Centre for Tuberculosis Research, Cape Town, 7505, South Africa.,Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7505, South Africa
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13
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Oluwole OG, Kuivaniemi H, Abrahams S, Haylett WL, Vorster AA, van Heerden CJ, Kenyon CP, Tabb DL, Fawale MB, Sunmonu TA, Ajose A, Olaogun MO, Rossouw AC, van Hillegondsberg LS, Carr J, Ross OA, Komolafe MA, Tromp G, Bardien S. Targeted next-generation sequencing identifies novel variants in candidate genes for Parkinson's disease in Black South African and Nigerian patients. BMC Med Genet 2020; 21:23. [PMID: 32019516 PMCID: PMC7001245 DOI: 10.1186/s12881-020-0953-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Accepted: 01/10/2020] [Indexed: 02/06/2023]
Abstract
BACKGROUND The prevalence of Parkinson's disease (PD) is increasing in sub-Saharan Africa, but little is known about the genetics of PD in these populations. Due to their unique ancestry and diversity, sub-Saharan African populations have the potential to reveal novel insights into the pathobiology of PD. In this study, we aimed to characterise the genetic variation in known and novel PD genes in a group of Black South African and Nigerian patients. METHODS We recruited 33 Black South African and 14 Nigerian PD patients, and screened them for sequence variants in 751 genes using an Ion AmpliSeq™ Neurological Research panel. We used bcftools to filter variants and annovar software for the annotation. Rare variants were prioritised using MetaLR and MetaSVM prediction scores. The effect of a variant on ATP13A2's protein structure was investigated by molecular modelling. RESULTS We identified 14,655 rare variants with a minor allele frequency ≤ 0.01, which included 2448 missense variants. Notably, no common pathogenic mutations were identified in these patients. Also, none of the known PD-associated mutations were found highlighting the need for more studies in African populations. Altogether, 54 rare variants in 42 genes were considered deleterious and were prioritized, based on MetaLR and MetaSVM scores, for follow-up studies. Protein modelling showed that the S1004R variant in ATP13A2 possibly alters the conformation of the protein. CONCLUSIONS We identified several rare variants predicted to be deleterious in sub-Saharan Africa PD patients; however, further studies are required to determine the biological effects of these variants and their possible role in PD. Studies such as these are important to elucidate the genetic aetiology of this disorder in patients of African ancestry.
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Affiliation(s)
- Oluwafemi G Oluwole
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
| | - Helena Kuivaniemi
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
| | - Shameemah Abrahams
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
| | - William L Haylett
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
- Division of Endocrinology, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Alvera A Vorster
- DNA Sequencing Unit, Central Analytical Facility, Stellenbosch University, Stellenbosch, South Africa
| | - Carel J van Heerden
- DNA Sequencing Unit, Central Analytical Facility, Stellenbosch University, Stellenbosch, South Africa
| | - Colin P Kenyon
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
- Bioinformatics Unit, South African Tuberculosis Bioinformatics Initiative, Stellenbosch University, Cape Town, South Africa
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, Stellenbosch University, Cape Town, South Africa
- South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa
| | - David L Tabb
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
- Bioinformatics Unit, South African Tuberculosis Bioinformatics Initiative, Stellenbosch University, Cape Town, South Africa
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, Stellenbosch University, Cape Town, South Africa
- South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa
- Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, South Africa
| | - Michael B Fawale
- Neurology Unit, Department of Medicine, College of Health Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Taofiki A Sunmonu
- Neurology Unit, Department of Medicine, Federal Medical Centre, Owo, Nigeria
| | - Abiodun Ajose
- Department of Chemical Pathology, College of Health Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Matthew O Olaogun
- Department of Medical Rehabilitation, College of Health Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Anastasia C Rossouw
- Division of Neurology, Department of Medicine, Faculty of Health Sciences, Walter Sisulu University, East London, South Africa
| | - Ludo S van Hillegondsberg
- Division of Neurology, Department of Medicine, Faculty of Health Sciences, Walter Sisulu University, East London, South Africa
- Division of Neurology, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Jonathan Carr
- Division of Neurology, Department of Medicine, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Owen A Ross
- Department of Neuroscience, Mayo Clinic, Jacksonville, Florida, USA
- Department of Clinical Genomics, Mayo Clinic College of Medicine, Jacksonville, Florida, USA
| | - Morenikeji A Komolafe
- Neurology Unit, Department of Medicine, College of Health Sciences, Obafemi Awolowo University, Ile-Ife, Nigeria
| | - Gerard Tromp
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa.
- Bioinformatics Unit, South African Tuberculosis Bioinformatics Initiative, Stellenbosch University, Cape Town, South Africa.
- DST-NRF Centre of Excellence for Biomedical Tuberculosis Research, Stellenbosch University, Cape Town, South Africa.
- South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Cape Town, South Africa.
- Centre for Bioinformatics and Computational Biology, Stellenbosch University, Stellenbosch, South Africa.
| | - Soraya Bardien
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa.
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14
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Nweke EE, Naicker P, Aron S, Stoychev S, Devar J, Tabb DL, Omoshoro-Jones J, Smith M, Candy G. SWATH-MS based proteomic profiling of pancreatic ductal adenocarcinoma tumours reveals the interplay between the extracellular matrix and related intracellular pathways. PLoS One 2020; 15:e0240453. [PMID: 33048956 PMCID: PMC7553299 DOI: 10.1371/journal.pone.0240453] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Accepted: 09/27/2020] [Indexed: 02/06/2023] Open
Abstract
Pancreatic cancer accounts for 2.8% of new cancer cases worldwide and is projected to become the second leading cause of cancer-related deaths by 2030. Patients of African ancestry appear to be at an increased risk for pancreatic ductal adenocarcinoma (PDAC), with more severe disease and outcomes. The purpose of this study was to map the proteomic and genomic landscape of a cohort of PDAC patients of African ancestry. Thirty tissues (15 tumours and 15 normal adjacent tissues) were obtained from consenting South African PDAC patients. Optimisation of the sample preparation method allowed for the simultaneous extraction of high-purity protein and DNA for SWATH-MS and OncoArray SNV analyses. We quantified 3402 proteins with 49 upregulated and 35 downregulated proteins at a minimum 2.1 fold change and FDR adjusted p-value (q-value) ≤ 0.01 when comparing tumour to normal adjacent tissue. Many of the upregulated proteins in the tumour samples are involved in extracellular matrix formation (ECM) and related intracellular pathways. In addition, proteins such as EMIL1, KBTB2, and ZCCHV involved in the regulation of ECM proteins were observed to be dysregulated in pancreatic tumours. Downregulation of pathways involved in oxygen and carbon dioxide transport were observed. Genotype data showed missense mutations in some upregulated proteins, such as MYPN, ESTY2 and SERPINB8. Approximately 11% of the dysregulated proteins, including ISLR, BP1, PTK7 and OLFL3, were predicted to be secretory proteins. These findings help in further elucidating the biology of PDAC and may aid in identifying future plausible markers for the disease.
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Affiliation(s)
- Ekene Emmanuel Nweke
- Department of Surgery, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- * E-mail:
| | - Previn Naicker
- Department of Biosciences, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - Shaun Aron
- Sydney Brenner Institute for Molecular Bioscience, University of the Witwatersrand, Johannesburg, South Africa
| | - Stoyan Stoychev
- Department of Biosciences, Council for Scientific and Industrial Research, Pretoria, South Africa
| | - John Devar
- Department of Surgery, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - David L. Tabb
- Bioinformatics Unit, South African Tuberculosis Bioinformatics Initiative, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Stellenbosch University, Cape Town, South Africa
| | - Jones Omoshoro-Jones
- Department of Surgery, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Martin Smith
- Department of Surgery, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Geoffrey Candy
- Department of Surgery, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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15
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Duffy FJ, Weiner J, Hansen S, Tabb DL, Suliman S, Thompson E, Maertzdorf J, Shankar S, Tromp G, Parida S, Dover D, Axthelm MK, Sutherland JS, Dockrell HM, Ottenhoff THM, Scriba TJ, Picker LJ, Walzl G, Kaufmann SHE, Zak DE. Immunometabolic Signatures Predict Risk of Progression to Active Tuberculosis and Disease Outcome. Front Immunol 2019; 10:527. [PMID: 30967866 PMCID: PMC6440524 DOI: 10.3389/fimmu.2019.00527] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2018] [Accepted: 02/27/2019] [Indexed: 12/24/2022] Open
Abstract
There remains a pressing need for biomarkers that can predict who will progress to active tuberculosis (TB) after exposure to Mycobacterium tuberculosis (MTB) bacterium. By analyzing cohorts of household contacts of TB index cases (HHCs) and a stringent non-human primate (NHP) challenge model, we evaluated whether integration of blood transcriptional profiling with serum metabolomic profiling can provide new understanding of disease processes and enable improved prediction of TB progression. Compared to either alone, the combined application of pre-existing transcriptome- and metabolome-based signatures more accurately predicted TB progression in the HHC cohorts and more accurately predicted disease severity in the NHPs. Pathway and data-driven correlation analyses of the integrated transcriptional and metabolomic datasets further identified novel immunometabolomic signatures significantly associated with TB progression in HHCs and NHPs, implicating cortisol, tryptophan, glutathione, and tRNA acylation networks. These results demonstrate the power of multi-omics analysis to provide new insights into complex disease processes.
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Affiliation(s)
- Fergal J Duffy
- Center for Global Infectious Disease Research, Seattle Childrens Research Institute, Seattle, WA, United States
| | - January Weiner
- Max Planck Institute for Infection Biology, Berlin, Germany
| | - Scott Hansen
- Oregon Health and Science University, Portland, OR, United States
| | - David L Tabb
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Stellenbosch, South Africa
| | - Sara Suliman
- Department of Pathology, South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine & Division of Immunology, University of Cape Town, Cape Town, South Africa
| | - Ethan Thompson
- Center for Infectious Disease Research, Seattle, WA, United States
| | | | - Smitha Shankar
- Center for Infectious Disease Research, Seattle, WA, United States
| | - Gerard Tromp
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Stellenbosch, South Africa
| | - Shreemanta Parida
- Max Planck Institute for Infection Biology, Berlin, Germany.,Translational Medicine & Global Health Consulting, Berlin, Germany
| | - Drew Dover
- Center for Global Infectious Disease Research, Seattle Childrens Research Institute, Seattle, WA, United States
| | | | - Jayne S Sutherland
- Vaccines & Immunity Theme, Medical Research Council Unit, Fajara, Gambia
| | - Hazel M Dockrell
- Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Tom H M Ottenhoff
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands
| | - Thomas J Scriba
- Department of Pathology, South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine & Division of Immunology, University of Cape Town, Cape Town, South Africa
| | - Louis J Picker
- Oregon Health and Science University, Portland, OR, United States
| | - Gerhard Walzl
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative (SATBBI), Center for Bioinformatics and Computational Biology, DST/NRF Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council Centre for Tuberculosis Research, Stellenbosch University, Stellenbosch, South Africa
| | | | - Daniel E Zak
- Center for Infectious Disease Research, Seattle, WA, United States
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16
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Guo X, Li Z, Yao Q, Mueller RS, Eng JK, Tabb DL, Hervey WJ, Pan C. Sipros Ensemble improves database searching and filtering for complex metaproteomics. Bioinformatics 2018; 34:795-802. [PMID: 29028897 PMCID: PMC6192206 DOI: 10.1093/bioinformatics/btx601] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2017] [Accepted: 09/19/2017] [Indexed: 01/14/2023] Open
Abstract
Motivation Complex microbial communities can be characterized by metagenomics and metaproteomics.
However, metagenome assemblies often generate enormous, and yet incomplete, protein
databases, which undermines the identification of peptides and proteins in
metaproteomics. This challenge calls for increased discrimination of true
identifications from false identifications by database searching and filtering
algorithms in metaproteomics. Results Sipros Ensemble was developed here for metaproteomics using an ensemble approach. Three
diverse scoring functions from MyriMatch, Comet and the original Sipros were
incorporated within a single database searching engine. Supervised classification with
logistic regression was used to filter database searching results. Benchmarking with
soil and marine microbial communities demonstrated a higher number of peptide and
protein identifications by Sipros Ensemble than MyriMatch/Percolator, Comet/Percolator,
MS-GF+/Percolator, Comet & MyriMatch/iProphet and Comet & MyriMatch &
MS-GF+/iProphet. Sipros Ensemble was computationally efficient and scalable on
supercomputers. Availability and implementation Freely available under the GNU GPL license at http://sipros.omicsbio.org. Supplementary information Supplementary data are
available at Bioinformatics online.
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Affiliation(s)
- Xuan Guo
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37996, USA.,Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA.,Department of Computer Science and Engineering, University of North Texas, Denton, TX 76203, USA
| | - Zhou Li
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37996, USA.,Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Qiuming Yao
- Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Ryan S Mueller
- Department of Microbiology, Oregon State University, Corvallis, OR 97331, USA
| | - Jimmy K Eng
- Proteomics Resource, University of Washington, Seattle, WA 98195, USA
| | - 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
| | - William Judson Hervey
- Naval Research Laboratory, Center for Bio/Molecular Science & Engineering (Code 6910), Washington, DC, 20375, USA
| | - Chongle Pan
- Graduate School of Genome Science and Technology, University of Tennessee, Knoxville, TN 37996, USA.,Computer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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17
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Bittremieux W, Tabb DL, Impens F, Staes A, Timmerman E, Martens L, Laukens K. Quality control in mass spectrometry-based proteomics. Mass Spectrom Rev 2018; 37:697-711. [PMID: 28802010 DOI: 10.1002/mas.21544] [Citation(s) in RCA: 50] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2017] [Revised: 07/24/2017] [Accepted: 07/24/2017] [Indexed: 05/21/2023]
Abstract
Mass spectrometry is a highly complex analytical technique and mass spectrometry-based proteomics experiments can be subject to a large variability, which forms an obstacle to obtaining accurate and reproducible results. Therefore, a comprehensive and systematic approach to quality control is an essential requirement to inspire confidence in the generated results. A typical mass spectrometry experiment consists of multiple different phases including the sample preparation, liquid chromatography, mass spectrometry, and bioinformatics stages. We review potential sources of variability that can impact the results of a mass spectrometry experiment occurring in all of these steps, and we discuss how to monitor and remedy the negative influences on the experimental results. Furthermore, we describe how specialized quality control samples of varying sample complexity can be incorporated into the experimental workflow and how they can be used to rigorously assess detailed aspects of the instrument performance.
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Affiliation(s)
- Wout Bittremieux
- Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (Biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
| | - David L Tabb
- Division of Molecular Biology and Human Genetics, Stellenbosch University Faculty of Medicine and Health Sciences, Tygerberg Hospital, Cape Town, South Africa
| | - Francis Impens
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - An Staes
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Evy Timmerman
- VIB Proteomics Core, Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium
- Faculty of Medicine and Health Sciences, Department of Biochemistry, Ghent University, Ghent, Belgium
- Bioinformatics Institute Ghent, Ghent University, Zwijnaarde, Belgium
| | - Kris Laukens
- Department of Mathematics and Computer Science, University of Antwerp, Antwerp, Belgium
- Biomedical Informatics Research Center Antwerp (Biomina), University of Antwerp/Antwerp University Hospital, Edegem, Belgium
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18
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Duffy FJ, Thompson E, Downing K, Suliman S, Mayanja-Kizza H, Boom WH, Thiel B, Weiner Iii J, Kaufmann SHE, Dover D, Tabb DL, Dockrell HM, Ottenhoff THM, Tromp G, Scriba TJ, Zak DE, Walzl G. A Serum Circulating miRNA Signature for Short-Term Risk of Progression to Active Tuberculosis Among Household Contacts. Front Immunol 2018; 9:661. [PMID: 29706954 PMCID: PMC5908968 DOI: 10.3389/fimmu.2018.00661] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Accepted: 03/19/2018] [Indexed: 12/21/2022] Open
Abstract
Biomarkers that predict who among recently Mycobacterium tuberculosis (MTB)-exposed individuals will progress to active tuberculosis are urgently needed. Intracellular microRNAs (miRNAs) regulate the host response to MTB and circulating miRNAs (c-miRNAs) have been developed as biomarkers for other diseases. We performed machine-learning analysis of c-miRNA measurements in the serum of adult household contacts (HHCs) of TB index cases from South Africa and Uganda and developed a c-miRNA-based signature of risk for progression to active TB. This c-miRNA-based signature significantly discriminated HHCs within 6 months of progression to active disease from HHCs that remained healthy in an independent test set [ROC area under the ROC curve (AUC) 0.74, progressors < 6 Mo to active TB and ROC AUC 0.66, up to 24 Mo to active TB], and complements the predictions of a previous cellular mRNA-based signature of TB risk.
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Affiliation(s)
- Fergal J Duffy
- The Center for Infectious Disease Research, Seattle, WA, United States
| | - Ethan Thompson
- The Center for Infectious Disease Research, Seattle, WA, United States
| | - Katrina Downing
- South African Tuberculosis Vaccine Initiative, Division of Immunology, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Sara Suliman
- South African Tuberculosis Vaccine Initiative, Division of Immunology, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Harriet Mayanja-Kizza
- Department of Medicine, Makerere University, Kampala, Uganda.,Department of Microbiology, Makerere University, Kampala, Uganda
| | - W Henry Boom
- Case Western Reserve University, Cleveland, OH, United States
| | - Bonnie Thiel
- Case Western Reserve University, Cleveland, OH, United States
| | | | | | - Drew Dover
- The Center for Infectious Disease Research, Seattle, WA, United States
| | - David L Tabb
- DST/NRF Centre of Excellence for Biomedical TB Research and MRC Centre for TB Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Hazel M Dockrell
- Department of Immunology and Infection, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Tom H M Ottenhoff
- Department of Infectious Diseases, Leiden University Medical Center, Leiden, Netherlands
| | - Gerard Tromp
- DST/NRF Centre of Excellence for Biomedical TB Research and MRC Centre for TB Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
| | - Thomas J Scriba
- South African Tuberculosis Vaccine Initiative, Division of Immunology, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Daniel E Zak
- The Center for Infectious Disease Research, Seattle, WA, United States
| | - Gerhard Walzl
- DST/NRF Centre of Excellence for Biomedical TB Research and MRC Centre for TB Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, South Africa
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19
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Bell L, Calder B, Hiller R, Klein A, Soares NC, Stoychev SH, Vorster BC, Tabb DL. Challenges and Opportunities for Biological Mass Spectrometry Core Facilities in the Developing World. J Biomol Tech 2018; 29:4-15. [PMID: 29623005 DOI: 10.7171/jbt.18-2901-003] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The developing world is seeing rapid growth in the availability of biological mass spectrometry (MS), particularly through core facilities. As proteomics and metabolomics becomes locally feasible for investigators in these nations, application areas associated with high burden in these nations, such as infectious disease, will see greatly increased research output. This article evaluates the rapid growth of MS in South Africa (currently approaching 20 laboratories) as a model for establishing MS core facilities in other nations of the developing world. Facilities should emphasize new services rather than new instruments. The reduction of the delays associated with reagent and other supply acquisition would benefit both facilities and the users who make use of their services. Instrument maintenance and repair, often mediated by an in-country business for an international vendor, is also likely to operate on a slower schedule than in the wealthiest nations. A key challenge to facilities in the developing world is educating potential facility users in how best to design experiments for proteomics and metabolomics, what reagents are most likely to introduce problematic artifacts, and how to interpret results from the facility. Here, we summarize the experience of 6 different institutions to raise the level of biological MS available to researchers in South Africa.
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Affiliation(s)
- Liam Bell
- Centre for Proteomic and Genomic Research, Observatory, Cape Town 7925, South Africa
| | - Bridget Calder
- University of Cape Town, Observatory, Cape Town 7925, South Africa
| | - Reinhard Hiller
- Centre for Proteomic and Genomic Research, Observatory, Cape Town 7925, South Africa
| | - Ashwil Klein
- University of the Western Cape, Bellville, Cape Town 7925, South Africa
| | - Nelson C Soares
- University of Cape Town, Observatory, Cape Town 7925, South Africa
| | - Stoyan H Stoychev
- Council for Scientific and Industrial Research, Pretoria 0001, South Africa
| | - Barend C Vorster
- Centre for Human Metabolomics, North-West University, Potchefstroom 2520, South Africa; and
| | - David L Tabb
- Department of Science and Technology/National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, South African Medical Research Council 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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20
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Baker MR, Ching T, Tabb DL, Li QX. Characterization of Plant Glycoproteins: Analysis of Plant Glycopeptide Mass Spectrometry Data with plantGlycoMS, a Package in the R Statistical Computing Environment. Methods Mol Biol 2018; 1789:205-220. [PMID: 29916082 DOI: 10.1007/978-1-4939-7856-4_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
plantGlycoMS is a set of tools, implemented in R, which is used to assess and validate glycopeptide spectrum matches (gPSMs). Validity of gPSMs is based on characteristic fragmentation patterns of glycopeptides (gPSMvalidator), adherence of the glycan moiety to the known N-glycan biosynthesis pathway in plants (pGlycoFilter), and elution of the glycopeptide within the observed retention time window of other glycopeptides sharing the same peptide backbone (rt.Restrict). plantGlycoMS also contains a tool for relative quantitation of glycoforms based on selected ion chromatograms of glycopeptide ion precursors in the mass spectrometry level 1 data (glycoRQ). This protocol walks the user through this workflow with example mass spectrometry data obtained for a plant glycoprotein.
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Affiliation(s)
- Margaret R Baker
- Department of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Travers Ching
- Department of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI, USA
| | - David L Tabb
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, SA MRC Centre for TB Research, DST/NRF Centre of Excellence for Biomedical TB Research, Stellenbosch University, Cape Town, South Africa
| | - Qing X Li
- Department of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, HI, USA.
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21
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Zhou JY, Chen L, Zhang B, Tian Y, Liu T, Thomas SN, Chen L, Schnaubelt M, Boja E, Hiltke T, Kinsinger CR, Rodriguez H, Davies SR, Li S, Snider JE, Erdmann-Gilmore P, Tabb DL, Townsend RR, Ellis MJ, Rodland KD, Smith RD, Carr SA, Zhang Z, Chan DW, Zhang H. Quality Assessments of Long-Term Quantitative Proteomic Analysis of Breast Cancer Xenograft Tissues. J Proteome Res 2017; 16:4523-4530. [PMID: 29124938 DOI: 10.1021/acs.jproteome.7b00362] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Clinical proteomics requires large-scale analysis of human specimens to achieve statistical significance. We evaluated the long-term reproducibility of an iTRAQ (isobaric tags for relative and absolute quantification)-based quantitative proteomics strategy using one channel for reference across all samples in different iTRAQ sets. A total of 148 liquid chromatography tandem mass spectrometric (LC-MS/MS) analyses were completed, generating six 2D LC-MS/MS data sets for human-in-mouse breast cancer xenograft tissues representative of basal and luminal subtypes. Such large-scale studies require the implementation of robust metrics to assess the contributions of technical and biological variability in the qualitative and quantitative data. Accordingly, we derived a quantification confidence score based on the quality of each peptide-spectrum match to remove quantification outliers from each analysis. After combining confidence score filtering and statistical analysis, reproducible protein identification and quantitative results were achieved from LC-MS/MS data sets collected over a 7-month period. This study provides the first quality assessment on long-term stability and technical considerations for study design of a large-scale clinical proteomics project.
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Affiliation(s)
- Jian-Ying Zhou
- Department of Pathology, Johns Hopkins University , Baltimore, Maryland 21231, United States
| | - Lijun Chen
- Department of Pathology, Johns Hopkins University , Baltimore, Maryland 21231, United States
| | - Bai Zhang
- Department of Pathology, Johns Hopkins University , Baltimore, Maryland 21231, United States
| | - Yuan Tian
- Department of Pathology, Johns Hopkins University , Baltimore, Maryland 21231, United States
| | - Tao Liu
- Biological Sciences Division, Pacific Northwest National Laboratory , Richland, Washington 99352, United States
| | - Stefani N Thomas
- Department of Pathology, Johns Hopkins University , Baltimore, Maryland 21231, United States
| | - Li Chen
- Department of Pathology, Johns Hopkins University , Baltimore, Maryland 21231, United States
| | - Michael Schnaubelt
- Department of Pathology, Johns Hopkins University , Baltimore, Maryland 21231, United States
| | - Emily Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute , Bethesda, Maryland 20892, United States
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute , Bethesda, Maryland 20892, United States
| | - Christopher R Kinsinger
- Office of Cancer Clinical Proteomics Research, National Cancer Institute , Bethesda, Maryland 20892, United States
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute , Bethesda, Maryland 20892, United States
| | - Sherri R Davies
- Department of Internal Medicine, Washington University School of Medicine , St. Louis, Missouri 63110, United States
| | - Shunqiang Li
- Department of Internal Medicine, Washington University School of Medicine , St. Louis, Missouri 63110, United States
| | - Jacqueline E Snider
- Department of Internal Medicine, Washington University School of Medicine , St. Louis, Missouri 63110, United States
| | - Petra Erdmann-Gilmore
- Department of Internal Medicine, Washington University School of Medicine , St. Louis, Missouri 63110, United States
| | - David L Tabb
- Department of Biomedical Informatics, Vanderbilt University Medical School , Nashville, Tennessee 37232, United States
| | - R Reid Townsend
- Department of Internal Medicine, Washington University School of Medicine , St. Louis, Missouri 63110, United States
| | - Matthew J Ellis
- Department of Internal Medicine, Washington University School of Medicine , St. Louis, Missouri 63110, United States
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory , Richland, Washington 99352, United States
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory , Richland, Washington 99352, United States
| | - Steven A Carr
- The Broad Institute of MIT and Harvard , Cambridge, Massachusetts 02142, United States
| | - Zhen Zhang
- Department of Pathology, Johns Hopkins University , Baltimore, Maryland 21231, United States
| | - Daniel W Chan
- Department of Pathology, Johns Hopkins University , Baltimore, Maryland 21231, United States
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University , Baltimore, Maryland 21231, United States
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22
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Deutsch EW, Orchard S, Binz PA, Bittremieux W, Eisenacher M, Hermjakob H, Kawano S, Lam H, Mayer G, Menschaert G, Perez-Riverol Y, Salek RM, Tabb DL, Tenzer S, Vizcaíno JA, Walzer M, Jones AR. Proteomics Standards Initiative: Fifteen Years of Progress and Future Work. J Proteome Res 2017; 16:4288-4298. [PMID: 28849660 PMCID: PMC5715286 DOI: 10.1021/acs.jproteome.7b00370] [Citation(s) in RCA: 69] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The Proteomics Standards Initiative (PSI) of the Human Proteome Organization (HUPO) has now been developing and promoting open community standards and software tools in the field of proteomics for 15 years. Under the guidance of the chair, cochairs, and other leadership positions, the PSI working groups are tasked with the development and maintenance of community standards via special workshops and ongoing work. Among the existing ratified standards, the PSI working groups continue to update PSI-MI XML, MITAB, mzML, mzIdentML, mzQuantML, mzTab, and the MIAPE (Minimum Information About a Proteomics Experiment) guidelines with the advance of new technologies and techniques. Furthermore, new standards are currently either in the final stages of completion (proBed and proBAM for proteogenomics results as well as PEFF) or in early stages of design (a spectral library standard format, a universal spectrum identifier, the qcML quality control format, and the Protein Expression Interface (PROXI) web services Application Programming Interface). In this work we review the current status of all of these aspects of the PSI, describe synergies with other efforts such as the ProteomeXchange Consortium, the Human Proteome Project, and the metabolomics community, and provide a look at future directions of the PSI.
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Affiliation(s)
- Eric W Deutsch
- Institute for Systems Biology , Seattle, Washington 98109, United States
| | - Sandra Orchard
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Pierre-Alain Binz
- CHUV Centre Hospitalier Universitaire Vaudois , 1011 Lausanne, Switzerland
| | - Wout Bittremieux
- Department of Mathematics and Computer Science, University of Antwerp , Middelheimlaan 1, 2020 Antwerp, Belgium
| | - Martin Eisenacher
- Medizinisches Proteom Center (MPC), Ruhr-Universität Bochum , D-44801 Bochum, Germany
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom.,State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, National Center for Protein Sciences, Beijing , Beijing 102206, China
| | - Shin Kawano
- Database Center for Life Science, Joint Support Center for Data Science Research, Research Organization of Information and Systems , Kashiwa, Chiba 277-0871, Japan
| | - Henry Lam
- Division of Biomedical Engineering, The Hong Kong University of Science and Technology , Clear Water Bay, Hong Kong, P. R. China.,Department of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology , Clear Water Bay, Hong Kong, P. R. China
| | - Gerhard Mayer
- Medizinisches Proteom Center (MPC), Ruhr-Universität Bochum , D-44801 Bochum, Germany
| | - Gerben Menschaert
- Lab of Bioinformatics and Computational Genomics (BioBix), Faculty of Bioscience Engineering, Ghent University , 9000 Ghent, Belgium
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Reza M Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - David L Tabb
- SA MRC Centre for TB Research, DST/NRF Centre of Excellence for Biomedical TB Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University , Cape Town, South Africa
| | - Stefan Tenzer
- Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz , 55131 Mainz, Germany
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Mathias Walzer
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Andrew R Jones
- Institute of Integrative Biology, University of Liverpool , South Wirral L64 4AY, United Kingdom
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23
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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24
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Thompson EG, Du Y, Malherbe ST, Shankar S, Braun J, Valvo J, Ronacher K, Tromp G, Tabb DL, Alland D, Shenai S, Via LE, Warwick J, Aderem A, Scriba TJ, Winter J, Walzl G, Zak DE. Host blood RNA signatures predict the outcome of tuberculosis treatment. Tuberculosis (Edinb) 2017; 107:48-58. [PMID: 29050771 PMCID: PMC5658513 DOI: 10.1016/j.tube.2017.08.004] [Citation(s) in RCA: 112] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 08/01/2017] [Accepted: 08/08/2017] [Indexed: 01/05/2023]
Abstract
Biomarkers for tuberculosis treatment outcome will assist in guiding individualized treatment and evaluation of new therapies. To identify candidate biomarkers, RNA sequencing of whole blood from a well-characterized TB treatment cohort was performed. Application of a validated transcriptional correlate of risk for TB revealed symmetry in host gene expression during progression from latent TB infection to active TB disease and resolution of disease during treatment, including return to control levels after drug therapy. The symmetry was also seen in a TB disease signature, constructed from the TB treatment cohort, that also functioned as a strong correlate of risk. Both signatures identified patients at risk of treatment failure 1–4 weeks after start of therapy. Further mining of the transcriptomes revealed an association between treatment failure and suppressed expression of mitochondrial genes before treatment initiation, leading to development of a novel baseline (pre-treatment) signature of treatment failure. These novel host responses to TB treatment were integrated into a five-gene real-time PCR-based signature that captures the clinically relevant responses to TB treatment and provides a convenient platform for stratifying patients according to their risk of treatment failure. Furthermore, this 5-gene signature is shown to correlate with the pulmonary inflammatory state (as measured by PET-CT) and can complement sputum-based Gene Xpert for patient stratification, providing a rapid and accurate alternative to current methods.
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Affiliation(s)
| | - Ying Du
- The Center for Infectious Disease Research, Seattle, WA, USA
| | - Stephanus T Malherbe
- Department of Science and Technology, National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, Stellenbosch University, Cape Town, South Africa; South African Medical Research Council, Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Smitha Shankar
- The Center for Infectious Disease Research, Seattle, WA, USA
| | - Jackie Braun
- The Center for Infectious Disease Research, Seattle, WA, USA
| | - Joe Valvo
- The Center for Infectious Disease Research, Seattle, WA, USA
| | - Katharina Ronacher
- Department of Science and Technology, National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, Stellenbosch University, Cape Town, South Africa; South African Medical Research Council, Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Mater Medical Research Institute, The University of Queensland, Brisbane, Australia
| | - Gerard Tromp
- Department of Science and Technology, National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, Stellenbosch University, Cape Town, South Africa; South African Medical Research Council, Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - David L Tabb
- Department of Science and Technology, National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, Stellenbosch University, Cape Town, South Africa; South African Medical Research Council, Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - David Alland
- Center for Emerging Pathogens, Department of Medicine, Rutgers New Jersey Medical School, Rutgers Biomedical & Health Sciences, Newark, NJ, USA
| | - Shubhada Shenai
- Center for Emerging Pathogens, Department of Medicine, Rutgers New Jersey Medical School, Rutgers Biomedical & Health Sciences, Newark, NJ, USA
| | - Laura E Via
- Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, Division of Intramural Research, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA; Institute of Infectious Disease and Molecular Medicine, Department of Clinical Laboratory Sciences, University of Cape Town, Cape Town, South Africa
| | - James Warwick
- Western Cape Academic Positron Emission Tomography-Computed Tomography Centre, Tygerberg Academic Hospital, Cape Town, South Africa; Division of Nuclear Medicine, Department of Medical Imaging and Clinical Oncology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Alan Aderem
- The Center for Infectious Disease Research, Seattle, WA, USA
| | - Thomas J Scriba
- South African Tuberculosis Vaccine Initiative, Institute of Infectious Disease and Molecular Medicine & Division of Immunology, Department of Pathology, University of Cape Town, Cape Town, South Africa
| | - Jill Winter
- Catalysis Foundation for Health, Emeryville, CA, USA
| | - Gerhard Walzl
- Department of Science and Technology, National Research Foundation Centre of Excellence for Biomedical Tuberculosis Research, Stellenbosch University, Cape Town, South Africa; South African Medical Research Council, Centre for Tuberculosis Research, Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Daniel E Zak
- The Center for Infectious Disease Research, Seattle, WA, USA.
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25
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Bittremieux W, Walzer M, Tenzer S, Zhu W, Salek RM, Eisenacher M, Tabb DL. The Human Proteome Organization-Proteomics Standards Initiative Quality Control Working Group: Making Quality Control More Accessible for Biological Mass Spectrometry. Anal Chem 2017; 89:4474-4479. [PMID: 28318237 DOI: 10.1021/acs.analchem.6b04310] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
To have confidence in results acquired during biological mass spectrometry experiments, a systematic approach to quality control is of vital importance. Nonetheless, until now, only scattered initiatives have been undertaken to this end, and these individual efforts have often not been complementary. To address this issue, the Human Proteome Organization-Proteomics Standards Initiative has established a new working group on quality control at its meeting in the spring of 2016. The goal of this working group is to provide a unifying framework for quality control data. The initial focus will be on providing a community-driven standardized file format for quality control. For this purpose, the previously proposed qcML format will be adapted to support a variety of use cases for both proteomics and metabolomics applications, and it will be established as an official PSI format. An important consideration is to avoid enforcing restrictive requirements on quality control but instead provide the basic technical necessities required to support extensive quality control for any type of mass spectrometry-based workflow. We want to emphasize that this is an open community effort, and we seek participation from all scientists with an interest in this field.
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Affiliation(s)
- Wout Bittremieux
- Department of Mathematics and Computer Science, University of Antwerp , Middelheimlaan 1, 2020 Antwerp, Belgium.,Biomedical Informatics Research Center Antwerp (biomina), University of Antwerp/Antwerp University Hospital , Wilrijkstraat 10, 2650 Edegem, Belgium
| | - Mathias Walzer
- Department of Computer Science, University of Tübingen , Tübingen 72076, Germany.,Center for Bioinformatics, University of Tübingen , Tübingen 72074, Germany
| | - Stefan Tenzer
- Institute for Immunology, University Medical Center of the Johannes-Gutenberg University Mainz D 55131, Germany
| | - Weimin Zhu
- National Center for Protein Science , No. 38, Science Park Road, Changping District, Beijing 102206, China
| | - Reza M Salek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, United Kingdom
| | - Martin Eisenacher
- Medical Bioinformatics, Medizinisches Proteom-Center, Ruhr-University Bochum , Bochum 44801, Germany
| | - David L Tabb
- Division of Molecular Biology and Human Genetics, Stellenbosch University Faculty of Medicine and Health Sciences , Tygerberg Hospital, Francie Van Zijl Drive, Cape Town 7505, South Africa
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26
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Wills TA, Baucum AJ, Louderback KM, Chen Y, Pasek JG, Delpire E, Tabb DL, Colbran RJ, Winder DG. Chronic intermittent alcohol disrupts the GluN2B-associated proteome and specifically regulates group I mGlu receptor-dependent long-term depression. Addict Biol 2017; 22:275-290. [PMID: 26549202 PMCID: PMC4860359 DOI: 10.1111/adb.12319] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2015] [Revised: 08/31/2015] [Accepted: 09/11/2015] [Indexed: 02/03/2023]
Abstract
N-Methyl-d-aspartate receptors (NMDARs) are major targets of both acute and chronic alcohol, as well as regulators of plasticity in a number of brain regions. Aberrant plasticity may contribute to the treatment resistance and high relapse rates observed in alcoholics. Recent work suggests that chronic alcohol treatment preferentially modulates both the expression and subcellular localization of NMDARs containing the GluN2B subunit. Signaling through synaptic and extrasynaptic GluN2B-NMDARs has already been implicated in the pathophysiology of various other neurological disorders. NMDARs interact with a large number of proteins at the glutamate synapse, and a better understanding of how alcohol modulates this proteome is needed. We employed a discovery-based proteomic approach in subcellular fractions of hippocampal tissue from chronic intermittent alcohol (CIE)-exposed C57Bl/6J mice to gain insight into alcohol-induced changes in GluN2B signaling complexes. Protein enrichment analyses revealed changes in the association of post-synaptic proteins, including scaffolding, glutamate receptor and PDZ-domain binding proteins with GluN2B. In particular, GluN2B interaction with metabotropic glutamate (mGlu)1/5 receptor-dependent long-term depression (LTD)-associated proteins such as Arc and Homer 1 was increased, while GluA2 was decreased. Accordingly, we found a lack of mGlu1/5 -induced LTD while α1 -adrenergic receptor-induced LTD remained intact in hippocampal CA1 following CIE. These data suggest that CIE specifically disrupts mGlu1/5 -LTD, representing a possible connection between NMDAR and mGlu receptor signaling. These studies not only demonstrate a new way in which alcohol can modulate plasticity in the hippocampus but also emphasize the utility of this discovery-based proteomic approach to generate new hypotheses regarding alcohol-related mechanisms.
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Affiliation(s)
- Tiffany A. Wills
- Department of Cell Biology & Anatomy, Louisiana State University Health Sciences Center, New Orleans, LA 70112
| | - Anthony J. Baucum
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN 46202
| | | | - Yaoyi Chen
- Department of Biochemical Informatics, Vanderbilt University School of Medicine, Nashville TN 37232
| | - Johanna G. Pasek
- Department of Molecular Physiology & Biophysics, Vanderbilt University School of Medicine, Nashville TN 37232
| | - Eric Delpire
- Department of Molecular Physiology & Biophysics, Vanderbilt University School of Medicine, Nashville TN 37232
- Vanderbilt Brain Institute, Vanderbilt University School of Medicine, Nashville TN 37232
- J.F. Kennedy Center for Research on Human Development, Vanderbilt University School of Medicine, Nashville TN 37232
- Department of Anesthesiology, Vanderbilt University School of Medicine, Nashville TN 37232
| | - David L. Tabb
- Department of Biochemical Informatics, Vanderbilt University School of Medicine, Nashville TN 37232
| | - Roger J. Colbran
- Department of Molecular Physiology & Biophysics, Vanderbilt University School of Medicine, Nashville TN 37232
- Vanderbilt Brain Institute, Vanderbilt University School of Medicine, Nashville TN 37232
- J.F. Kennedy Center for Research on Human Development, Vanderbilt University School of Medicine, Nashville TN 37232
| | - Danny G. Winder
- Department of Molecular Physiology & Biophysics, Vanderbilt University School of Medicine, Nashville TN 37232
- Vanderbilt Brain Institute, Vanderbilt University School of Medicine, Nashville TN 37232
- J.F. Kennedy Center for Research on Human Development, Vanderbilt University School of Medicine, Nashville TN 37232
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27
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Audain E, Uszkoreit J, Sachsenberg T, Pfeuffer J, Liang X, Hermjakob H, Sanchez A, Eisenacher M, Reinert K, Tabb DL, Kohlbacher O, Perez-Riverol Y. In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics. J Proteomics 2017; 150:170-182. [DOI: 10.1016/j.jprot.2016.08.002] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2016] [Revised: 07/30/2016] [Accepted: 08/02/2016] [Indexed: 12/24/2022]
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28
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Griss J, Perez-Riverol Y, Lewis S, Tabb DL, Dianes JA, Del-Toro N, Rurik M, Walzer MW, Kohlbacher O, Hermjakob H, Wang R, Vizcaíno JA. Recognizing millions of consistently unidentified spectra across hundreds of shotgun proteomics datasets. Nat Methods 2016; 13:651-656. [PMID: 27493588 PMCID: PMC4968634 DOI: 10.1038/nmeth.3902] [Citation(s) in RCA: 114] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Mass spectrometry (MS) is the main technology used in proteomics approaches. However, on average 75% of spectra analysed in an MS experiment remain unidentified. We propose to use spectrum clustering at a large-scale to shed a light on these unidentified spectra. PRoteomics IDEntifications database (PRIDE) Archive is one of the largest MS proteomics public data repositories worldwide. By clustering all tandem MS spectra publicly available in PRIDE Archive, coming from hundreds of datasets, we were able to consistently characterize three distinct groups of spectra: 1) incorrectly identified spectra, 2) spectra correctly identified but below the set scoring threshold, and 3) truly unidentified spectra. Using a multitude of complementary analysis approaches, we were able to identify less than 20% of the consistently unidentified spectra. The complete spectrum clustering results are available through the new version of the PRIDE Cluster resource (http://www.ebi.ac.uk/pride/cluster). This resource is intended, among other aims, to encourage and simplify further investigation into these unidentified spectra.
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Affiliation(s)
- Johannes Griss
- Division of Immunology, Allergy and Infectious Diseases, Department of Dermatology, Medical University of Vienna, Austria; European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Steve Lewis
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - David L Tabb
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville
| | - José A Dianes
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Noemi Del-Toro
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Marc Rurik
- Dept. of Computer Science, University of Tübingen, Germany; Center for Bioinformatics, University of Tübingen, Germany
| | - Mathias W Walzer
- Dept. of Computer Science, University of Tübingen, Germany; Center for Bioinformatics, University of Tübingen, Germany
| | - Oliver Kohlbacher
- Dept. of Computer Science, University of Tübingen, Germany; Center for Bioinformatics, University of Tübingen, Germany; Quantitative Biology Center, University of Tübingen, Germany; Max Planck Institute for Developmental Biology, Germany
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom; National Center for Protein Sciences, Beijing, China
| | - Rui Wang
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
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29
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Kochen MA, Chambers MC, Holman JD, Nesvizhskii AI, Weintraub ST, Belisle JT, Islam MN, Griss J, Tabb DL. Greazy: Open-Source Software for Automated Phospholipid Tandem Mass Spectrometry Identification. Anal Chem 2016; 88:5733-41. [PMID: 27186799 DOI: 10.1021/acs.analchem.6b00021] [Citation(s) in RCA: 46] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Lipid identification from data produced with high-throughput technologies is essential to the elucidation of the roles played by lipids in cellular function and disease. Software tools for identifying lipids from tandem mass (MS/MS) spectra have been developed, but they are often costly or lack the sophistication of their proteomics counterparts. We have developed Greazy, an open source tool for the automated identification of phospholipids from MS/MS spectra, that utilizes methods similar to those developed for proteomics. From user-supplied parameters, Greazy builds a phospholipid search space and associated theoretical MS/MS spectra. Experimental spectra are scored against search space lipids with similar precursor masses using a peak score based on the hypergeometric distribution and an intensity score utilizing the percentage of total ion intensity residing in matching peaks. The LipidLama component filters the results via mixture modeling and density estimation. We assess Greazy's performance against the NIST 2014 metabolomics library, observing high accuracy in a search of multiple lipid classes. We compare Greazy/LipidLama against the commercial lipid identification software LipidSearch and show that the two platforms differ considerably in the sets of identified spectra while showing good agreement on those spectra identified by both. Lastly, we demonstrate the utility of Greazy/LipidLama with different instruments. We searched data from replicates of alveolar type 2 epithelial cells obtained with an Orbitrap and from human serum replicates generated on a quadrupole-time-of-flight (Q-TOF). These findings substantiate the application of proteomics derived methods to the identification of lipids. The software is available from the ProteoWizard repository: http://tiny.cc/bumbershoot-vc12-bin64 .
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Affiliation(s)
- Michael A Kochen
- Department of Biomedical Informatics, Vanderbilt University , Nashville, Tennessee 37203, United States
| | - Matthew C Chambers
- Department of Biomedical Informatics, Vanderbilt University , Nashville, Tennessee 37203, United States
| | - Jay D Holman
- Department of Biomedical Informatics, Vanderbilt University , Nashville, Tennessee 37203, United States
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan , Ann Arbor, Michigan 48109, United States
| | - Susan T Weintraub
- Department of Biochemistry, UT Health Science Center at San Antonio , San Antonio, Texas 78229, United States
| | - John T Belisle
- Department of Microbiology, Immunology and Pathology, Colorado State University , Fort Collins, Colorado 80523, United States
| | - M Nurul Islam
- Department of Microbiology, Immunology and Pathology, Colorado State University , Fort Collins, Colorado 80523, United States
| | - Johannes Griss
- European Bioinformatics Institute (EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge, U.K. CB10 1SD.,Department of Dermatology, Medical University of Vienna , 1090 Vienna, Austria
| | - David L Tabb
- Department of Biomedical Informatics, Vanderbilt University , Nashville, Tennessee 37203, United States
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30
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Baker MR, Tabb DL, Ching T, Zimmerman LJ, Sakharov IY, Li QX. Site-Specific N-Glycosylation Characterization of Windmill Palm Tree Peroxidase Using Novel Tools for Analysis of Plant Glycopeptide Mass Spectrometry Data. J Proteome Res 2016; 15:2026-38. [DOI: 10.1021/acs.jproteome.6b00205] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Margaret R. Baker
- Department
of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, Hawaii 96822, United States
| | - David L. Tabb
- Department
of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee 37205, United States
| | - Travers Ching
- Department
of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, Hawaii 96822, United States
| | - Lisa J. Zimmerman
- Department
of Biochemistry, Vanderbilt University School of Medicine, Nashville, Tennessee 37205, United States
| | - Ivan Y. Sakharov
- Department
of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia
| | - Qing X. Li
- Department
of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, Honolulu, Hawaii 96822, United States
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31
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Tabb DL, Wang X, Carr SA, Clauser KR, Mertins P, Chambers MC, Holman JD, Wang J, Zhang B, Zimmerman LJ, Chen X, Gunawardena HP, Davies SR, Ellis MJC, Li S, Townsend RR, Boja ES, Ketchum KA, Kinsinger CR, Mesri M, Rodriguez H, Liu T, Kim S, McDermott JE, Payne SH, Petyuk VA, Rodland KD, Smith RD, Yang F, Chan DW, Zhang B, Zhang H, Zhang Z, Zhou JY, Liebler DC. Reproducibility of Differential Proteomic Technologies in CPTAC Fractionated Xenografts. J Proteome Res 2015; 15:691-706. [PMID: 26653538 PMCID: PMC4779376 DOI: 10.1021/acs.jproteome.5b00859] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC) employed a pair of reference xenograft proteomes for initial platform validation and ongoing quality control of its data collection for The Cancer Genome Atlas (TCGA) tumors. These two xenografts, representing basal and luminal-B human breast cancer, were fractionated and analyzed on six mass spectrometers in a total of 46 replicates divided between iTRAQ and label-free technologies, spanning a total of 1095 LC-MS/MS experiments. These data represent a unique opportunity to evaluate the stability of proteomic differentiation by mass spectrometry over many months of time for individual instruments or across instruments running dissimilar workflows. We evaluated iTRAQ reporter ions, label-free spectral counts, and label-free extracted ion chromatograms as strategies for data interpretation (source code is available from http://homepages.uc.edu/~wang2x7/Research.htm ). From these assessments, we found that differential genes from a single replicate were confirmed by other replicates on the same instrument from 61 to 93% of the time. When comparing across different instruments and quantitative technologies, using multiple replicates, differential genes were reproduced by other data sets from 67 to 99% of the time. Projecting gene differences to biological pathways and networks increased the degree of similarity. These overlaps send an encouraging message about the maturity of technologies for proteomic differentiation.
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Affiliation(s)
| | - Xia Wang
- Department of Mathematical Sciences, University of Cincinnati , Cincinnati, Ohio 45221, United States
| | - Steven A Carr
- Proteomics Platform, Broad Institute of MIT and Harvard , Cambridge, Massachusetts 02142, United States
| | - Karl R Clauser
- Proteomics Platform, Broad Institute of MIT and Harvard , Cambridge, Massachusetts 02142, United States
| | - Philipp Mertins
- Proteomics Platform, Broad Institute of MIT and Harvard , Cambridge, Massachusetts 02142, United States
| | | | | | | | | | | | - Xian Chen
- Department of Biochemistry and Biophysics, University of North Carolina , Chapel Hill, North Carolina 27599, United States
| | - Harsha P Gunawardena
- Department of Biochemistry and Biophysics, University of North Carolina , Chapel Hill, North Carolina 27599, United States
| | - Sherri R Davies
- Department of Medicine, Washington University , St. Louis, Missouri 63110, United States
| | - Matthew J C Ellis
- Department of Medicine, Washington University , St. Louis, Missouri 63110, United States
| | - Shunqiang Li
- Department of Medicine, Washington University , St. Louis, Missouri 63110, United States
| | - R Reid Townsend
- Department of Medicine, Washington University , St. Louis, Missouri 63110, United States
| | - Emily S Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute , Bethesda, Maryland 20892, United States
| | - Karen A Ketchum
- Enterprise Science and Computing, Inc. , Rockville, Maryland 20850, United States
| | - Christopher R Kinsinger
- Office of Cancer Clinical Proteomics Research, National Cancer Institute , Bethesda, Maryland 20892, United States
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute , Bethesda, Maryland 20892, United States
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute , Bethesda, Maryland 20892, United States
| | - Tao Liu
- Division of Biological Sciences, Pacific Northwest National Laboratory , Richland, Washington 99352, United States
| | - Sangtae Kim
- Division of Biological Sciences, Pacific Northwest National Laboratory , Richland, Washington 99352, United States
| | - Jason E McDermott
- Division of Biological Sciences, Pacific Northwest National Laboratory , Richland, Washington 99352, United States
| | - Samuel H Payne
- Division of Biological Sciences, Pacific Northwest National Laboratory , Richland, Washington 99352, United States
| | - Vladislav A Petyuk
- Division of Biological Sciences, Pacific Northwest National Laboratory , Richland, Washington 99352, United States
| | - Karin D Rodland
- Division of Biological Sciences, Pacific Northwest National Laboratory , Richland, Washington 99352, United States
| | - Richard D Smith
- Division of Biological Sciences, Pacific Northwest National Laboratory , Richland, Washington 99352, United States
| | - Feng Yang
- Division of Biological Sciences, Pacific Northwest National Laboratory , Richland, Washington 99352, United States
| | - Daniel W Chan
- JHMI and Division of Clinical Chemistry, Johns Hopkins University , Baltimore, Maryland 21231, United States
| | - Bai Zhang
- JHMI and Division of Clinical Chemistry, Johns Hopkins University , Baltimore, Maryland 21231, United States
| | - Hui Zhang
- JHMI and Division of Clinical Chemistry, Johns Hopkins University , Baltimore, Maryland 21231, United States
| | - Zhen Zhang
- JHMI and Division of Clinical Chemistry, Johns Hopkins University , Baltimore, Maryland 21231, United States
| | - Jian-Ying Zhou
- JHMI and Division of Clinical Chemistry, Johns Hopkins University , Baltimore, Maryland 21231, United States
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32
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Wang X, Slebos RJC, Chambers MC, Tabb DL, Liebler DC, Zhang B. proBAMsuite, a Bioinformatics Framework for Genome-Based Representation and Analysis of Proteomics Data. Mol Cell Proteomics 2015; 15:1164-75. [PMID: 26657539 PMCID: PMC4813696 DOI: 10.1074/mcp.m115.052860] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2015] [Indexed: 01/13/2023] Open
Abstract
To facilitate genome-based representation and analysis of proteomics data, we developed a new bioinformatics framework, proBAMsuite, in which a central component is the protein BAM (proBAM) file format for organizing peptide spectrum matches (PSMs)1 within the context of the genome. proBAMsuite also includes two R packages, proBAMr and proBAMtools, for generating and analyzing proBAM files, respectively. Applying proBAMsuite to three recently published proteomics datasets, we demonstrated its utility in facilitating efficient genome-based sharing, interpretation, and integration of proteomics data. First, the interpretation of proteomics data is significantly enhanced with the rich genomic annotation information. Second, PSMs can be easily reannotated using user-specified gene annotation schemes and assembled into both protein and gene identifications. Third, using the genome as a common reference, proBAMsuite facilitates seamless proteomics and proteogenomics data integration. Finally, proBAM files can be readily visualized in genome browsers and thus bring proteomics data analysis to a general audience beyond the proteomics community. Results from this study establish proBAMsuite as a useful bioinformatics framework for proteomics and proteogenomics research.
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Affiliation(s)
| | - Robbert J C Slebos
- §Department of Biochemistry, ¶Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt-Ingram Cancer Center, Nashville, TN 37232
| | | | - David L Tabb
- From the ‡Department of Biomedical Informatics, §Department of Biochemistry
| | - Daniel C Liebler
- From the ‡Department of Biomedical Informatics, §Department of Biochemistry, ¶Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt-Ingram Cancer Center, Nashville, TN 37232
| | - Bing Zhang
- From the ‡Department of Biomedical Informatics, ‖Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232;
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33
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Basak T, Vega-Montoto L, Zimmerman LJ, Tabb DL, Hudson BG, Vanacore RM. Comprehensive Characterization of Glycosylation and Hydroxylation of Basement Membrane Collagen IV by High-Resolution Mass Spectrometry. J Proteome Res 2015; 15:245-58. [PMID: 26593852 DOI: 10.1021/acs.jproteome.5b00767] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Collagen IV is the main structural protein that provides a scaffold for assembly of basement membrane proteins. Posttranslational modifications such as hydroxylation of proline and lysine and glycosylation of lysine are essential for the functioning of collagen IV triple-helical molecules. These modifications are highly abundant posing a difficult challenge for in-depth characterization of collagen IV using conventional proteomics approaches. Herein, we implemented an integrated pipeline combining high-resolution mass spectrometry with different fragmentation techniques and an optimized bioinformatics workflow to study posttranslational modifications in mouse collagen IV. We achieved 82% sequence coverage for the α1 chain, mapping 39 glycosylated hydroxylysine, 148 4-hydroxyproline, and seven 3-hydroxyproline residues. Further, we employed our pipeline to map the modifications on human collagen IV and achieved 85% sequence coverage for the α1 chain, mapping 35 glycosylated hydroxylysine, 163 4-hydroxyproline, and 14 3-hydroxyproline residues. Although lysine glycosylation heterogeneity was observed in both mouse and human, 21 conserved sites were identified. Likewise, five 3-hydroxyproline residues were conserved between mouse and human, suggesting that these modification sites are important for collagen IV function. Collectively, these are the first comprehensive maps of hydroxylation and glycosylation sites in collagen IV, which lay the foundation for dissecting the key role of these modifications in health and disease.
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Affiliation(s)
- Trayambak Basak
- Department of Medicine, Division of Nephrology and Hypertension, ‡Center for Matrix Biology, §Department of Biochemistry, and ⊥Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, Tennessee 37232, United States
| | - Lorenzo Vega-Montoto
- Department of Medicine, Division of Nephrology and Hypertension, ‡Center for Matrix Biology, §Department of Biochemistry, and ⊥Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, Tennessee 37232, United States
| | - Lisa J Zimmerman
- Department of Medicine, Division of Nephrology and Hypertension, ‡Center for Matrix Biology, §Department of Biochemistry, and ⊥Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, Tennessee 37232, United States
| | - David L Tabb
- Department of Medicine, Division of Nephrology and Hypertension, ‡Center for Matrix Biology, §Department of Biochemistry, and ⊥Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, Tennessee 37232, United States
| | - Billy G Hudson
- Department of Medicine, Division of Nephrology and Hypertension, ‡Center for Matrix Biology, §Department of Biochemistry, and ⊥Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, Tennessee 37232, United States
| | - Roberto M Vanacore
- Department of Medicine, Division of Nephrology and Hypertension, ‡Center for Matrix Biology, §Department of Biochemistry, and ⊥Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, Tennessee 37232, United States
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34
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Ruggles KV, Tang Z, Wang X, Grover H, Askenazi M, Teubl J, Cao S, McLellan MD, Clauser KR, Tabb DL, Mertins P, Slebos R, Erdmann-Gilmore P, Li S, Gunawardena HP, Xie L, Liu T, Zhou JY, Sun S, Hoadley KA, Perou CM, Chen X, Davies SR, Maher CA, Kinsinger CR, Rodland KD, Zhang H, Zhang Z, Ding L, Townsend RR, Rodriguez H, Chan D, Smith RD, Liebler DC, Carr SA, Payne S, Ellis MJ, Fenyő D. An Analysis of the Sensitivity of Proteogenomic Mapping of Somatic Mutations and Novel Splicing Events in Cancer. Mol Cell Proteomics 2015; 15:1060-71. [PMID: 26631509 DOI: 10.1074/mcp.m115.056226] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2015] [Indexed: 11/06/2022] Open
Abstract
Improvements in mass spectrometry (MS)-based peptide sequencing provide a new opportunity to determine whether polymorphisms, mutations, and splice variants identified in cancer cells are translated. Herein, we apply a proteogenomic data integration tool (QUILTS) to illustrate protein variant discovery using whole genome, whole transcriptome, and global proteome datasets generated from a pair of luminal and basal-like breast-cancer-patient-derived xenografts (PDX). The sensitivity of proteogenomic analysis for singe nucleotide variant (SNV) expression and novel splice junction (NSJ) detection was probed using multiple MS/MS sample process replicates defined here as an independent tandem MS experiment using identical sample material. Despite analysis of over 30 sample process replicates, only about 10% of SNVs (somatic and germline) detected by both DNA and RNA sequencing were observed as peptides. An even smaller proportion of peptides corresponding to NSJ observed by RNA sequencing were detected (<0.1%). Peptides mapping to DNA-detected SNVs without a detectable mRNA transcript were also observed, suggesting that transcriptome coverage was incomplete (∼80%). In contrast to germline variants, somatic variants were less likely to be detected at the peptide level in the basal-like tumor than in the luminal tumor, raising the possibility of differential translation or protein degradation effects. In conclusion, this large-scale proteogenomic integration allowed us to determine the degree to which mutations are translated and identify gaps in sequence coverage, thereby benchmarking current technology and progress toward whole cancer proteome and transcriptome analysis.
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Affiliation(s)
- Kelly V Ruggles
- From the ‡New York University School of Medicine, New York, NY
| | - Zuojian Tang
- From the ‡New York University School of Medicine, New York, NY
| | - Xuya Wang
- From the ‡New York University School of Medicine, New York, NY
| | - Himanshu Grover
- From the ‡New York University School of Medicine, New York, NY
| | | | - Jennifer Teubl
- From the ‡New York University School of Medicine, New York, NY
| | - Song Cao
- ¶Washington University in St. Louis, St. Louis, MO
| | | | | | - David L Tabb
- **Vanderbilt University School of Medicine, Nashville, TN
| | | | - Robbert Slebos
- **Vanderbilt University School of Medicine, Nashville, TN
| | | | - Shunqiang Li
- ¶Washington University in St. Louis, St. Louis, MO
| | | | - Ling Xie
- ‡‡Universtiy of North Carolina School of Medicine, Chapel Hill, NC
| | - Tao Liu
- §§Pacific Northwest National Laboratory, Richland, WA
| | | | | | | | - Charles M Perou
- ‡‡Universtiy of North Carolina School of Medicine, Chapel Hill, NC
| | - Xian Chen
- ‡‡Universtiy of North Carolina School of Medicine, Chapel Hill, NC
| | | | | | | | | | - Hui Zhang
- ¶¶Johns Hopkins University, Baltimore, MD
| | - Zhen Zhang
- ¶¶Johns Hopkins University, Baltimore, MD
| | - Li Ding
- ¶Washington University in St. Louis, St. Louis, MO
| | | | - Henry Rodriguez
- ‖‖Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD
| | | | | | | | | | - Samuel Payne
- §§Pacific Northwest National Laboratory, Richland, WA;
| | | | - David Fenyő
- From the ‡New York University School of Medicine, New York, NY;
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35
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Abstract
Since its introduction in 1994, SEQUEST has gained many important new capabilities, and a host of successor algorithms have built upon its successes. This Account and Perspective maps the evolution of this important tool and charts the relationships among contributions to the SEQUEST legacy. Many of the changes represented improvements in computing speed by clusters and graphics cards. Mass spectrometry innovations in mass accuracy and activation methods led to shifts in fragment modeling and scoring strategies. These changes, as well as the movement of laboratories and lab members, have led to great diversity among the members of the SEQUEST family. Graphical Abstract ᅟ.
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Affiliation(s)
- David L Tabb
- School of Medicine, Vanderbilt University, Nashville, TN, 37232-8575, USA.
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36
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Bennett KL, Wang X, Bystrom CE, Chambers MC, Andacht TM, Dangott LJ, Elortza F, Leszyk J, Molina H, Moritz RL, Phinney BS, Thompson JW, Bunger MK, Tabb DL. The 2012/2013 ABRF Proteomic Research Group Study: Assessing Longitudinal Intralaboratory Variability in Routine Peptide Liquid Chromatography Tandem Mass Spectrometry Analyses. Mol Cell Proteomics 2015; 14:3299-309. [PMID: 26435129 DOI: 10.1074/mcp.o115.051888] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2015] [Indexed: 11/06/2022] Open
Abstract
Questions concerning longitudinal data quality and reproducibility of proteomic laboratories spurred the Protein Research Group of the Association of Biomolecular Resource Facilities (ABRF-PRG) to design a study to systematically assess the reproducibility of proteomic laboratories over an extended period of time. Developed as an open study, initially 64 participants were recruited from the broader mass spectrometry community to analyze provided aliquots of a six bovine protein tryptic digest mixture every month for a period of nine months. Data were uploaded to a central repository, and the operators answered an accompanying survey. Ultimately, 45 laboratories submitted a minimum of eight LC-MSMS raw data files collected in data-dependent acquisition (DDA) mode. No standard operating procedures were enforced; rather the participants were encouraged to analyze the samples according to usual practices in the laboratory. Unlike previous studies, this investigation was not designed to compare laboratories or instrument configuration, but rather to assess the temporal intralaboratory reproducibility. The outcome of the study was reassuring with 80% of the participating laboratories performing analyses at a medium to high level of reproducibility and quality over the 9-month period. For the groups that had one or more outlying experiments, the major contributing factor that correlated to the survey data was the performance of preventative maintenance prior to the LC-MSMS analyses. Thus, the Protein Research Group of the Association of Biomolecular Resource Facilities recommends that laboratories closely scrutinize the quality control data following such events. Additionally, improved quality control recording is imperative. This longitudinal study provides evidence that mass spectrometry-based proteomics is reproducible. When quality control measures are strictly adhered to, such reproducibility is comparable among many disparate groups. Data from the study are available via ProteomeXchange under the accession code PXD002114.
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Affiliation(s)
- Keiryn L Bennett
- From the ‡CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria;
| | - Xia Wang
- §University of Cincinnati, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, Ohio, 45221-0025
| | - Cory E Bystrom
- ¶Cleveland HeartLab, Inc., Research and Development, Cleveland HeartLab, Inc., Cleveland, Ohio, 44103
| | - Matthew C Chambers
- ‖Vanderbilt University, Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, 37232
| | - Tracy M Andacht
- **Centers for Disease Control and Prevention, Emergency Response Branch, Division of Laboratory Sciences, National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, 30341
| | - Larry J Dangott
- ‡‡Texas A&M University, Department of Biochemistry & Biophysics, Texas A&M University, College Station, Texas, 77843
| | - Félix Elortza
- §§CIC bioGUNE, Centro de Investigacion Cooperativa en Biociencias, ProteoRed-ISCIII, Bilbao, Spain
| | - John Leszyk
- ¶¶University of Massachusetts, Department of Biochemistry and Molecular Pharmacology Proteomics and Mass Spectrometry Facility, University of Massachusetts Medical School, Shrewsbury, Massachusetts, 01545
| | - Henrik Molina
- ‖‖The Rockefeller University, Proteomics Resource Center, The Rockefeller University, New York, New York, 10065
| | | | - Brett S Phinney
- University of California, Davis, Proteomics Core, University of California-Davis Genome Center, Davis, California, 95616
| | - J Will Thompson
- Duke University, Proteomics and Metabolomics Core Facility, Duke University Medical Center, Durham, North Carolina, 27708
| | | | - David L Tabb
- ‖Vanderbilt University, Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, 37232;
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37
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Gibbons BC, Chambers MC, Monroe ME, Tabb DL, Payne SH. Correcting systematic bias and instrument measurement drift with mzRefinery. Bioinformatics 2015; 31:3838-40. [PMID: 26243018 PMCID: PMC4653383 DOI: 10.1093/bioinformatics/btv437] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2015] [Accepted: 07/21/2015] [Indexed: 11/12/2022] Open
Abstract
MOTIVATION Systematic bias in mass measurement adversely affects data quality and negates the advantages of high precision instruments. RESULTS We introduce the mzRefinery tool for calibration of mass spectrometry data files. Using confident peptide spectrum matches, three different calibration methods are explored and the optimal transform function is chosen. After calibration, systematic bias is removed and the mass measurement errors are centered at 0 ppm. Because it is part of the ProteoWizard package, mzRefinery can read and write a wide variety of file formats. AVAILABILITY AND IMPLEMENTATION The mzRefinery tool is part of msConvert, available with the ProteoWizard open source package at http://proteowizard.sourceforge.net/ CONTACT samuel.payne@pnnl.gov. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Bryson C Gibbons
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA 99354 and
| | - Matthew C Chambers
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Matthew E Monroe
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA 99354 and
| | - David L Tabb
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Samuel H Payne
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland WA 99354 and
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38
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Slebos RJC, Wang X, Wang X, Zhang B, Tabb DL, Liebler DC. Corrigendum: Proteomic analysis of colon and rectal carcinoma using standard and customized databases. Sci Data 2015. [PMID: 26217491 PMCID: PMC4508824 DOI: 10.1038/sdata.2015.37] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
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39
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Woo S, Cha SW, Bonissone S, Na S, Tabb DL, Pevzner PA, Bafna V. Advanced Proteogenomic Analysis Reveals Multiple Peptide Mutations and Complex Immunoglobulin Peptides in Colon Cancer. J Proteome Res 2015; 14:3555-67. [PMID: 26139413 DOI: 10.1021/acs.jproteome.5b00264] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Aiming toward an improved understanding of the regulation of proteins in cancer, recent studies from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) have focused on analyzing cancer tissue using proteomic technologies and workflows. Although many proteogenomics approaches for the study of cancer samples have been proposed, serious methodological challenges remain, especially in the identification of multiple mutational variants or structural variations such as fusion gene events. In addition, although immune system genes play an important role in cancer, identification of IgG peptides remains challenging in proteomic data sets. Here, we describe an integrative proteogenomic method that extends the limit of proteogenomic searches to identify multiple variant peptides as well as immunoglobulin gene variations/rearrangements using customized mining of RNA-seq data. Our results also provide the first extensive characterization of tumor immune response and demonstrate the potential of this method to improve the molecular characterization of tumor subtypes.
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Affiliation(s)
| | | | | | | | - David L Tabb
- Department of Biomedical Informatics, Vanderbilt University , Nashville, Tennessee 37203, United States
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40
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Slebos RJC, Wang X, Wang X, Wang X, Zhang B, Tabb DL, Liebler DC. Proteomic analysis of colon and rectal carcinoma using standard and customized databases. Sci Data 2015; 2:150022. [PMID: 26110064 PMCID: PMC4477697 DOI: 10.1038/sdata.2015.22] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2014] [Accepted: 02/17/2015] [Indexed: 12/17/2022] Open
Abstract
Understanding proteomic differences underlying the different phenotypic classes of colon and rectal carcinoma is important and may eventually lead to a better assessment of clinical behavior of these cancers. We here present a comprehensive description of the proteomic data obtained from 90 colon and rectal carcinomas previously subjected to genomic analysis by The Cancer Genome Atlas (TCGA). Here, the primary instrument files and derived secondary data files are compiled and presented in forms that will allow further analyses of the biology of colon and rectal carcinoma. We also discuss new challenges in processing these large proteomic datasets for relevant proteins and protein variants.
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Affiliation(s)
- Robbert J C Slebos
- Department of Biochemistry, Vanderbilt University School of Medicine , Nashville, TN 37232, USA ; Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt-Ingram Cancer Center , Nashville, TN 37232, USA
| | - Xia Wang
- Department of Mathematical Sciences, University of Cincinnati , Cincinnati, OH 45221, USA
| | - Xiaojing Wang
- Department of Biomedical Informatics, Vanderbilt University School of Medicine , Nashville, TN 37232, USA
| | - Xaojing Wang
- Department of Biomedical Informatics, Vanderbilt University School of Medicine , Nashville, TN 37232, USA
| | - Bing Zhang
- Department of Biomedical Informatics, Vanderbilt University School of Medicine , Nashville, TN 37232, USA
| | - David L Tabb
- Department of Biomedical Informatics, Vanderbilt University School of Medicine , Nashville, TN 37232, USA
| | - Daniel C Liebler
- Department of Biochemistry, Vanderbilt University School of Medicine , Nashville, TN 37232, USA ; Jim Ayers Institute for Precancer Detection and Diagnosis, Vanderbilt-Ingram Cancer Center , Nashville, TN 37232, USA
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41
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French WR, Zimmerman LJ, Schilling B, Gibson BW, Miller CA, Townsend RR, Sherrod SD, Goodwin CR, McLean JA, Tabb DL. Wavelet-based peak detection and a new charge inference procedure for MS/MS implemented in ProteoWizard's msConvert. J Proteome Res 2014; 14:1299-307. [PMID: 25411686 PMCID: PMC4324452 DOI: 10.1021/pr500886y] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
![]()
We
report the implementation of high-quality signal processing
algorithms into ProteoWizard, an efficient, open-source software package
designed for analyzing proteomics tandem mass spectrometry data. Specifically,
a new wavelet-based peak-picker (CantWaiT) and a precursor charge
determination algorithm (Turbocharger) have been implemented. These
additions into ProteoWizard provide universal tools that are independent
of vendor platform for tandem mass spectrometry analyses and have
particular utility for intralaboratory studies requiring the advantages
of different platforms convergent on a particular workflow or for
interlaboratory investigations spanning multiple platforms. We compared
results from these tools to those obtained using vendor and commercial
software, finding that in all cases our algorithms resulted in a comparable
number of identified peptides for simple and complex samples measured
on Waters, Agilent, and AB SCIEX quadrupole time-of-flight and Thermo
Q-Exactive mass spectrometers. The mass accuracy of matched precursor
ions also compared favorably with vendor and commercial tools. Additionally,
typical analysis runtimes (∼1–100 ms per MS/MS spectrum)
were short enough to enable the practical use of these high-quality
signal processing tools for large clinical and research data sets.
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Affiliation(s)
- William R French
- Department of Biomedical Informatics, Vanderbilt University Medical Center , Nashville, Tennessee 37232-8340, United States
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42
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Seymour SL, Farrah T, Binz PA, Chalkley RJ, Cottrell JS, Searle BC, Tabb DL, Vizcaíno JA, Prieto G, Uszkoreit J, Eisenacher M, Martínez-Bartolomé S, Ghali F, Jones AR. A standardized framing for reporting protein identifications in mzIdentML 1.2. Proteomics 2014; 14:2389-99. [PMID: 25092112 DOI: 10.1002/pmic.201400080] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2014] [Revised: 07/02/2014] [Accepted: 07/31/2014] [Indexed: 11/09/2022]
Abstract
Inferring which protein species have been detected in bottom-up proteomics experiments has been a challenging problem for which solutions have been maturing over the past decade. While many inference approaches now function well in isolation, comparing and reconciling the results generated across different tools remains difficult. It presently stands as one of the greatest barriers in collaborative efforts such as the Human Proteome Project and public repositories such as the PRoteomics IDEntifications (PRIDE) database. Here we present a framework for reporting protein identifications that seeks to improve capabilities for comparing results generated by different inference tools. This framework standardizes the terminology for describing protein identification results, associated with the HUPO-Proteomics Standards Initiative (PSI) mzIdentML standard, while still allowing for differing methodologies to reach that final state. It is proposed that developers of software for reporting identification results will adopt this terminology in their outputs. While the new terminology does not require any changes to the core mzIdentML model, it represents a significant change in practice, and, as such, the rules will be released via a new version of the mzIdentML specification (version 1.2) so that consumers of files are able to determine whether the new guidelines have been adopted by export software.
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43
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Abstract
After raw data have been captured by mass spectrometers in biological LC-MS/MS experiments, they must be converted from vendor-specific binary files to open-format files for manipulation by most software. This protocol details the use of ProteoWizard software for this conversion, taking format features, coding options, and vendor particularities into account. This protocol will aid researchers in preparing their data for analysis by database search engines and other bioinformatics tools.
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Affiliation(s)
- Jerry D Holman
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee
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44
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Wang X, Chambers MC, Vega-Montoto LJ, Bunk DM, Stein SE, Tabb DL. QC metrics from CPTAC raw LC-MS/MS data interpreted through multivariate statistics. Anal Chem 2014; 86:2497-509. [PMID: 24494671 PMCID: PMC3982976 DOI: 10.1021/ac4034455] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
![]()
Shotgun proteomics experiments integrate
a complex sequence of
processes, any of which can introduce variability. Quality metrics
computed from LC-MS/MS data have relied upon identifying MS/MS scans,
but a new mode for the QuaMeter software produces metrics that are
independent of identifications. Rather than evaluating each metric
independently, we have created a robust multivariate statistical toolkit
that accommodates the correlation structure of these metrics and allows
for hierarchical relationships among data sets. The framework enables
visualization and structural assessment of variability. Study 1 for
the Clinical Proteomics Technology Assessment for Cancer (CPTAC),
which analyzed three replicates of two common samples at each of two
time points among 23 mass spectrometers in nine laboratories, provided
the data to demonstrate this framework, and CPTAC Study 5 provided
data from complex lysates under Standard Operating Procedures (SOPs)
to complement these findings. Identification-independent quality metrics
enabled the differentiation of sites and run-times through robust
principal components analysis and subsequent factor analysis. Dissimilarity
metrics revealed outliers in performance, and a nested ANOVA model
revealed the extent to which all metrics or individual metrics were
impacted by mass spectrometer and run time. Study 5 data revealed
that even when SOPs have been applied, instrument-dependent variability
remains prominent, although it may be reduced, while within-site variability
is reduced significantly. Finally, identification-independent quality
metrics were shown to be predictive of identification sensitivity
in these data sets. QuaMeter and the associated multivariate framework
are available from http://fenchurch.mc.vanderbilt.edu and http://homepages.uc.edu/~wang2x7/, respectively.
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Affiliation(s)
- Xia Wang
- Department of Mathematical Sciences, University of Cincinnati , Cincinnati, Ohio 45221, United States
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45
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Alcendor DJ, Block FE, Cliffel DE, Daniels JS, Ellacott KLJ, Goodwin CR, Hofmeister LH, Li D, Markov DA, May JC, McCawley LJ, McLaughlin B, McLean JA, Niswender KD, Pensabene V, Seale KT, Sherrod SD, Sung HJ, Tabb DL, Webb DJ, Wikswo JP. Neurovascular unit on a chip: implications for translational applications. Stem Cell Res Ther 2013; 4 Suppl 1:S18. [PMID: 24564885 PMCID: PMC4029462 DOI: 10.1186/scrt379] [Citation(s) in RCA: 49] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
The blood-brain barrier (BBB) dynamically controls exchange between the brain and the body, but this interaction cannot be studied directly in the intact human brain or sufficiently represented by animal models. Most existing in vitro BBB models do not include neurons and glia with other BBB elements and do not adequately predict drug efficacy and toxicity. Under the National Institutes of Health Microtissue Initiative, we are developing a three-dimensional, multicompartment, organotypic microphysiological system representative of a neurovascular unit of the brain. The neurovascular unit system will serve as a model to study interactions between the central nervous system neurons and the cerebral spinal fluid (CSF) compartment, all coupled to a realistic blood-surrogate supply and venous return system that also incorporates circulating immune cells and the choroid plexus. Hence all three critical brain barriers will be recapitulated: blood-brain, brain-CSF, and blood-CSF. Primary and stem cell-derived human cells will interact with a variety of agents to produce critical chemical communications across the BBB and between brain regions. Cytomegalovirus, a common herpesvirus, will be used as an initial model of infections regulated by the BBB. This novel technological platform, which combines innovative microfluidics, cell culture, analytical instruments, bioinformatics, control theory, neuroscience, and drug discovery, will replicate chemical communication, molecular trafficking, and inflammation in the brain. The platform will enable targeted and clinically relevant nutritional and pharmacologic interventions for or prevention of such chronic diseases as obesity and acute injury such as stroke, and will uncover potential adverse effects of drugs. If successful, this project will produce clinically useful technologies and reveal new insights into how the brain receives, modifies, and is affected by drugs, other neurotropic agents, and diseases.
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46
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Sirbu BM, McDonald WH, Dungrawala H, Badu-Nkansah A, Kavanaugh GM, Chen Y, Tabb DL, Cortez D. Identification of proteins at active, stalled, and collapsed replication forks using isolation of proteins on nascent DNA (iPOND) coupled with mass spectrometry. J Biol Chem 2013; 288:31458-67. [PMID: 24047897 DOI: 10.1074/jbc.m113.511337] [Citation(s) in RCA: 170] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
Both DNA and chromatin need to be duplicated during each cell division cycle. Replication happens in the context of defects in the DNA template and other forms of replication stress that present challenges to both genetic and epigenetic inheritance. The replication machinery is highly regulated by replication stress responses to accomplish this goal. To identify important replication and stress response proteins, we combined isolation of proteins on nascent DNA (iPOND) with quantitative mass spectrometry. We identified 290 proteins enriched on newly replicated DNA at active, stalled, and collapsed replication forks. Approximately 16% of these proteins are known replication or DNA damage response proteins. Genetic analysis indicates that several of the newly identified proteins are needed to facilitate DNA replication, especially under stressed conditions. Our data provide a useful resource for investigators studying DNA replication and the replication stress response and validate the use of iPOND combined with mass spectrometry as a discovery tool.
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47
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Chen YY, Chambers MC, Li M, Ham AJL, Turner JL, Zhang B, Tabb DL. IDPQuantify: combining precursor intensity with spectral counts for protein and peptide quantification. J Proteome Res 2013; 12:4111-21. [PMID: 23879310 DOI: 10.1021/pr400438q] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Differentiating and quantifying protein differences in complex samples produces significant challenges in sensitivity and specificity. Label-free quantification can draw from two different information sources: precursor intensities and spectral counts. Intensities are accurate for calculating protein relative abundance, but values are often missing due to peptides that are identified sporadically. Spectral counting can reliably reproduce difference lists, but differentiating peptides or quantifying all but the most concentrated protein changes is usually beyond its abilities. Here we developed new software, IDPQuantify, to align multiple replicates using principal component analysis, extract accurate precursor intensities from MS data, and combine intensities with spectral counts for significant gains in differentiation and quantification. We have applied IDPQuantify to three comparative proteomic data sets featuring gold standard protein differences spiked in complicated backgrounds. The software is able to associate peptides with peaks that are otherwise left unidentified to increase the efficiency of protein quantification, especially for low-abundance proteins. By combing intensities with spectral counts from IDPicker, it gains an average of 30% more true positive differences among top differential proteins. IDPQuantify quantifies protein relative abundance accurately in these test data sets to produce good correlations between known and measured concentrations.
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Affiliation(s)
- Yao-Yi Chen
- Department of Biomedical Informatics, Vanderbilt University Medical School, Nashville, Tennessee 37232-8575, United States
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48
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Perez-Riverol Y, Sánchez A, Noda J, Borges D, Carvalho PC, Wang R, Vizcaíno JA, Betancourt L, Ramos Y, Duarte G, Nogueira FCS, González LJ, Padrón G, Tabb DL, Hermjakob H, Domont GB, Besada V. HI-bone: a scoring system for identifying phenylisothiocyanate-derivatized peptides based on precursor mass and high intensity fragment ions. Anal Chem 2013; 85:3515-20. [PMID: 23448308 DOI: 10.1021/ac303239g] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Peptide sequence matching algorithms used for peptide identification by tandem mass spectrometry (MS/MS) enumerate theoretical peptides from the database, predict their fragment ions, and match them to the experimental MS/MS spectra. Here, we present an approach for scoring MS/MS identifications based on the high mass accuracy matching of precursor ions, the identification of a high intensity b1 fragment ion, and partial sequence tags from phenylthiocarbamoyl-derivatized peptides. This derivatization process boosts the b1 fragment ion signal, which turns it into a powerful feature for peptide identification. We demonstrate the effectiveness of our scoring system by implementing it on a computational tool called "HI-bone" and by identifying mass spectra of an Escherichia coli sample acquired on an Orbitrap Velos instrument using Higher-energy C-trap dissociation. Following this strategy, we identified 1614 peptide spectrum matches with a peptide false discovery rate (FDR) below 1%. These results were significantly higher than those from Mascot and SEQUEST using a similar FDR.
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Affiliation(s)
- Yasset Perez-Riverol
- Department of Proteomics, Center for Genetic Engineering and Biotechnology, Cubanacán, Playa, Ciudad de la Habana, Cuba
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49
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Wang D, Dasari S, Chambers MC, Holman JD, Chen K, Liebler DC, Orton DJ, Purvine SO, Monroe ME, Chung CY, Rose KL, Tabb DL. Basophile: accurate fragment charge state prediction improves peptide identification rates. Genomics Proteomics Bioinformatics 2013; 11:86-95. [PMID: 23499924 PMCID: PMC3737598 DOI: 10.1016/j.gpb.2012.11.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2012] [Revised: 11/03/2012] [Accepted: 11/22/2012] [Indexed: 01/14/2023]
Abstract
In shotgun proteomics, database search algorithms rely on fragmentation models to predict fragment ions that should be observed for a given peptide sequence. The most widely used strategy (Naive model) is oversimplified, cleaving all peptide bonds with equal probability to produce fragments of all charges below that of the precursor ion. More accurate models, based on fragmentation simulation, are too computationally intensive for on-the-fly use in database search algorithms. We have created an ordinal-regression-based model called Basophile that takes fragment size and basic residue distribution into account when determining the charge retention during CID/higher-energy collision induced dissociation (HCD) of charged peptides. This model improves the accuracy of predictions by reducing the number of unnecessary fragments that are routinely predicted for highly-charged precursors. Basophile increased the identification rates by 26% (on average) over the Naive model, when analyzing triply-charged precursors from ion trap data. Basophile achieves simplicity and speed by solving the prediction problem with an ordinal regression equation, which can be incorporated into any database search software for shotgun proteomic identification.
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Affiliation(s)
- Dong Wang
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
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50
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Chambers MC, Maclean B, Burke R, Amodei D, Ruderman DL, Neumann S, Gatto L, Fischer B, Pratt B, Egertson J, Hoff K, Kessner D, Tasman N, Shulman N, Frewen B, Baker TA, Brusniak MY, Paulse C, Creasy D, Flashner L, Kani K, Moulding C, Seymour SL, Nuwaysir LM, Lefebvre B, Kuhlmann F, Roark J, Rainer P, Detlev S, Hemenway T, Huhmer A, Langridge J, Connolly B, Chadick T, Holly K, Eckels J, Deutsch EW, Moritz RL, Katz JE, Agus DB, MacCoss M, Tabb DL, Mallick P. A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol 2013; 30:918-20. [PMID: 23051804 PMCID: PMC3471674 DOI: 10.1038/nbt.2377] [Citation(s) in RCA: 2192] [Impact Index Per Article: 199.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
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