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Metatla I, Roger K, Chhuon C, Ceccacci S, Chapelle M, Pierre-Olivier Schmit, Demichev V, Guerrera IC. Neat plasma proteomics: getting the best out of the worst. Clin Proteomics 2024; 21:22. [PMID: 38475715 DOI: 10.1186/s12014-024-09477-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 02/29/2024] [Indexed: 03/14/2024] Open
Abstract
Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using S-Trap and analyzed on Evosep One and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searches using DIA-NN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhance protein identification in neat plasma samples. Globally, our optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC-MS platform used.
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Affiliation(s)
- Ines Metatla
- Proteomics Platform Necker, Université Paris Cité-Structure Fédérative de Recherche Necker, INSERM US24/CNRS UAR3633, 75015, Paris, France
| | - Kevin Roger
- Proteomics Platform Necker, Université Paris Cité-Structure Fédérative de Recherche Necker, INSERM US24/CNRS UAR3633, 75015, Paris, France
| | - Cerina Chhuon
- Proteomics Platform Necker, Université Paris Cité-Structure Fédérative de Recherche Necker, INSERM US24/CNRS UAR3633, 75015, Paris, France
| | - Sara Ceccacci
- Proteomics Platform Necker, Université Paris Cité-Structure Fédérative de Recherche Necker, INSERM US24/CNRS UAR3633, 75015, Paris, France
| | - Manuel Chapelle
- Bruker Daltonique SA, 34 Rue de l'Industrie, 67166, Wissembourg Cedex, France
| | | | - Vadim Demichev
- Department of Biochemistry, Charité-Universitätsmedizin Berlin, 10117, Berlin, Germany
| | - Ida Chiara Guerrera
- Proteomics Platform Necker, Université Paris Cité-Structure Fédérative de Recherche Necker, INSERM US24/CNRS UAR3633, 75015, Paris, France.
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2
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White MEH, Sinn LR, Jones DM, de Folter J, Aulakh SK, Wang Z, Flynn HR, Krüger L, Tober-Lau P, Demichev V, Kurth F, Mülleder M, Blanchard V, Messner CB, Ralser M. Oxonium ion scanning mass spectrometry for large-scale plasma glycoproteomics. Nat Biomed Eng 2024; 8:233-247. [PMID: 37474612 DOI: 10.1038/s41551-023-01067-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 06/15/2023] [Indexed: 07/22/2023]
Abstract
Protein glycosylation, a complex and heterogeneous post-translational modification that is frequently dysregulated in disease, has been difficult to analyse at scale. Here we report a data-independent acquisition technique for the large-scale mass-spectrometric quantification of glycopeptides in plasma samples. The technique, which we named 'OxoScan-MS', identifies oxonium ions as glycopeptide fragments and exploits a sliding-quadrupole dimension to generate comprehensive and untargeted oxonium ion maps of precursor masses assigned to fragment ions from non-enriched plasma samples. By applying OxoScan-MS to quantify 1,002 glycopeptide features in the plasma glycoproteomes from patients with COVID-19 and healthy controls, we found that severe COVID-19 induces differential glycosylation in IgA, haptoglobin, transferrin and other disease-relevant plasma glycoproteins. OxoScan-MS may allow for the quantitative mapping of glycoproteomes at the scale of hundreds to thousands of samples.
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Affiliation(s)
- Matthew E H White
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Ludwig R Sinn
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - D Marc Jones
- Bioinformatics and Computational Biology Laboratory, The Francis Crick Institute, London, UK
- Department of Basic and Clinical Neuroscience, Maurice Wohl Clinical Neuroscience Institute, London, UK
| | - Joost de Folter
- Software Engineering and Artificial Intelligence Technology Platform, The Francis Crick Institute, London, UK
| | - Simran Kaur Aulakh
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Ziyue Wang
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Helen R Flynn
- Mass Spectrometry Proteomics Science Technology Platform, The Francis Crick Institute, London, UK
| | - Lynn Krüger
- Institute of Diagnostic Laboratory Medicine, Charité - Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Human Medicine, Medical School Berlin, Berlin, Germany
| | - Pinkus Tober-Lau
- Department of Infectious Diseases and Critical Care Medicine, Charité - Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Vadim Demichev
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Florian Kurth
- Department of Infectious Diseases and Critical Care Medicine, Charité - Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Michael Mülleder
- Core Facility High-throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Véronique Blanchard
- Institute of Diagnostic Laboratory Medicine, Charité - Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Human Medicine, Medical School Berlin, Berlin, Germany
| | - Christoph B Messner
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
- Precision Proteomic Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland.
| | - Markus Ralser
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- Max Planck Institute for Molecular Genetics, Berlin, Germany.
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3
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Szyrwiel L, Gille C, Mülleder M, Demichev V, Ralser M. Fast proteomics with dia-PASEF and analytical flow-rate chromatography. Proteomics 2024; 24:e2300100. [PMID: 37287406 DOI: 10.1002/pmic.202300100] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 05/22/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023]
Abstract
Increased throughput in proteomic experiments can improve accessibility of proteomic platforms, reduce costs, and facilitate new approaches in systems biology and biomedical research. Here we propose combination of analytical flow rate chromatography with ion mobility separation of peptide ions, data-independent acquisition, and data analysis with the DIA-NN software suite, to achieve high-quality proteomic experiments from limited sample amounts, at a throughput of up to 400 samples per day. For instance, when benchmarking our workflow using a 500-μL/min flow rate and 3-min chromatographic gradients, we report the quantification of 5211 proteins from 2 μg of a mammalian cell-line standard at high quantitative accuracy and precision. We further used this platform to analyze blood plasma samples from a cohort of COVID-19 inpatients, using a 3-min chromatographic gradient and alternating column regeneration on a dual pump system. The method delivered a comprehensive view of the COVID-19 plasma proteome, allowing classification of the patients according to disease severity and revealing plasma biomarker candidates.
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Affiliation(s)
- Lukasz Szyrwiel
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christoph Gille
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Core Facility High-Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Mülleder
- Core Facility High-Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Vadim Demichev
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
- Max Planck Institute for Molecular Genetics, Berlin, Germany
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4
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Huang J, Staes A, Impens F, Demichev V, Van Breusegem F, Gevaert K, Willems P. CysQuant: Simultaneous quantification of cysteine oxidation and protein abundance using data dependent or independent acquisition mass spectrometry. Redox Biol 2023; 67:102908. [PMID: 37793239 PMCID: PMC10562924 DOI: 10.1016/j.redox.2023.102908] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 09/20/2023] [Accepted: 09/26/2023] [Indexed: 10/06/2023] Open
Abstract
Protein cysteinyl thiols are susceptible to reduction-oxidation reactions that can influence protein function. Accurate quantification of cysteine oxidation is therefore crucial for decoding protein redox regulation. Here, we present CysQuant, a novel approach for simultaneous quantification of cysteine oxidation degrees and protein abundancies. CysQuant involves light/heavy iodoacetamide isotopologues for differential labeling of reduced and reversibly oxidized cysteines analyzed by data-dependent acquisition (DDA) or data-independent acquisition mass spectrometry (DIA-MS). Using plexDIA with in silico predicted spectral libraries, we quantified an average of 18% cysteine oxidation in Arabidopsis thaliana by DIA-MS, including a subset of highly oxidized cysteines forming disulfide bridges in AlphaFold2 predicted structures. Applying CysQuant to Arabidopsis seedlings exposed to excessive light, we successfully quantified the well-established increased reduction of Calvin-Benson cycle enzymes and discovered yet uncharacterized redox-sensitive disulfides in chloroplastic enzymes. Overall, CysQuant is a highly versatile tool for assessing the cysteine modification status that can be widely applied across various mass spectrometry platforms and organisms.
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Affiliation(s)
- Jingjing Huang
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium; Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium
| | - An Staes
- Department of Biomolecular Medicine, Ghent University, 9052, Ghent, Belgium; Center for Medical Biotechnology, VIB, 9052, Ghent, Belgium; VIB Proteomics Core, 9052, Ghent, Belgium
| | - Francis Impens
- Department of Biomolecular Medicine, Ghent University, 9052, Ghent, Belgium; Center for Medical Biotechnology, VIB, 9052, Ghent, Belgium; VIB Proteomics Core, 9052, Ghent, Belgium
| | - Vadim Demichev
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, 10117, Berlin, Germany
| | - Frank Van Breusegem
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium; Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium
| | - Kris Gevaert
- Department of Biomolecular Medicine, Ghent University, 9052, Ghent, Belgium; Center for Medical Biotechnology, VIB, 9052, Ghent, Belgium.
| | - Patrick Willems
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052, Ghent, Belgium; Center for Plant Systems Biology, VIB, 9052, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, 9052, Ghent, Belgium; Center for Medical Biotechnology, VIB, 9052, Ghent, Belgium.
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5
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Kleeman SO, Thakir TM, Demestichas B, Mourikis N, Loiero D, Ferrer M, Bankier S, Riazat-Kesh YJ, Lee H, Chantzichristos D, Regan C, Preall J, Sinha S, Rosin N, Yipp B, de Almeida LG, Biernaskie J, Dufour A, Tober-Lau P, Ruusalepp A, Bjorkegren JL, Ralser M, Kurth F, Demichev V, Heywood T, Gao Q, Johannsson G, Koelzer VH, Walker BR, Meyer HV, Janowitz T. Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy. Cell Genom 2023; 3:100347. [PMID: 37601967 PMCID: PMC10435381 DOI: 10.1016/j.xgen.2023.100347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 03/23/2023] [Accepted: 05/30/2023] [Indexed: 08/22/2023]
Abstract
Cystatin C (CyC), a secreted cysteine protease inhibitor, has unclear biological functions. Many patients exhibit elevated plasma CyC levels, particularly during glucocorticoid (GC) treatment. This study links GCs with CyC's systemic regulation by utilizing genome-wide association and structural equation modeling to determine CyC production genetics in the UK Biobank. Both CyC production and a polygenic score (PGS) capturing predisposition to CyC production were associated with increased all-cause and cancer-specific mortality. We found that the GC receptor directly targets CyC, leading to GC-responsive CyC secretion in macrophages and cancer cells. CyC-knockout tumors displayed significantly reduced growth and diminished recruitment of TREM2+ macrophages, which have been connected to cancer immunotherapy failure. Furthermore, the CyC-production PGS predicted checkpoint immunotherapy failure in 685 patients with metastatic cancer from combined clinical trial cohorts. In conclusion, CyC may act as a GC effector pathway via TREM2+ macrophage recruitment and may be a potential target for combination cancer immunotherapy.
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Affiliation(s)
- Sam O. Kleeman
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | | | | | - Dominik Loiero
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Miriam Ferrer
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Sean Bankier
- BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | | | - Hassal Lee
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Dimitrios Chantzichristos
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Endocrinology Diabetes and Metabolism, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Claire Regan
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | | | - Sarthak Sinha
- Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
| | - Nicole Rosin
- Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
| | - Bryan Yipp
- Department of Critical Care Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Luiz G.N. de Almeida
- Department of Biochemistry and Molecular Biology and Physiology and Pharmacology, University of Calgary, Calgary, AB, Canada
| | - Jeff Biernaskie
- Department of Comparative Biology and Experimental Medicine, Faculty of Veterinary Medicine, University of Calgary, Calgary, AB, Canada
- Department of Surgery, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Antoine Dufour
- Department of Biochemistry and Molecular Biology and Physiology and Pharmacology, University of Calgary, Calgary, AB, Canada
| | | | - Arno Ruusalepp
- Department of Cardiac Surgery, Tartu University Hospital, Tartu, Estonia
| | - Johan L.M. Bjorkegren
- Department of Genetics & Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Markus Ralser
- Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Florian Kurth
- Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Todd Heywood
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Qing Gao
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Gudmundur Johannsson
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Endocrinology Diabetes and Metabolism, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Viktor H. Koelzer
- Department of Pathology and Molecular Pathology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
- Department of Oncology and Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Brian R. Walker
- BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, UK
- Translational & Clinical Research Institute, Newcastle University, Newcastle upon Tyne, UK
| | | | - Tobias Janowitz
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
- Cancer Institute, Northwell Health, New Hyde Park, NY, USA
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6
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Yang KL, Yu F, Teo GC, Li K, Demichev V, Ralser M, Nesvizhskii AI. MSBooster: improving peptide identification rates using deep learning-based features. Nat Commun 2023; 14:4539. [PMID: 37500632 PMCID: PMC10374903 DOI: 10.1038/s41467-023-40129-9] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 07/06/2023] [Indexed: 07/29/2023] Open
Abstract
Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.
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Affiliation(s)
- Kevin L Yang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Kai Li
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Vadim Demichev
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Markus Ralser
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
- Nuffield Department of Medicine, The Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Max Planck Institute for Molecular Genetics, Berlin, Germany
| | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
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7
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Vernardis SI, Demichev V, Lemke O, Grüning NM, Messner C, White M, Pietzner M, Peluso A, Collet TH, Henning E, Gille C, Campbell A, Hayward C, Porteous DJ, Marioni RE, Mülleder M, Zelezniak A, Wareham NJ, Langenberg C, Farooqi IS, Ralser M. The Impact of Acute Nutritional Interventions on the Plasma Proteome. J Clin Endocrinol Metab 2023; 108:2087-2098. [PMID: 36658456 PMCID: PMC10348471 DOI: 10.1210/clinem/dgad031] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 01/14/2023] [Accepted: 01/17/2023] [Indexed: 01/21/2023]
Abstract
CONTEXT Humans respond profoundly to changes in diet, while nutrition and environment have a great impact on population health. It is therefore important to deeply characterize the human nutritional responses. OBJECTIVE Endocrine parameters and the metabolome of human plasma are rapidly responding to acute nutritional interventions such as caloric restriction or a glucose challenge. It is less well understood whether the plasma proteome would be equally dynamic, and whether it could be a source of corresponding biomarkers. METHODS We used high-throughput mass spectrometry to determine changes in the plasma proteome of i) 10 healthy, young, male individuals in response to 2 days of acute caloric restriction followed by refeeding; ii) 200 individuals of the Ely epidemiological study before and after a glucose tolerance test at 4 time points (0, 30, 60, 120 minutes); and iii) 200 random individuals from the Generation Scotland study. We compared the proteomic changes detected with metabolome data and endocrine parameters. RESULTS Both caloric restriction and the glucose challenge substantially impacted the plasma proteome. Proteins responded across individuals or in an individual-specific manner. We identified nutrient-responsive plasma proteins that correlate with changes in the metabolome, as well as with endocrine parameters. In particular, our study highlights the role of apolipoprotein C1 (APOC1), a small, understudied apolipoprotein that was affected by caloric restriction and dominated the response to glucose consumption and differed in abundance between individuals with and without type 2 diabetes. CONCLUSION Our study identifies APOC1 as a dominant nutritional responder in humans and highlights the interdependency of acute nutritional response proteins and the endocrine system.
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Affiliation(s)
- Spyros I Vernardis
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1HT, UK
| | - Vadim Demichev
- Department of Biochemistry, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Oliver Lemke
- Department of Biochemistry, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Nana-Maria Grüning
- Department of Biochemistry, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Christoph Messner
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1HT, UK
| | - Matt White
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1HT, UK
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, CB2 0SL, UK
- Computational Medicine, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Alina Peluso
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1HT, UK
| | - Tinh-Hai Collet
- Metabolic Research Laboratories and National Institute for Health Research Cambridge Biomedical Research Centre, Wellcome-Medical Research Council Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge, Cambridge, CB2 0QQ, UK
- Service of Endocrinology, Diabetology, Nutrition and Therapeutic Education, Department of Medicine, Geneva University Hospitals, 1211 Geneva, Switzerland
| | - Elana Henning
- Metabolic Research Laboratories and National Institute for Health Research Cambridge Biomedical Research Centre, Wellcome-Medical Research Council Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Christoph Gille
- Department of Biochemistry, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Aleksej Zelezniak
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1HT, UK
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
- Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius SE-412 96, Lithuania
- Randall Centre for Cell & Molecular Biophysics, King's College London, New Hunt's House, Guy's Campus, SE1 1UL London, UK
| | | | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, CB2 0SL, UK
- Computational Medicine, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
- Precision Healthcare University Research Institute, Queen Mary University of London, London, E1 1HH, UK
| | - I Sadaf Farooqi
- Metabolic Research Laboratories and National Institute for Health Research Cambridge Biomedical Research Centre, Wellcome-Medical Research Council Institute of Metabolic Science, Addenbrooke's Hospital, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Markus Ralser
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, NW1 1HT, UK
- Department of Biochemistry, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
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8
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Yu F, Teo GC, Kong AT, Fröhlich K, Li GX, Demichev V, Nesvizhskii AI. Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform. Nat Commun 2023; 14:4154. [PMID: 37438352 PMCID: PMC10338508 DOI: 10.1038/s41467-023-39869-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 06/28/2023] [Indexed: 07/14/2023] Open
Abstract
Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.
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Affiliation(s)
- Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Andy T Kong
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Klemens Fröhlich
- Proteomics Core Facility, Biozentrum, University of Basel, Basel, Switzerland
| | - Ginny Xiaohe Li
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Vadim Demichev
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
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9
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Messner CB, Demichev V, Muenzner J, Aulakh SK, Barthel N, Röhl A, Herrera-Domínguez L, Egger AS, Kamrad S, Hou J, Tan G, Lemke O, Calvani E, Szyrwiel L, Mülleder M, Lilley KS, Boone C, Kustatscher G, Ralser M. The proteomic landscape of genome-wide genetic perturbations. Cell 2023; 186:2018-2034.e21. [PMID: 37080200 PMCID: PMC7615649 DOI: 10.1016/j.cell.2023.03.026] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 01/20/2023] [Accepted: 03/21/2023] [Indexed: 04/22/2023]
Abstract
Functional genomic strategies have become fundamental for annotating gene function and regulatory networks. Here, we combined functional genomics with proteomics by quantifying protein abundances in a genome-scale knockout library in Saccharomyces cerevisiae, using data-independent acquisition mass spectrometry. We find that global protein expression is driven by a complex interplay of (1) general biological properties, including translation rate, protein turnover, the formation of protein complexes, growth rate, and genome architecture, followed by (2) functional properties, such as the connectivity of a protein in genetic, metabolic, and physical interaction networks. Moreover, we show that functional proteomics complements current gene annotation strategies through the assessment of proteome profile similarity, protein covariation, and reverse proteome profiling. Thus, our study reveals principles that govern protein expression and provides a genome-spanning resource for functional annotation.
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Affiliation(s)
- Christoph B Messner
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK; Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, 7265 Davos, Switzerland
| | - Vadim Demichev
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK; Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany; Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge CB2 1QW, UK
| | - Julia Muenzner
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Simran K Aulakh
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK
| | - Natalie Barthel
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Annika Röhl
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | | | - Anna-Sophia Egger
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK
| | - Stephan Kamrad
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK
| | - Jing Hou
- The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Guihong Tan
- The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada
| | - Oliver Lemke
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Enrica Calvani
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK
| | - Lukasz Szyrwiel
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK; Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Michael Mülleder
- Charité Universitätsmedizin, Core Facility - High Throughput Mass Spectrometry, 10117 Berlin, Germany
| | - Kathryn S Lilley
- Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge CB2 1QW, UK
| | - Charles Boone
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5S3E1, Canada; The Donnelly Centre, University of Toronto, Toronto, ON M5S3E1, Canada; RIKEN Center for Sustainable Resource Science, Wako, 351-0198 Saitama, Japan
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh EH9 3BF, Scotland, UK.
| | - Markus Ralser
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW1 1AT, UK; Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany; The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK; Max Planck Institute for Molecular Genetics, 14195 Berlin, Germany.
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10
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Messner CB, Demichev V, Wang Z, Hartl J, Kustatscher G, Mülleder M, Ralser M. Mass spectrometry-based high-throughput proteomics and its role in biomedical studies and systems biology. Proteomics 2023; 23:e2200013. [PMID: 36349817 DOI: 10.1002/pmic.202200013] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Revised: 10/13/2022] [Accepted: 10/13/2022] [Indexed: 11/11/2022]
Abstract
There are multiple reasons why the next generation of biological and medical studies require increasing numbers of samples. Biological systems are dynamic, and the effect of a perturbation depends on the genetic background and environment. As a consequence, many conditions need to be considered to reach generalizable conclusions. Moreover, human population and clinical studies only reach sufficient statistical power if conducted at scale and with precise measurement methods. Finally, many proteins remain without sufficient functional annotations, because they have not been systematically studied under a broad range of conditions. In this review, we discuss the latest technical developments in mass spectrometry (MS)-based proteomics that facilitate large-scale studies by fast and efficient chromatography, fast scanning mass spectrometers, data-independent acquisition (DIA), and new software. We further highlight recent studies which demonstrate how high-throughput (HT) proteomics can be applied to capture biological diversity, to annotate gene functions or to generate predictive and prognostic models for human diseases.
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Affiliation(s)
- Christoph B Messner
- Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, Davos, Switzerland
| | - Vadim Demichev
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Ziyue Wang
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Johannes Hartl
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Georg Kustatscher
- Wellcome Centre for Cell Biology, University of Edinburgh, Max Born Crescent, Edinburgh, Scotland, UK
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Institute of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK
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11
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Talwar D, Miller CG, Grossmann J, Szyrwiel L, Schwecke T, Demichev V, Mikecin Drazic AM, Mayakonda A, Lutsik P, Veith C, Milsom MD, Müller-Decker K, Mülleder M, Ralser M, Dick TP. The GAPDH redox switch safeguards reductive capacity and enables survival of stressed tumour cells. Nat Metab 2023; 5:660-676. [PMID: 37024754 PMCID: PMC10132988 DOI: 10.1038/s42255-023-00781-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Accepted: 03/09/2023] [Indexed: 04/08/2023]
Abstract
Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) is known to contain an active-site cysteine residue undergoing oxidation in response to hydrogen peroxide, leading to rapid inactivation of the enzyme. Here we show that human and mouse cells expressing a GAPDH mutant lacking this redox switch retain catalytic activity but are unable to stimulate the oxidative pentose phosphate pathway and enhance their reductive capacity. Specifically, we find that anchorage-independent growth of cells and spheroids is limited by an elevation of endogenous peroxide levels and is largely dependent on a functional GAPDH redox switch. Likewise, tumour growth in vivo is limited by peroxide stress and suppressed when the GAPDH redox switch is disabled in tumour cells. The induction of additional intratumoural oxidative stress by chemo- or radiotherapy synergized with the deactivation of the GAPDH redox switch. Mice lacking the GAPDH redox switch exhibit altered fatty acid metabolism in kidney and heart, apparently in compensation for the lack of the redox switch. Together, our findings demonstrate the physiological and pathophysiological relevance of oxidative GAPDH inactivation in mammals.
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Affiliation(s)
- Deepti Talwar
- Division of Redox Regulation, DKFZ-ZMBH Alliance, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Colin G Miller
- Division of Redox Regulation, DKFZ-ZMBH Alliance, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Justus Grossmann
- Department of Biochemistry, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Lukasz Szyrwiel
- Department of Biochemistry, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Torsten Schwecke
- Department of Biochemistry, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Vadim Demichev
- Department of Biochemistry, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Ana-Matea Mikecin Drazic
- Division of Experimental Hematology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM), Heidelberg, Germany
| | - Anand Mayakonda
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Pavlo Lutsik
- Division of Cancer Epigenomics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Laboratory of Computational Cancer Biology and Epigenomics, Department of Oncology, Catholic University (KU) Leuven, Leuven, Belgium
| | - Carmen Veith
- Division of Redox Regulation, DKFZ-ZMBH Alliance, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael D Milsom
- Division of Experimental Hematology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM), Heidelberg, Germany
| | - Karin Müller-Decker
- Core Facility Tumor Models, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Mülleder
- Core Facility High Throughput Mass Spectrometry, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
| | - Markus Ralser
- Department of Biochemistry, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | - Tobias P Dick
- Division of Redox Regulation, DKFZ-ZMBH Alliance, German Cancer Research Center (DKFZ), Heidelberg, Germany.
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
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12
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Gatto L, Aebersold R, Cox J, Demichev V, Derks J, Emmott E, Franks AM, Ivanov AR, Kelly RT, Khoury L, Leduc A, MacCoss MJ, Nemes P, Perlman DH, Petelski AA, Rose CM, Schoof EM, Van Eyk J, Vanderaa C, Yates JR, Slavov N. Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments. Nat Methods 2023; 20:375-386. [PMID: 36864200 PMCID: PMC10130941 DOI: 10.1038/s41592-023-01785-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 01/24/2023] [Indexed: 03/04/2023]
Abstract
Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition and data analysis. We expect that broadly accepted community guidelines and standardized metrics will enhance rigor, data quality and alignment between laboratories. Here we propose best practices, quality controls and data-reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics. Resources and discussion forums are available at https://single-cell.net/guidelines .
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Affiliation(s)
- Laurent Gatto
- Computational Biology and Bioinformatics Unit, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Juergen Cox
- Max Planck Institute of Biochemistry, Martinsried, Germany
| | | | - Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Edward Emmott
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, University of Liverpool, Liverpool, UK
| | - Alexander M Franks
- Department of Statistics and Applied Probability, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Alexander R Ivanov
- Department of Chemistry and Chemical Biology, Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA, USA
| | - Ryan T Kelly
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA
| | - Luke Khoury
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | | | - Peter Nemes
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, USA
| | - David H Perlman
- Merck Exploratory Science Center, Merck Sharp & Dohme Corp., Cambridge, MA, USA
| | - Aleksandra A Petelski
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
- Parallel Squared Technology Institute, Watertown, MA, USA
| | - Christopher M Rose
- Department of Microchemistry, Proteomics and Lipidomics, Genentech Inc., South San Francisco, CA, USA
| | - Erwin M Schoof
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | | | - Christophe Vanderaa
- Computational Biology and Bioinformatics Unit, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium
| | - John R Yates
- Departments of Molecular Medicine and Neurobiology, the Scripps Research Institute, La Jolla, CA, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA.
- Parallel Squared Technology Institute, Watertown, MA, USA.
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13
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Kamrad S, Correia-Melo C, Szyrwiel L, Aulakh SK, Bähler J, Demichev V, Mülleder M, Ralser M. Metabolic heterogeneity and cross-feeding within isogenic yeast populations captured by DILAC. Nat Microbiol 2023; 8:441-454. [PMID: 36797484 PMCID: PMC9981460 DOI: 10.1038/s41564-022-01304-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Accepted: 12/13/2022] [Indexed: 02/18/2023]
Abstract
Genetically identical cells are known to differ in many physiological parameters such as growth rate and drug tolerance. Metabolic specialization is believed to be a cause of such phenotypic heterogeneity, but detection of metabolically divergent subpopulations remains technically challenging. We developed a proteomics-based technology, termed differential isotope labelling by amino acids (DILAC), that can detect producer and consumer subpopulations of a particular amino acid within an isogenic cell population by monitoring peptides with multiple occurrences of the amino acid. We reveal that young, morphologically undifferentiated yeast colonies contain subpopulations of lysine producers and consumers that emerge due to nutrient gradients. Deconvoluting their proteomes using DILAC, we find evidence for in situ cross-feeding where rapidly growing cells ferment and provide the more slowly growing, respiring cells with ethanol. Finally, by combining DILAC with fluorescence-activated cell sorting, we show that the metabolic subpopulations diverge phenotypically, as exemplified by a different tolerance to the antifungal drug amphotericin B. Overall, DILAC captures previously unnoticed metabolic heterogeneity and provides experimental evidence for the role of metabolic specialization and cross-feeding interactions as a source of phenotypic heterogeneity in isogenic cell populations.
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Affiliation(s)
- Stephan Kamrad
- Department of Biochemistry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Clara Correia-Melo
- Department of Biochemistry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Lukasz Szyrwiel
- Department of Biochemistry, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Simran Kaur Aulakh
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Jürg Bähler
- Institute of Healthy Ageing and Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Vadim Demichev
- Department of Biochemistry, Charité Universitätsmedizin Berlin, Berlin, Germany
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Michael Mülleder
- Core Facility-High-Throughput Mass Spectrometry, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Markus Ralser
- Department of Biochemistry, Charité Universitätsmedizin Berlin, Berlin, Germany.
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
- The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
- Max Planck Institute for Molecular Genetics, Berlin, Germany.
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14
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Correia-Melo C, Kamrad S, Tengölics R, Messner CB, Trebulle P, Townsend S, Jayasree Varma S, Freiwald A, Heineike BM, Campbell K, Herrera-Dominguez L, Kaur Aulakh S, Szyrwiel L, Yu JSL, Zelezniak A, Demichev V, Mülleder M, Papp B, Alam MT, Ralser M. Cell-cell metabolite exchange creates a pro-survival metabolic environment that extends lifespan. Cell 2023; 186:63-79.e21. [PMID: 36608659 DOI: 10.1016/j.cell.2022.12.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 09/07/2022] [Accepted: 12/05/2022] [Indexed: 01/07/2023]
Abstract
Metabolism is deeply intertwined with aging. Effects of metabolic interventions on aging have been explained with intracellular metabolism, growth control, and signaling. Studying chronological aging in yeast, we reveal a so far overlooked metabolic property that influences aging via the exchange of metabolites. We observed that metabolites exported by young cells are re-imported by chronologically aging cells, resulting in cross-generational metabolic interactions. Then, we used self-establishing metabolically cooperating communities (SeMeCo) as a tool to increase metabolite exchange and observed significant lifespan extensions. The longevity of the SeMeCo was attributable to metabolic reconfigurations in methionine consumer cells. These obtained a more glycolytic metabolism and increased the export of protective metabolites that in turn extended the lifespan of cells that supplied them with methionine. Our results establish metabolite exchange interactions as a determinant of cellular aging and show that metabolically cooperating cells can shape the metabolic environment to extend their lifespan.
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Affiliation(s)
- Clara Correia-Melo
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK; Department of Biochemistry, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany.
| | - Stephan Kamrad
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Roland Tengölics
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network, Szeged 6726, Hungary; HCEMM-BRC Metabolic Systems Biology Lab, Szeged 6726, Hungary
| | - Christoph B Messner
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Precision Proteomics Center, Swiss Institute of Allergy and Asthma Research (SIAF), University of Zurich, 7265 Davos, Switzerland
| | - Pauline Trebulle
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK; The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - StJohn Townsend
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Department of Biochemistry, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany
| | | | - Anja Freiwald
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany; Core Facility - High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Benjamin M Heineike
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK; The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK; Quantitative Gene Expression Research Group, MRC London Institute of Medical Sciences (LMS), London W12 0HS, UK; Quantitative Gene Expression Research Group, Institute of Clinical Sciences (ICS), Faculty of Medicine, Imperial College London, London SW2 2AZ, UK
| | - Kate Campbell
- Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK
| | - Lucía Herrera-Dominguez
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Simran Kaur Aulakh
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK; The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK
| | - Lukasz Szyrwiel
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Department of Biochemistry, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Jason S L Yu
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK
| | - Aleksej Zelezniak
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Gothenburg, Sweden; Randall Centre for Cell & Molecular Biophysics, King's College London, New Hunt's House, Guy's Campus, London SE1 1UL, UK; Institute of Biotechnology, Life Sciences Center, Vilnius University, Vilnius 10257, Lithuania
| | - Vadim Demichev
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK; Department of Biochemistry, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Michael Mülleder
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK; Core Facility - High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany
| | - Balázs Papp
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre, Eötvös Loránd Research Network, Szeged 6726, Hungary; HCEMM-BRC Metabolic Systems Biology Lab, Szeged 6726, Hungary
| | - Mohammad Tauqeer Alam
- Department of Biology, College of Science, United Arab Emirates University, P.O.Box 15551, Al-Ain, United Arab Emirates
| | - Markus Ralser
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London NW1 1AT, UK; Department of Biochemistry, University of Cambridge, Cambridge CB2 1QW, UK; Department of Biochemistry, Charité - Universitätsmedizin Berlin, 10117 Berlin, Germany; The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK.
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15
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Derks J, Leduc A, Wallmann G, Huffman RG, Willetts M, Khan S, Specht H, Ralser M, Demichev V, Slavov N. Increasing the throughput of sensitive proteomics by plexDIA. Nat Biotechnol 2023; 41:50-59. [PMID: 35835881 PMCID: PMC9839897 DOI: 10.1038/s41587-022-01389-w] [Citation(s) in RCA: 61] [Impact Index Per Article: 61.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 06/13/2022] [Indexed: 01/22/2023]
Abstract
Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.
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Affiliation(s)
- Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA.
| | - Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Georg Wallmann
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | - R Gray Huffman
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | | | - Saad Khan
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Harrison Specht
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Markus Ralser
- Charité - Universitätsmedizin Berlin, Berlin, Germany
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | | | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA, USA.
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16
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Gindlhuber J, Tomin T, Wiesenhofer F, Zacharias M, Liesinger L, Demichev V, Kratochwill K, Gorkiewicz G, Schittmayer M, Birner-Gruenberger R. Proteomic profiling of end-stage COVID-19 lung biopsies. Clin Proteomics 2022; 19:46. [PMID: 36526981 PMCID: PMC9758034 DOI: 10.1186/s12014-022-09386-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 12/07/2022] [Indexed: 12/23/2022] Open
Abstract
The outbreak of a novel coronavirus (SARS-CoV-2) in 2019 led to a worldwide pandemic, which remains an integral part of our lives to this day. Coronavirus disease (COVID-19) is a flu like condition, often accompanied by high fever and respiratory distress. In some cases, conjointly with other co-morbidities, COVID-19 can become severe, leading to lung arrest and even death. Although well-known from a clinical standpoint, the mechanistic understanding of lethal COVID-19 is still rudimentary. Studying the pathology and changes on a molecular level associated with the resulting COVID-19 disease is impeded by the highly infectious nature of the virus and the concomitant sampling challenges. We were able to procure COVID-19 post-mortem lung tissue specimens by our collaboration with the BSL-3 laboratory of the Biobanking and BioMolecular resources Research Infrastructure Austria which we subjected to state-of-the-art quantitative proteomic analysis to better understand the pulmonary manifestations of lethal COVID-19. Lung tissue samples from age-matched non-COVID-19 patients who died within the same period were used as controls. Samples were subjected to parallel accumulation-serial fragmentation combined with data-independent acquisition (diaPASEF) on a timsTOF Pro and obtained raw data was processed using DIA-NN software. Here we report that terminal COVID-19 patients display an increase in inflammation, acute immune response and blood clot formation (with concomitant triggering of fibrinolysis). Furthermore, we describe that COVID-19 diseased lungs undergo severe extracellular matrix restructuring, which was corroborated on the histopathological level. However, although undergoing an injury, diseased lungs seem to have impaired proliferative and tissue repair signalling, with several key kinase-mediated signalling pathways being less active. This might provide a mechanistic link to post-acute sequelae of COVID-19 (PASC; "Long COVID"). Overall, we emphasize the importance of histopathological patient stratification when interpreting molecular COVID-19 data.
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Affiliation(s)
- Juergen Gindlhuber
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Tamara Tomin
- Institute of Chemical Technologies and Analytics, Faculty of Technical Chemistry, Technische Universität Wien, Vienna, Austria
| | - Florian Wiesenhofer
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
- Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Martin Zacharias
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Laura Liesinger
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Vadim Demichev
- Institute of Biochemistry, Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Klaus Kratochwill
- Christian Doppler Laboratory for Molecular Stress Research in Peritoneal Dialysis, Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, Vienna, Austria
- Division of Pediatric Nephrology and Gastroenterology, Department of Pediatrics and Adolescent Medicine, Comprehensive Center for Pediatrics, Medical University of Vienna, Vienna, Austria
| | - Gregor Gorkiewicz
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria
| | - Matthias Schittmayer
- Institute of Chemical Technologies and Analytics, Faculty of Technical Chemistry, Technische Universität Wien, Vienna, Austria.
| | - Ruth Birner-Gruenberger
- Diagnostic and Research Institute of Pathology, Medical University of Graz, Graz, Austria.
- Institute of Chemical Technologies and Analytics, Faculty of Technical Chemistry, Technische Universität Wien, Vienna, Austria.
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17
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Aramburu IV, Hoving D, Vernardis SI, Tin MC, Ioannou M, Temkin MI, De Vasconcelos NM, Demichev V, Helbig ET, Lippert L, Stahl K, White M, Radbruch H, Ihlow J, Horst D, Chiesa ST, Deanfield JE, David S, Bode C, Kurth F, Ralser M, Papayannopoulos V. Functional proteomic profiling links deficient DNA clearance with increased mortality in individuals with severe COVID-19 pneumonia. Immunity 2022; 55:2436-2453.e5. [PMID: 36462503 PMCID: PMC9671605 DOI: 10.1016/j.immuni.2022.11.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 09/01/2022] [Accepted: 11/15/2022] [Indexed: 11/18/2022]
Abstract
The factors that influence survival during severe infection are unclear. Extracellular chromatin drives pathology, but the mechanisms enabling its accumulation remain elusive. Here, we show that in murine sepsis models, splenocyte death interferes with chromatin clearance through the release of the DNase I inhibitor actin. Actin-mediated inhibition was compensated by upregulation of DNase I or the actin scavenger gelsolin. Splenocyte death and neutrophil extracellular trap (NET) clearance deficiencies were prevalent in individuals with severe COVID-19 pneumonia or microbial sepsis. Activity tracing by plasma proteomic profiling uncovered an association between low NET clearance and increased COVID-19 pathology and mortality. Low NET clearance activity with comparable proteome associations was prevalent in healthy donors with low-grade inflammation, implicating defective chromatin clearance in the development of cardiovascular disease and linking COVID-19 susceptibility to pre-existing conditions. Hence, the combination of aberrant chromatin release with defects in protective clearance mechanisms lead to poor survival outcomes.
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Affiliation(s)
| | - Dennis Hoving
- The Francis Crick Institute, Antimicrobial Defence Laboratory, London, UK
| | - Spyros I. Vernardis
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, UK
| | - Martha C.F. Tin
- The Francis Crick Institute, Antimicrobial Defence Laboratory, London, UK
| | - Marianna Ioannou
- The Francis Crick Institute, Antimicrobial Defence Laboratory, London, UK
| | - Mia I. Temkin
- The Francis Crick Institute, Antimicrobial Defence Laboratory, London, UK
| | | | - Vadim Demichev
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, UK
| | - Elisa Theresa Helbig
- Charité – Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Lena Lippert
- Charité – Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Klaus Stahl
- Department of Gastroenterology, Hepatology and Endocrinology, Medical School Hannover, Hannover, Germany
| | - Matthew White
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, UK
| | - Helena Radbruch
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Neuropathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Jana Ihlow
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - David Horst
- Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Scott T. Chiesa
- Institute of Cardiovascular Science, University College London, London, UK
| | - John E. Deanfield
- Institute of Cardiovascular Science, University College London, London, UK
| | - Sascha David
- Institute for Intensive Care Medicine, University Hospital Zurich, Zurich, Switzerland
| | - Christian Bode
- Department of Anaesthesiology and Critical Care, University Hospital Bonn, Bonn, Germany
| | - Florian Kurth
- Charité – Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Markus Ralser
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, UK,Charité – Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
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18
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Yu JSL, Heineike BM, Hartl J, Aulakh SK, Correia-Melo C, Lehmann A, Lemke O, Agostini F, Lee CT, Demichev V, Messner CB, Mülleder M, Ralser M. Inorganic sulfur fixation via a new homocysteine synthase allows yeast cells to cooperatively compensate for methionine auxotrophy. PLoS Biol 2022; 20:e3001912. [PMID: 36455053 PMCID: PMC9757880 DOI: 10.1371/journal.pbio.3001912] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/16/2022] [Accepted: 11/14/2022] [Indexed: 12/03/2022] Open
Abstract
The assimilation, incorporation, and metabolism of sulfur is a fundamental process across all domains of life, yet how cells deal with varying sulfur availability is not well understood. We studied an unresolved conundrum of sulfur fixation in yeast, in which organosulfur auxotrophy caused by deletion of the homocysteine synthase Met17p is overcome when cells are inoculated at high cell density. In combining the use of self-establishing metabolically cooperating (SeMeCo) communities with proteomic, genetic, and biochemical approaches, we discovered an uncharacterized gene product YLL058Wp, herein named Hydrogen Sulfide Utilizing-1 (HSU1). Hsu1p acts as a homocysteine synthase and allows the cells to substitute for Met17p by reassimilating hydrosulfide ions leaked from met17Δ cells into O-acetyl-homoserine and forming homocysteine. Our results show that cells can cooperate to achieve sulfur fixation, indicating that the collective properties of microbial communities facilitate their basic metabolic capacity to overcome sulfur limitation.
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Affiliation(s)
- Jason S. L. Yu
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Benjamin M. Heineike
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Johannes Hartl
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Simran K. Aulakh
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Clara Correia-Melo
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Andrea Lehmann
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Oliver Lemke
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Federica Agostini
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Cory T. Lee
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Vadim Demichev
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
| | - Christoph B. Messner
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, United Kingdom
| | - Michael Mülleder
- Core Facility—High Throughput Mass Spectrometry, Charité Universitätsmedizin, Berlin, Germany
| | - Markus Ralser
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, United Kingdom
- Department of Biochemistry, Charité Universitätsmedizin, Berlin, Germany
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19
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Kleeman SO, Cordioli M, Timmers PRJ, Khan A, Tober-Lau P, Kurth F, Demichev V, Meyer HV, Wilson JF, Ralser M, Kiryluk K, Ganna A, Baillie K, Janowitz T. Cystatin C is associated with adverse COVID-19 outcomes in diverse populations. iScience 2022; 25:105040. [PMID: 36062073 PMCID: PMC9428108 DOI: 10.1016/j.isci.2022.105040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 06/01/2022] [Accepted: 08/25/2022] [Indexed: 12/15/2022] Open
Abstract
COVID-19 has highly variable clinical courses. The search for prognostic host factors for COVID-19 outcome is a priority. We performed logistic regression for ICU admission against a polygenic score (PGS) for Cystatin C (CyC) production in patients with COVID-19. We analyzed the predictive value of longitudinal plasma CyC levels in an independent cohort of patients hospitalized with COVID-19. In four cohorts spanning European and African ancestry populations, we identified a significant association between CyC-production PGS and odds of critical illness (n cases=2,319), with the strongest association captured in the UKB cohort (OR 2.13, 95% CI 1.58-2.87, p=7.12e-7). Plasma proteomics from an independent cohort of hospitalized COVID-19 patients (n cases = 131) demonstrated that CyC production was associated with COVID-specific mortality (p=0.0007). Our findings suggest that CyC may be useful for stratification of patients and it has functional role in the host response to COVID-19. Cystatin C varies independently of renal function in COVID-19 A polygenic score for cystatin C production predicts critical COVID-19 illness Elevated serum cystatin C is associated with COVID-19 mortality
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20
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Demichev V, Szyrwiel L, Yu F, Teo GC, Rosenberger G, Niewienda A, Ludwig D, Decker J, Kaspar-Schoenefeld S, Lilley KS, Mülleder M, Nesvizhskii AI, Ralser M. dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts. Nat Commun 2022; 13:3944. [PMID: 35803928 PMCID: PMC9270362 DOI: 10.1038/s41467-022-31492-0] [Citation(s) in RCA: 87] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2021] [Accepted: 06/20/2022] [Indexed: 11/28/2022] Open
Abstract
The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200 ng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation.
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Affiliation(s)
- Vadim Demichev
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany.
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
- Department of Biochemistry and Milner Therapeutics Institute, University of Cambridge, Cambridge, UK.
| | - Lukasz Szyrwiel
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | | | - Agathe Niewienda
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Daniela Ludwig
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jens Decker
- Bruker Daltonics GmbH & Co. KG, Bremen, Germany
| | | | - Kathryn S Lilley
- Department of Biochemistry and Milner Therapeutics Institute, University of Cambridge, Cambridge, UK
| | - Michael Mülleder
- Core Facility High-Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Markus Ralser
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
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21
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Wang Z, Cryar A, Lemke O, Tober-Lau P, Ludwig D, Helbig ET, Hippenstiel S, Sander LE, Blake D, Lane CS, Sayers RL, Mueller C, Zeiser J, Townsend S, Demichev V, Mülleder M, Kurth F, Sirka E, Hartl J, Ralser M. A multiplex protein panel assay for severity prediction and outcome prognosis in patients with COVID-19: An observational multi-cohort study. EClinicalMedicine 2022; 49:101495. [PMID: 35702332 PMCID: PMC9181834 DOI: 10.1016/j.eclinm.2022.101495] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 05/11/2022] [Accepted: 05/17/2022] [Indexed: 12/15/2022] Open
Abstract
BACKGROUND Global healthcare systems continue to be challenged by the COVID-19 pandemic, and there is a need for clinical assays that can help optimise resource allocation, support treatment decisions, and accelerate the development and evaluation of new therapies. METHODS We developed a multiplexed proteomics assay for determining disease severity and prognosis in COVID-19. The assay quantifies up to 50 peptides, derived from 30 known and newly introduced COVID-19-related protein markers, in a single measurement using routine-lab compatible analytical flow rate liquid chromatography and multiple reaction monitoring (LC-MRM). We conducted two observational studies in patients with COVID-19 hospitalised at Charité - Universitätsmedizin Berlin, Germany before (from March 1 to 26, 2020, n=30) and after (from April 4 to November 19, 2020, n=164) dexamethasone became standard of care. The study is registered in the German and the WHO International Clinical Trials Registry (DRKS00021688). FINDINGS The assay produces reproducible (median inter-batch CV of 10.9%) absolute quantification of 47 peptides with high sensitivity (median LLOQ of 143 ng/ml) and accuracy (median 96.8%). In both studies, the assay reproducibly captured hallmarks of COVID-19 infection and severity, as it distinguished healthy individuals, mild, moderate, and severe COVID-19. In the post-dexamethasone cohort, the assay predicted survival with an accuracy of 0.83 (108/130), and death with an accuracy of 0.76 (26/34) in the median 2.5 weeks before the outcome, thereby outperforming compound clinical risk assessments such as SOFA, APACHE II, and ABCS scores. INTERPRETATION Disease severity and clinical outcomes of patients with COVID-19 can be stratified and predicted by the routine-applicable panel assay that combines known and novel COVID-19 biomarkers. The prognostic value of this assay should be prospectively assessed in larger patient cohorts for future support of clinical decisions, including evaluation of sample flow in routine setting. The possibility to objectively classify COVID-19 severity can be helpful for monitoring of novel therapies, especially in early clinical trials. FUNDING This research was funded in part by the European Research Council (ERC) under grant agreement ERC-SyG-2020 951475 (to M.R) and by the Wellcome Trust (IA 200829/Z/16/Z to M.R.). The work was further supported by the Ministry of Education and Research (BMBF) as part of the National Research Node 'Mass Spectrometry in Systems Medicine (MSCoresys)', under grant agreements 031L0220 and 161L0221. J.H. was supported by a Swiss National Science Foundation (SNSF) Postdoc Mobility fellowship (project number 191052). This study was further supported by the BMBF grant NaFoUniMedCOVID-19 - NUM-NAPKON, FKZ: 01KX2021. The study was co-funded by the UK's innovation agency, Innovate UK, under project numbers 75594 and 56328.
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Affiliation(s)
- Ziyue Wang
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Am Chariteplatz 1, 10117 Berlin, Germany
| | - Adam Cryar
- Inoviv, Mappin House, 4 Winsley St, London, United Kingdom
| | - Oliver Lemke
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Am Chariteplatz 1, 10117 Berlin, Germany
| | - Pinkus Tober-Lau
- Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Daniela Ludwig
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Am Chariteplatz 1, 10117 Berlin, Germany
| | - Elisa Theresa Helbig
- Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Stefan Hippenstiel
- Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
| | - Leif-Erik Sander
- Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
- Berlin Institute of Health at the Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | | | | | - Christoph Mueller
- Agilent Technologies Sales & Services GmbH & Co. KG, Waldbronn, Germany
| | - Johannes Zeiser
- Agilent Technologies Sales & Services GmbH & Co. KG, Waldbronn, Germany
| | - StJohn Townsend
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Am Chariteplatz 1, 10117 Berlin, Germany
| | - Vadim Demichev
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Am Chariteplatz 1, 10117 Berlin, Germany
| | - Michael Mülleder
- Core Facility – High-Throughput Mass Spectrometry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Am Chariteplatz 1, 10117 Berlin, Germany
| | - Florian Kurth
- Department of Infectious Diseases and Respiratory Medicine, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Augustenburger Platz 1, 13353 Berlin, Germany
- Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine, and Department of Medicine I, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
- Corresponding author: Florian Kurth, Charité - Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Augustenburger Platz 1, 13353 Berlin, Germany. Tel.: +49 (0)30 450 553052.
| | - Ernestas Sirka
- Inoviv, Mappin House, 4 Winsley St, London, United Kingdom
- Corresponding author: Ernestas Sirka, Inoviv, Mappin House, 4 Winsley St, London W1W 8HF, United Kingdom, Tel.: +44 (0)20 3239 0178.
| | - Johannes Hartl
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Am Chariteplatz 1, 10117 Berlin, Germany
- Corresponding author: Johannes Hartl, Charité – Universitätsmedizin Berlin, Department of Biochemistry, Charitéplatz 1, 10117 Berlin, Germany. Tel.: +49 (0)30 450 528317.
| | - Markus Ralser
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Am Chariteplatz 1, 10117 Berlin, Germany
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Corresponding author: Markus Ralser, Charité – Universitätsmedizin Berlin, Department of Biochemistry, Charitéplatz 1, 10117 Berlin, Germany. Tel.: +49 (0)30 450 528141
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22
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Gao H, Liu Y, Demichev V, Tate S, Chen C, Zhu J, Lu C, Ralser M, Guo T, Zhu Y. Optimization of Microflow LC Coupled with Scanning SWATH and Its Application in Hepatocellular Carcinoma Tissues. J Proteome Res 2022; 21:1686-1693. [PMID: 35653712 DOI: 10.1021/acs.jproteome.2c00078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Scanning SWATH coupled with normal-flow LC has been recently introduced for high-content, high-throughput proteomics analysis, which requires a relatively large amount of sample injection. Here we established the microflow LC coupled with Scanning SWATH for samples with relatively small quantities. First, we optimized several key parameters of the LC and MS settings, including C18 particle size for the analytical column, LC gradient and flow rate, as well as effective ion accumulation time and isolation window width for MS acquisition. We then compared the optimized Scanning SWATH method with the conventional variable window SWATH (referred to as SWATH) method. Results showed that the total ion chromatogram signals in Scanning SWATH were 10 times higher than that of SWATH, and Scanning SWATH identified 12.2-22.2% more peptides than SWATH. Finally, we employed 120 min Scanning SWATH to acquire the proteomes of 62 formalin-fixed, paraffin-embedded (FFPE) tissue samples from 31 patients with hepatocellular carcinoma (HCC). Altogether, 92 334 peptides and 8516 proteins were quantified. Besides the reported biomarkers, including ANXA2, MCM7, SUOX, and AKR1B10, we identified new potential HCC biomarkers such as CST5, TP53, CEBPB, and E2F4. Taken together, we present an optimal workflow integrating microflow LC and Scanning SWATH that effectively improves the protein identification and quantitation.
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Affiliation(s)
- Huanhuan Gao
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang Province, China
| | - Youqi Liu
- Westlake Omics (Hangzhou) Biotechnology Co., Ltd., No. 1 Yunmeng Road, Cloud Town, Xihu District, Hangzhou 310024, Zhejiang Province, China
| | - Vadim Demichev
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London WC2N 5DU, U.K.,Department of Biochemistry, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 10115, Germany
| | | | | | - Jiang Zhu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, Hubei, China
| | - Cong Lu
- Center for Stem Cell Research and Application, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430000, Hubei, China
| | - Markus Ralser
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London WC2N 5DU, U.K.,Department of Biochemistry, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin 10115, Germany
| | - Tiannan Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang Province, China
| | - Yi Zhu
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou 310024, Zhejiang Province, China.,Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou 310024, Zhejiang Province, China
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23
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Steger M, Karayel Ö, Demichev V. Ubiquitinomics: history, methods and applications in basic research and drug discovery. Proteomics 2022; 22:e2200074. [PMID: 35353442 DOI: 10.1002/pmic.202200074] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 03/21/2022] [Accepted: 03/23/2022] [Indexed: 11/08/2022]
Abstract
The ubiquitin-proteasome system (UPS) was discovered about 40 years ago and is known to regulate a multitude of cellular processes including protein homeostasis. ubiquitylated proteins are recognized by downstream effectors, resulting in alterations of protein abundance, activity, or localization. Not surprisingly, the ubiquitylation machinery is dysregulated in numerous diseases, including cancers and neurodegeneration. Mass spectrometry (MS)-based proteomics has emerged as a transformative technology for characterizing protein ubiquitylation in an unbiased fashion. Here, we provide an overview of the different MS-based approaches for studying protein ubiquitylation. We review various methods for enriching and quantifying ubiquitin modifications at the peptide or protein level, outline MS acquisition and data processing approaches and discuss key challenges. Finally, we examine how MS-based ubiquitinomics can aid both basic biology and drug discovery research. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Martin Steger
- Evotec München GmbH, Martinsried, 82152, Germany.,Present address: Max Planck Institute of Biochemistry, Martinsried, 82152, Germany
| | - Özge Karayel
- Max Planck Institute of Biochemistry, Martinsried, 82152, Germany.,Current address: Department of Physiological Chemistry, Genentech, South San Francisco, CA, 94080, USA
| | - Vadim Demichev
- Charité - Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany
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24
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Yu JSL, Correia-Melo C, Zorrilla F, Herrera-Dominguez L, Wu MY, Hartl J, Campbell K, Blasche S, Kreidl M, Egger AS, Messner CB, Demichev V, Freiwald A, Mülleder M, Howell M, Berman J, Patil KR, Alam MT, Ralser M. Microbial communities form rich extracellular metabolomes that foster metabolic interactions and promote drug tolerance. Nat Microbiol 2022; 7:542-555. [PMID: 35314781 PMCID: PMC8975748 DOI: 10.1038/s41564-022-01072-5] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 01/28/2022] [Indexed: 12/30/2022]
Abstract
Microbial communities are composed of cells of varying metabolic capacity, and regularly include auxotrophs that lack essential metabolic pathways. Through analysis of auxotrophs for amino acid biosynthesis pathways in microbiome data derived from >12,000 natural microbial communities obtained as part of the Earth Microbiome Project (EMP), and study of auxotrophic–prototrophic interactions in self-establishing metabolically cooperating yeast communities (SeMeCos), we reveal a metabolically imprinted mechanism that links the presence of auxotrophs to an increase in metabolic interactions and gains in antimicrobial drug tolerance. As a consequence of the metabolic adaptations necessary to uptake specific metabolites, auxotrophs obtain altered metabolic flux distributions, export more metabolites and, in this way, enrich community environments in metabolites. Moreover, increased efflux activities reduce intracellular drug concentrations, allowing cells to grow in the presence of drug levels above minimal inhibitory concentrations. For example, we show that the antifungal action of azoles is greatly diminished in yeast cells that uptake metabolites from a metabolically enriched environment. Our results hence provide a mechanism that explains why cells are more robust to drug exposure when they interact metabolically. Using microbiome data analysis and a self-establishing metabolically cooperating yeast community model, the authors show that the presence of auxotrophs in a microbial community increases metabolic interactions between cells and fosters antimicrobial drug tolerance.
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Affiliation(s)
- Jason S L Yu
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Clara Correia-Melo
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.,Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Francisco Zorrilla
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.,Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Lucia Herrera-Dominguez
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.,Department of Biochemistry, Charité University Medicine, Berlin, Germany
| | - Mary Y Wu
- High-Throughput Screening, The Francis Crick Institute, London, UK
| | - Johannes Hartl
- Department of Biochemistry, Charité University Medicine, Berlin, Germany
| | - Kate Campbell
- Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Sonja Blasche
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.,Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Marco Kreidl
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Anna-Sophia Egger
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Christoph B Messner
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.,Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Vadim Demichev
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.,Department of Biochemistry, University of Cambridge, Cambridge, UK
| | - Anja Freiwald
- Department of Biochemistry, Charité University Medicine, Berlin, Germany.,Core Facility - High Throughput Mass Spectrometry, Charité University Medicine, Berlin, Germany
| | - Michael Mülleder
- Core Facility - High Throughput Mass Spectrometry, Charité University Medicine, Berlin, Germany
| | - Michael Howell
- High-Throughput Screening, The Francis Crick Institute, London, UK
| | - Judith Berman
- Shmunis School of Biomedical and Cancer Research, George S. Wise Faculty of Life Sciences, Tel Aviv University, Ramat Aviv, Israel
| | - Kiran R Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK.,Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Mohammad Tauqeer Alam
- Department of Biology, College of Science, United Arab Emirates University, Al-Ain, UAE. .,Warwick Medical School, University of Warwick, Coventry, UK.
| | - Markus Ralser
- The Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK. .,Department of Biochemistry, Charité University Medicine, Berlin, Germany. .,Core Facility - High Throughput Mass Spectrometry, Charité University Medicine, Berlin, Germany.
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25
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Georg P, Astaburuaga-García R, Bonaguro L, Brumhard S, Michalick L, Lippert LJ, Kostevc T, Gäbel C, Schneider M, Streitz M, Demichev V, Gemünd I, Barone M, Tober-Lau P, Helbig ET, Hillus D, Petrov L, Stein J, Dey HP, Paclik D, Iwert C, Mülleder M, Aulakh SK, Djudjaj S, Bülow RD, Mei HE, Schulz AR, Thiel A, Hippenstiel S, Saliba AE, Eils R, Lehmann I, Mall MA, Stricker S, Röhmel J, Corman VM, Beule D, Wyler E, Landthaler M, Obermayer B, von Stillfried S, Boor P, Demir M, Wesselmann H, Suttorp N, Uhrig A, Müller-Redetzky H, Nattermann J, Kuebler WM, Meisel C, Ralser M, Schultze JL, Aschenbrenner AC, Thibeault C, Kurth F, Sander LE, Blüthgen N, Sawitzki B. Complement activation induces excessive T cell cytotoxicity in severe COVID-19. Cell 2022; 185:493-512.e25. [PMID: 35032429 PMCID: PMC8712270 DOI: 10.1016/j.cell.2021.12.040] [Citation(s) in RCA: 100] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 10/22/2021] [Accepted: 12/22/2021] [Indexed: 12/20/2022]
Abstract
Severe COVID-19 is linked to both dysfunctional immune response and unrestrained immunopathology, and it remains unclear whether T cells contribute to disease pathology. Here, we combined single-cell transcriptomics and single-cell proteomics with mechanistic studies to assess pathogenic T cell functions and inducing signals. We identified highly activated CD16+ T cells with increased cytotoxic functions in severe COVID-19. CD16 expression enabled immune-complex-mediated, T cell receptor-independent degranulation and cytotoxicity not found in other diseases. CD16+ T cells from COVID-19 patients promoted microvascular endothelial cell injury and release of neutrophil and monocyte chemoattractants. CD16+ T cell clones persisted beyond acute disease maintaining their cytotoxic phenotype. Increased generation of C3a in severe COVID-19 induced activated CD16+ cytotoxic T cells. Proportions of activated CD16+ T cells and plasma levels of complement proteins upstream of C3a were associated with fatal outcome of COVID-19, supporting a pathological role of exacerbated cytotoxicity and complement activation in COVID-19.
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Affiliation(s)
- Philipp Georg
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Rosario Astaburuaga-García
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany; IRI Life Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Lorenzo Bonaguro
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany; Systems Medicine, Deutsches Zentrum für Neurodegenerativen Erkrankungen (DZNE), Bonn, Germany
| | - Sophia Brumhard
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Laura Michalick
- Institute of Physiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Lena J Lippert
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Tomislav Kostevc
- Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christiane Gäbel
- Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Maria Schneider
- Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Mathias Streitz
- Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Vadim Demichev
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK; Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany; Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge, UK
| | - Ioanna Gemünd
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany; PRECISE Platform for Genomics and Epigenomics at DZNE, University of Bonn, Bonn, Germany; Department of Microbiology and Immunology, The University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Victoria, Australia
| | - Matthias Barone
- Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Pinkus Tober-Lau
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Elisa T Helbig
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - David Hillus
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Lev Petrov
- Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Julia Stein
- Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Hannah-Philine Dey
- Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Daniela Paclik
- Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christina Iwert
- Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Michael Mülleder
- Core Facility, High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Simran Kaur Aulakh
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Sonja Djudjaj
- Institute of Pathology, University Clinic Aachen, RWTH Aachen, Aachen, Germany
| | - Roman D Bülow
- Institute of Pathology, University Clinic Aachen, RWTH Aachen, Aachen, Germany
| | - Henrik E Mei
- Mass Cytometry Laboratory, DRFZ Berlin, A Leibniz Institute, Berlin, Germany
| | - Axel R Schulz
- Mass Cytometry Laboratory, DRFZ Berlin, A Leibniz Institute, Berlin, Germany
| | - Andreas Thiel
- Si-M/"Der Simulierte Mensch" a Science Framework of Technische Universität Berlin and Charité - Universitätsmedizin Berlin, Berlin, Germany; Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt - Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany
| | - Stefan Hippenstiel
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Antoine-Emmanuel Saliba
- Helmholtz Institute for RNA-based Infection Research (HIRI), Helmholtz Center for Infection Research (HZI), Würzburg, Germany
| | - Roland Eils
- Center for Digital Health, Berlin Institute of Health (BIH), Charité - Universitätsmedizin Berlin, Berlin, Germany; German Center for Lung Research (DZL), Berlin, Germany
| | - Irina Lehmann
- Center for Digital Health, Berlin Institute of Health (BIH), Charité - Universitätsmedizin Berlin, Berlin, Germany; German Center for Lung Research (DZL), Berlin, Germany
| | - Marcus A Mall
- Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany; German Center for Lung Research (DZL), Associated Partner, Berlin, Germany; Berlin Institute of Health (BIH), Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastian Stricker
- Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Jobst Röhmel
- Department of Pediatric Respiratory Medicine, Immunology and Critical Care Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Victor M Corman
- Institute of Virology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Dieter Beule
- Core Unit Bioinformatics, Berlin Institute of Health (BIH), Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Emanuel Wyler
- Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Markus Landthaler
- IRI Life Sciences, Humboldt-Universität zu Berlin, Berlin, Germany; Max-Delbrück-Center for Molecular Medicine in the Helmholtz Association, Berlin Institute for Medical Systems Biology, Berlin, Germany
| | - Benedikt Obermayer
- Core Unit Bioinformatics, Berlin Institute of Health (BIH), Charité - Universitätsmedizin Berlin, Berlin, Germany
| | | | - Peter Boor
- Institute of Pathology, University Clinic Aachen, RWTH Aachen, Aachen, Germany; Department of Nephrology, University Clinic Aachen, RWTH Aachen, Aachen, Germany; Electron Microscopy Facility, University Clinic Aachen, RWTH Aachen, Aachen, Germany
| | - Münevver Demir
- Department of Hepatology and Gastroenterology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Hans Wesselmann
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Norbert Suttorp
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany; German Center for Lung Research (DZL), Gießen, Germany
| | - Alexander Uhrig
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Holger Müller-Redetzky
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Jacob Nattermann
- Department of Internal Medicine I, University Hospital Bonn, Bonn, Germany
| | - Wolfgang M Kuebler
- Institute of Physiology, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Christian Meisel
- Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Department of Immunology, Labor Berlin, Charité Vivantes, Berlin, Germany
| | - Markus Ralser
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK; Department of Biochemistry, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Joachim L Schultze
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany; Systems Medicine, Deutsches Zentrum für Neurodegenerativen Erkrankungen (DZNE), Bonn, Germany; PRECISE Platform for Genomics and Epigenomics at DZNE, University of Bonn, Bonn, Germany
| | - Anna C Aschenbrenner
- Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany; Systems Medicine, Deutsches Zentrum für Neurodegenerativen Erkrankungen (DZNE), Bonn, Germany; PRECISE Platform for Genomics and Epigenomics at DZNE, University of Bonn, Bonn, Germany; Department of Internal Medicine, Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Charlotte Thibeault
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Florian Kurth
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany; Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine, Department of Medicine I, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany
| | - Leif E Sander
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Nils Blüthgen
- Institute of Pathology, Charité - Universitätsmedizin Berlin, Berlin, Germany; IRI Life Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Birgit Sawitzki
- Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Berlin, Germany; Berlin Institute of Health (BIH), Charité - Universitätsmedizin Berlin, Berlin, Germany.
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26
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Wang Z, Mülleder M, Batruch I, Chelur A, Textoris-Taube K, Schwecke T, Hartl J, Causon J, Castro-Perez J, Demichev V, Tate S, Ralser M. High-throughput proteomics of nanogram-scale samples with Zeno SWATH MS. eLife 2022; 11:83947. [PMID: 36449390 PMCID: PMC9711518 DOI: 10.7554/elife.83947] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2022] [Accepted: 11/08/2022] [Indexed: 12/03/2022] Open
Abstract
The possibility to record proteomes in high throughput and at high quality has opened new avenues for biomedical research, drug discovery, systems biology, and clinical translation. However, high-throughput proteomic experiments often require high sample amounts and can be less sensitive compared to conventional proteomic experiments. Here, we introduce and benchmark Zeno SWATH MS, a data-independent acquisition technique that employs a linear ion trap pulsing (Zeno trap pulsing) to increase the sensitivity in high-throughput proteomic experiments. We demonstrate that when combined with fast micro- or analytical flow-rate chromatography, Zeno SWATH MS increases protein identification with low sample amounts. For instance, using 20 min micro-flow-rate chromatography, Zeno SWATH MS identified more than 5000 proteins consistently, and with a coefficient of variation of 6%, from a 62.5 ng load of human cell line tryptic digest. Using 5 min analytical flow-rate chromatography (800 µl/min), Zeno SWATH MS identified 4907 proteins from a triplicate injection of 2 µg of a human cell lysate, or more than 3000 proteins from a 250 ng tryptic digest. Zeno SWATH MS hence facilitates sensitive high-throughput proteomic experiments with low sample amounts, mitigating the current bottlenecks of high-throughput proteomics.
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Affiliation(s)
- Ziyue Wang
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu BerlinBerlinGermany
| | - Michael Mülleder
- Core Facility – High-Throughput Mass Spectrometry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Core Facility – High-Throughput Mass SpectrometryBerlinGermany
| | | | | | - Kathrin Textoris-Taube
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu BerlinBerlinGermany,Core Facility – High-Throughput Mass Spectrometry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Core Facility – High-Throughput Mass SpectrometryBerlinGermany
| | - Torsten Schwecke
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu BerlinBerlinGermany
| | - Johannes Hartl
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu BerlinBerlinGermany
| | | | | | - Vadim Demichev
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu BerlinBerlinGermany
| | | | - Markus Ralser
- Department of Biochemistry, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu BerlinBerlinGermany,The Wellcome Centre for Human Genetics, Nuffield Department of Medicine, University of OxfordOxfordUnited Kingdom
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27
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Demichev V, Tober-Lau P, Nazarenko T, Lemke O, Kaur Aulakh S, Whitwell HJ, Röhl A, Freiwald A, Mittermaier M, Szyrwiel L, Ludwig D, Correia-Melo C, Lippert LJ, Helbig ET, Stubbemann P, Olk N, Thibeault C, Grüning NM, Blyuss O, Vernardis S, White M, Messner CB, Joannidis M, Sonnweber T, Klein SJ, Pizzini A, Wohlfarter Y, Sahanic S, Hilbe R, Schaefer B, Wagner S, Machleidt F, Garcia C, Ruwwe-Glösenkamp C, Lingscheid T, Bosquillon de Jarcy L, Stegemann MS, Pfeiffer M, Jürgens L, Denker S, Zickler D, Spies C, Edel A, Müller NB, Enghard P, Zelezniak A, Bellmann-Weiler R, Weiss G, Campbell A, Hayward C, Porteous DJ, Marioni RE, Uhrig A, Zoller H, Löffler-Ragg J, Keller MA, Tancevski I, Timms JF, Zaikin A, Hippenstiel S, Ramharter M, Müller-Redetzky H, Witzenrath M, Suttorp N, Lilley K, Mülleder M, Sander LE, Kurth F, Ralser M. A proteomic survival predictor for COVID-19 patients in intensive care. PLOS Digit Health 2022; 1:e0000007. [PMID: 36812516 PMCID: PMC9931303 DOI: 10.1371/journal.pdig.0000007] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 11/18/2021] [Indexed: 02/07/2023]
Abstract
Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care.
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Affiliation(s)
- Vadim Demichev
- Charité–Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, United Kingdom
- The University of Cambridge, Department of Biochemistry and Cambridge Centre for Proteomics, Cambridge, United Kingdom
| | - Pinkus Tober-Lau
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Tatiana Nazarenko
- University College London, Department of Mathematics, London, United Kingdom
- University College London, Department of Women’s Cancer, EGA Institute for Women’s Health, London, United Kingdom
| | - Oliver Lemke
- Charité–Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany
| | - Simran Kaur Aulakh
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, United Kingdom
| | - Harry J. Whitwell
- National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
- Lobachevsky University, Laboratory of Systems Medicine of Healthy Ageing, Nizhny Novgorod, Russia
- Imperial College London, Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, London, United Kingdom
| | - Annika Röhl
- Charité–Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany
| | - Anja Freiwald
- Charité–Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany
| | - Mirja Mittermaier
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
- Berlin Institute of Health, Berlin, Germany
| | - Lukasz Szyrwiel
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, United Kingdom
| | - Daniela Ludwig
- Charité–Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany
| | - Clara Correia-Melo
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, United Kingdom
| | - Lena J. Lippert
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Elisa T. Helbig
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Paula Stubbemann
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Nadine Olk
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Charlotte Thibeault
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Nana-Maria Grüning
- Charité–Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany
| | - Oleg Blyuss
- Lobachevsky University, Department of Applied Mathematics, Nizhny Novgorod, Russia
- University of Hertfordshire, School of Physics, Astronomy and Mathematics, Hatfield, United Kingdom
- Sechenov First Moscow State Medical University, Department of Paediatrics and Paediatric Infectious Diseases, Moscow, Russia
| | - Spyros Vernardis
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, United Kingdom
| | - Matthew White
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, United Kingdom
| | - Christoph B. Messner
- Charité–Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, United Kingdom
| | - Michael Joannidis
- Medical University Innsbruck, Division of Intensive Care and Emergency Medicine, Department of Internal Medicine, Innsbruck, Austria
| | - Thomas Sonnweber
- Medical University of Innsbruck, Department of Internal Medicine II, Innsbruck, Austria
| | - Sebastian J. Klein
- Medical University Innsbruck, Division of Intensive Care and Emergency Medicine, Department of Internal Medicine, Innsbruck, Austria
| | - Alex Pizzini
- Medical University of Innsbruck, Department of Internal Medicine II, Innsbruck, Austria
| | - Yvonne Wohlfarter
- Medical University of Innsbruck, Institute of Human Genetics, Innsbruck, Austria
| | - Sabina Sahanic
- Medical University of Innsbruck, Department of Internal Medicine II, Innsbruck, Austria
| | - Richard Hilbe
- Medical University of Innsbruck, Department of Internal Medicine II, Innsbruck, Austria
| | - Benedikt Schaefer
- Medical University of Innsbruck, Christian Doppler Laboratory for Iron and Phosphate Biology, Department of Internal Medicine I, Innsbruck, Austria
| | - Sonja Wagner
- Medical University of Innsbruck, Christian Doppler Laboratory for Iron and Phosphate Biology, Department of Internal Medicine I, Innsbruck, Austria
| | - Felix Machleidt
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Carmen Garcia
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Christoph Ruwwe-Glösenkamp
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Tilman Lingscheid
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Laure Bosquillon de Jarcy
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Miriam S. Stegemann
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Moritz Pfeiffer
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Linda Jürgens
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Sophy Denker
- Charité–Universitätsmedizin Berlin, Medical Department of Hematology, Oncology & Tumor Immunology, Virchow Campus & Molekulares Krebsforschungszentrum, Berlin, Germany
| | - Daniel Zickler
- Charité–Universitätsmedizin Berlin, Department of Nephrology and Internal Intensive Care Medicine, Berlin, Germany
| | - Claudia Spies
- Charité–Universitätsmedizin Berlin, Department of Anesthesiology and Intensive Care, Berlin, Germany
| | - Andreas Edel
- Charité–Universitätsmedizin Berlin, Department of Anesthesiology and Intensive Care, Berlin, Germany
| | - Nils B. Müller
- Charité–Universitätsmedizin Berlin, Department of Nephrology and Internal Intensive Care Medicine, Berlin, Germany
| | - Philipp Enghard
- Charité–Universitätsmedizin Berlin, Department of Nephrology and Internal Intensive Care Medicine, Berlin, Germany
| | - Aleksej Zelezniak
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, United Kingdom
- Chalmers University of Technology, Department of Biology and Biological Engineering, Gothenburg, Sweden
| | - Rosa Bellmann-Weiler
- Medical University of Innsbruck, Department of Internal Medicine II, Innsbruck, Austria
| | - Günter Weiss
- Medical University of Innsbruck, Department of Internal Medicine II, Innsbruck, Austria
| | - Archie Campbell
- University of Edinburgh, Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, United Kingdom
- University of Edinburgh, Usher Institute, Edinburgh, United Kingdom
| | - Caroline Hayward
- University of Edinburgh, MRC Human Genetics Unit, Institute of Genetics and Cancer, Edinburgh, United Kingdom
| | - David J. Porteous
- University of Edinburgh, Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, United Kingdom
- University of Edinburgh, Usher Institute, Edinburgh, United Kingdom
| | - Riccardo E. Marioni
- University of Edinburgh, Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, United Kingdom
| | - Alexander Uhrig
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Heinz Zoller
- Medical University of Innsbruck, Christian Doppler Laboratory for Iron and Phosphate Biology, Department of Internal Medicine I, Innsbruck, Austria
| | - Judith Löffler-Ragg
- Medical University of Innsbruck, Department of Internal Medicine II, Innsbruck, Austria
| | - Markus A. Keller
- Medical University of Innsbruck, Institute of Human Genetics, Innsbruck, Austria
| | - Ivan Tancevski
- Medical University of Innsbruck, Department of Internal Medicine II, Innsbruck, Austria
| | - John F. Timms
- University College London, Department of Women’s Cancer, EGA Institute for Women’s Health, London, United Kingdom
| | - Alexey Zaikin
- University College London, Department of Mathematics, London, United Kingdom
- University College London, Department of Women’s Cancer, EGA Institute for Women’s Health, London, United Kingdom
- Lobachevsky University, Laboratory of Systems Medicine of Healthy Ageing, Nizhny Novgorod, Russia
- Centre for Analysis of Complex Systems, Sechenov First Moscow State Medical University, Moscow, Russia
| | - Stefan Hippenstiel
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
- German Centre for Lung Research, Germany
| | - Michael Ramharter
- Bernhard Nocht Institute for Tropical Medicine, Department of Tropical Medicine, and University Medical Center Hamburg-Eppendorf, Department of Medicine, Hamburg, Germany
| | - Holger Müller-Redetzky
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Martin Witzenrath
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
- German Centre for Lung Research, Germany
| | - Norbert Suttorp
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
- German Centre for Lung Research, Germany
| | - Kathryn Lilley
- The University of Cambridge, Department of Biochemistry and Cambridge Centre for Proteomics, Cambridge, United Kingdom
| | - Michael Mülleder
- Charité–Universitätsmedizin Berlin, Core Facility—High-Throughput Mass Spectrometry, Berlin, Germany
| | - Leif Erik Sander
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
- German Centre for Lung Research, Germany
| | | | - Florian Kurth
- Charité–Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
- Bernhard Nocht Institute for Tropical Medicine, Department of Tropical Medicine, and University Medical Center Hamburg-Eppendorf, Department of Medicine, Hamburg, Germany
- * E-mail:
| | - Markus Ralser
- Charité–Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, United Kingdom
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28
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Demichev V, Tober-Lau P, Lemke O, Nazarenko T, Thibeault C, Whitwell H, Röhl A, Freiwald A, Szyrwiel L, Ludwig D, Correia-Melo C, Aulakh SK, Helbig ET, Stubbemann P, Lippert LJ, Grüning NM, Blyuss O, Vernardis S, White M, Messner CB, Joannidis M, Sonnweber T, Klein SJ, Pizzini A, Wohlfarter Y, Sahanic S, Hilbe R, Schaefer B, Wagner S, Mittermaier M, Machleidt F, Garcia C, Ruwwe-Glösenkamp C, Lingscheid T, Bosquillon de Jarcy L, Stegemann MS, Pfeiffer M, Jürgens L, Denker S, Zickler D, Enghard P, Zelezniak A, Campbell A, Hayward C, Porteous DJ, Marioni RE, Uhrig A, Müller-Redetzky H, Zoller H, Löffler-Ragg J, Keller MA, Tancevski I, Timms JF, Zaikin A, Hippenstiel S, Ramharter M, Witzenrath M, Suttorp N, Lilley K, Mülleder M, Sander LE, Ralser M, Kurth F. A time-resolved proteomic and prognostic map of COVID-19. Cell Syst 2021; 12:780-794.e7. [PMID: 34139154 PMCID: PMC8201874 DOI: 10.1016/j.cels.2021.05.005] [Citation(s) in RCA: 92] [Impact Index Per Article: 30.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Revised: 03/24/2021] [Accepted: 05/07/2021] [Indexed: 12/14/2022]
Abstract
COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.
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Affiliation(s)
- Vadim Demichev
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany; The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK; The University of Cambridge, Department of Biochemistry and Cambridge Centre for Proteomics, Cambridge CB21GA, UK
| | - Pinkus Tober-Lau
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Oliver Lemke
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Tatiana Nazarenko
- University College London, Department of Mathematics, London WC1E 6BT, UK; University College London, Department of Women's Cancer, EGA Institute for Women'S Health, London WC1E 6BT, UK
| | - Charlotte Thibeault
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Harry Whitwell
- National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW72AZ, UK; Lobachevsky University, Department of Applied Mathematics, Nizhny Novgorod 603105, Russia; Imperial College London, Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, London SW7 2AZ, UK
| | - Annika Röhl
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Anja Freiwald
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Lukasz Szyrwiel
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK
| | - Daniela Ludwig
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Clara Correia-Melo
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK
| | - Simran Kaur Aulakh
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK
| | - Elisa T Helbig
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Paula Stubbemann
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Lena J Lippert
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Nana-Maria Grüning
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany
| | - Oleg Blyuss
- Lobachevsky University, Department of Applied Mathematics, Nizhny Novgorod 603105, Russia; University of Hertfordshire, School of Physics, Astronomy and Mathematics, Hatfield AL10 9AB, UK; Sechenov First Moscow State Medical University, Department of Paediatrics and Paediatric Infectious Diseases, Moscow 119435, Russia
| | - Spyros Vernardis
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK
| | - Matthew White
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK
| | - Christoph B Messner
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany; The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK
| | - Michael Joannidis
- Medical University Innsbruck, Division of Intensive Care and Emergency Medicine, Department of Internal Medicine, 6020 Innsbruck, Austria
| | - Thomas Sonnweber
- Medical University of Innsbruck, Department of Internal Medicine II, 6020 Innsbruck, Austria
| | - Sebastian J Klein
- Medical University Innsbruck, Division of Intensive Care and Emergency Medicine, Department of Internal Medicine, 6020 Innsbruck, Austria
| | - Alex Pizzini
- Medical University of Innsbruck, Department of Internal Medicine II, 6020 Innsbruck, Austria
| | - Yvonne Wohlfarter
- Medical University of Innsbruck, Institute of Human Genetics, 6020 Innsbruck, Austria
| | - Sabina Sahanic
- Medical University of Innsbruck, Department of Internal Medicine II, 6020 Innsbruck, Austria
| | - Richard Hilbe
- Medical University of Innsbruck, Department of Internal Medicine II, 6020 Innsbruck, Austria
| | - Benedikt Schaefer
- Medical University of Innsbruck, Christian Doppler Laboratory for Iron and Phosphate Biology, Department of Internal Medicine I, 6020 Innsbruck, Austria
| | - Sonja Wagner
- Medical University of Innsbruck, Christian Doppler Laboratory for Iron and Phosphate Biology, Department of Internal Medicine I, 6020 Innsbruck, Austria
| | - Mirja Mittermaier
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany; Berlin Institute of Health, 10178 Berlin, Germany
| | - Felix Machleidt
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Carmen Garcia
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Christoph Ruwwe-Glösenkamp
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Tilman Lingscheid
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Laure Bosquillon de Jarcy
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Miriam S Stegemann
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Moritz Pfeiffer
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Linda Jürgens
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Sophy Denker
- Charité Universitätsmedizin Berlin, Medical Department of Hematology, Oncology & Tumor Immunology, Virchow Campus & Molekulares Krebsforschungszentrum, 13353 Berlin, Germany; Berlin Institute of Health, 10178 Berlin, Germany
| | - Daniel Zickler
- Charité Universitätsmedizin Berlin, Department of Nephrology and Internal Intensive Care Medicine, 10117 Berlin, Germany
| | - Philipp Enghard
- Charité Universitätsmedizin Berlin, Department of Nephrology and Internal Intensive Care Medicine, 10117 Berlin, Germany
| | - Aleksej Zelezniak
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK; Chalmers Tekniska Högskola, Department of Biology and Biological Engineering, SE-412 96 Gothenburg, Sweden
| | - Archie Campbell
- University of Edinburgh, Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, Edinburgh EH4 2XU, UK; University of Edinburgh, Usher Institute, Edinburgh EH16 4UX, UK
| | - Caroline Hayward
- University of Edinburgh, MRC Human Genetics Unit, Institute of Genetics and Cancer, Edinburgh EH4 2XU, UK
| | - David J Porteous
- University of Edinburgh, Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, Edinburgh EH4 2XU, UK; University of Edinburgh, Usher Institute, Edinburgh EH16 4UX, UK
| | - Riccardo E Marioni
- University of Edinburgh, Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, Edinburgh EH4 2XU, UK
| | - Alexander Uhrig
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Holger Müller-Redetzky
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Heinz Zoller
- Medical University of Innsbruck, Christian Doppler Laboratory for Iron and Phosphate Biology, Department of Internal Medicine I, 6020 Innsbruck, Austria
| | - Judith Löffler-Ragg
- Medical University of Innsbruck, Department of Internal Medicine II, 6020 Innsbruck, Austria
| | - Markus A Keller
- Medical University of Innsbruck, Institute of Human Genetics, 6020 Innsbruck, Austria
| | - Ivan Tancevski
- Medical University of Innsbruck, Department of Internal Medicine II, 6020 Innsbruck, Austria
| | - John F Timms
- University College London, Department of Women's Cancer, EGA Institute for Women'S Health, London WC1E 6BT, UK
| | - Alexey Zaikin
- University College London, Department of Mathematics, London WC1E 6BT, UK; University College London, Department of Women's Cancer, EGA Institute for Women'S Health, London WC1E 6BT, UK; Lobachevsky University, Laboratory of Systems Medicine of Healthy Ageing, Nizhny Novgorod 603105, Russia
| | - Stefan Hippenstiel
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany; German Centre for Lung Research, 35392 Gießen, Germany
| | - Michael Ramharter
- Bernhard Nocht Institute for Tropical Medicine, Department of Tropical Medicine, and University Medical Center Hamburg-Eppendorf, Department of Medicine, 20359 Hamburg, Germany
| | - Martin Witzenrath
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany; German Centre for Lung Research, 35392 Gießen, Germany
| | - Norbert Suttorp
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany; German Centre for Lung Research, 35392 Gießen, Germany
| | - Kathryn Lilley
- The University of Cambridge, Department of Biochemistry and Cambridge Centre for Proteomics, Cambridge CB21GA, UK
| | - Michael Mülleder
- Charité - Universitätsmedizin Berlin, Core Facility - High-Throughput Mass Spectrometry, 10117 Berlin, Germany
| | - Leif Erik Sander
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany; German Centre for Lung Research, 35392 Gießen, Germany
| | - Markus Ralser
- Charité Universitätsmedizin Berlin, Department of Biochemistry, 10117 Berlin, Germany; The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK.
| | - Florian Kurth
- Charité Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany; Bernhard Nocht Institute for Tropical Medicine, Department of Tropical Medicine, and University Medical Center Hamburg-Eppendorf, Department of Medicine, 20359 Hamburg, Germany
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29
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Völlmy F, van den Toorn H, Zenezini Chiozzi R, Zucchetti O, Papi A, Volta CA, Marracino L, Vieceli Dalla Sega F, Fortini F, Demichev V, Tober-Lau P, Campo G, Contoli M, Ralser M, Kurth F, Spadaro S, Rizzo P, Heck AJ. A serum proteome signature to predict mortality in severe COVID-19 patients. Life Sci Alliance 2021; 4:4/9/e202101099. [PMID: 34226277 PMCID: PMC8321673 DOI: 10.26508/lsa.202101099] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 06/24/2021] [Accepted: 06/24/2021] [Indexed: 12/12/2022] Open
Abstract
Here, we recorded serum proteome profiles of 33 severe COVID-19 patients admitted to respiratory and intensive care units because of respiratory failure. We received, for most patients, blood samples just after admission and at two more later time points. With the aim to predict treatment outcome, we focused on serum proteins different in abundance between the group of survivors and non-survivors. We observed that a small panel of about a dozen proteins were significantly different in abundance between these two groups. The four structurally and functionally related type-3 cystatins AHSG, FETUB, histidine-rich glycoprotein, and KNG1 were all more abundant in the survivors. The family of inter-α-trypsin inhibitors, ITIH1, ITIH2, ITIH3, and ITIH4, were all found to be differentially abundant in between survivors and non-survivors, whereby ITIH1 and ITIH2 were more abundant in the survivor group and ITIH3 and ITIH4 more abundant in the non-survivors. ITIH1/ITIH2 and ITIH3/ITIH4 also showed opposite trends in protein abundance during disease progression. We defined an optimal panel of nine proteins for mortality risk assessment. The prediction power of this mortality risk panel was evaluated against two recent COVID-19 serum proteomics studies on independent cohorts measured in other laboratories in different countries and observed to perform very well in predicting mortality also in these cohorts. This panel may not be unique for COVID-19 as some of the proteins in the panel have previously been annotated as mortality markers in aging and in other diseases caused by different pathogens, including bacteria.
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Affiliation(s)
- Franziska Völlmy
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands.,Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Henk van den Toorn
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands.,Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Riccardo Zenezini Chiozzi
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands.,Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Ottavio Zucchetti
- Cardiology Unit, Azienda Ospedaliero-Universitaria di Ferrara, University of Ferrara, Ferrara, Italy
| | - Alberto Papi
- Respiratory Section, Department of Translational Medicine, University of Ferrara, Ferrara, Italy and Respiratory Disease Unit, Azienda Ospedaliero-Universitaria di Ferrara, Ferrara, Italy
| | - Carlo Alberto Volta
- Department of Translational Medicine University of Ferrara, Ferrara, Italy and Intensive Care Unit, Azienda Ospedaliero-Universitaria di Ferrara, Italy
| | - Luisa Marracino
- Department of Translational Medicine and Laboratory for Technology of Advanced Therapies (LTTA), University of Ferrara, Ferrara, Italy
| | | | | | - Vadim Demichev
- Charité-Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany.,The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, UK.,The University of Cambridge, Department of Biochemistry and Cambridge Centre for Proteomics, Cambridge, UK
| | - Pinkus Tober-Lau
- Charité-Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany
| | - Gianluca Campo
- Cardiology Unit, Azienda Ospedaliero-Universitaria di Ferrara, University of Ferrara, Ferrara, Italy.,Maria Cecilia Hospital, GVM Care & Research, Cotignola, Italy
| | - Marco Contoli
- Respiratory Section, Department of Translational Medicine, University of Ferrara, Ferrara, Italy and Respiratory Disease Unit, Azienda Ospedaliero-Universitaria di Ferrara, Ferrara, Italy
| | - Markus Ralser
- Charité-Universitätsmedizin Berlin, Department of Biochemistry, Berlin, Germany.,The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London, UK
| | - Florian Kurth
- Charité-Universitätsmedizin Berlin, Department of Infectious Diseases and Respiratory Medicine, Berlin, Germany.,National Phenome Centre and Imperial Clinical Phenotyping Centre, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Savino Spadaro
- Department of Translational Medicine University of Ferrara, Ferrara, Italy and Intensive Care Unit, Azienda Ospedaliero-Universitaria di Ferrara, Italy
| | - Paola Rizzo
- Department of Translational Medicine and Laboratory for Technology of Advanced Therapies (LTTA), University of Ferrara, Ferrara, Italy.,Maria Cecilia Hospital, GVM Care & Research, Cotignola, Italy
| | - Albert Jr Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands .,Netherlands Proteomics Center, Utrecht, The Netherlands
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30
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Messner CB, Demichev V, Bloomfield N, Yu JSL, White M, Kreidl M, Egger AS, Freiwald A, Ivosev G, Wasim F, Zelezniak A, Jürgens L, Suttorp N, Sander LE, Kurth F, Lilley KS, Mülleder M, Tate S, Ralser M. Ultra-fast proteomics with Scanning SWATH. Nat Biotechnol 2021; 39:846-854. [PMID: 33767396 PMCID: PMC7611254 DOI: 10.1038/s41587-021-00860-4] [Citation(s) in RCA: 119] [Impact Index Per Article: 39.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 02/18/2021] [Indexed: 01/31/2023]
Abstract
Accurate quantification of the proteome remains challenging for large sample series and longitudinal experiments. We report a data-independent acquisition method, Scanning SWATH, that accelerates mass spectrometric (MS) duty cycles, yielding quantitative proteomes in combination with short gradients and high-flow (800 µl min-1) chromatography. Exploiting a continuous movement of the precursor isolation window to assign precursor masses to tandem mass spectrometry (MS/MS) fragment traces, Scanning SWATH increases precursor identifications by ~70% compared to conventional data-independent acquisition (DIA) methods on 0.5-5-min chromatographic gradients. We demonstrate the application of ultra-fast proteomics in drug mode-of-action screening and plasma proteomics. Scanning SWATH proteomes capture the mode of action of fungistatic azoles and statins. Moreover, we confirm 43 and identify 11 new plasma proteome biomarkers of COVID-19 severity, advancing patient classification and biomarker discovery. Thus, our results demonstrate a substantial acceleration and increased depth in fast proteomic experiments that facilitate proteomic drug screens and clinical studies.
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Affiliation(s)
- Christoph B Messner
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Vadim Demichev
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge, UK
| | | | - Jason S L Yu
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Matthew White
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Marco Kreidl
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Anna-Sophia Egger
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
| | - Anja Freiwald
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Core Facility - High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | | | | | - Aleksej Zelezniak
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Linda Jürgens
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Norbert Suttorp
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Leif Erik Sander
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | - Florian Kurth
- Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
- Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine & I. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Kathryn S Lilley
- Department of Biochemistry, Cambridge Centre for Proteomics, University of Cambridge, Cambridge, UK
| | - Michael Mülleder
- Core Facility - High Throughput Mass Spectrometry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
| | | | - Markus Ralser
- Molecular Biology of Metabolism Laboratory, The Francis Crick Institute, London, UK.
- Department of Biochemistry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
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31
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Messner CB, Demichev V, Wendisch D, Michalick L, White M, Freiwald A, Textoris-Taube K, Vernardis SI, Egger AS, Kreidl M, Ludwig D, Kilian C, Agostini F, Zelezniak A, Thibeault C, Pfeiffer M, Hippenstiel S, Hocke A, von Kalle C, Campbell A, Hayward C, Porteous DJ, Marioni RE, Langenberg C, Lilley KS, Kuebler WM, Mülleder M, Drosten C, Suttorp N, Witzenrath M, Kurth F, Sander LE, Ralser M. Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection. Cell Syst 2020; 11:11-24.e4. [PMID: 32619549 PMCID: PMC7264033 DOI: 10.1016/j.cels.2020.05.012] [Citation(s) in RCA: 336] [Impact Index Per Article: 84.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 05/22/2020] [Accepted: 05/27/2020] [Indexed: 02/06/2023]
Abstract
The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated clinical laboratories. Our low-cost workflow handles up to 180 samples per day, enables high precision quantification, and reduces batch effects for large-scale and longitudinal studies. We use our platform on samples collected from a cohort of early hospitalized cases of the SARS-CoV-2 pandemic and identify 27 potential biomarkers that are differentially expressed depending on the WHO severity grade of COVID-19. They include complement factors, the coagulation system, inflammation modulators, and pro-inflammatory factors upstream and downstream of interleukin 6. All protocols and software for implementing our approach are freely available. In total, this work supports the development of routine proteomic assays to aid clinical decision making and generate hypotheses about potential COVID-19 therapeutic targets.
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Affiliation(s)
- Christoph B Messner
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK
| | - Vadim Demichev
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK; Department of Biochemistry, The University of Cambridge, Cambridge CB21GA, UK
| | - Daniel Wendisch
- Charité Universitätsmedizin, Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Laura Michalick
- Charité Universitätsmedizin, Institute of Physiology, 10117 Berlin, Germany
| | - Matthew White
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK
| | - Anja Freiwald
- Charité Universitätsmedizin, Core Facility - High-Throughput Mass Spectrometry, 10117 Berlin, Germany; Charité Universitätsmedizin, Department of Biochemistry, 10117 Berlin, Germany
| | - Kathrin Textoris-Taube
- Charité Universitätsmedizin, Core Facility - High-Throughput Mass Spectrometry, 10117 Berlin, Germany
| | - Spyros I Vernardis
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK
| | - Anna-Sophia Egger
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK
| | - Marco Kreidl
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK
| | - Daniela Ludwig
- Charité Universitätsmedizin, Department of Biochemistry, 10117 Berlin, Germany
| | - Christiane Kilian
- Charité Universitätsmedizin, Department of Biochemistry, 10117 Berlin, Germany
| | - Federica Agostini
- Charité Universitätsmedizin, Department of Biochemistry, 10117 Berlin, Germany
| | - Aleksej Zelezniak
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg 412 96, Sweden
| | - Charlotte Thibeault
- Charité Universitätsmedizin, Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Moritz Pfeiffer
- Charité Universitätsmedizin, Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Stefan Hippenstiel
- Charité Universitätsmedizin, Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Andreas Hocke
- Charité Universitätsmedizin, Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Christof von Kalle
- Berlin Institute of Health (BIH) and Charité Universitätsmedizin, Clinical Study Center (CSC), 10117 Berlin, Germany
| | - Archie Campbell
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK; Usher Institute, University of Edinburgh, Nine, Edinburgh Bioquarter, 9 Little France Road, Edinburgh EH16 4UX, UK
| | - Caroline Hayward
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - David J Porteous
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Riccardo E Marioni
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK
| | - Claudia Langenberg
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK; MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Kathryn S Lilley
- Department of Biochemistry, The University of Cambridge, Cambridge CB21GA, UK
| | - Wolfgang M Kuebler
- Charité Universitätsmedizin, Institute of Physiology, 10117 Berlin, Germany
| | - Michael Mülleder
- Charité Universitätsmedizin, Core Facility - High-Throughput Mass Spectrometry, 10117 Berlin, Germany
| | - Christian Drosten
- Charité Universitätsmedizin, Department of Virology, 10117 Berlin, Germany
| | - Norbert Suttorp
- Charité Universitätsmedizin, Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Martin Witzenrath
- Charité Universitätsmedizin, Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Florian Kurth
- Charité Universitätsmedizin, Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany; Department of Tropical Medicine, Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany
| | - Leif Erik Sander
- Charité Universitätsmedizin, Berlin, Department of Infectious Diseases and Respiratory Medicine, 10117 Berlin, Germany
| | - Markus Ralser
- The Francis Crick Institute, Molecular Biology of Metabolism Laboratory, London NW11AT, UK; Charité Universitätsmedizin, Department of Biochemistry, 10117 Berlin, Germany.
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32
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Demichev V, Messner CB, Vernardis SI, Lilley KS, Ralser M. DIA-NN: neural networks and interference correction enable deep proteome coverage in high throughput. Nat Methods 2020; 17:41-44. [PMID: 31768060 PMCID: PMC6949130 DOI: 10.1038/s41592-019-0638-x] [Citation(s) in RCA: 655] [Impact Index Per Article: 163.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Accepted: 10/09/2019] [Indexed: 01/12/2023]
Abstract
We present an easy-to-use integrated software suite, DIA-NN, that exploits deep neural networks and new quantification and signal correction strategies for the processing of data-independent acquisition (DIA) proteomics experiments. DIA-NN improves the identification and quantification performance in conventional DIA proteomic applications, and is particularly beneficial for high-throughput applications, as it is fast and enables deep and confident proteome coverage when used in combination with fast chromatographic methods.
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Affiliation(s)
- Vadim Demichev
- Department of Biochemistry and The Milner Therapeutics Institute, University of Cambridge, Cambridge, UK
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK
| | - Christoph B Messner
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK
| | - Spyros I Vernardis
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK
| | - Kathryn S Lilley
- Department of Biochemistry and The Milner Therapeutics Institute, University of Cambridge, Cambridge, UK
| | - Markus Ralser
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK.
- Department of Biochemistry, Charité Universitätsmedizin Berlin, Berlin, Germany.
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33
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Zelezniak A, Vowinckel J, Capuano F, Messner CB, Demichev V, Polowsky N, Mülleder M, Kamrad S, Klaus B, Keller MA, Ralser M. Machine Learning Predicts the Yeast Metabolome from the Quantitative Proteome of Kinase Knockouts. Cell Syst 2018; 7:269-283.e6. [PMID: 30195436 PMCID: PMC6167078 DOI: 10.1016/j.cels.2018.08.001] [Citation(s) in RCA: 62] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 05/29/2018] [Accepted: 07/31/2018] [Indexed: 02/08/2023]
Abstract
A challenge in solving the genotype-to-phenotype relationship is to predict a cell’s metabolome, believed to correlate poorly with gene expression. Using comparative quantitative proteomics, we found that differential protein expression in 97 Saccharomyces cerevisiae kinase deletion strains is non-redundant and dominated by abundance changes in metabolic enzymes. Associating differential enzyme expression landscapes to corresponding metabolomes using network models provided reasoning for poor proteome-metabolome correlations; differential protein expression redistributes flux control between many enzymes acting in concert, a mechanism not captured by one-to-one correlation statistics. Mapping these regulatory patterns using machine learning enabled the prediction of metabolite concentrations, as well as identification of candidate genes important for the regulation of metabolism. Overall, our study reveals that a large part of metabolism regulation is explained through coordinated enzyme expression changes. Our quantitative data indicate that this mechanism explains more than half of metabolism regulation and underlies the interdependency between enzyme levels and metabolism, which renders the metabolome a predictable phenotype. The proteome of kinase knockouts is dominated by enzyme abundance changes The enzyme expression profiles of kinase knockouts are non-redundant Metabolism is regulated by many expression changes acting in concert Machine learning accurately predicts the metabolome from enzyme abundance
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Affiliation(s)
- Aleksej Zelezniak
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK; Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jakob Vowinckel
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK; Biognosys AG, Schlieren, Switzerland
| | - Floriana Capuano
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Christoph B Messner
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK
| | - Vadim Demichev
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Nicole Polowsky
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Michael Mülleder
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK
| | - Stephan Kamrad
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Genetics, Evolution and Environment, University College London, London, UK
| | - Bernd Klaus
- Centre for Statistical Data Analysis, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Markus A Keller
- Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK; Medical University of Innsbruck, Innsbruck, Austria
| | - Markus Ralser
- The Francis Crick Institute, Molecular Biology of Metabolism laboratory, London, UK; Department of Biochemistry and Cambridge Systems Biology Centre, University of Cambridge, Cambridge, UK; Department of Biochemistry, Charité Universitaetsmedizin Berlin, Berlin, Germany.
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