1
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Siraj A, Bouwmeester R, Declercq A, Welp L, Chernev A, Wulf A, Urlaub H, Martens L, Degroeve S, Kohlbacher O, Sachsenberg T. Intensity and retention time prediction improves the rescoring of protein-nucleic acid cross-links. Proteomics 2024; 24:e2300144. [PMID: 38629965 DOI: 10.1002/pmic.202300144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 12/29/2023] [Accepted: 01/05/2024] [Indexed: 04/19/2024]
Abstract
In protein-RNA cross-linking mass spectrometry, UV or chemical cross-linking introduces stable bonds between amino acids and nucleic acids in protein-RNA complexes that are then analyzed and detected in mass spectra. This analytical tool delivers valuable information about RNA-protein interactions and RNA docking sites in proteins, both in vitro and in vivo. The identification of cross-linked peptides with oligonucleotides of different length leads to a combinatorial increase in search space. We demonstrate that the peptide retention time prediction tasks can be transferred to the task of cross-linked peptide retention time prediction using a simple amino acid composition encoding, yielding improved identification rates when the prediction error is included in rescoring. For the more challenging task of including fragment intensity prediction of cross-linked peptides in the rescoring, we obtain, on average, a similar improvement. Further improvement in the encoding and fine-tuning of retention time and intensity prediction models might lead to further gains, and merit further research.
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Affiliation(s)
- Arslan Siraj
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany
- Institute for Biological and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Robbin Bouwmeester
- Department of Biomolecular Medicine, Ghent University, Gent, Belgium
- VIB-UGent Center for Medical Biotechnology, VIB, Gent, Belgium
| | - Arthur Declercq
- Department of Biomolecular Medicine, Ghent University, Gent, Belgium
- VIB-UGent Center for Medical Biotechnology, VIB, Gent, Belgium
| | - Luisa Welp
- Bioanalytical Mass Spectrometry, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Bioanalytics, Institute of Clinical Chemistry, University Medical Center Göttingen, Göttingen, Germany
| | - Aleksandar Chernev
- Bioanalytical Mass Spectrometry, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Alexander Wulf
- Bioanalytical Mass Spectrometry, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
| | - Henning Urlaub
- Bioanalytical Mass Spectrometry, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
- Bioanalytics, Institute of Clinical Chemistry, University Medical Center Göttingen, Göttingen, Germany
| | - Lennart Martens
- Department of Biomolecular Medicine, Ghent University, Gent, Belgium
- VIB-UGent Center for Medical Biotechnology, VIB, Gent, Belgium
| | - Sven Degroeve
- Department of Biomolecular Medicine, Ghent University, Gent, Belgium
- VIB-UGent Center for Medical Biotechnology, VIB, Gent, Belgium
| | - Oliver Kohlbacher
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany
- Institute for Biological and Medical Informatics, University of Tübingen, Tübingen, Germany
| | - Timo Sachsenberg
- Department of Computer Science, Applied Bioinformatics, University of Tübingen, Tübingen, Germany
- Institute for Biological and Medical Informatics, University of Tübingen, Tübingen, Germany
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2
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Zhu C, Liu LY, Yamaguchi TN, Zhu H, Hugh-White R, Livingstone J, Patel Y, Kislinger T, Boutros PC. moPepGen: Rapid and Comprehensive Proteoform Identification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.28.587261. [PMID: 38585946 PMCID: PMC10996593 DOI: 10.1101/2024.03.28.587261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Gene expression is a multi-step transformation of biological information from its storage form (DNA) into functional forms (protein and some RNAs). Regulatory activities at each step of this transformation multiply a single gene into a myriad of proteoforms. Proteogenomics is the study of how genomic and transcriptomic variation creates this proteoform diversity, and is limited by the challenges of modeling the complexities of gene-expression. We therefore created moPepGen, a graph-based algorithm that comprehensively enumerates proteoforms in linear time. moPepGen works with multiple technologies, in multiple species and on all types of genetic and transcriptomic data. In human cancer proteomes, it detects and quantifies previously unobserved noncanonical peptides arising from germline and somatic genomic variants, noncoding open reading frames, RNA fusions and RNA circularization. By enabling efficient identification and quantitation of previously hidden proteins in both existing and new proteomic data, moPepGen facilitates all proteogenomics applications. It is available at: https://github.com/uclahs-cds/package-moPepGen.
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Affiliation(s)
- Chenghao Zhu
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, CA, USA
- Department of Urology, University of California, Los Angeles, CA, USA
| | - Lydia Y. Liu
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
| | - Takafumi N. Yamaguchi
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, CA, USA
| | - Helen Zhu
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
| | - Rupert Hugh-White
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, CA, USA
| | - Julie Livingstone
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, CA, USA
| | - Yash Patel
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, CA, USA
| | - Thomas Kislinger
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Paul C. Boutros
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, CA, USA
- Institute for Precision Health, University of California, Los Angeles, CA, USA
- Department of Urology, University of California, Los Angeles, CA, USA
- Department of Medical Biophysics, University of Toronto, Toronto, Canada
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3
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Lampe RH, Coale TH, Forsch KO, Jabre LJ, Kekuewa S, Bertrand EM, Horák A, Oborník M, Rabines AJ, Rowland E, Zheng H, Andersson AJ, Barbeau KA, Allen AE. Short-term acidification promotes diverse iron acquisition and conservation mechanisms in upwelling-associated phytoplankton. Nat Commun 2023; 14:7215. [PMID: 37940668 PMCID: PMC10632500 DOI: 10.1038/s41467-023-42949-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Accepted: 10/26/2023] [Indexed: 11/10/2023] Open
Abstract
Coastal upwelling regions are among the most productive marine ecosystems but may be threatened by amplified ocean acidification. Increased acidification is hypothesized to reduce iron bioavailability for phytoplankton thereby expanding iron limitation and impacting primary production. Here we show from community to molecular levels that phytoplankton in an upwelling region respond to short-term acidification exposure with iron uptake pathways and strategies that reduce cellular iron demand. A combined physiological and multi-omics approach was applied to trace metal clean incubations that introduced 1200 ppm CO2 for up to four days. Although variable, molecular-level responses indicate a prioritization of iron uptake pathways that are less hindered by acidification and reductions in iron utilization. Growth, nutrient uptake, and community compositions remained largely unaffected suggesting that these mechanisms may confer short-term resistance to acidification; however, we speculate that cellular iron demand is only temporarily satisfied, and longer-term acidification exposure without increased iron inputs may result in increased iron stress.
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Affiliation(s)
- Robert H Lampe
- Integrative Oceanography Division, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
- Microbial and Environmental Genomics, J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Tyler H Coale
- Integrative Oceanography Division, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
- Microbial and Environmental Genomics, J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Kiefer O Forsch
- Geosciences Research Division, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Loay J Jabre
- Department of Biology and Institute for Comparative Genomics, Dalhousie University, 1355 Oxford St, Halifax, NS, B3H 4R2, Canada
| | - Samuel Kekuewa
- Geosciences Research Division, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Erin M Bertrand
- Department of Biology and Institute for Comparative Genomics, Dalhousie University, 1355 Oxford St, Halifax, NS, B3H 4R2, Canada
| | - Aleš Horák
- Biology Centre, Institute of Parasitology, Czech Academy of Sciences, 370 05, České Budějovice, CZ, Czechia
- Faculty of Science, University of South Bohemia, 370 05, České Budějovice, CZ, Czechia
| | - Miroslav Oborník
- Biology Centre, Institute of Parasitology, Czech Academy of Sciences, 370 05, České Budějovice, CZ, Czechia
- Faculty of Science, University of South Bohemia, 370 05, České Budějovice, CZ, Czechia
| | - Ariel J Rabines
- Integrative Oceanography Division, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
- Microbial and Environmental Genomics, J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Elden Rowland
- Department of Biology and Institute for Comparative Genomics, Dalhousie University, 1355 Oxford St, Halifax, NS, B3H 4R2, Canada
| | - Hong Zheng
- Microbial and Environmental Genomics, J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA
| | - Andreas J Andersson
- Geosciences Research Division, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Katherine A Barbeau
- Geosciences Research Division, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Andrew E Allen
- Integrative Oceanography Division, Scripps Institution of Oceanography, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
- Microbial and Environmental Genomics, J. Craig Venter Institute, 4120 Capricorn Lane, La Jolla, CA, 92037, USA.
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4
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Postoenko VI, Garibova LA, Levitsky LI, Bubis JA, Gorshkov MV, Ivanov MV. IQMMA: Efficient MS1 Intensity Extraction Pipeline Using Multiple Feature Detection Algorithms for DDA Proteomics. J Proteome Res 2023; 22:2827-2835. [PMID: 37579078 DOI: 10.1021/acs.jproteome.3c00075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2023]
Abstract
One of the key steps in data dependent acquisition (DDA) proteomics is detection of peptide isotopic clusters, also called "features", in MS1 spectra and matching them to MS/MS-based peptide identifications. A number of peptide feature detection tools became available in recent years, each relying on its own matching algorithm. Here, we provide an integrated solution, the intensity-based Quantitative Mix and Match Approach (IQMMA), which integrates a number of untargeted peptide feature detection algorithms and returns the most probable intensity values for the MS/MS-based identifications. IQMMA was tested using available proteomic data acquired for both well-characterized (ground truth) and real-world biological samples, including a mix of Yeast and E. coli digests spiked at different concentrations into the Human K562 digest used as a background, and a set of glioblastoma cell lines. Three open-source feature detection algorithms were integrated: Dinosaur, biosaur2, and OpenMS FeatureFinder. None of them was found optimal when applied individually to all the data sets employed in this work; however, their combined use in IQMMA improved efficiency of subsequent protein quantitation. The software implementing IQMMA is freely available at https://github.com/PostoenkoVI/IQMMA under Apache 2.0 license.
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Affiliation(s)
- Valeriy I Postoenko
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russia
- Moscow Institute of Physics and Technology, National Research University, G. Dolgoprudny, Institutsky Lane 9, Dolgoprudny 141701, Russia
| | - Leyla A Garibova
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russia
- Moscow Institute of Physics and Technology, National Research University, G. Dolgoprudny, Institutsky Lane 9, Dolgoprudny 141701, Russia
| | - Lev I Levitsky
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russia
| | - Julia A Bubis
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russia
| | - Mikhail V Gorshkov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russia
| | - Mark V Ivanov
- V. L. Talrose Institute for Energy Problems of Chemical Physics, N. N. Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, Moscow 119334, Russia
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5
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Kontou EE, Walter A, Alka O, Pfeuffer J, Sachsenberg T, Mohite OS, Nuhamunada M, Kohlbacher O, Weber T. UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis. J Cheminform 2023; 15:52. [PMID: 37173725 PMCID: PMC10176759 DOI: 10.1186/s13321-023-00724-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 04/27/2023] [Indexed: 05/15/2023] Open
Abstract
Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC-MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets.
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Affiliation(s)
- Eftychia E Kontou
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, 2800, Kgs. Lyngby, Denmark
| | - Axel Walter
- Applied Bioinformatics, Department of Computer Science, Eberhard Karls University Tübingen, Sand 14, 72076, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, 72076, Tübingen, Germany
| | - Oliver Alka
- Applied Bioinformatics, Department of Computer Science, Eberhard Karls University Tübingen, Sand 14, 72076, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, 72076, Tübingen, Germany
| | - Julianus Pfeuffer
- Visual and Data-Centric Computing, Zuse Institute Berlin, Takustr. 7, 14195, Berlin, Germany
- Algorithmic Bioinformatics, Freie Universität Berlin, Takustr. 9, 14195, Berlin, Germany
| | - Timo Sachsenberg
- Applied Bioinformatics, Department of Computer Science, Eberhard Karls University Tübingen, Sand 14, 72076, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, 72076, Tübingen, Germany
| | - Omkar S Mohite
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, 2800, Kgs. Lyngby, Denmark
| | - Matin Nuhamunada
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, 2800, Kgs. Lyngby, Denmark
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, Eberhard Karls University Tübingen, Sand 14, 72076, Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Sand 14, 72076, Tübingen, Germany
- Translational Bioinformatics, University Hospital Tübingen, Schaffhausenstr. 77, 72072, Tübingen, Germany
| | - Tilmann Weber
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet Building 220, 2800, Kgs. Lyngby, Denmark.
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6
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He J, Liu O, Guo X. Deep Learning Based MS2 Feature Detection for Data-Independent Shotgun Proteomics. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2022; 2022:2342-2348. [PMID: 37635836 PMCID: PMC10457098 DOI: 10.1109/bibm55620.2022.9995258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/29/2023]
Abstract
Accuracy of peptide identification in LC-MS analysis is crucial for information regarding the aspects of proteins that aid in biomarker discovery and the profiling of complex proteomes. The detection of peptide fragment ions in tandem mass spectrometry is still challenging given that current tools were not created or tested for the low-abundance, low-peak fragments of peptides found in MS2 data. Feature detection, a crucial pre-processing step in the LC-MS analysis pipeline that quantifies peptides by their mass-to-charge ratio, retention time, and intensity, is particularly challenging due to the overlapping nature of peptides and weak signals that are often indistinguishable from noises, thus creating a reliance on rigid mathematical structures and heuristics. In this study, we developed a deep-learning-based model with an innovative sliding window process that enables high-resolution processing of quantitative MS/MS data to conduct MS2 feature detection. Experimental results show that our model can produce more accurate values and identifications than existing feature detection tools, as well as a high rate of true positive features quantified. Therefore, we believe that our model illustrates the advantages of deep learning techniques applied towards computational proteomics.
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Affiliation(s)
- Jonathan He
- Department of Computer Science and Engineering, Univeristy of North Texas, Denton, USA
| | - Olivia Liu
- Department of Computer Science and Engineering, Univeristy of North Texas, Denton, USA
| | - Xuan Guo
- Department of Computer Science and Engineering, Univeristy of North Texas, Denton, USA
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7
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Rebelo-Guiomar P, Pellegrino S, Dent KC, Sas-Chen A, Miller-Fleming L, Garone C, Van Haute L, Rogan JF, Dinan A, Firth AE, Andrews B, Whitworth AJ, Schwartz S, Warren AJ, Minczuk M. A late-stage assembly checkpoint of the human mitochondrial ribosome large subunit. Nat Commun 2022; 13:929. [PMID: 35177605 PMCID: PMC8854578 DOI: 10.1038/s41467-022-28503-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 01/20/2022] [Indexed: 12/04/2022] Open
Abstract
Many cellular processes, including ribosome biogenesis, are regulated through post-transcriptional RNA modifications. Here, a genome-wide analysis of the human mitochondrial transcriptome shows that 2’-O-methylation is limited to residues of the mitoribosomal large subunit (mtLSU) 16S mt-rRNA, introduced by MRM1, MRM2 and MRM3, with the modifications installed by the latter two proteins being interdependent. MRM2 controls mitochondrial respiration by regulating mitoribosome biogenesis. In its absence, mtLSU particles (visualized by cryo-EM at the resolution of 2.6 Å) present disordered RNA domains, partial occupancy of bL36m and bound MALSU1:L0R8F8:mtACP anti-association module, allowing five mtLSU biogenesis intermediates with different intersubunit interface configurations to be placed along the assembly pathway. However, mitoribosome biogenesis does not depend on the methyltransferase activity of MRM2. Disruption of the MRM2 Drosophila melanogaster orthologue leads to mitochondria-related developmental arrest. This work identifies a key checkpoint during mtLSU assembly, essential to maintain mitochondrial homeostasis. Rebelo-Guiomar et al. unveil late stage assembly intermediates of the human mitochondrial ribosome by inactivating the methyltransferase MRM2 in cells. Absence of MRM2 impairs organismal homeostasis, while its catalytic activity is dispensable for mitoribosomal biogenesis.
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Affiliation(s)
- Pedro Rebelo-Guiomar
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Keith Peters Building, Hills Rd, Cambridge, CB2 0XY, UK
| | - Simone Pellegrino
- Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Campus, Keith Peters Building, Hills Rd, Cambridge, CB2 0XY, UK.,Wellcome Trust - MRC Stem Cell Institute, Cambridge Biomedical Campus, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK.,Department of Haematology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK
| | - Kyle C Dent
- Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Campus, Keith Peters Building, Hills Rd, Cambridge, CB2 0XY, UK.,Wellcome Trust - MRC Stem Cell Institute, Cambridge Biomedical Campus, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK.,Department of Haematology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK.,MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Francis Crick Avenue, Cambridge, CB2 0QH, UK
| | - Aldema Sas-Chen
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 76100, Israel.,Shmunis School of Biomedicine and Cancer Research, The George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, 6997801, Israel
| | - Leonor Miller-Fleming
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Keith Peters Building, Hills Rd, Cambridge, CB2 0XY, UK
| | - Caterina Garone
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Keith Peters Building, Hills Rd, Cambridge, CB2 0XY, UK.,Department of Medical and Surgical Sciences, University of Bologna, Bologna, 40137, Italy
| | - Lindsey Van Haute
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Keith Peters Building, Hills Rd, Cambridge, CB2 0XY, UK
| | - Jack F Rogan
- STORM Therapeutics Limited, Babraham Research Campus, Moneta Building, Cambridge, CB22 3AT, UK
| | - Adam Dinan
- Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK
| | - Andrew E Firth
- Department of Pathology, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QP, UK
| | - Byron Andrews
- STORM Therapeutics Limited, Babraham Research Campus, Moneta Building, Cambridge, CB22 3AT, UK
| | - Alexander J Whitworth
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Keith Peters Building, Hills Rd, Cambridge, CB2 0XY, UK
| | - Schraga Schwartz
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 76100, Israel
| | - Alan J Warren
- Cambridge Institute for Medical Research, University of Cambridge, Cambridge Biomedical Campus, Keith Peters Building, Hills Rd, Cambridge, CB2 0XY, UK.,Wellcome Trust - MRC Stem Cell Institute, Cambridge Biomedical Campus, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK.,Department of Haematology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Jeffrey Cheah Biomedical Centre, Puddicombe Way, Cambridge, CB2 0AW, UK
| | - Michal Minczuk
- MRC Mitochondrial Biology Unit, University of Cambridge, Cambridge Biomedical Campus, Keith Peters Building, Hills Rd, Cambridge, CB2 0XY, UK.
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8
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Proteomic traits vary across taxa in a coastal Antarctic phytoplankton bloom. THE ISME JOURNAL 2022; 16:569-579. [PMID: 34482372 PMCID: PMC8776772 DOI: 10.1038/s41396-021-01084-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 07/13/2021] [Accepted: 08/02/2021] [Indexed: 02/07/2023]
Abstract
Production and use of proteins is under strong selection in microbes, but it is unclear how proteome-level traits relate to ecological strategies. We identified and quantified proteomic traits of eukaryotic microbes and bacteria through an Antarctic phytoplankton bloom using in situ metaproteomics. Different taxa, rather than different environmental conditions, formed distinct clusters based on their ribosomal and photosynthetic proteomic proportions, and we propose that these characteristics relate to ecological differences. We defined and used a proteomic proxy for regulatory cost, which showed that SAR11 had the lowest regulatory cost of any taxa we observed at our summertime Southern Ocean study site. Haptophytes had lower regulatory cost than diatoms, which may underpin haptophyte-to-diatom bloom progression in the Ross Sea. We were able to make these proteomic trait inferences by assessing various sources of bias in metaproteomics, providing practical recommendations for researchers in the field. We have quantified several proteomic traits (ribosomal and photosynthetic proteomic proportions, regulatory cost) in eukaryotic and bacterial taxa, which can then be incorporated into trait-based models of microbial communities that reflect resource allocation strategies.
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9
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Yamashita K, Umezawa T. Phosphoproteomic Approaches to Evaluate ABA Signaling. Methods Mol Biol 2022; 2462:163-179. [PMID: 35152388 DOI: 10.1007/978-1-0716-2156-1_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Abscisic acid (ABA) is a major phytohormone that regulates various processes in plants (e.g., seed dormancy/germination, abiotic/biotic stress responses). As protein phosphorylation is involved in the major pathways of ABA signaling, it is necessary to elucidate the phosphosignaling pathway involved in the ABA response. Phosphoproteomics enables determination of the proteins phosphorylated in vivo, and recent studies have applied a comparative phosphoproteomic approach to analyze ABA signaling in plants. For example, ABA-responsive phosphoproteins were identified in barley embryos. Furthermore, a phosphoproteomic approach is useful for screening protein kinase substrates by comparative analysis using kinase knockout mutants. Here, some technical points regarding phosphoproteomic analyses of ABA responses in plants are described.
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Affiliation(s)
- Kota Yamashita
- Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan
| | - Taishi Umezawa
- Graduate School of Bio-Applications and Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo, Japan.
- Faculty of Agriculture, Tokyo University of Agriculture and Technology, Tokyo, Japan.
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10
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Jahn M, Crang N, Janasch M, Hober A, Forsström B, Kimler K, Mattausch A, Chen Q, Asplund-Samuelsson J, Hudson EP. Protein allocation and utilization in the versatile chemolithoautotroph Cupriavidus necator. eLife 2021; 10:69019. [PMID: 34723797 PMCID: PMC8591527 DOI: 10.7554/elife.69019] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 10/30/2021] [Indexed: 12/12/2022] Open
Abstract
Bacteria must balance the different needs for substrate assimilation, growth
functions, and resilience in order to thrive in their environment. Of all
cellular macromolecules, the bacterial proteome is by far the most important
resource and its size is limited. Here, we investigated how the highly versatile
'knallgas' bacterium Cupriavidus necator reallocates protein
resources when grown on different limiting substrates and with different growth
rates. We determined protein quantity by mass spectrometry and estimated enzyme
utilization by resource balance analysis modeling. We found that C.
necator invests a large fraction of its proteome in functions that
are hardly utilized. Of the enzymes that are utilized, many are present in
excess abundance. One prominent example is the strong expression of CBB cycle
genes such as Rubisco during growth on fructose. Modeling and mutant competition
experiments suggest that CO2-reassimilation through Rubisco does not
provide a fitness benefit for heterotrophic growth, but is rather an investment
in readiness for autotrophy.
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Affiliation(s)
- Michael Jahn
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Nick Crang
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Markus Janasch
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Andreas Hober
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Björn Forsström
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Kyle Kimler
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Alexander Mattausch
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Qi Chen
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Johannes Asplund-Samuelsson
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Elton Paul Hudson
- School of Engineering Sciences in Chemistry, Biotechnology and Health, Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
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11
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Proteomic Advances in Cereal and Vegetable Crops. Molecules 2021; 26:molecules26164924. [PMID: 34443513 PMCID: PMC8401599 DOI: 10.3390/molecules26164924] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/06/2021] [Accepted: 08/10/2021] [Indexed: 01/06/2023] Open
Abstract
The importance of vegetables in human nutrition, such as cereals, which in many cases represent the main source of daily energy for humans, added to the impact that the incessant increase in demographic pressure has on the demand for these plant foods, entails the search for new technologies that can alleviate this pressure on markets while reducing the carbon footprint of related activities. Plant proteomics arises as a response to these problems, and through research and the application of new technologies, it attempts to enhance areas of food science that are fundamental for the optimization of processes. This review aims to present the different approaches and tools of proteomics in the investigation of new methods for the development of vegetable crops. In the last two decades, different studies in the control of the quality of crops have reported very interesting results that can help us to verify parameters as important as food safety, the authenticity of the products, or the increase in the yield by early detection of diseases. A strategic plan that encourages the incorporation of these new methods into the industry will be essential to promote the use of proteomics and all the advantages it offers in the optimization of processes and the solution of problems.
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12
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McCain JSP, Tagliabue A, Susko E, Achterberg EP, Allen AE, Bertrand EM. Cellular costs underpin micronutrient limitation in phytoplankton. SCIENCE ADVANCES 2021; 7:7/32/eabg6501. [PMID: 34362734 PMCID: PMC8346223 DOI: 10.1126/sciadv.abg6501] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 06/22/2021] [Indexed: 05/08/2023]
Abstract
Micronutrients control phytoplankton growth in the ocean, influencing carbon export and fisheries. It is currently unclear how micronutrient scarcity affects cellular processes and how interdependence across micronutrients arises. We show that proximate causes of micronutrient growth limitation and interdependence are governed by cumulative cellular costs of acquiring and using micronutrients. Using a mechanistic proteomic allocation model of a polar diatom focused on iron and manganese, we demonstrate how cellular processes fundamentally underpin micronutrient limitation, and how they interact and compensate for each other to shape cellular elemental stoichiometry and resource interdependence. We coupled our model with metaproteomic and environmental data, yielding an approach for estimating biogeochemical metrics, including taxon-specific growth rates. Our results show that cumulative cellular costs govern how environmental conditions modify phytoplankton growth.
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Affiliation(s)
- J Scott P McCain
- Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada.
- Centre for Comparative Genomics and Evolutionary Bioinformatics, Dalhousie University, Halifax, Nova Scotia, Canada
| | | | - Edward Susko
- Centre for Comparative Genomics and Evolutionary Bioinformatics, Dalhousie University, Halifax, Nova Scotia, Canada
- Department of Mathematics and Statistics, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Eric P Achterberg
- GEOMAR Helmholtz Center for Ocean Research Kiel, Wischhofstrasse 1-3, 24148 Kiel, Germany
| | - Andrew E Allen
- Microbial and Environmental Genomics, J. Craig Venter Institute, La Jolla, CA 92037, USA
- Integrative Oceanography Division, Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA 92037, USA
| | - Erin M Bertrand
- Department of Biology, Dalhousie University, Halifax, Nova Scotia, Canada.
- Centre for Comparative Genomics and Evolutionary Bioinformatics, Dalhousie University, Halifax, Nova Scotia, Canada
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13
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Svecla M, Garrone G, Faré F, Aletti G, Norata GD, Beretta G. DDASSQ: an open-source, multiple peptide sequencing strategy for label free quantification based on an OpenMS pipeline in the KNIME analytics platform. Proteomics 2021; 21:e2000319. [PMID: 34312990 PMCID: PMC8459258 DOI: 10.1002/pmic.202000319] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Revised: 07/08/2021] [Accepted: 07/12/2021] [Indexed: 11/16/2022]
Abstract
In this study we investigated the performance of a computational pipeline for protein identification and label free quantification (LFQ) of LC–MS/MS data sets from experimental animal tissue samples, as well as the impact of its specific peptide search combinatorial approach. The full pipeline workflow was composed of peptide search engine adapters based on different identification algorithms, in the frame of the open‐source OpenMS software running within the KNIME analytics platform. Two different in silico tryptic digestion, database‐search assisted approaches (X!Tandem and MS‐GF+), de novo peptide sequencing based on Novor and consensus library search (SpectraST), were tested for the processing of LC‐MS/MS raw data files obtained from proteomic LC‐MS experiments done on proteolytic extracts from mouse ex vivo liver samples. The results from proteomic LFQ were compared to those based on the application of the two software tools MaxQuant and Proteome Discoverer for protein inference and label‐free data analysis in shotgun proteomics. Data are available via ProteomeXchange with identifier PXD025097.
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Affiliation(s)
- Monika Svecla
- Department of Excellence of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy
| | | | | | - Giacomo Aletti
- Department of Environmental Science and Policy, University of Milan, Milan, Italy
| | - Giuseppe Danilo Norata
- Department of Excellence of Pharmacological and Biomolecular Sciences, University of Milan, Milan, Italy.,Centro Studio Aterosclerosi, Bassini Hospital, Cinisello Balsamo, Milan, Italy
| | - Giangiacomo Beretta
- Department of Environmental Science and Policy, University of Milan, Milan, Italy
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14
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Karlsen J, Asplund-Samuelsson J, Jahn M, Vitay D, Hudson EP. Slow Protein Turnover Explains Limited Protein-Level Response to Diurnal Transcriptional Oscillations in Cyanobacteria. Front Microbiol 2021; 12:657379. [PMID: 34194405 PMCID: PMC8237939 DOI: 10.3389/fmicb.2021.657379] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 03/22/2021] [Indexed: 12/31/2022] Open
Abstract
Metabolically engineered cyanobacteria have the potential to mitigate anthropogenic CO2 emissions by converting CO2 into renewable fuels and chemicals. Yet, better understanding of metabolic regulation in cyanobacteria is required to develop more productive strains that can make industrial scale-up economically feasible. The aim of this study was to find the cause for the previously reported inconsistency between oscillating transcription and constant protein levels under day-night growth conditions. To determine whether translational regulation counteracts transcriptional changes, Synechocystis sp. PCC 6803 was cultivated in an artificial day-night setting and the level of transcription, translation and protein was measured across the genome at different time points using mRNA sequencing, ribosome profiling and quantitative proteomics. Furthermore, the effect of protein turnover on the amplitude of protein oscillations was investigated through in silico simulations using a protein mass balance model. Our experimental analysis revealed that protein oscillations were not dampened by translational regulation, as evidenced by high correlation between translational and transcriptional oscillations (r = 0.88) and unchanged protein levels. Instead, model simulations showed that these observations can be attributed to a slow protein turnover, which reduces the effect of protein synthesis oscillations on the protein level. In conclusion, these results suggest that cyanobacteria have evolved to govern diurnal metabolic shifts through allosteric regulatory mechanisms in order to avoid the energy burden of replacing the proteome on a daily basis. Identification and manipulation of such mechanisms could be part of a metabolic engineering strategy for overproduction of chemicals.
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Affiliation(s)
- Jan Karlsen
- Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.,Science for Life Laboratory, Stockholm, Sweden
| | - Johannes Asplund-Samuelsson
- Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.,Science for Life Laboratory, Stockholm, Sweden
| | - Michael Jahn
- Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.,Science for Life Laboratory, Stockholm, Sweden
| | - Dóra Vitay
- Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.,Science for Life Laboratory, Stockholm, Sweden.,Biosyntia ApS, Copenhagen, Denmark
| | - Elton P Hudson
- Department of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden.,Science for Life Laboratory, Stockholm, Sweden
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15
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Marcu A, Bichmann L, Kuchenbecker L, Kowalewski DJ, Freudenmann LK, Backert L, Mühlenbruch L, Szolek A, Lübke M, Wagner P, Engler T, Matovina S, Wang J, Hauri-Hohl M, Martin R, Kapolou K, Walz JS, Velz J, Moch H, Regli L, Silginer M, Weller M, Löffler MW, Erhard F, Schlosser A, Kohlbacher O, Stevanović S, Rammensee HG, Neidert MC. HLA Ligand Atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy. J Immunother Cancer 2021; 9:e002071. [PMID: 33858848 PMCID: PMC8054196 DOI: 10.1136/jitc-2020-002071] [Citation(s) in RCA: 100] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/25/2021] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND The human leucocyte antigen (HLA) complex controls adaptive immunity by presenting defined fractions of the intracellular and extracellular protein content to immune cells. Understanding the benign HLA ligand repertoire is a prerequisite to define safe T-cell-based immunotherapies against cancer. Due to the poor availability of benign tissues, if available, normal tissue adjacent to the tumor has been used as a benign surrogate when defining tumor-associated antigens. However, this comparison has proven to be insufficient and even resulted in lethal outcomes. In order to match the tumor immunopeptidome with an equivalent counterpart, we created the HLA Ligand Atlas, the first extensive collection of paired HLA-I and HLA-II immunopeptidomes from 227 benign human tissue samples. This dataset facilitates a balanced comparison between tumor and benign tissues on HLA ligand level. METHODS Human tissue samples were obtained from 16 subjects at autopsy, five thymus samples and two ovary samples originating from living donors. HLA ligands were isolated via immunoaffinity purification and analyzed in over 1200 liquid chromatography mass spectrometry runs. Experimentally and computationally reproducible protocols were employed for data acquisition and processing. RESULTS The initial release covers 51 HLA-I and 86 HLA-II allotypes presenting 90,428 HLA-I- and 142,625 HLA-II ligands. The HLA allotypes are representative for the world population. We observe that immunopeptidomes differ considerably between tissues and individuals on source protein and HLA-ligand level. Moreover, we discover 1407 HLA-I ligands from non-canonical genomic regions. Such peptides were previously described in tumors, peripheral blood mononuclear cells (PBMCs), healthy lung tissues and cell lines. In a case study in glioblastoma, we show that potential on-target off-tumor adverse events in immunotherapy can be avoided by comparing tumor immunopeptidomes to the provided multi-tissue reference. CONCLUSION Given that T-cell-based immunotherapies, such as CAR-T cells, affinity-enhanced T cell transfer, cancer vaccines and immune checkpoint inhibition, have significant side effects, the HLA Ligand Atlas is the first step toward defining tumor-associated targets with an improved safety profile. The resource provides insights into basic and applied immune-associated questions in the context of cancer immunotherapy, infection, transplantation, allergy and autoimmunity. It is publicly available and can be browsed in an easy-to-use web interface at https://hla-ligand-atlas.org .
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Affiliation(s)
- Ana Marcu
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Leon Bichmann
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Leon Kuchenbecker
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Daniel Johannes Kowalewski
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
| | - Lena Katharina Freudenmann
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - Linus Backert
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Lena Mühlenbruch
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - András Szolek
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
| | - Maren Lübke
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
| | - Philipp Wagner
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Department of Obstetrics and Gynecology, University Hospital of Tübingen, Tübingen, Germany
| | - Tobias Engler
- Department of Obstetrics and Gynecology, University Hospital of Tübingen, Tübingen, Germany
| | - Sabine Matovina
- Department of Obstetrics and Gynecology, University Hospital of Tübingen, Tübingen, Germany
| | - Jian Wang
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Mathias Hauri-Hohl
- Pediatric Stem Cell Transplantation, University Children's Hospital Zurich, Zurich, Switzerland
| | - Roland Martin
- Neuroimmunology and MS Research, Neurology Clinic, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Konstantina Kapolou
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Juliane Sarah Walz
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- Clinical Collaboration Unit Translational Immunology, German Cancer Consortium (DKTK), University Hospital of Tübingen, Tübingen, Germany
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology (IKP) and Robert Bosch Center for Tumor Diseases (RBCT), Stuttgart, Germany
| | - Julia Velz
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Holger Moch
- Department of Pathology and Molecular Pathology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Luca Regli
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
| | - Manuela Silginer
- Clinical Neuroscience Center and Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Michael Weller
- Clinical Neuroscience Center and Department of Neurology, University Hospital and University of Zurich, Zurich, Switzerland
| | - Markus W Löffler
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
- Department of General, Visceral and Transplant Surgery, University Hospital of Tübingen, Tübingen, Germany
- Department of Clinical Pharmacology, University of Hospital Tübingen, Tübingen, Germany
| | - Florian Erhard
- Institute for Virology and Immunobiology, Julius-Maximilians-University Würzburg, Würzburg, Bayern, Germany
| | - Andreas Schlosser
- Rudolf Virchow Center - Center for Integrative and Translational Bioimaging, Julius-Maximilians-University Würzburg, Würzburg, Germany
| | - Oliver Kohlbacher
- Applied Bioinformatics, Department of Computer Science, University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
- Institute for Bioinformatics and Medical Informatics, University of Tübingen, Tübingen, Germany
- Quantitative Biology Center (QBiC), University of Tübingen, Tübingen, Germany
- Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany
- Cluster of Excellence Machine Learning in the Sciences (EXC 2064), University of Tübingen, Tübingen, Germany
- Institute for Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
| | - Stefan Stevanović
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - Hans-Georg Rammensee
- Department of Immunology, Interfaculty Institute for Cell Biology, University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180) "Image-Guided and Functionally Instructed Tumor Therapies", University of Tübingen, Tübingen, Germany
- DKFZ Partner Site Tübingen, German Cancer Consortium (DKTK), Tübingen, Germany
| | - Marian Christoph Neidert
- Clinical Neuroscience Center and Department of Neurosurgery, University Hospital and University of Zurich, Zurich, Switzerland
- Department of Neurosurgery, Cantonal Hospital St.Gallen, St.Gallen, Switzerland
- Neuroscience Center Zurich (ZNZ), University of Zurich and ETH Zurich, Zurich, Switzerland
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16
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Kösters M, Leufken J, Leidel SA. SMITER-A Python Library for the Simulation of LC-MS/MS Experiments. Genes (Basel) 2021; 12:396. [PMID: 33799543 PMCID: PMC8000309 DOI: 10.3390/genes12030396] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/05/2021] [Accepted: 03/08/2021] [Indexed: 12/24/2022] Open
Abstract
SMITER (Synthetic mzML writer) is a Python-based command-line tool designed to simulate liquid-chromatography-coupled tandem mass spectrometry LC-MS/MS runs. It enables the simulation of any biomolecule amenable to mass spectrometry (MS) since all calculations are based on chemical formulas. SMITER features a modular design, allowing for an easy implementation of different noise and fragmentation models. By default, SMITER uses an established noise model and offers several methods for peptide fragmentation, and two models for nucleoside fragmentation and one for lipid fragmentation. Due to the rich Python ecosystem, other modules, e.g., for retention time (RT) prediction, can easily be implemented for the tailored simulation of any molecule of choice. This facilitates the generation of defined gold-standard LC-MS/MS datasets for any type of experiment. Such gold standards, where the ground truth is known, are required in computational mass spectrometry to test new algorithms and to improve parameters of existing ones. Similarly, gold-standard datasets can be used to evaluate analytical challenges, e.g., by predicting co-elution and co-fragmentation of molecules. As these challenges hinder the detection or quantification of co-eluents, a comprehensive simulation can identify and thus, prevent such difficulties before performing actual MS experiments. SMITER allows the creation of such datasets easily, fast, and efficiently.
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Affiliation(s)
| | | | - Sebastian A. Leidel
- Department of Chemistry, Biochemistry and Pharmaceutical Sciences (DCBP), University of Bern, Freiestrasse 3, 3012 Bern, Switzerland; (M.K.); (J.L.)
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17
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Sénécaut N, Alves G, Weisser H, Lignières L, Terrier S, Yang-Crosson L, Poulain P, Lelandais G, Yu YK, Camadro JM. Novel Insights into Quantitative Proteomics from an Innovative Bottom-Up Simple Light Isotope Metabolic (bSLIM) Labeling Data Processing Strategy. J Proteome Res 2021; 20:1476-1487. [PMID: 33573382 PMCID: PMC8459934 DOI: 10.1021/acs.jproteome.0c00478] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Simple light isotope metabolic labeling (SLIM labeling) is an innovative method to quantify variations in the proteome based on an original in vivo labeling strategy. Heterotrophic cells grown in U-[12C] as the sole source of carbon synthesize U-[12C]-amino acids, which are incorporated into proteins, giving rise to U-[12C]-proteins. This results in a large increase in the intensity of the monoisotope ion of peptides and proteins, thus allowing higher identification scores and protein sequence coverage in mass spectrometry experiments. This method, initially developed for signal processing and quantification of the incorporation rate of 12C into peptides, was based on a multistep process that was difficult to implement for many laboratories. To overcome these limitations, we developed a new theoretical background to analyze bottom-up proteomics data using SLIM-labeling (bSLIM) and established simple procedures based on open-source software, using dedicated OpenMS modules, and embedded R scripts to process the bSLIM experimental data. These new tools allow computation of both the 12C abundance in peptides to follow the kinetics of protein labeling and the molar fraction of unlabeled and 12C-labeled peptides in multiplexing experiments to determine the relative abundance of proteins extracted under different biological conditions. They also make it possible to consider incomplete 12C labeling, such as that observed in cells with nutritional requirements for nonlabeled amino acids. These tools were validated on an experimental dataset produced using various yeast strains of Saccharomyces cerevisiae and growth conditions. The workflows are built on the implementation of appropriate calculation modules in a KNIME working environment. These new integrated tools provide a convenient framework for the wider use of the SLIM-labeling strategy.
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Affiliation(s)
- Nicolas Sénécaut
- ≪ Mitochondria, Metals, and Oxidative Stress ≫ Group, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
| | - Gelio Alves
- National Center for Biotechnology Information, NLM, NIH, Bethesda, Maryland 20894, United States
| | | | - Laurent Lignières
- ProteoSeine@IJM, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
| | - Samuel Terrier
- ProteoSeine@IJM, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
| | - Lilian Yang-Crosson
- ≪ Mitochondria, Metals, and Oxidative Stress ≫ Group, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
| | - Pierre Poulain
- ≪ Mitochondria, Metals, and Oxidative Stress ≫ Group, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
| | - Gaëlle Lelandais
- Institut de Biologie Intégrative de la Cellule, 91190 Orsay, France
| | - Yi-Kuo Yu
- National Center for Biotechnology Information, NLM, NIH, Bethesda, Maryland 20894, United States
| | - Jean-Michel Camadro
- ≪ Mitochondria, Metals, and Oxidative Stress ≫ Group, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
- ProteoSeine@IJM, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
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18
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Fu J, Luo Y, Mou M, Zhang H, Tang J, Wang Y, Zhu F. Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection. Curr Drug Targets 2021; 21:34-54. [PMID: 31433754 DOI: 10.2174/1389450120666190821160207] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 07/17/2019] [Accepted: 07/24/2019] [Indexed: 12/13/2022]
Abstract
BACKGROUND Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets. OBJECTIVE The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics. METHODS Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics. RESULTS In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed. CONCLUSION In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Hongning Zhang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China.,School of Pharmaceutical Sciences and Innovative Drug Research Centre, Chongqing University, Chongqing 401331, China
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19
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Millikin RJ, Shortreed MR, Scalf M, Smith LM. A Bayesian Null Interval Hypothesis Test Controls False Discovery Rates and Improves Sensitivity in Label-Free Quantitative Proteomics. J Proteome Res 2020; 19:1975-1981. [PMID: 32243168 DOI: 10.1021/acs.jproteome.9b00796] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Statistical significance tests are a common feature in quantitative proteomics workflows. The Student's t-test is widely used to compute the statistical significance of a protein's change between two groups of samples. However, the t-test's null hypothesis asserts that the difference in means between two groups is exactly zero, often marking small but uninteresting fold-changes as statistically significant. Compensations to address this issue are widely used in quantitative proteomics, but we suggest that a replacement of the t-test with a Bayesian approach offers a better path forward. In this article, we describe a Bayesian hypothesis test in which the null hypothesis is an interval rather than a single point at zero; the width of the interval is estimated from population statistics. The improved sensitivity of the method substantially increases the number of truly changing proteins detected in two benchmark data sets (ProteomeXchange identifiers PXD005590 and PXD016470). The method has been implemented within FlashLFQ, an open-source software program that quantifies bottom-up proteomics search results obtained from any search tool. FlashLFQ is rapid, sensitive, and accurate and is available both as an easy-to-use graphical user interface (Windows) and as a command-line tool (Windows/Linux/OSX).
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Affiliation(s)
- Robert J Millikin
- Department of Chemistry, University of Wisconsin, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Michael R Shortreed
- Department of Chemistry, University of Wisconsin, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Mark Scalf
- Department of Chemistry, University of Wisconsin, 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin, 1101 University Avenue, Madison, Wisconsin 53706, United States
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20
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Mische SM, Fisher NC, Meyn SM, Sol-Church K, Hegstad-Davies RL, Weis-Garcia F, Adams M, Ashton JM, Delventhal KM, Dragon JA, Holmes L, Jagtap P, Kubow KE, Mason CE, Palmblad M, Searle BC, Turck CW, Knudtson KL. A Review of the Scientific Rigor, Reproducibility, and Transparency Studies Conducted by the ABRF Research Groups. J Biomol Tech 2020; 31:11-26. [PMID: 31969795 PMCID: PMC6959150 DOI: 10.7171/jbt.20-3101-003] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Shared research resource facilities, also known as core laboratories (Cores), are responsible for generating a significant and growing portion of the research data in academic biomedical research institutions. Cores represent a central repository for institutional knowledge management, with deep expertise in the strengths and limitations of technology and its applications. They inherently support transparency and scientific reproducibility by protecting against cognitive bias in research design and data analysis, and they have institutional responsibility for the conduct of research (research ethics, regulatory compliance, and financial accountability) performed in their Cores. The Association of Biomolecular Resource Facilities (ABRF) is a FASEB-member scientific society whose members are scientists and administrators that manage or support Cores. The ABRF Research Groups (RGs), representing expertise for an array of cutting-edge and established technology platforms, perform multicenter research studies to determine and communicate best practices and community-based standards. This review provides a summary of the contributions of the ABRF RGs to promote scientific rigor and reproducibility in Cores from the published literature, ABRF meetings, and ABRF RGs communications.
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Affiliation(s)
- Sheenah M. Mische
- New York University (NYU) Langone Medical Center, New
York, New York 10016, USA
| | - Nancy C. Fisher
- University of North Carolina at Chapel Hill, Chapel
Hill, North Carolina 27599, USA
| | - Susan M. Meyn
- Vanderbilt University Medical Center, Nashville,
Tennessee 37212, USA
| | - Katia Sol-Church
- University of Virginia School of Medicine,
Charlottesville, Virginia 22908, USA
| | | | | | - Marie Adams
- Van Andel Institute, Grand Rapids, Michigan 49503,
USA
| | - John M. Ashton
- University of Rochester Medical Center, West
Henrietta, New York 14642, USA
| | - Kym M. Delventhal
- Stowers Institute for Medical Research, Kansas City,
Missouri 64110, USA
| | | | - Laura Holmes
- Stowers Institute for Medical Research, Kansas City,
Missouri 64110, USA
| | - Pratik Jagtap
- University of Minnesota, Minneapolis, Minnesota
55455, USA
| | | | | | - Magnus Palmblad
- Leiden University Medical Center, Leiden 2333, The
Netherlands
| | - Brian C. Searle
- Institute for Systems Biology, Seattle, Washington
98109, USA
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21
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Zhao L, Cong X, Zhai L, Hu H, Xu JY, Zhao W, Zhu M, Tan M, Ye BC. Comparative evaluation of label-free quantification strategies. J Proteomics 2020; 215:103669. [DOI: 10.1016/j.jprot.2020.103669] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Revised: 12/28/2019] [Accepted: 01/22/2020] [Indexed: 12/17/2022]
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22
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Wein S, Andrews B, Sachsenberg T, Santos-Rosa H, Kohlbacher O, Kouzarides T, Garcia BA, Weisser H. A computational platform for high-throughput analysis of RNA sequences and modifications by mass spectrometry. Nat Commun 2020; 11:926. [PMID: 32066737 PMCID: PMC7026122 DOI: 10.1038/s41467-020-14665-7] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Accepted: 01/22/2020] [Indexed: 02/06/2023] Open
Abstract
The field of epitranscriptomics continues to reveal how post-transcriptional modification of RNA affects a wide variety of biological phenomena. A pivotal challenge in this area is the identification of modified RNA residues within their sequence contexts. Mass spectrometry (MS) offers a comprehensive solution by using analogous approaches to shotgun proteomics. However, software support for the analysis of RNA MS data is inadequate at present and does not allow high-throughput processing. Existing software solutions lack the raw performance and statistical grounding to efficiently handle the numerous modifications found on RNA. We present a free and open-source database search engine for RNA MS data, called NucleicAcidSearchEngine (NASE), that addresses these shortcomings. We demonstrate the capability of NASE to reliably identify a wide range of modified RNA sequences in four original datasets of varying complexity. In human tRNA, we characterize over 20 different modification types simultaneously and find many cases of incomplete modification.
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Affiliation(s)
- Samuel Wein
- Epigenetics Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Bioinformatics Tübingen, University of Tübingen, Tübingen, Germany
| | - Byron Andrews
- STORM Therapeutics Limited, Moneta Building, Babraham Research Campus, Cambridge, UK
| | - Timo Sachsenberg
- Applied Bioinformatics, Department for Computer Science, University of Tübingen, Tübingen, Germany
| | | | - Oliver Kohlbacher
- Center for Bioinformatics Tübingen, University of Tübingen, Tübingen, Germany
- Applied Bioinformatics, Department for Computer Science, University of Tübingen, Tübingen, Germany
- Quantitative Biology Center, University of Tübingen, Tübingen, Germany
- Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen, Germany
- Translational Bioinformatics, University Hospital Tübingen, Tübingen, Germany
| | | | - Benjamin A Garcia
- Epigenetics Program, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Hendrik Weisser
- STORM Therapeutics Limited, Moneta Building, Babraham Research Campus, Cambridge, UK.
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23
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Hulstaert N, Shofstahl J, Sachsenberg T, Walzer M, Barsnes H, Martens L, Perez-Riverol Y. ThermoRawFileParser: Modular, Scalable, and Cross-Platform RAW File Conversion. J Proteome Res 2019; 19:537-542. [PMID: 31755270 DOI: 10.1021/acs.jproteome.9b00328] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
The field of computational proteomics is approaching the big data age, driven both by a continuous growth in the number of samples analyzed per experiment as well as by the growing amount of data obtained in each analytical run. In order to process these large amounts of data, it is increasingly necessary to use elastic compute resources such as Linux-based cluster environments and cloud infrastructures. Unfortunately, the vast majority of cross-platform proteomics tools are not able to operate directly on the proprietary formats generated by the diverse mass spectrometers. Here, we present ThermoRawFileParser, an open-source, cross-platform tool that converts Thermo RAW files into open file formats such as MGF and the HUPO-PSI standard file format mzML. To ensure the broadest possible availability and to increase integration capabilities with popular workflow systems such as Galaxy or Nextflow, we have also built Conda package and BioContainers container around ThermoRawFileParser. In addition, we implemented a user-friendly interface (ThermoRawFileParserGUI) for those users not familiar with command-line tools. Finally, we performed a benchmark of ThermoRawFileParser and msconvert to verify that the converted mzML files contain reliable quantitative results.
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Affiliation(s)
- Niels Hulstaert
- VIB-UGent Center for Medical Biotechnology, VIB , Ghent B-9000 , Belgium.,Department of Biomolecular Medicine , Ghent University , Ghent B-9000 , Belgium
| | - Jim Shofstahl
- Thermo Fisher Scientific , 355 River Oaks Parkway , San Jose , California 95134 , United States
| | - Timo Sachsenberg
- Applied Bioinformatics, Department for Computer Science , University of Tuebingen , Sand 14 , 72076 Tuebingen , Germany
| | - Mathias Walzer
- European Molecular Biology Laboratory , European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD , United Kingdom
| | - Harald Barsnes
- Computational Biology Unit (CBU), Department of Informatics , University of Bergen , Bergen 5020 , Norway.,Proteomics Unit (PROBE), Department of Biomedicine , University of Bergen , Bergen 5020 , Norway
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB , Ghent B-9000 , Belgium.,Department of Biomolecular Medicine , Ghent University , Ghent B-9000 , Belgium
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory , European Bioinformatics Institute (EMBL-EBI) , Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD , United Kingdom
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24
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Wang X, Shen S, Rasam SS, Qu J. MS1 ion current-based quantitative proteomics: A promising solution for reliable analysis of large biological cohorts. MASS SPECTROMETRY REVIEWS 2019; 38:461-482. [PMID: 30920002 PMCID: PMC6849792 DOI: 10.1002/mas.21595] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2018] [Accepted: 02/28/2019] [Indexed: 05/04/2023]
Abstract
The rapidly-advancing field of pharmaceutical and clinical research calls for systematic, molecular-level characterization of complex biological systems. To this end, quantitative proteomics represents a powerful tool but an optimal solution for reliable large-cohort proteomics analysis, as frequently involved in pharmaceutical/clinical investigations, is urgently needed. Large-cohort analysis remains challenging owing to the deteriorating quantitative quality and snowballing missing data and false-positive discovery of altered proteins when sample size increases. MS1 ion current-based methods, which have become an important class of label-free quantification techniques during the past decade, show considerable potential to achieve reproducible protein measurements in large cohorts with high quantitative accuracy/precision. Nonetheless, in order to fully unleash this potential, several critical prerequisites should be met. Here we provide an overview of the rationale of MS1-based strategies and then important considerations for experimental and data processing techniques, with the emphasis on (i) efficient and reproducible sample preparation and LC separation; (ii) sensitive, selective and high-resolution MS detection; iii)accurate chromatographic alignment; (iv) sensitive and selective generation of quantitative features; and (v) optimal post-feature-generation data quality control. Prominent technical developments in these aspects are discussed. Finally, we reviewed applications of MS1-based strategy in disease mechanism studies, biomarker discovery, and pharmaceutical investigations.
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Affiliation(s)
- Xue Wang
- Department of Cell Stress BiologyRoswell Park Cancer InstituteBuffaloNew York
| | - Shichen Shen
- Department of Pharmaceutical SciencesUniversity at BuffaloState University of New YorkNew YorkNew York
| | - Sailee Suryakant Rasam
- Department of Biochemistry, University at BuffaloState University of New YorkNew YorkNew York
| | - Jun Qu
- Department of Cell Stress BiologyRoswell Park Cancer InstituteBuffaloNew York
- Department of Pharmaceutical SciencesUniversity at BuffaloState University of New YorkNew YorkNew York
- Department of Biochemistry, University at BuffaloState University of New YorkNew YorkNew York
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25
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Bichmann L, Nelde A, Ghosh M, Heumos L, Mohr C, Peltzer A, Kuchenbecker L, Sachsenberg T, Walz JS, Stevanović S, Rammensee HG, Kohlbacher O. MHCquant: Automated and Reproducible Data Analysis for Immunopeptidomics. J Proteome Res 2019; 18:3876-3884. [DOI: 10.1021/acs.jproteome.9b00313] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
| | | | | | | | | | | | | | | | | | - Stefan Stevanović
- German Cancer Consortium (DKTK), DKFZ Partner Site, Tübingen 72076, Germany
| | | | - Oliver Kohlbacher
- Biomolecular Interactions, Max Planck Institute for Developmental Biology, Tübingen 72076, Germany
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26
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Tang J, Fu J, Wang Y, Luo Y, Yang Q, Li B, Tu G, Hong J, Cui X, Chen Y, Yao L, Xue W, Zhu F. Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains. Mol Cell Proteomics 2019; 18:1683-1699. [PMID: 31097671 PMCID: PMC6682996 DOI: 10.1074/mcp.ra118.001169] [Citation(s) in RCA: 93] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Revised: 04/28/2019] [Indexed: 12/13/2022] Open
Abstract
The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.
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Affiliation(s)
- Jing Tang
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China; ¶Department of Bioinformatics, Chongqing Medical University, Chongqing 400016, China
| | - Jianbo Fu
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Qingxia Yang
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Bo Li
- §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Gao Tu
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Jiajun Hong
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Xuejiao Cui
- §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Yuzong Chen
- ‖Department of Pharmacy, National University of Singapore, Singapore 117543, Singapore
| | - Lixia Yao
- **Department of Health Sciences Research, Mayo Clinic, Rochester MN 55905, United States
| | - Weiwei Xue
- §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China
| | - Feng Zhu
- ‡College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; §School of Pharmaceutical Sciences, Chongqing University, Chongqing 401331, China.
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27
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Chen AT, Franks A, Slavov N. DART-ID increases single-cell proteome coverage. PLoS Comput Biol 2019; 15:e1007082. [PMID: 31260443 PMCID: PMC6625733 DOI: 10.1371/journal.pcbi.1007082] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 07/12/2019] [Accepted: 05/06/2019] [Indexed: 01/09/2023] Open
Abstract
Analysis by liquid chromatography and tandem mass spectrometry (LC-MS/MS) can identify and quantify thousands of proteins in microgram-level samples, such as those comprised of thousands of cells. This process, however, remains challenging for smaller samples, such as the proteomes of single mammalian cells, because reduced protein levels reduce the number of confidently sequenced peptides. To alleviate this reduction, we developed Data-driven Alignment of Retention Times for IDentification (DART-ID). DART-ID implements principled Bayesian frameworks for global retention time (RT) alignment and for incorporating RT estimates towards improved confidence estimates of peptide-spectrum-matches. When applied to bulk or to single-cell samples, DART-ID increased the number of data points by 30-50% at 1% FDR, and thus decreased missing data. Benchmarks indicate excellent quantification of peptides upgraded by DART-ID and support their utility for quantitative analysis, such as identifying cell types and cell-type specific proteins. The additional datapoints provided by DART-ID boost the statistical power and double the number of proteins identified as differentially abundant in monocytes and T-cells. DART-ID can be applied to diverse experimental designs and is freely available at http://dart-id.slavovlab.net.
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Affiliation(s)
- Albert Tian Chen
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America
- Barnett Institute, Northeastern University, Boston, Massachusetts, United States of America
| | - Alexander Franks
- Department of Statistics and Applied Probability, University of California Santa Barbara, California, United States of America
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America
- Barnett Institute, Northeastern University, Boston, Massachusetts, United States of America
- Department of Biology, Northeastern University, Boston, Massachusetts, United States of America
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28
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Lorentzen LG, Chuang CY, Rogowska-Wrzesinska A, Davies MJ. Identification and quantification of sites of nitration and oxidation in the key matrix protein laminin and the structural consequences of these modifications. Redox Biol 2019; 24:101226. [PMID: 31154162 PMCID: PMC6543125 DOI: 10.1016/j.redox.2019.101226] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 05/06/2019] [Accepted: 05/19/2019] [Indexed: 01/01/2023] Open
Abstract
Laminin is a major protein of the basement membrane (BM), a specialized extracellular matrix (ECM) of the artery wall. The potent oxidizing and nitrating agent peroxynitrous acid (ONOOH) is formed at sites of inflammation, and data implicate ONOOH in ECM damage and cardiovascular disease. Co-localization of 3-nitrotyrosine, a product of ONOOH-mediated tyrosine (Tyr) modification, and laminin has been reported in human atherosclerotic lesions. The sites and consequences of 3-nitrotyrosine (and related nitrated tryptophan) formation on laminin, it's self-assembly and cell interactions are poorly understood. In this study murine laminin-111 was exposed to ONOOH (1–500-fold molar excess). Nitration sites were mapped and quantified using LC-MS/MS. Mono-nitration was detected at 148 sites (126 Tyr, 22 Trp), and di-nitration at 14 sites. Label-free quantification showed enhanced nitration with increasing oxidant doses. Tyr nitration was ∼10-fold greater than at Trp. CO2 modulated damage in a site-specific manner, with most sites less extensively nitrated. 119 mono-nitration sites were identified with CO2 present, and no unique sites were detected. 23 di-nitration sites were detected, with 15 unique to the presence of CO2. Extensive modification was detected at sites involved in cell adhesion, protein-protein interactions and self-polymerization. Tyr-145 on the γ1 chain was extensively nitrated, and endothelial cells exhibited decreased adhesion to a nitrated peptide modelling this site. Modification of residues involved in self-polymerization interfered with the formation of ordered polymers as detected by scanning electron microscopy. These laminin modifications may contribute to endothelial cell dysfunction and modulate ECM structure and assembly, and thereby contribute to atherogenesis. Laminin is a major extracellular matrix protein of the artery wall. Peroxynitrous acid exposure gives nitration of tyrosine and tryptophan residues. CO2 both increases and decreases damage depending of the reaction site. LC-MS/MS used to map modifications to protein structure and functional domains. Sites for cell adhesion, protein interactions and self-polymerization are modified.
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Affiliation(s)
- Lasse G Lorentzen
- Dept. of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Christine Y Chuang
- Dept. of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Adelina Rogowska-Wrzesinska
- Dept. of Biochemistry and Molecular Biology and VILLUM Center for Bioanalytical Sciences, University of Southern Denmark, Odense, Denmark
| | - Michael J Davies
- Dept. of Biomedical Sciences, University of Copenhagen, Copenhagen, Denmark.
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29
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Doblmann J, Dusberger F, Imre R, Hudecz O, Stanek F, Mechtler K, Dürnberger G. apQuant: Accurate Label-Free Quantification by Quality Filtering. J Proteome Res 2018; 18:535-541. [PMID: 30351950 DOI: 10.1021/acs.jproteome.8b00113] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Label-free quantification of shotgun proteomics data is a frequently used strategy, offering high dynamic range, sensitivity, and the ability to compare a high number of samples without additional labeling effort. Here, we present a bioinformatics approach that significantly improves label-free quantification results. We employ Percolator to assess the quality of quantified peptides. This allows to extract accurate and reliable quantitative results based on false discovery rate. Benchmarking our approach on previously published public data shows that it considerably outperforms currently available algorithms. apQuant is available free of charge as a node for Proteome Discoverer.
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Affiliation(s)
- Johannes Doblmann
- Research Institute of Molecular Pathology (IMP) , Vienna Biocenter (VBC) , Campus-Vienna-Biocenter 1 , 1030 Vienna , Austria
| | - Frederico Dusberger
- Research Institute of Molecular Pathology (IMP) , Vienna Biocenter (VBC) , Campus-Vienna-Biocenter 1 , 1030 Vienna , Austria
| | - Richard Imre
- Research Institute of Molecular Pathology (IMP) , Vienna Biocenter (VBC) , Campus-Vienna-Biocenter 1 , 1030 Vienna , Austria.,Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA) , Vienna Biocenter (VBC) , Dr. Bohr-Gasse 3 , 1030 Vienna , Austria
| | - Otto Hudecz
- Research Institute of Molecular Pathology (IMP) , Vienna Biocenter (VBC) , Campus-Vienna-Biocenter 1 , 1030 Vienna , Austria.,Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA) , Vienna Biocenter (VBC) , Dr. Bohr-Gasse 3 , 1030 Vienna , Austria
| | - Florian Stanek
- Research Institute of Molecular Pathology (IMP) , Vienna Biocenter (VBC) , Campus-Vienna-Biocenter 1 , 1030 Vienna , Austria
| | - Karl Mechtler
- Research Institute of Molecular Pathology (IMP) , Vienna Biocenter (VBC) , Campus-Vienna-Biocenter 1 , 1030 Vienna , Austria.,Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA) , Vienna Biocenter (VBC) , Dr. Bohr-Gasse 3 , 1030 Vienna , Austria
| | - Gerhard Dürnberger
- Research Institute of Molecular Pathology (IMP) , Vienna Biocenter (VBC) , Campus-Vienna-Biocenter 1 , 1030 Vienna , Austria.,Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA) , Vienna Biocenter (VBC) , Dr. Bohr-Gasse 3 , 1030 Vienna , Austria.,Gregor Mendel Institute of Molecular Plant Biology (GMI) , Vienna Biocenter (VBC) , Dr. Bohr-Gasse 3 , 1030 Vienna , Austria
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30
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Fu J, Tang J, Wang Y, Cui X, Yang Q, Hong J, Li X, Li S, Chen Y, Xue W, Zhu F. Discovery of the Consistently Well-Performed Analysis Chain for SWATH-MS Based Pharmacoproteomic Quantification. Front Pharmacol 2018; 9:681. [PMID: 29997509 PMCID: PMC6028727 DOI: 10.3389/fphar.2018.00681] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Accepted: 06/05/2018] [Indexed: 12/20/2022] Open
Abstract
Sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS) has emerged as one of the most popular techniques for label-free proteome quantification in current pharmacoproteomic research. It provides more comprehensive detection and more accurate quantitation of proteins comparing with the traditional techniques. The performance of SWATH-MS is highly susceptible to the selection of processing method. Till now, ≥27 methods (transformation, normalization, and missing-value imputation) are sequentially applied to construct numerous analysis chains for SWATH-MS, but it is still not clear which analysis chain gives the optimal quantification performance. Herein, the performances of 560 analysis chains for quantifying pharmacoproteomic data were comprehensively assessed. Firstly, the most complete set of the publicly available SWATH-MS based pharmacoproteomic data were collected by comprehensive literature review. Secondly, substantial variations among the performances of various analysis chains were observed, and the consistently well-performed analysis chains (CWPACs) across various datasets were for the first time generalized. Finally, the log and power transformations sequentially followed by the total ion current normalization were discovered as one of the best performed analysis chains for the quantification of SWATH-MS based pharmacoproteomic data. In sum, the CWPACs identified here provided important guidance to the quantification of proteomic data and could therefore facilitate the cutting-edge research in any pharmacoproteomic studies requiring SWATH-MS technique.
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Affiliation(s)
- Jianbo Fu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Jing Tang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xuejiao Cui
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Qingxia Yang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Jiajun Hong
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Xiaoxu Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Shuang Li
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Yuzong Chen
- Bioinformatics and Drug Design Group, Department of Pharmacy, Center for Computational Science and Engineering, National University of Singapore, Singapore, Singapore
| | - Weiwei Xue
- School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.,School of Pharmaceutical Sciences and Collaborative Innovation Center for Brain Science, Chongqing University, Chongqing, China
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31
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Shen S, Wang X, Orsburn BC, Qu J. How could IonStar challenge the current status quo of quantitative proteomics in large sample cohorts? Expert Rev Proteomics 2018; 15:541-543. [PMID: 29911452 DOI: 10.1080/14789450.2018.1490646] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
Affiliation(s)
- Shichen Shen
- a Department of Pharmaceutical Sciences , School of Pharmacy and Pharmaceutical Sciences, University at Buffalo , Buffalo , NY , USA.,b New York State Center of Excellence in Bioinformatics & Life Sciences , Buffalo , NY , USA
| | - Xue Wang
- b New York State Center of Excellence in Bioinformatics & Life Sciences , Buffalo , NY , USA.,c Department of Cell Stress Biology , Roswell Park Cancer Institute , Buffalo , NY , USA
| | - Benjamin C Orsburn
- d Cancer Research Technology Program, Frederick National Laboratory for Cancer Research , Frederick , Maryland , USA
| | - Jun Qu
- a Department of Pharmaceutical Sciences , School of Pharmacy and Pharmaceutical Sciences, University at Buffalo , Buffalo , NY , USA.,b New York State Center of Excellence in Bioinformatics & Life Sciences , Buffalo , NY , USA
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32
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IonStar enables high-precision, low-missing-data proteomics quantification in large biological cohorts. Proc Natl Acad Sci U S A 2018; 115:E4767-E4776. [PMID: 29743190 DOI: 10.1073/pnas.1800541115] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Reproducible quantification of large biological cohorts is critical for clinical/pharmaceutical proteomics yet remains challenging because most prevalent methods suffer from drastically declined commonly quantified proteins and substantially deteriorated quantitative quality as cohort size expands. MS2-based data-independent acquisition approaches represent tremendous advancements in reproducible protein measurement, but often with limited depth. We developed IonStar, an MS1-based quantitative approach enabling in-depth, high-quality quantification of large cohorts by combining efficient/reproducible experimental procedures with unique data-processing components, such as efficient 3D chromatographic alignment, sensitive and selective direct ion current extraction, and stringent postfeature generation quality control. Compared with several popular label-free methods, IonStar exhibited far lower missing data (0.1%), superior quantitative accuracy/precision [∼5% intragroup coefficient of variation (CV)], the widest protein abundance range, and the highest sensitivity/specificity for identifying protein changes (<5% false altered-protein discovery) in a benchmark sample set (n = 20). We demonstrated the usage of IonStar by a large-scale investigation of traumatic injuries and pharmacological treatments in rat brains (n = 100), quantifying >7,000 unique protein groups (>99.8% without missing data across the 100 samples) with a low false discovery rate (FDR), two or more unique peptides per protein, and high quantitative precision. IonStar represents a reliable and robust solution for precise and reproducible protein measurement in large cohorts.
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33
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Millikin RJ, Solntsev SK, Shortreed MR, Smith LM. Ultrafast Peptide Label-Free Quantification with FlashLFQ. J Proteome Res 2017; 17:386-391. [PMID: 29083185 DOI: 10.1021/acs.jproteome.7b00608] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The rapid and accurate quantification of peptides is a critical element of modern proteomics that has become increasingly challenging as proteomic data sets grow in size and complexity. We present here FlashLFQ, a computer program for high-speed label-free quantification of peptides following a search of bottom-up mass spectrometry data. FlashLFQ is approximately an order of magnitude faster than established label-free quantification methods. The increased speed makes it practical to base quantification upon all of the charge states for a given peptide rather than solely upon the charge state that was selected for MS2 fragmentation. This increases the number of quantified peptides, improves replicate-to-replicate reproducibility, and increases quantitative accuracy. We integrated FlashLFQ into the graphical user interface of the MetaMorpheus search software, allowing it to work together with the global post-translational modification discovery (G-PTM-D) engine to accurately quantify modified peptides. FlashLFQ is also available as a NuGet package, facilitating its integration into other software, and as a standalone command line software program for the quantification of search results from other programs (e.g., MaxQuant).
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Affiliation(s)
- Robert J Millikin
- Department of Chemistry, University of Wisconsin , 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Stefan K Solntsev
- Department of Chemistry, University of Wisconsin , 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Michael R Shortreed
- Department of Chemistry, University of Wisconsin , 1101 University Avenue, Madison, Wisconsin 53706, United States
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin , 1101 University Avenue, Madison, Wisconsin 53706, United States.,Genome Center of Wisconsin, University of Wisconsin , 425G Henry Mall, Room 3420, Madison, Wisconsin 53706, United States
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