1
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Liu J, Bao C, Zhang J, Han Z, Fang H, Lu H. Artificial intelligence with mass spectrometry-based multimodal molecular profiling methods for advancing therapeutic discovery of infectious diseases. Pharmacol Ther 2024; 263:108712. [PMID: 39241918 DOI: 10.1016/j.pharmthera.2024.108712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2024] [Revised: 07/22/2024] [Accepted: 09/03/2024] [Indexed: 09/09/2024]
Abstract
Infectious diseases, driven by a diverse array of pathogens, can swiftly undermine public health systems. Accurate diagnosis and treatment of infectious diseases-centered around the identification of biomarkers and the elucidation of disease mechanisms-are in dire need of more versatile and practical analytical approaches. Mass spectrometry (MS)-based molecular profiling methods can deliver a wealth of information on a range of functional molecules, including nucleic acids, proteins, and metabolites. While MS-driven omics analyses can yield vast datasets, the sheer complexity and multi-dimensionality of MS data can significantly hinder the identification and characterization of functional molecules within specific biological processes and events. Artificial intelligence (AI) emerges as a potent complementary tool that can substantially enhance the processing and interpretation of MS data. AI applications in this context lead to the reduction of spurious signals, the improvement of precision, the creation of standardized analytical frameworks, and the increase of data integration efficiency. This critical review emphasizes the pivotal roles of MS based omics strategies in the discovery of biomarkers and the clarification of infectious diseases. Additionally, the review underscores the transformative ability of AI techniques to enhance the utility of MS-based molecular profiling in the field of infectious diseases by refining the quality and practicality of data produced from omics analyses. In conclusion, we advocate for a forward-looking strategy that integrates AI with MS-based molecular profiling. This integration aims to transform the analytical landscape and the performance of biological molecule characterization, potentially down to the single-cell level. Such advancements are anticipated to propel the development of AI-driven predictive models, thus improving the monitoring of diagnostics and therapeutic discovery for the ongoing challenge related to infectious diseases.
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Affiliation(s)
- Jingjing Liu
- School of Chinese Medicine, Hong Kong Traditional Chinese Medicine Phenome Research Center, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong 999077, China
| | - Chaohui Bao
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Jiaxin Zhang
- School of Chinese Medicine, Hong Kong Traditional Chinese Medicine Phenome Research Center, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong 999077, China
| | - Zeguang Han
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Hai Fang
- Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.
| | - Haitao Lu
- School of Chinese Medicine, Hong Kong Traditional Chinese Medicine Phenome Research Center, State Key Laboratory of Environmental and Biological Analysis, Hong Kong Baptist University, Hong Kong 999077, China; Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China.
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2
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Wang CR, McFarlane LO, Pukala TL. Exploring snake venoms beyond the primary sequence: From proteoforms to protein-protein interactions. Toxicon 2024; 247:107841. [PMID: 38950738 DOI: 10.1016/j.toxicon.2024.107841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Revised: 06/26/2024] [Accepted: 06/28/2024] [Indexed: 07/03/2024]
Abstract
Snakebite envenomation has been a long-standing global issue that is difficult to treat, largely owing to the flawed nature of current immunoglobulin-based antivenom therapy and the complexity of snake venoms as sophisticated mixtures of bioactive proteins and peptides. Comprehensive characterisation of venom compositions is essential to better understanding snake venom toxicity and inform effective and rationally designed antivenoms. Additionally, a greater understanding of snake venom composition will likely unearth novel biologically active proteins and peptides that have promising therapeutic or biotechnological applications. While a bottom-up proteomic workflow has been the main approach for cataloguing snake venom compositions at the toxin family level, it is unable to capture snake venom heterogeneity in the form of protein isoforms and higher-order protein interactions that are important in driving venom toxicity but remain underexplored. This review aims to highlight the importance of understanding snake venom heterogeneity beyond the primary sequence, in the form of post-translational modifications that give rise to different proteoforms and the myriad of higher-order protein complexes in snake venoms. We focus on current top-down proteomic workflows to identify snake venom proteoforms and further discuss alternative or novel separation, instrumentation, and data processing strategies that may improve proteoform identification. The current higher-order structural characterisation techniques implemented for snake venom proteins are also discussed; we emphasise the need for complementary and higher resolution structural bioanalytical techniques such as mass spectrometry-based approaches, X-ray crystallography and cryogenic electron microscopy, to elucidate poorly characterised tertiary and quaternary protein structures. We envisage that the expansion of the snake venom characterisation "toolbox" with top-down proteomics and high-resolution protein structure determination techniques will be pivotal in advancing structural understanding of snake venoms towards the development of improved therapeutic and biotechnology applications.
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Affiliation(s)
- C Ruth Wang
- Discipline of Chemistry, School of Physics, Chemistry and Earth Sciences, The University of Adelaide, Adelaide, 5005, Australia
| | - Lewis O McFarlane
- Discipline of Chemistry, School of Physics, Chemistry and Earth Sciences, The University of Adelaide, Adelaide, 5005, Australia
| | - Tara L Pukala
- Discipline of Chemistry, School of Physics, Chemistry and Earth Sciences, The University of Adelaide, Adelaide, 5005, Australia.
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3
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Urban J, Joeres R, Thomès L, Thomsson KA, Bojar D. Navigating the maze of mass spectra: a machine-learning guide to identifying diagnostic ions in O-glycan analysis. Anal Bioanal Chem 2024:10.1007/s00216-024-05500-9. [PMID: 39180595 DOI: 10.1007/s00216-024-05500-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2024] [Revised: 08/06/2024] [Accepted: 08/09/2024] [Indexed: 08/26/2024]
Abstract
Structural details of oligosaccharides, or glycans, often carry biological relevance, which is why they are typically elucidated using tandem mass spectrometry. Common approaches to distinguish isomers rely on diagnostic glycan fragments for annotating topologies or linkages. Diagnostic fragments are often only known informally among practitioners or stem from individual studies, with unclear validity or generalizability, causing annotation heterogeneity and hampering new analysts. Drawing on a curated set of 237,000 O-glycomics spectra, we here present a rule-based machine learning workflow to uncover quantifiably valid and generalizable diagnostic fragments. This results in fragmentation rules to robustly distinguish common O-glycan isomers for reduced glycans in negative ion mode. We envision this resource to improve glycan annotation accuracy and concomitantly make annotations more transparent and homogeneous across analysts.
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Affiliation(s)
- James Urban
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Roman Joeres
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Helmholtz Institute for Pharmaceutical Research Saarland, Helmholtz Centre for Infection Research, Saarbrücken, Germany
- Center for Bioinformatics, Saarland University, Saarbrücken, Germany
| | - Luc Thomès
- ULR 7364 - RADEME - Maladies RAres du DÉveloppement embryonnaire et du Métabolisme, CHU Lille, University Lille, 59000, Lille, France
| | - Kristina A Thomsson
- Proteomics Core Facility at Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Daniel Bojar
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden.
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden.
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4
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Beltrao P, Van Den Bossche T, Gabriels R, Holstein T, Kockmann T, Nameni A, Panse C, Schlapbach R, Lautenbacher L, Mattanovich M, Nesvizhskii A, Van Puyvelde B, Scheid J, Schwämmle V, Strauss M, Susmelj AK, The M, Webel H, Wilhelm M, Winkelhardt D, Wolski WE, Xi M. Proceedings of the EuBIC-MS developers meeting 2023. J Proteomics 2024; 305:105246. [PMID: 38964537 DOI: 10.1016/j.jprot.2024.105246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 06/19/2024] [Accepted: 06/27/2024] [Indexed: 07/06/2024]
Abstract
The 2023 European Bioinformatics Community for Mass Spectrometry (EuBIC-MS) Developers Meeting was held from January 15th to January 20th, 2023, in Congressi Stefano Franscin at Monte Verità in Ticino, Switzerland. The participants were scientists and developers working in computational mass spectrometry (MS), metabolomics, and proteomics. The 5-day program was split between introductory keynote lectures and parallel hackathon sessions focusing on "Artificial Intelligence in proteomics" to stimulate future directions in the MS-driven omics areas. During the latter, the participants developed bioinformatics tools and resources addressing outstanding needs in the community. The hackathons allowed less experienced participants to learn from more advanced computational MS experts and actively contribute to highly relevant research projects. We successfully produced several new tools applicable to the proteomics community by improving data analysis and facilitating future research.
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Affiliation(s)
| | - Tim Van Den Bossche
- VIB-UGent Center for Medical Biotechnology, 9052 Zwijnaarde, Belgium; Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, 9052 Zwijnaarde, Belgium; Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Tanja Holstein
- VIB-UGent Center for Medical Biotechnology, 9052 Zwijnaarde, Belgium; Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Tobias Kockmann
- Functional Genomics Center Zürich, ETH Zürich/University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Alireza Nameni
- VIB-UGent Center for Medical Biotechnology, 9052 Zwijnaarde, Belgium; Department of Biomolecular Medicine, Ghent University, 9000 Ghent, Belgium
| | - Christian Panse
- Functional Genomics Center Zürich, ETH Zürich/University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland; Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Amphipole, 1015 Lausanne, Switzerland.
| | - Ralph Schlapbach
- Functional Genomics Center Zürich, ETH Zürich/University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland
| | - Ludwig Lautenbacher
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 3, D - 85354 Freising, Germany
| | - Matthias Mattanovich
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, DK-2200 Copenhagen, Denmark
| | - Alexey Nesvizhskii
- Departments of Pathology and Computational Medicine and Bioinfoirmatics, University of Michigan, Ann Arbor, MI 48105, USA
| | - Bart Van Puyvelde
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Ottergemsesteenweg 460, BE-9000 Ghent, Belgium
| | - Jonas Scheid
- Department of Peptide-based Immunotherapy, Institute of Immunology, University and University hospital Tübingen, Auf der Morgenstelle 15, D-72076 Tübingen, Germany; Quantitative Biology Center (QBiC), University of Tübingen, Auf der Morgenstelle 10, D-72076 Tübingen, Germany
| | - Veit Schwämmle
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Campusvej 55, 5230 Odense, Denmark
| | - Maximilian Strauss
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Matthew The
- TUM School of Life Sciences Technische Universität München, D - 85354 Freising, Germany
| | - Henry Webel
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 3, D - 85354 Freising, Germany
| | | | - Witold E Wolski
- Functional Genomics Center Zürich, ETH Zürich/University of Zürich, Winterthurerstrasse 190, CH-8057 Zürich, Switzerland; Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Amphipole, 1015 Lausanne, Switzerland
| | - Muyao Xi
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Blegdamsvej 3B, DK-2200 Copenhagen, Denmark
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5
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DeBono NJ, Moh ESX, Packer NH. Experimentally Determined Diagnostic Ions for Identification of Peptide Glycotopes. J Proteome Res 2024; 23:2661-2673. [PMID: 38888225 DOI: 10.1021/acs.jproteome.3c00858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
The analysis of the structures of glycans present on glycoproteins is an essential component for determining glycoprotein function; however, detailed glycan structural assignment on glycopeptides from proteomics mass spectrometric data remains challenging. Glycoproteomic analysis by mass spectrometry currently can provide significant, yet incomplete, information about the glycans present, including the glycan monosaccharide composition and in some circumstances the site(s) of glycosylation. Advancements in mass spectrometric resolution, using high-mass accuracy instrumentation and tailored MS/MS fragmentation parameters, coupled with a dedicated definition of diagnostic fragmentation ions have enabled the determination of some glycan structural features, or glycotopes, expressed on glycopeptides. Here we present a collation of diagnostic glycan fragments produced by traditional positive-ion-mode reversed-phase LC-ESI MS/MS proteomic workflows and describe the specific fragmentation energy settings required to identify specific glycotopes presented on N- or O-linked glycopeptides in a typical proteomics MS/MS experiment.
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Affiliation(s)
- Nicholas J DeBono
- ARC Centre of Excellence in Synthetic Biology, School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Edward S X Moh
- ARC Centre of Excellence in Synthetic Biology, School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Nicolle H Packer
- ARC Centre of Excellence in Synthetic Biology, School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia
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6
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Kalhor M, Lapin J, Picciani M, Wilhelm M. Rescoring Peptide Spectrum Matches: Boosting Proteomics Performance by Integrating Peptide Property Predictors Into Peptide Identification. Mol Cell Proteomics 2024; 23:100798. [PMID: 38871251 PMCID: PMC11269915 DOI: 10.1016/j.mcpro.2024.100798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 05/26/2024] [Accepted: 06/09/2024] [Indexed: 06/15/2024] Open
Abstract
Rescoring of peptide spectrum matches originating from database search engines enabled by peptide property predictors is exceeding the performance of peptide identification from traditional database search engines. In contrast to the peptide spectrum match scores calculated by traditional database search engines, rescoring peptide spectrum matches generates scores based on comparing observed and predicted peptide properties, such as fragment ion intensities and retention times. These newly generated scores enable a more efficient discrimination between correct and incorrect peptide spectrum matches. This approach was shown to lead to substantial improvements in the number of confidently identified peptides, facilitating the analysis of challenging datasets in various fields such as immunopeptidomics, metaproteomics, proteogenomics, and single-cell proteomics. In this review, we summarize the key elements leading up to the recent introduction of multiple data-driven rescoring pipelines. We provide an overview of relevant post-processing rescoring tools, introduce prominent data-driven rescoring pipelines for various applications, and highlight limitations, opportunities, and future perspectives of this approach and its impact on mass spectrometry-based proteomics.
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Affiliation(s)
- Mostafa Kalhor
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Joel Lapin
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mario Picciani
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Mathias Wilhelm
- Computational Mass Spectrometry, TUM School of Life Sciences, Technical University of Munich, Freising, Germany; Munich Data Science Institute, Technical University of Munich, Garching, Germany.
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7
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Burton NR, Backus KM. Functionalizing tandem mass tags for streamlining click-based quantitative chemoproteomics. Commun Chem 2024; 7:80. [PMID: 38600184 PMCID: PMC11006884 DOI: 10.1038/s42004-024-01162-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 03/27/2024] [Indexed: 04/12/2024] Open
Abstract
Mapping the ligandability or potential druggability of all proteins in the human proteome is a central goal of mass spectrometry-based covalent chemoproteomics. Achieving this ambitious objective requires high throughput and high coverage sample preparation and liquid chromatography-tandem mass spectrometry analysis for hundreds to thousands of reactive compounds and chemical probes. Conducting chemoproteomic screens at this scale benefits from technical innovations that achieve increased sample throughput. Here we realize this vision by establishing the silane-based cleavable linkers for isotopically-labeled proteomics-tandem mass tag (sCIP-TMT) proteomic platform, which is distinguished by early sample pooling that increases sample preparation throughput. sCIP-TMT pairs a custom click-compatible sCIP capture reagent that is readily functionalized in high yield with commercially available TMT reagents. Synthesis and benchmarking of a 10-plex set of sCIP-TMT reveal a substantial decrease in sample preparation time together with high coverage and high accuracy quantification. By screening a focused set of four cysteine-reactive electrophiles, we demonstrate the utility of sCIP-TMT for chemoproteomic target hunting, identifying 789 total liganded cysteines. Distinguished by its compatibility with established enrichment and quantification protocols, we expect sCIP-TMT will readily translate to a wide range of covalent chemoproteomic applications.
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Affiliation(s)
- Nikolas R Burton
- Department of Biological Chemistry, David Geffen School of Medicine, UCLA, Los Angeles CA, USA
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA
| | - Keriann M Backus
- Department of Biological Chemistry, David Geffen School of Medicine, UCLA, Los Angeles CA, USA.
- Department of Chemistry and Biochemistry, UCLA, Los Angeles, CA, USA.
- Molecular Biology Institute, UCLA, Los Angeles, CA, USA.
- DOE Institute for Genomics and Proteomics, UCLA, Los Angeles, CA, USA.
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, UCLA, Los Angeles, CA, USA.
- Jonsson Comprehensive Cancer Center, UCLA, Los Angeles, CA, USA.
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8
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Kespohl B, Hegele AL, Düsterhöft S, Bakker H, Buettner FFR, Hartig R, Lokau J, Garbers C. Molecular characterization of the craniosynostosis-associated interleukin-11 receptor variants p.T306_S308dup and p.E364_V368del. FEBS J 2024; 291:1667-1683. [PMID: 37994264 DOI: 10.1111/febs.17015] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 11/02/2023] [Accepted: 11/21/2023] [Indexed: 11/24/2023]
Abstract
Interleukin-11 (IL-11) is a member of the IL-6 family of cytokines and is an important factor for bone homeostasis. IL-11 binds to and signals via the membrane-bound IL-11 receptor (IL-11R, classic signaling) or soluble forms of the IL-11R (sIL-11R, trans-signaling). Mutations in the IL11RA gene, which encodes the IL-11R, are associated with craniosynostosis, a human condition in which one or several of the sutures close prematurely, resulting in malformation of the skull. The biological mechanisms of how mutations within the IL-11R are linked to craniosynostosis are mostly unexplored. In this study, we analyze two variants of the IL-11R described in craniosynostosis patients: p.T306_S308dup, which results in a duplication of three amino-acid residues within the membrane-proximal fibronectin type III domain, and p.E364_V368del, which results in a deletion of five amino-acid residues in the so-called stalk region adjacent to the plasma membrane. The stalk region connects the three extracellular domains to the transmembrane and intracellular region of the IL-11R and contains cleavage sites for different proteases that generate sIL-11R variants. Using a combination of bioinformatics and different biochemical, molecular, and cell biology methods, we show that the IL-11R-T306_S308dup variant does not mature correctly, is intracellularly retained, and does not reach the cell surface. In contrast, the IL-11R-E364_V368del variant is fully biologically active and processed normally by proteases, thus allowing classic and trans-signaling of IL-11. Our results provide evidence that mutations within the IL11RA gene may not be causative for craniosynostosis and suggest that other regulatory mechanism(s) are involved but remain to be identified.
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Affiliation(s)
- Birte Kespohl
- Department of Pathology, Medical Faculty, Otto-von-Guericke-University Magdeburg, Germany
| | - Anna-Lena Hegele
- Department of Pathology, Medical Faculty, Otto-von-Guericke-University Magdeburg, Germany
| | - Stefan Düsterhöft
- Institute of Molecular Pharmacology, RWTH Aachen University, Germany
| | - Hans Bakker
- Institute of Clinical Biochemistry, Hannover Medical School, Germany
| | - Falk F R Buettner
- Institute of Clinical Biochemistry, Hannover Medical School, Germany
| | - Roland Hartig
- Institute for Molecular and Clinical Immunology and Service Unit Multiparametric Bioimaging and Cytometry, Medical Faculty, University of Magdeburg, Germany
| | - Juliane Lokau
- Department of Pathology, Medical Faculty, Otto-von-Guericke-University Magdeburg, Germany
- Institute of Clinical Biochemistry, Hannover Medical School, Germany
| | - Christoph Garbers
- Institute of Clinical Biochemistry, Hannover Medical School, Germany
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9
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Lou R, Shui W. Acquisition and Analysis of DIA-Based Proteomic Data: A Comprehensive Survey in 2023. Mol Cell Proteomics 2024; 23:100712. [PMID: 38182042 PMCID: PMC10847697 DOI: 10.1016/j.mcpro.2024.100712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/27/2023] [Accepted: 01/02/2024] [Indexed: 01/07/2024] Open
Abstract
Data-independent acquisition (DIA) mass spectrometry (MS) has emerged as a powerful technology for high-throughput, accurate, and reproducible quantitative proteomics. This review provides a comprehensive overview of recent advances in both the experimental and computational methods for DIA proteomics, from data acquisition schemes to analysis strategies and software tools. DIA acquisition schemes are categorized based on the design of precursor isolation windows, highlighting wide-window, overlapping-window, narrow-window, scanning quadrupole-based, and parallel accumulation-serial fragmentation-enhanced DIA methods. For DIA data analysis, major strategies are classified into spectrum reconstruction, sequence-based search, library-based search, de novo sequencing, and sequencing-independent approaches. A wide array of software tools implementing these strategies are reviewed, with details on their overall workflows and scoring approaches at different steps. The generation and optimization of spectral libraries, which are critical resources for DIA analysis, are also discussed. Publicly available benchmark datasets covering global proteomics and phosphoproteomics are summarized to facilitate performance evaluation of various software tools and analysis workflows. Continued advances and synergistic developments of versatile components in DIA workflows are expected to further enhance the power of DIA-based proteomics.
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Affiliation(s)
- Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, China; School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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10
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Wiest A, Kielkowski P. Cu-Catalyzed Azide-Alkyne-Thiol Reaction Forms Ubiquitous Background in Chemical Proteomic Studies. J Am Chem Soc 2024; 146:2151-2159. [PMID: 38214237 PMCID: PMC10811670 DOI: 10.1021/jacs.3c11780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/13/2024]
Abstract
We report here a Cu-catalyzed azide-alkyne-thiol reaction forming thiotriazoles as the major byproduct under widely used bio-orthogonal protein labeling "click" conditions. The development of Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) had a tremendous impact on many biological discoveries. However, the considered chemoselectivity of CuAAC is hampered by the high reactivity of cysteine free thiols, yielding thiotriazole protein conjugates. The reaction byproducts generate false-positive protein hits in functional proteomic studies. The reported detail investigation of conjugates between chemical probes containing terminal alkynes, azide tags, and cell lysates reveals the formation of thiotriazoles, which can be readily detected by in-gel fluorescence scanning or after peptide and protein enrichment by mass spectrometry-based proteomics. In protein level identification and quantification experiments, the produced fluorescent bands or enriched proteins may not result from the important enzymatically driven reaction and can be falsely assigned as hits. This study provides a complete list of the most common background proteins. The knowledge of this previously overlooked reactivity now leads to the introduction of modified CuAAC conditions, which avoids the undesired product formation, diminishes the background, and hence improves the signal-to-noise ratio.
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Affiliation(s)
- Andreas Wiest
- Department of Chemistry, LMU Munich, Würmtalstr. 201, 81375 Munich, Germany
| | - Pavel Kielkowski
- Department of Chemistry, LMU Munich, Würmtalstr. 201, 81375 Munich, Germany
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11
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Burton NR, Polasky DA, Shikwana F, Ofori S, Yan T, Geiszler DJ, Veiga Leprevost FD, Nesvizhskii AI, Backus KM. Solid-Phase Compatible Silane-Based Cleavable Linker Enables Custom Isobaric Quantitative Chemoproteomics. J Am Chem Soc 2023; 145:21303-21318. [PMID: 37738129 DOI: 10.1021/jacs.3c05797] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/24/2023]
Abstract
Mass spectrometry-based chemoproteomics has emerged as an enabling technology for functional biology and drug discovery. To address limitations of established chemoproteomics workflows, including cumbersome reagent synthesis and low throughput sample preparation, here, we established the silane-based cleavable isotopically labeled proteomics (sCIP) method. The sCIP method is enabled by a high yielding and scalable route to dialkoxydiphenylsilane fluorenylmethyloxycarbonyl (DADPS-Fmoc)-protected amino acid building blocks, which enable the facile synthesis of customizable, isotopically labeled, and chemically cleavable biotin capture reagents. sCIP is compatible with both MS1- and MS2-based quantitation, and the sCIP-MS2 method is distinguished by its click-assembled isobaric tags in which the reporter group is encoded in the sCIP capture reagent and balancer in the pan cysteine-reactive probe. The sCIP-MS2 workflow streamlines sample preparation with early stage isobaric labeling and sample pooling, allowing for high coverage and increased sample throughput via customized low cost six-plex sample multiplexing. When paired with a custom FragPipe data analysis workflow and applied to cysteine-reactive fragment screens, sCIP proteomics revealed established and unprecedented cysteine-ligand pairs, including the discovery that mitochondrial uncoupling agent FCCP acts as a covalent-reversible cysteine-reactive electrophile.
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Affiliation(s)
- Nikolas R Burton
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, United States
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Daniel A Polasky
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Flowreen Shikwana
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, United States
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Samuel Ofori
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Tianyang Yan
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, United States
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
| | - Daniel J Geiszler
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
| | | | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Keriann M Backus
- Department of Biological Chemistry, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095, United States
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, California 90095, United States
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, California 90095, United States
- DOE Institute for Genomics and Proteomics, University of California, Los Angeles, Los Angeles, California 90095, United States
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, California 90095, United States
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, California 90095, United States
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