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Bu L, He L, Wang X, Du G, Wu R, Liu W. Proteomic Analysis Provides a New Sight Into the CRABP1 Expression in the Pathogenesis of Hirschsprung Disease. Biochem Genet 2024:10.1007/s10528-024-10913-3. [PMID: 39298027 DOI: 10.1007/s10528-024-10913-3] [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: 04/08/2024] [Accepted: 09/09/2024] [Indexed: 09/21/2024]
Abstract
Hirschsprung's disease (HSCR) is the most common developmental disorder of the enteric nervous system and its etiology and pathogenesis remain largely unknown. This study aims to identify the differential proteomic patterns linked to the occurrence and development of Hirschsprung disease in colonic tissues. Biopsies were obtained from the aganglionic colon in human HSCR and the corresponding ganglionic colon segments for direct quantitative determination of the data-independent acquisition (DIA) followed by bioinformatics analysis. The differentially expressed main proteins were confirmed by Western blot and immunostaining. A total of 5832 proteins were identified in human colon tissues. Among them, 97 differentially expressed proteins (DEP) with fold change (FC) > 1.2 were screened, including 18 upregulated proteins and 79 downregulated proteins, and GO and KEGG enrichment analyses were performed on differential proteins. By comparing down-regulated proteins with highly connected protein nodes in the PPI network with those related to intracellular metabolic processes in the above analysis, we identified cellular retinoic acid binding protein 1(CRABP1). Its expression was verified in the aganglionic part of the colon by western blotting in an expanded sample set (P = 0.0031). The immunostaining results revealed that CRABP1 was highly expressed in the myenteric plexus ganglion in ganglionic colons compared to aganglionic segments (P = 0.0004). This study demonstrated the down-regulation of CRABP1 in the aganglionic hindgut of HSCR, which could provide potential markers or promising new candidate actors for the pathogenesis of HSCR.
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Affiliation(s)
- Lingyun Bu
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, China
| | - Lingxiao He
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, China
| | - Xiaoqing Wang
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, China
| | - Guoqiang Du
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, China
| | - Rongde Wu
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, China
| | - Wei Liu
- Department of Pediatric Surgery, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, China.
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2
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Wang S, Di Y, Yang Y, Salovska B, Li W, Hu L, Yin J, Shao W, Zhou D, Cheng J, Liu D, Yang H, Liu Y. PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals. CELL REPORTS METHODS 2024; 4:100859. [PMID: 39255793 PMCID: PMC11440062 DOI: 10.1016/j.crmeth.2024.100859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 05/13/2024] [Accepted: 08/15/2024] [Indexed: 09/12/2024]
Abstract
To support PTM proteomic analysis and annotation in different species, we developed PTMoreR, a user-friendly tool that considers the surrounding amino acid sequences of PTM sites during BLAST, enabling a motif-centric analysis across species. By controlling sequence window similarity, PTMoreR can map phosphoproteomic results between any two species, perform site-level functional enrichment analysis, and generate kinase-substrate networks. We demonstrate that the majority of real P-sites in mice can be inferred from experimentally derived human P-sites with PTMoreR mapping. Furthermore, the compositions of 129 mammalian phosphoproteomes can also be predicted using PTMoreR. The method also identifies cross-species phosphorylation events that occur on proteins with an increased tendency to respond to the environmental factors. Moreover, the classic kinase motifs can be extracted across mammalian species, offering an evolutionary angle for refining current motifs. PTMoreR supports PTM proteomics in non-human species and facilitates quantitative phosphoproteomic analysis.
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Affiliation(s)
- Shisheng Wang
- Department of Pulmonary and Critical Care Medicine, Proteomics-Metabolomics Analysis Platform, and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yi Di
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Yin Yang
- Department of Pulmonary and Critical Care Medicine, Proteomics-Metabolomics Analysis Platform, and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Barbora Salovska
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Liqiang Hu
- Department of Pulmonary and Critical Care Medicine, Proteomics-Metabolomics Analysis Platform, and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Jiahui Yin
- Information Research Institute, Tongji University, Shanghai 200092, China
| | - Wenguang Shao
- State Key Laboratory of Microbial Metabolism, School of Life Science & Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Dong Zhou
- Department of Medicine, Division of Nephrology, University of Connecticut School of Medicine, Farmington, CT 06030, USA
| | - Jingqiu Cheng
- Department of Pulmonary and Critical Care Medicine, Proteomics-Metabolomics Analysis Platform, and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Dan Liu
- Department of Pulmonary and Critical Care Medicine, Proteomics-Metabolomics Analysis Platform, and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China; State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Hao Yang
- Department of Pulmonary and Critical Care Medicine, Proteomics-Metabolomics Analysis Platform, and NHC Key Lab of Transplant Engineering and Immunology, West China Hospital, Sichuan University, Chengdu 610041, China.
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA; Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA; Department of Biomedical Informatics & Data Science, Yale Univeristy School of Medicine, New Haven, CT 06510, USA.
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3
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Rosenberger G, Li W, Turunen M, He J, Subramaniam PS, Pampou S, Griffin AT, Karan C, Kerwin P, Murray D, Honig B, Liu Y, Califano A. Network-based elucidation of colon cancer drug resistance mechanisms by phosphoproteomic time-series analysis. Nat Commun 2024; 15:3909. [PMID: 38724493 PMCID: PMC11082183 DOI: 10.1038/s41467-024-47957-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 04/16/2024] [Indexed: 05/12/2024] Open
Abstract
Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. Leveraging progress in proteomic technologies and network-based methodologies, we introduce Virtual Enrichment-based Signaling Protein-activity Analysis (VESPA)-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and use it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogating tumor-specific enzyme/substrate interactions accurately infers kinase and phosphatase activity, based on their substrate phosphorylation state, effectively accounting for signal crosstalk and sparse phosphoproteome coverage. The analysis elucidates time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring, experimentally confirmed by CRISPR knock-out assays, suggesting broad applicability to cancer and other diseases.
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Affiliation(s)
- George Rosenberger
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Mikko Turunen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jing He
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Prem S Subramaniam
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Pampou
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Patrick Kerwin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
- Zuckerman Mind Brain and Behavior Institute, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA.
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA.
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.
- Chan Zuckerberg Biohub New York, New York, NY, USA.
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4
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Sing JC, Charkow J, AlHigaylan M, Horecka I, Xu L, Röst HL. MassDash: A Web-Based Dashboard for Data-Independent Acquisition Mass Spectrometry Visualization. J Proteome Res 2024. [PMID: 38684072 DOI: 10.1021/acs.jproteome.4c00026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
With the increased usage and diversity of methods and instruments being applied to analyze Data-Independent Acquisition (DIA) data, visualization is becoming increasingly important to validate automated software results. Here we present MassDash, a cross-platform DIA mass spectrometry visualization and validation software for comparing features and results across popular tools. MassDash provides a web-based interface and Python package for interactive feature visualizations and summary report plots across multiple automated DIA feature detection tools, including OpenSwath, DIA-NN, and dreamDIA. Furthermore, MassDash processes peptides on the fly, enabling interactive visualization of peptides across dozens of runs simultaneously on a personal computer. MassDash supports various multidimensional visualizations across retention time, ion mobility, m/z, and intensity, providing additional insights into the data. The modular framework is easily extendable, enabling rapid algorithm development of novel peak-picker techniques, such as deep-learning-based approaches and refinement of existing tools. MassDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash, and a demo version can be accessed at https://massdash.streamlit.app.
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Affiliation(s)
- Justin C Sing
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada
| | - Joshua Charkow
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada
| | - Mohammed AlHigaylan
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada
| | - Ira Horecka
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada
| | - Leon Xu
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada
| | - Hannes L Röst
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5G 1A8, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario M5G 1A8, Canada
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5
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Joyce AW, Searle BC. Computational approaches to identify sites of phosphorylation. Proteomics 2024; 24:e2300088. [PMID: 37897210 DOI: 10.1002/pmic.202300088] [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: 06/15/2023] [Revised: 10/07/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
Due to their oftentimes ambiguous nature, phosphopeptide positional isomers can present challenges in bottom-up mass spectrometry-based workflows as search engine scores alone are often not enough to confidently distinguish them. Additional scoring algorithms can remedy this by providing confidence metrics in addition to these search results, reducing ambiguity. Here we describe challenges to interpreting phosphoproteomics data and review several different approaches to determine sites of phosphorylation for both data-dependent and data-independent acquisition-based workflows. Finally, we discuss open questions regarding neutral losses, gas-phase rearrangement, and false localization rate estimation experienced by both types of acquisition workflows and best practices for managing ambiguity in phosphosite determination.
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Affiliation(s)
- Alex W Joyce
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
| | - Brian C Searle
- Department of Biomedical Informatics, The Ohio State University Medical Center, Columbus, Ohio, USA
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA
- Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio, USA
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6
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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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7
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Kitata RB, Yang JC, Chen YJ. Advances in data-independent acquisition mass spectrometry towards comprehensive digital proteome landscape. MASS SPECTROMETRY REVIEWS 2023; 42:2324-2348. [PMID: 35645145 DOI: 10.1002/mas.21781] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/09/2021] [Revised: 12/17/2021] [Accepted: 01/21/2022] [Indexed: 06/15/2023]
Abstract
The data-independent acquisition mass spectrometry (DIA-MS) has rapidly evolved as a powerful alternative for highly reproducible proteome profiling with a unique strength of generating permanent digital maps for retrospective analysis of biological systems. Recent advancements in data analysis software tools for the complex DIA-MS/MS spectra coupled to fast MS scanning speed and high mass accuracy have greatly expanded the sensitivity and coverage of DIA-based proteomics profiling. Here, we review the evolution of the DIA-MS techniques, from earlier proof-of-principle of parallel fragmentation of all-ions or ions in selected m/z range, the sequential window acquisition of all theoretical mass spectra (SWATH-MS) to latest innovations, recent development in computation algorithms for data informatics, and auxiliary tools and advanced instrumentation to enhance the performance of DIA-MS. We further summarize recent applications of DIA-MS and experimentally-derived as well as in silico spectra library resources for large-scale profiling to facilitate biomarker discovery and drug development in human diseases with emphasis on the proteomic profiling coverage. Toward next-generation DIA-MS for clinical proteomics, we outline the challenges in processing multi-dimensional DIA data set and large-scale clinical proteomics, and continuing need in higher profiling coverage and sensitivity.
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Affiliation(s)
| | - Jhih-Ci Yang
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Applied Chemistry, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, Taiwan
- Sustainable Chemical Science and Technology, Taiwan International Graduate Program, Academia Sinica and National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Chemistry, National Taiwan University, Taipei, Taiwan
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8
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Searle BC, Chien A, Koller A, Hawke D, Herren AW, Kim Kim J, Lee KA, Leib RD, Nelson AJ, Patel P, Ren JM, Stemmer PM, Zhu Y, Neely BA, Patel B. A Multipathway Phosphopeptide Standard for Rapid Phosphoproteomics Assay Development. Mol Cell Proteomics 2023; 22:100639. [PMID: 37657519 PMCID: PMC10561125 DOI: 10.1016/j.mcpro.2023.100639] [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: 03/28/2023] [Revised: 08/22/2023] [Accepted: 08/24/2023] [Indexed: 09/03/2023] Open
Abstract
Recent advances in methodology have made phosphopeptide analysis a tractable problem for many proteomics researchers. There are now a wide variety of robust and accessible enrichment strategies to generate phosphoproteomes while free or inexpensive software tools for quantitation and site localization have simplified phosphoproteome analysis workflow tremendously. As a research group under the Association for Biomolecular Resource Facilities umbrella, the Proteomics Standards Research Group has worked to develop a multipathway phosphopeptide standard based on a mixture of heavy-labeled phosphopeptides designed to enable researchers to rapidly develop assays. This mixture contains 131 mass spectrometry vetted phosphopeptides specifically chosen to cover as many known biologically interesting phosphosites as possible from seven different signaling networks: AMPK signaling, death and apoptosis signaling, ErbB signaling, insulin/insulin-like growth factor-1 signaling, mTOR signaling, PI3K/AKT signaling, and stress (p38/SAPK/JNK) signaling. Here, we describe a characterization of this mixture spiked into a HeLa tryptic digest stimulated with both epidermal growth factor and insulin-like growth factor-1 to activate the MAPK and PI3K/AKT/mTOR pathways. We further demonstrate a comparison of phosphoproteomic profiling of HeLa performed independently in five labs using this phosphopeptide mixture with data-independent acquisition. Despite different experimental and instrumentation processes, we found that labs could produce reproducible, harmonized datasets by reporting measurements as ratios to the standard, while intensity measurements showed lower consistency between labs even after normalization. Our results suggest that widely available, biologically relevant phosphopeptide standards can act as a quantitative "yardstick" across laboratories and sample preparations enabling experimental designs larger than a single laboratory can perform. Raw data files are publicly available in the MassIVE dataset MSV000090564.
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Affiliation(s)
- Brian C Searle
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio, USA; Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio, USA.
| | - Allis Chien
- Mass Spectrometry Center, Stanford University, Stanford, California, USA
| | | | | | - Anthony W Herren
- UC Davis Genome Center, Proteomics Core, University of California Davis, Davis California, USA
| | - Jenny Kim Kim
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York, USA
| | - Kimberly A Lee
- Cell Signaling Technology, Inc, Danvers, Massachusetts, USA
| | - Ryan D Leib
- Mass Spectrometry Center, Stanford University, Stanford, California, USA
| | | | - Purvi Patel
- Herbert Irving Comprehensive Cancer Center, Columbia University Medical Center, New York, New York, USA
| | - Jian Min Ren
- Cell Signaling Technology, Inc, Danvers, Massachusetts, USA
| | - Paul M Stemmer
- Department of Pharmaceutical Sciences, Wayne State University, Detroit, Michigan, USA
| | - Yiying Zhu
- Cell Signaling Technology, Inc, Danvers, Massachusetts, USA
| | - Benjamin A Neely
- National Institute of Standards and Technology, Charleston, South Carolina, USA
| | - Bhavin Patel
- Thermo Fisher Scientific, Rockford, Illinois, USA
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9
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Downs M, Zaia J, Sethi MK. Mass spectrometry methods for analysis of extracellular matrix components in neurological diseases. MASS SPECTROMETRY REVIEWS 2023; 42:1848-1875. [PMID: 35719114 PMCID: PMC9763553 DOI: 10.1002/mas.21792] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 04/12/2022] [Accepted: 05/24/2022] [Indexed: 06/15/2023]
Abstract
The brain extracellular matrix (ECM) is a highly glycosylated environment and plays important roles in many processes including cell communication, growth factor binding, and scaffolding. The formation of structures such as perineuronal nets (PNNs) is critical in neuroprotection and neural plasticity, and the formation of molecular networks is dependent in part on glycans. The ECM is also implicated in the neuropathophysiology of disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), and Schizophrenia (SZ). As such, it is of interest to understand both the proteomic and glycomic makeup of healthy and diseased brain ECM. Further, there is a growing need for site-specific glycoproteomic information. Over the past decade, sample preparation, mass spectrometry, and bioinformatic methods have been developed and refined to provide comprehensive information about the glycoproteome. Core ECM molecules including versican, hyaluronan and proteoglycan link proteins, and tenascin are dysregulated in AD, PD, and SZ. Glycomic changes such as differential sialylation, sulfation, and branching are also associated with neurodegeneration. A more thorough understanding of the ECM and its proteomic, glycomic, and glycoproteomic changes in brain diseases may provide pathways to new therapeutic options.
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Affiliation(s)
- Margaret Downs
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University, Boston, Massachusetts, USA
| | - Joseph Zaia
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University, Boston, Massachusetts, USA
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - Manveen K Sethi
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University, Boston, Massachusetts, USA
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10
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He Q, Zhong CQ, Li X, Guo H, Li Y, Gao M, Yu R, Liu X, Zhang F, Guo D, Ye F, Guo T, Shuai J, Han J. Dear-DIA XMBD: Deep Autoencoder Enables Deconvolution of Data-Independent Acquisition Proteomics. RESEARCH (WASHINGTON, D.C.) 2023; 6:0179. [PMID: 37377457 PMCID: PMC10292580 DOI: 10.34133/research.0179] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 06/01/2023] [Indexed: 06/29/2023]
Abstract
Data-independent acquisition (DIA) technology for protein identification from mass spectrometry and related algorithms is developing rapidly. The spectrum-centric analysis of DIA data without the use of spectra library from data-dependent acquisition data represents a promising direction. In this paper, we proposed an untargeted analysis method, Dear-DIAXMBD, for direct analysis of DIA data. Dear-DIAXMBD first integrates the deep variational autoencoder and triplet loss to learn the representations of the extracted fragment ion chromatograms, then uses the k-means clustering algorithm to aggregate fragments with similar representations into the same classes, and finally establishes the inverted index tables to determine the precursors of fragment clusters between precursors and peptides and between fragments and peptides. We show that Dear-DIAXMBD performs superiorly with the highly complicated DIA data of different species obtained by different instrument platforms. Dear-DIAXMBD is publicly available at https://github.com/jianweishuai/Dear-DIA-XMBD.
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Affiliation(s)
- Qingzu He
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health) and Wenzhou Institute,
University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
| | - Chuan-Qi Zhong
- School of Life Sciences,
Xiamen University, Xiamen 361102, China
- State Key Laboratory of Cellular Stress Biology,
Innovation Center for Cell Signaling Network, Xiamen 361102, China
| | - Xiang Li
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
- State Key Laboratory of Cellular Stress Biology,
Innovation Center for Cell Signaling Network, Xiamen 361102, China
| | - Huan Guo
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
| | - Yiming Li
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
| | - Mingxuan Gao
- Department of Computer Science,
Xiamen University, Xiamen 361005, China
| | - Rongshan Yu
- Department of Computer Science,
Xiamen University, Xiamen 361005, China
- National Institute for Data Science in Health and Medicine, School of Medicine,
Xiamen University, Xiamen 361102, China
| | - Xianming Liu
- Bruker (Beijing) Scientific Technology Co. Ltd., Beijing, China
| | - Fangfei Zhang
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences,
Westlake University, 18 Shilongshan Road, Hangzhou 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China
| | - Donghui Guo
- Department of Electronic Engineering,
Xiamen University, Xiamen 361005, China
| | - Fangfu Ye
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health) and Wenzhou Institute,
University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
| | - Tiannan Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences,
Westlake University, 18 Shilongshan Road, Hangzhou 310024, China
- Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, 18 Shilongshan Road, Hangzhou 310024, China
- Westlake Omics Ltd., Yunmeng Road 1, Hangzhou, China
| | - Jianwei Shuai
- Department of Physics, and Fujian Provincial Key Laboratory for Soft Functional Materials Research,
Xiamen University, Xiamen 361005, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health) and Wenzhou Institute,
University of Chinese Academy of Sciences, Wenzhou, Zhejiang 325001, China
- State Key Laboratory of Cellular Stress Biology,
Innovation Center for Cell Signaling Network, Xiamen 361102, China
- National Institute for Data Science in Health and Medicine, School of Medicine,
Xiamen University, Xiamen 361102, China
| | - Jiahuai Han
- School of Life Sciences,
Xiamen University, Xiamen 361102, China
- State Key Laboratory of Cellular Stress Biology,
Innovation Center for Cell Signaling Network, Xiamen 361102, China
- National Institute for Data Science in Health and Medicine, School of Medicine,
Xiamen University, Xiamen 361102, China
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11
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Zhou W, Li W, Wang S, Salovska B, Hu Z, Tao B, Di Y, Punyamurtula U, Turk BE, Sessa WC, Liu Y. An optogenetic-phosphoproteomic study reveals dynamic Akt1 signaling profiles in endothelial cells. Nat Commun 2023; 14:3803. [PMID: 37365174 PMCID: PMC10293293 DOI: 10.1038/s41467-023-39514-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Accepted: 06/07/2023] [Indexed: 06/28/2023] Open
Abstract
The serine/threonine kinase AKT is a central node in cell signaling. While aberrant AKT activation underlies the development of a variety of human diseases, how different patterns of AKT-dependent phosphorylation dictate downstream signaling and phenotypic outcomes remains largely enigmatic. Herein, we perform a systems-level analysis that integrates methodological advances in optogenetics, mass spectrometry-based phosphoproteomics, and bioinformatics to elucidate how different intensity, duration, and pattern of Akt1 stimulation lead to distinct temporal phosphorylation profiles in vascular endothelial cells. Through the analysis of ~35,000 phosphorylation sites across multiple conditions precisely controlled by light stimulation, we identify a series of signaling circuits activated downstream of Akt1 and interrogate how Akt1 signaling integrates with growth factor signaling in endothelial cells. Furthermore, our results categorize kinase substrates that are preferably activated by oscillating, transient, and sustained Akt1 signals. We validate a list of phosphorylation sites that covaried with Akt1 phosphorylation across experimental conditions as potential Akt1 substrates. Our resulting dataset provides a rich resource for future studies on AKT signaling and dynamics.
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Affiliation(s)
- Wenping Zhou
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06510, USA
- Department of Cell Biology, Yale University School of Medicine, New Haven, CT, 06511, USA
- Vascular Biology & Therapeutics Program, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Wenxue Li
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06510, USA
- Cancer Biology Institute, Yale University School of Medicine, West Haven, CT, 06516, USA
| | - Shisheng Wang
- Department of Pulmonary and Critical Care Medicine, and Proteomics-Metabolomics Analysis Platform, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Barbora Salovska
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06510, USA
- Cancer Biology Institute, Yale University School of Medicine, West Haven, CT, 06516, USA
| | - Zhenyi Hu
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06510, USA
- Cancer Biology Institute, Yale University School of Medicine, West Haven, CT, 06516, USA
| | - Bo Tao
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06510, USA
- Vascular Biology & Therapeutics Program, Yale University School of Medicine, New Haven, CT, 06520, USA
| | - Yi Di
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06510, USA
- Cancer Biology Institute, Yale University School of Medicine, West Haven, CT, 06516, USA
| | - Ujwal Punyamurtula
- Master of Biotechnology ScM Program, Brown University, Providence, RI, 02912, USA
| | - Benjamin E Turk
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06510, USA
| | - William C Sessa
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06510, USA.
- Vascular Biology & Therapeutics Program, Yale University School of Medicine, New Haven, CT, 06520, USA.
| | - Yansheng Liu
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, 06510, USA.
- Cancer Biology Institute, Yale University School of Medicine, West Haven, CT, 06516, USA.
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12
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Di Y, Li W, Salovska B, Ba Q, Hu Z, Wang S, Liu Y. A basic phosphoproteomic-DIA workflow integrating precise quantification of phosphosites in systems biology. BIOPHYSICS REPORTS 2023; 9:82-98. [PMID: 37753060 PMCID: PMC10518521 DOI: 10.52601/bpr.2023.230007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 04/28/2023] [Indexed: 09/28/2023] Open
Abstract
Phosphorylation is one of the most important post-translational modifications (PTMs) of proteins, governing critical protein functions. Most human proteins have been shown to undergo phosphorylation, and phosphoproteomic studies have been widely applied due to recent advancements in high-resolution mass spectrometry technology. Although the experimental workflow for phosphoproteomics has been well-established, it would be useful to optimize and summarize a detailed, feasible protocol that combines phosphoproteomics and data-independent acquisition (DIA), along with follow-up data analysis procedures due to the recent instrumental and bioinformatic advances in measuring and understanding tens of thousands of site-specific phosphorylation events in a single experiment. Here, we describe an optimized Phos-DIA protocol, from sample preparation to bioinformatic analysis, along with practical considerations and experimental configurations for each step. The protocol is designed to be robust and applicable for both small-scale phosphoproteomic analysis and large-scale quantification of hundreds of samples for studies in systems biology and systems medicine.
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Affiliation(s)
- Yi Di
- Cancer Biology Institute, Yale University School of Medicine, West Haven, CT 06516, USA
| | - Wenxue Li
- Cancer Biology Institute, Yale University School of Medicine, West Haven, CT 06516, USA
| | - Barbora Salovska
- Cancer Biology Institute, Yale University School of Medicine, West Haven, CT 06516, USA
| | - Qian Ba
- Cancer Biology Institute, Yale University School of Medicine, West Haven, CT 06516, USA
- Current address: Laboratory Center, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 200071, China
| | - Zhenyi Hu
- Cancer Biology Institute, Yale University School of Medicine, West Haven, CT 06516, USA
| | - Shisheng Wang
- Department of Pulmonary and Critical Care Medicine, and Proteomics-Metabolomics Analysis Platform, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yansheng Liu
- Cancer Biology Institute, Yale University School of Medicine, West Haven, CT 06516, USA
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06510, USA
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13
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Zong Y, Wang Y, Yang Y, Zhao D, Wang X, Shen C, Qiao L. DeepFLR facilitates false localization rate control in phosphoproteomics. Nat Commun 2023; 14:2269. [PMID: 37080984 PMCID: PMC10119288 DOI: 10.1038/s41467-023-38035-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 04/06/2023] [Indexed: 04/22/2023] Open
Abstract
Protein phosphorylation is a post-translational modification crucial for many cellular processes and protein functions. Accurate identification and quantification of protein phosphosites at the proteome-wide level are challenging, not least because efficient tools for protein phosphosite false localization rate (FLR) control are lacking. Here, we propose DeepFLR, a deep learning-based framework for controlling the FLR in phosphoproteomics. DeepFLR includes a phosphopeptide tandem mass spectrum (MS/MS) prediction module based on deep learning and an FLR assessment module based on a target-decoy approach. DeepFLR improves the accuracy of phosphopeptide MS/MS prediction compared to existing tools. Furthermore, DeepFLR estimates FLR accurately for both synthetic and biological datasets, and localizes more phosphosites than probability-based methods. DeepFLR is compatible with data from different organisms, instruments types, and both data-dependent and data-independent acquisition approaches, thus enabling FLR estimation for a broad range of phosphoproteomics experiments.
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Affiliation(s)
- Yu Zong
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Yuxin Wang
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
- Department of Computer Science, and Institute of Modern Languages and Linguistics, Fudan University, Shanghai, China
| | - Yi Yang
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | - Dan Zhao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China
| | | | | | - Liang Qiao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, China.
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14
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Franciosa G, Locard-Paulet M, Jensen LJ, Olsen JV. Recent advances in kinase signaling network profiling by mass spectrometry. Curr Opin Chem Biol 2023; 73:102260. [PMID: 36657259 DOI: 10.1016/j.cbpa.2022.102260] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Revised: 12/13/2022] [Accepted: 12/14/2022] [Indexed: 01/19/2023]
Abstract
Mass spectrometry-based phosphoproteomics is currently the leading methodology for the study of global kinase signaling. The scientific community is continuously releasing technological improvements for sensitive and fast identification of phosphopeptides, and their accurate quantification. To interpret large-scale phosphoproteomics data, numerous bioinformatic resources are available that help understanding kinase network functional role in biological systems upon perturbation. Some of these resources are databases of phosphorylation sites, protein kinases and phosphatases; others are bioinformatic algorithms to infer kinase activity, predict phosphosite functional relevance and visualize kinase signaling networks. In this review, we present the latest experimental and bioinformatic tools to profile protein kinase signaling networks and provide examples of their application in biomedicine.
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Affiliation(s)
- Giulia Franciosa
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marie Locard-Paulet
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lars J Jensen
- Disease Systems Biology Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jesper V Olsen
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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15
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Rosenberger G, Li W, Turunen M, He J, Subramaniam PS, Pampou S, Griffin AT, Karan C, Kerwin P, Murray D, Honig B, Liu Y, Califano A. Network-based elucidation of colon cancer drug resistance by phosphoproteomic time-series analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.15.528736. [PMID: 36824919 PMCID: PMC9949144 DOI: 10.1101/2023.02.15.528736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Aberrant signaling pathway activity is a hallmark of tumorigenesis and progression, which has guided targeted inhibitor design for over 30 years. Yet, adaptive resistance mechanisms, induced by rapid, context-specific signaling network rewiring, continue to challenge therapeutic efficacy. By leveraging progress in proteomic technologies and network-based methodologies, over the past decade, we developed VESPA-an algorithm designed to elucidate mechanisms of cell response and adaptation to drug perturbations-and used it to analyze 7-point phosphoproteomic time series from colorectal cancer cells treated with clinically-relevant inhibitors and control media. Interrogation of tumor-specific enzyme/substrate interactions accurately inferred kinase and phosphatase activity, based on their inferred substrate phosphorylation state, effectively accounting for signal cross-talk and sparse phosphoproteome coverage. The analysis elucidated time-dependent signaling pathway response to each drug perturbation and, more importantly, cell adaptive response and rewiring that was experimentally confirmed by CRISPRko assays, suggesting broad applicability to cancer and other diseases.
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Affiliation(s)
- George Rosenberger
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
| | - Mikko Turunen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Jing He
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Present address: Regeneron Genetics Center, Tarrytown, NY, USA
| | - Prem S Subramaniam
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Sergey Pampou
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Aaron T Griffin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- Medical Scientist Training Program, Columbia University Irving Medical Center, New York, NY, USA
| | - Charles Karan
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
| | - Patrick Kerwin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Diana Murray
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Barry Honig
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT, USA
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT, USA
| | - Andrea Califano
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
- J.P. Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Medicine, Columbia University Irving Medical Center, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, USA
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA
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16
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Salovska B, Gao E, Müller‐Dott S, Li W, Cordon CC, Wang S, Dugourd A, Rosenberger G, Saez‐Rodriguez J, Liu Y. Phosphoproteomic analysis of metformin signaling in colorectal cancer cells elucidates mechanism of action and potential therapeutic opportunities. Clin Transl Med 2023; 13:e1179. [PMID: 36781298 PMCID: PMC9925373 DOI: 10.1002/ctm2.1179] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/30/2022] [Accepted: 01/05/2023] [Indexed: 02/15/2023] Open
Abstract
BACKGROUND The biguanide drug metformin is a safe and widely prescribed drug for type 2 diabetes. Interestingly, hundreds of clinical trials have been set to evaluate the potential role of metformin in the prevention and treatment of cancer including colorectal cancer (CRC). However, the "metformin signaling" remains controversial. AIMS AND METHODS To interrogate cell signaling induced by metformin in CRC and explore the druggability of the metformin-rewired phosphorylation network, we performed integrative analysis of phosphoproteomics, bioinformatics, and cell proliferation assays on a panel of 12 molecularly heterogeneous CRC cell lines. Using the high-resolute data-independent analysis mass spectrometry (DIA-MS), we monitored a total of 10,142 proteins and 56,080 phosphosites (P-sites) in CRC cells upon a short- and a long-term metformin treatment. RESULTS AND CONCLUSIONS We found that metformin tended to primarily remodel cell signaling in the long-term and only minimally regulated the total proteome expression levels. Strikingly, the phosphorylation signaling response to metformin was highly heterogeneous in the CRC panel, based on a network analysis inferring kinase/phosphatase activities and cell signaling reconstruction. A "MetScore" was determined to assign the metformin relevance of each P-site, revealing new and robust phosphorylation nodes and pathways in metformin signaling. Finally, we leveraged the metformin P-site signature to identify pharmacodynamic interactions and confirmed a number of candidate metformin-interacting drugs, including navitoclax, a BCL-2/BCL-xL inhibitor. Together, we provide a comprehensive phosphoproteomic resource to explore the metformin-induced cell signaling for potential cancer therapeutics. This resource can be accessed at https://yslproteomics.shinyapps.io/Metformin/.
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Affiliation(s)
- Barbora Salovska
- Yale Cancer Biology InstituteYale UniversityWest HavenConnecticutUSA
| | - Erli Gao
- Yale Cancer Biology InstituteYale UniversityWest HavenConnecticutUSA
| | - Sophia Müller‐Dott
- Institute for Computational BiomedicineFaculty of MedicineHeidelberg University HospitalBioquant, Heidelberg UniversityHeidelbergGermany
| | - Wenxue Li
- Yale Cancer Biology InstituteYale UniversityWest HavenConnecticutUSA
| | | | - Shisheng Wang
- West China‐Washington Mitochondria and Metabolism Research CenterWest China HospitalSichuan UniversityChengduChina
| | - Aurelien Dugourd
- Institute for Computational BiomedicineFaculty of MedicineHeidelberg University HospitalBioquant, Heidelberg UniversityHeidelbergGermany
| | | | - Julio Saez‐Rodriguez
- Institute for Computational BiomedicineFaculty of MedicineHeidelberg University HospitalBioquant, Heidelberg UniversityHeidelbergGermany
| | - Yansheng Liu
- Yale Cancer Biology InstituteYale UniversityWest HavenConnecticutUSA
- Department of PharmacologyYale University School of MedicineNew HavenConnecticutUSA
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17
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Ba Q, Hei Y, Dighe A, Li W, Maziarz J, Pak I, Wang S, Wagner GP, Liu Y. Proteotype coevolution and quantitative diversity across 11 mammalian species. SCIENCE ADVANCES 2022; 8:eabn0756. [PMID: 36083897 PMCID: PMC9462687 DOI: 10.1126/sciadv.abn0756] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 07/25/2022] [Indexed: 06/15/2023]
Abstract
Evolutionary profiling has been largely limited to the nucleotide level. Using consistent proteomic methods, we quantified proteomic and phosphoproteomic layers in fibroblasts from 11 common mammalian species, with transcriptomes as reference. Covariation analysis indicates that transcript and protein expression levels and variabilities across mammals remarkably follow functional role, with extracellular matrix-associated expression being the most variable, demonstrating strong transcriptome-proteome coevolution. The biological variability of gene expression is universal at both interindividual and interspecies scales but to a different extent. RNA metabolic processes particularly show higher interspecies versus interindividual variation. Our results further indicate that while the ubiquitin-proteasome system is strongly conserved in mammals, lysosome-mediated protein degradation exhibits remarkable variation between mammalian lineages. In addition, the phosphosite profiles reveal a phosphorylation coevolution network independent of protein abundance.
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Affiliation(s)
- Qian Ba
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
| | - Yuanyuan Hei
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
| | - Anasuya Dighe
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Wenxue Li
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
| | - Jamie Maziarz
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Irene Pak
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
| | - Shisheng Wang
- West China-Washington Mitochondria and Metabolism Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Günter P. Wagner
- Yale Systems Biology Institute, West Haven, CT 06516, USA
- Department of Ecology and Evolutionary Biology, Yale University, New Haven, CT 06520, USA
- Department of Obstetrics, Gynecology and Reproductive Sciences, Yale University School of Medicine, New Haven, CT 06510, USA
- Department of Obstetrics and Gynecology, Wayne State University, Detroit, MI 48202, USA
| | - Yansheng Liu
- Yale Cancer Biology Institute, West Haven, CT 06516, USA
- Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06510, USA
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18
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Yang Y, Qiao L. Data-independent acquisition proteomics methods for analyzing post-translational modifications. Proteomics 2022; 23:e2200046. [PMID: 36036492 DOI: 10.1002/pmic.202200046] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 08/20/2022] [Accepted: 08/23/2022] [Indexed: 11/06/2022]
Abstract
Protein post-translational modifications (PTMs) increase the functional diversity of the cellular proteome. Accurate and high throughput identification and quantification of protein PTMs is a key task in proteomics research. Recent advancements in data-independent acquisition (DIA) mass spectrometry (MS) technology have achieved deep coverage and accurate quantification of proteins and PTMs. This review provides an overview of DIA data processing methods that cover three aspects of PTMs analysis, i.e., detection of PTMs, site localization, and characterization of complex modification moieties, such as glycosylation. In addition, a survey of deep learning methods that boost DIA-based PTMs analysis is presented, including in silico spectral library generation, as well as feature scoring and error rate control. The limitations and future directions of DIA methods for PTMs analysis are also discussed. Novel data analysis methods will take advantage of advanced MS instrumentation techniques to empower DIA MS for in-depth and accurate PTMs measurements. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Yi Yang
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, 200000, China
| | - Liang Qiao
- Department of Chemistry, and Shanghai Stomatological Hospital, Fudan University, Shanghai, 200000, China
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19
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Srinivasan A, Sing JC, Gingras AC, Röst HL. Improving Phosphoproteomics Profiling Using Data-Independent Mass Spectrometry. J Proteome Res 2022; 21:1789-1799. [PMID: 35877786 DOI: 10.1021/acs.jproteome.2c00172] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Mass spectrometry-based profiling of the phosphoproteome is a powerful method of identifying phosphorylation events at a systems level. Most phosphoproteomics studies have used data-dependent acquisition (DDA) mass spectrometry as their method of choice. In this Perspective, we review some recent studies benchmarking DDA and DIA methods for phosphoproteomics and discuss data analysis options for DIA phosphoproteomics. In order to evaluate the impact of data-dependent and data-independent acquisition (DIA) on identification and quantification, we analyze a previously published phosphopeptide-enriched data set consisting of 10 replicates acquired by DDA and DIA each. We find that though more unique identifications are made in DDA data, phosphopeptides are identified more consistently across replicates in DIA. We further discuss the challenges of identifying chromatographically coeluting phosphopeptide isomers and investigate the impact on reproducibility of identifying high-confidence site-localized phosphopeptides in replicates.
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Affiliation(s)
- Aparna Srinivasan
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Justin C Sing
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
| | - Anne-Claude Gingras
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario M5G 1X5, Canada
| | - Hannes L Röst
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada
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20
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Chao Y, Jiang W, Wang X, Wang X, Song J, Chen C, Zhou J, Huang Q, Hu J, Song Y. Discovery of efficacy biomarkers for non-small cell lung cancer with first-line anti-PD-1 immunotherapy by
data-independent acquisition mass spectrometry. Clin Exp Immunol 2022; 208:60-71. [PMID: 35348622 PMCID: PMC9113286 DOI: 10.1093/cei/uxac021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 03/01/2022] [Indexed: 01/12/2023] Open
Abstract
First-line immune checkpoint inhibitors (ICIs) have greatly ameliorated outcomes in non-small cell lung cancer (NSCLC). However, approximately a quarter of patients receiving ICIs demonstrate long-term clinical benefit, and the true responders have not been fully clarified by the existing biomarkers. To discover potential biomarkers treatment-related outcomes in plasma, mass spectrometry assay for the data-independent acquisition was analyzed plasma samples collected before the anti-PD-1 treatment. From July 2019 to January 2020, 15 patients with EGFR/ALK-negative NSCLC receiving first-line anti-programmed cell death protein 1 (PD-1) inhibitors were enrolled, and six healthy individuals have collected the plasma samples as control. We explored plasma proteome profiles and conducted stratified analyses by anti-PD-1 responders and non-responders. To validate the target proteins by ELISA, we recruited 22 additional independent patients and 15 healthy individuals from April 2021 to August 2021. By identifying biomarkers to predict better efficacy, we performed differential expression analysis in 12 responders and three non-responders. Compared with healthy individuals, hierarchical cluster analysis revealed plasma proteome profiles of NSCLC were markedly changed in 170 differentially expressed proteins. Furthermore, we discovered that SAA1, SAA2, S100A8, and S100A9 were noticeably increased among non-responders than responders, which may serve as predictive biomarkers with unfavorable responses. The validated results from all samples via ELISA have confirmed this observation. Identified a set of plasma-derived protein biomarkers (SAA1, SAA2, S100A8, and S100A9) that could potentially predict the efficacy in cohorts of patients with NSCLC treated with first-line anti-PD-1 inhibitors and deserves further prospective study.
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Affiliation(s)
- Yencheng Chao
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China
| | - Weipeng Jiang
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China
| | - Xiaocen Wang
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Xiaoyue Wang
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Juan Song
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Cuicui Chen
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jian Zhou
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Qihong Huang
- Shanghai Respiratory Research Institute, Department of Clinical Sciences, Cancer Center, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Jie Hu
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
| | - Yuanlin Song
- Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Lung Inflammation and Injury, Shanghai, China
- Shanghai Respiratory Research Institute, Shanghai, China
- National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
- Department of Pulmonary Medicine, Zhongshan Hospital, Qingpu Branch, Fudan University, Shanghai, China
- Jinshan Hospital of Fudan University, Shanghai, China
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21
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LeDuc RD, Deutsch EW, Binz PA, Fellers RT, Cesnik AJ, Klein JA, Van Den Bossche T, Gabriels R, Yalavarthi A, Perez-Riverol Y, Carver J, Bittremieux W, Kawano S, Pullman B, Bandeira N, Kelleher NL, Thomas PM, Vizcaíno JA. Proteomics Standards Initiative's ProForma 2.0: Unifying the Encoding of Proteoforms and Peptidoforms. J Proteome Res 2022; 21:1189-1195. [PMID: 35290070 PMCID: PMC7612572 DOI: 10.1021/acs.jproteome.1c00771] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
It is important for the proteomics community to have a standardized manner to represent all possible variations of a protein or peptide primary sequence, including natural, chemically induced, and artifactual modifications. The Human Proteome Organization Proteomics Standards Initiative in collaboration with several members of the Consortium for Top-Down Proteomics (CTDP) has developed a standard notation called ProForma 2.0, which is a substantial extension of the original ProForma notation developed by the CTDP. ProForma 2.0 aims to unify the representation of proteoforms and peptidoforms. ProForma 2.0 supports use cases needed for bottom-up and middle-/top-down proteomics approaches and allows the encoding of highly modified proteins and peptides using a human- and machine-readable string. ProForma 2.0 can be used to represent protein modifications in a specified or ambiguous location, designated by mass shifts, chemical formulas, or controlled vocabulary terms, including cross-links (natural and chemical) and atomic isotopes. Notational conventions are based on public controlled vocabularies and ontologies. The most up-to-date full specification document and information about software implementations are available at http://psidev.info/proforma.
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Affiliation(s)
- Richard D LeDuc
- National Resource for Translational and Developmental Proteomics, Northwestern University, Evanston, Illinois 60611, United States
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Pierre-Alain Binz
- Clinical Chemistry Service, Lausanne University Hospital, 1011 Lausanne, Switzerland
| | - Ryan T Fellers
- National Resource for Translational and Developmental Proteomics, Northwestern University, Evanston, Illinois 60611, United States
| | - Anthony J Cesnik
- Department of Genetics, Stanford University, Stanford, California 94305, United States
- Chan Zuckerberg Biohub, 499 Illinois Street, San Francisco, California 94158, United States
- SciLifeLab, School of Engineering Sciences in Chemistry Biotechnology and Health, KTH-Royal Institute of Technology, SE-171 21 Solna, Stockholm, Sweden 113 51
| | - Joshua A Klein
- Program for Bioinformatics, Boston University, Boston, Massachusetts 02215, United States
| | - Tim Van Den Bossche
- VIB-UGent Center for Medical Biotechnology, VIB, Technologiepark 75-FSVM II, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, VIB, Technologiepark 75-FSVM II, 9052 Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Arshika Yalavarthi
- National Resource for Translational and Developmental Proteomics, Northwestern University, Evanston, Illinois 60611, United States
| | - Yasset Perez-Riverol
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge CB10 1SD, United Kingdom
| | | | | | - Shin Kawano
- Toyama University of International Studies, Toyama, 930-1292 Toyama, Higashikuromaki, 6 5-1, Japan
- Database Center for Life Science, Joint Support-Center for Data Science Research, Research Organization of Information and Systems, Kashiwa, Chiba 277-0871, Japan
| | | | | | - Neil L Kelleher
- National Resource for Translational and Developmental Proteomics, Northwestern University, Evanston, Illinois 60611, United States
| | - Paul M Thomas
- National Resource for Translational and Developmental Proteomics, Northwestern University, Evanston, Illinois 60611, United States
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, EMBL-European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge CB10 1SD, United Kingdom
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22
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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: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [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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23
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Harney DJ, Larance M. Annotated Protein Database Using Known Cleavage Sites for Rapid Detection of Secreted Proteins. J Proteome Res 2022; 21:965-974. [DOI: 10.1021/acs.jproteome.1c00806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Dylan J. Harney
- Charles Perkins Centre and School of Life and Environmental Sciences, University of Sydney, 2006 Sydney, Australia
| | - Mark Larance
- Charles Perkins Centre and School of Life and Environmental Sciences, University of Sydney, 2006 Sydney, Australia
- Charles Perkins Centre and School of Medical Sciences, University of Sydney, 2006 Sydney, Australia
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24
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Liu Y. A peptidoform based proteomic strategy for studying functions of post-translational modifications. Proteomics 2022; 22:e2100316. [PMID: 34878717 PMCID: PMC8959388 DOI: 10.1002/pmic.202100316] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 11/30/2021] [Accepted: 12/02/2021] [Indexed: 02/03/2023]
Abstract
Protein post-translational modifications (PTMs) generate an enormous, but as yet undetermined, expansion of the produced proteoforms. In this Viewpoint, we firstly reviewed the concepts of proteoform and peptidoform. We show that many of the current PTM biological investigation and annotation studies largely follow a PTM site-specific rather than proteoform-specific approach. We further illustrate a potentially useful matching strategy in which a particular "modified peptidoform" is matched to the corresponding "unmodified peptidoform" as a reference for the quantitative analysis between samples and conditions. We suggest this strategy has the potential to provide more directly relevant information to learn the PTM site-specific biological functions. Accordingly, we advocate for the wider use of the nomenclature "peptidoform" in future bottom-up proteomic studies.
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Affiliation(s)
- Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA,Department of Pharmacology, Yale University, School of Medicine, New Haven, CT 06520, USA,Corresponding author:
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25
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Recent Developments in Clinical Plasma Proteomics—Applied to Cardiovascular Research. Biomedicines 2022; 10:biomedicines10010162. [PMID: 35052841 PMCID: PMC8773619 DOI: 10.3390/biomedicines10010162] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/05/2022] [Accepted: 01/12/2022] [Indexed: 01/27/2023] Open
Abstract
The human plasma proteome mirrors the physiological state of the cardiovascular system, a fact that has been used to analyze plasma biomarkers in routine analysis for the diagnosis and monitoring of cardiovascular diseases for decades. These biomarkers address, however, only a very limited subset of cardiovascular diseases, such as acute myocardial infarct or acute deep vein thrombosis, and clinical plasma biomarkers for the diagnosis and stratification cardiovascular diseases that are growing in incidence, such as heart failure and abdominal aortic aneurysm, do not exist and are urgently needed. The discovery of novel biomarkers in plasma has been hindered by the complexity of the human plasma proteome that again transforms into an extreme analytical complexity when it comes to the discovery of novel plasma biomarkers. This complexity is, however, addressed by recent achievements in technologies for analyzing the human plasma proteome, thereby facilitating the possibility for novel biomarker discoveries. The aims of this article is to provide an overview of the recent achievements in technologies for proteomic analysis of the human plasma proteome and their applications in cardiovascular medicine.
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26
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Hubbard EE, Heil LR, Merrihew GE, Chhatwal JP, Farlow MR, McLean CA, Ghetti B, Newell KL, Frosch MP, Bateman RJ, Larson EB, Keene CD, Perrin RJ, Montine TJ, MacCoss MJ, Julian RR. Does Data-Independent Acquisition Data Contain Hidden Gems? A Case Study Related to Alzheimer's Disease. J Proteome Res 2022; 21:118-131. [PMID: 34818016 PMCID: PMC8741752 DOI: 10.1021/acs.jproteome.1c00558] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
One of the potential benefits of using data-independent acquisition (DIA) proteomics protocols is that information not originally targeted by the study may be present and discovered by subsequent analysis. Herein, we reanalyzed DIA data originally recorded for global proteomic analysis to look for isomerized peptides, which occur as a result of spontaneous chemical modifications to long-lived proteins. Examination of a large set of human brain samples revealed a striking relationship between Alzheimer's disease (AD) status and isomerization of aspartic acid in a peptide from tau. Relative to controls, a surprising increase in isomer abundance was found in both autosomal dominant and sporadic AD samples. To explore potential mechanisms that might account for these observations, quantitative analysis of proteins related to isomerization repair and autophagy was performed. Differences consistent with reduced autophagic flux in AD-related samples relative to controls were found for numerous proteins, including most notably p62, a recognized indicator of autophagic inhibition. These results suggest, but do not conclusively demonstrate, that lower autophagic flux may be strongly associated with loss of function in AD brains. This study illustrates that DIA data may contain unforeseen results of interest and may be particularly useful for pilot studies investigating new research directions. In this case, a promising target for future investigations into the therapy and prevention of AD has been identified.
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Affiliation(s)
- Evan E. Hubbard
- Department of Chemistry, University of California, Riverside, California 92521, United States
| | - Lilian R. Heil
- Department of Genome Sciences, University of Washington, Seattle, Washington, 98195, United States
| | - Gennifer E. Merrihew
- Department of Genome Sciences, University of Washington, Seattle, Washington, 98195, United States
| | - Jasmeer P. Chhatwal
- Harvard Medical School, Massachusetts General Hospital, Department of Neurology, 15 Parkman St, Suite 835, Boston MA 02114
| | - Martin R. Farlow
- Department of Neurology, Indiana University School of Medicine, Indianapolis, Indiana, 46202
| | | | - Bernardino Ghetti
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202
| | - Kathy L. Newell
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, 46202
| | - Matthew P. Frosch
- C.S. Kubik Laboratory for Neuropathology, and Massachusetts Alzheimer Disease Research Center, Massachusetts General Hospital, Boston, MA 02114
| | - Randall J. Bateman
- Department of Neurology, Washington University School of Medicine, 660 South Euclid Avenue, Box 8111, St. Louis, 63110, Missouri, USA
| | - Eric B. Larson
- Kaiser Permanente Washington Health Research Institute and Department of Medicine, University of Washington, Seattle WA
| | - C. Dirk Keene
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington, 98195, United States
| | - Richard J. Perrin
- Department of Pathology and Immunology, Department of Neurology, Washington University School of Medicine, Saint Louis, Missouri 63110, United States
| | - Thomas J. Montine
- Department of Pathology, Stanford University, Stanford, CA, 94305, United States
| | - Michael J. MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington, 98195, United States
| | - Ryan R. Julian
- Department of Chemistry, University of California, Riverside, California 92521, United States,corresponding author:
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27
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Sun X, Zhu L, Qi X, Zhang H, Wu L, Wang J, Hou H. Cleavage sites and non-enzymatic self-degradation mechanism of ready-to-eat sea cucumber during storage. Food Chem 2021; 375:131722. [PMID: 34922275 DOI: 10.1016/j.foodchem.2021.131722] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 11/24/2021] [Accepted: 11/27/2021] [Indexed: 02/07/2023]
Abstract
The non-enzymatic degradation of ready-to-eat sea cucumber (RSC) was closely related to the quality of sea cucumber products. When stored at 37 °C for 0-30 d, the hardness of RSC decreased by 86.7% and the proportion of free water increased by 12.71%. The content of free hydroxyproline increased from 8.33 μg/g to 24.12 μg/g. Label-free quantitative proteomics analysis showed that protein was prone to break at the sites of G, Q, N, D, and L, and the peptide bonds in QI, DL, NL, RI, EF and SY were much more liable to break. Edman degradation method showed that the breakage sites of RSC were at S, D, H, E, and V. NL, NA and NG calculated by B3LYP/6-31G(d) showed that the relative free energies in the initial cyclization step were 53.20, 143.53 and 78.10 kcal/mol, respectively, which may be the rate-determining step for peptide bond cleavage.
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Affiliation(s)
- Xiao Sun
- College of Food Science and Engineering, Ocean University of China, No.5, Yu Shan Road, Qingdao, Shandong Province 266003, PR China
| | - Lulu Zhu
- College of Food Science and Engineering, Ocean University of China, No.5, Yu Shan Road, Qingdao, Shandong Province 266003, PR China
| | - Xin Qi
- College of Food Science and Engineering, Ocean University of China, No.5, Yu Shan Road, Qingdao, Shandong Province 266003, PR China
| | - Hongwei Zhang
- Technology Center of Qingdao Customs, No. 83, Xinyue Road, Qingdao, Shandong Province 266109, PR China
| | - Ling Wu
- Wuhu Midea Smart Kitchen Appliance Manufacturing Co., Ltd, Wuhu, Anhui Province 241012, PR China
| | - Jinhua Wang
- Wuhu Midea Smart Kitchen Appliance Manufacturing Co., Ltd, Wuhu, Anhui Province 241012, PR China
| | - Hu Hou
- College of Food Science and Engineering, Ocean University of China, No.5, Yu Shan Road, Qingdao, Shandong Province 266003, PR China; Laboratory for Marine Drugs and Bioproducts, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong Province 266237, PR China.
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28
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Lou R, Liu W, Li R, Li S, He X, Shui W. DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation. Nat Commun 2021; 12:6685. [PMID: 34795227 PMCID: PMC8602247 DOI: 10.1038/s41467-021-26979-1] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 10/26/2021] [Indexed: 12/27/2022] Open
Abstract
Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining heavily relies on a spectral library that in most cases is built on DDA analysis of the same sample. Construction of this project-specific DDA library impairs the analytical throughput, limits the proteome coverage, and increases the sample size for DIA phosphoproteomics. Herein we introduce a deep neural network, DeepPhospho, which conceptually differs from previous deep learning models to achieve accurate predictions of LC-MS/MS data for phosphopeptides. By leveraging in silico libraries generated by DeepPhospho, we establish a DIA workflow for phosphoproteome profiling which involves DIA data acquisition and data mining with DeepPhospho predicted libraries, thus circumventing the need of DDA library construction. Our DeepPhospho-empowered workflow substantially expands the phosphoproteome coverage while maintaining high quantification performance, which leads to the discovery of more signaling pathways and regulated kinases in an EGF signaling study than the DDA library-based approach. DeepPhospho is provided as a web server as well as an offline app to facilitate user access to model training, predictions and library generation.
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Affiliation(s)
- Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Weizhen Liu
- School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Rongjie Li
- School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China
| | - Shanshan Li
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China
| | - Xuming He
- School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
- Shanghai Engineering Research Center of Intelligent Vision and Imaging, Shanghai, 201210, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, 201210, China.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China.
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29
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Goetze S, Frey K, Rohrer L, Radosavljevic S, Krützfeldt J, Landmesser U, Bueter M, Pedrioli PGA, von Eckardstein A, Wollscheid B. Reproducible Determination of High-Density Lipoprotein Proteotypes. J Proteome Res 2021; 20:4974-4984. [PMID: 34677978 DOI: 10.1021/acs.jproteome.1c00429] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
High-density lipoprotein (HDL) is a heterogeneous mixture of blood-circulating multimolecular particles containing many different proteins, lipids, and RNAs. Recent advancements in mass spectrometry-based proteotype analysis show promise for the analysis of proteoforms across large patient cohorts. In order to create the required spectral libraries enabling these data-independent acquisition (DIA) strategies, HDL was isolated from the plasma of more than 300 patients with a multiplicity of physiological HDL states. HDL proteome spectral libraries consisting of 296 protein groups and more than 786 peptidoforms were established, and the performance of the DIA strategy was benchmarked for the detection of HDL proteotype differences between healthy individuals and a cohort of patients suffering from diabetes mellitus type 2 and/or coronary heart disease. Bioinformatic interrogation of the data using the generated spectral libraries showed that the DIA approach enabled robust HDL proteotype determination. HDL peptidoform analysis enabled by using spectral libraries allowed for the identification of post-translational modifications, such as in APOA1, which could affect HDL functionality. From a technical point of view, data analysis further shows that protein and peptide quantities are currently more discriminative between different HDL proteotypes than peptidoforms without further enrichment. Together, DIA-based HDL proteotyping enables the robust digitization of HDL proteotypes as a basis for the analysis of larger clinical cohorts.
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Affiliation(s)
- Sandra Goetze
- Institute of Translational Medicine (ITM), Department of Health Sciences and Technology (D-HEST), ETH Zurich, Zurich 8093, Switzerland.,Swiss Multi-Omics Center (SMOC), PHRT-CPAC, ETH Zurich, Zurich 8093, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne 1015, Switzerland
| | - Kathrin Frey
- Institute of Translational Medicine (ITM), Department of Health Sciences and Technology (D-HEST), ETH Zurich, Zurich 8093, Switzerland
| | - Lucia Rohrer
- Institute for Clinical Chemistry, University Hospital Zurich, Zurich 8091, Switzerland
| | - Silvija Radosavljevic
- Institute for Clinical Chemistry, University Hospital Zurich, Zurich 8091, Switzerland
| | - Jan Krützfeldt
- Division of Endocrinology, Diabetes and Clinical Nutrition, University Hospital Zurich, Zurich 8091, Switzerland
| | - Ulf Landmesser
- Department of Cardiology, Charité - Universitätsmedizin Berlin, Berlin 12203, Germany
| | - Marco Bueter
- Department of Surgery and Transplantation, University Hospital Zurich, Zurich 8091, Switzerland
| | - Patrick G A Pedrioli
- Institute of Translational Medicine (ITM), Department of Health Sciences and Technology (D-HEST), ETH Zurich, Zurich 8093, Switzerland.,Swiss Multi-Omics Center (SMOC), PHRT-CPAC, ETH Zurich, Zurich 8093, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne 1015, Switzerland
| | | | - Bernd Wollscheid
- Institute of Translational Medicine (ITM), Department of Health Sciences and Technology (D-HEST), ETH Zurich, Zurich 8093, Switzerland.,Swiss Multi-Omics Center (SMOC), PHRT-CPAC, ETH Zurich, Zurich 8093, Switzerland.,Swiss Institute of Bioinformatics (SIB), Lausanne 1015, Switzerland
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30
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Dong M, Lih TSM, Ao M, Hu Y, Chen SY, Eguez RV, Zhang H. Data-Independent Acquisition-Based Mass Spectrometry (DIA-MS) for Quantitative Analysis of Intact N-Linked Glycopeptides. Anal Chem 2021; 93:13774-13782. [PMID: 34622651 DOI: 10.1021/acs.analchem.1c01659] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
N-linked protein glycosylation is a key regulator in various biological functions. Previous studies have shown that aberrant glycosylation is associated with many diseases. Therefore, it is essential to elucidate protein modifications of glycosylation by quantitatively profiling intact N-linked glycopeptides. Data-independent acquisition (DIA) mass spectrometry (MS) is a cost-effective, flexible, and high-throughput method for global proteomics. However, substantial challenges are still present in the quantitative analysis of intact glycopeptides with high accuracy at high throughput. In this study, we have established a novel integrated platform for the DIA analysis of intact glycopeptides isolated from complex samples. The established analysis platform utilizes a well-designed DIA-MS method for raw data collection, a spectral library constructed specifically for intact glycopeptide quantification providing accurate results by the inclusion of Y ions for quantification and filtering of quantified intact glycopeptides with low-quality MS2 spectra automatically using a set of criteria. Intact glycopeptides isolated from human serum were used to evaluate the performance of the integrated platform. By utilizing 100 isolation windows for DIA data acquisition, a well-constructed human serum spectral library containing 1123 nonredundant intact glycopeptides with Y ions, and automated data inspection, 620 intact glycopeptides were quantified with high confidence from DIA-MS. In summary, our integrated platform can serve as a reliable quantitative tool for characterizing intact glycopeptides isolated from complex biological samples to assist our understanding of biological functions of N-linked glycosylation.
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Affiliation(s)
- Mingming Dong
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Tung-Shing Mamie Lih
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Minghui Ao
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Yingwei Hu
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Shao-Yung Chen
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States.,Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore Maryland 21218, United States
| | - Rodrigo Vargas Eguez
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States
| | - Hui Zhang
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21231, United States.,Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore Maryland 21218, United States
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31
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Yang Y, Yan G, Kong S, Wu M, Yang P, Cao W, Qiao L. GproDIA enables data-independent acquisition glycoproteomics with comprehensive statistical control. Nat Commun 2021; 12:6073. [PMID: 34663801 PMCID: PMC8523693 DOI: 10.1038/s41467-021-26246-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 09/23/2021] [Indexed: 12/26/2022] Open
Abstract
Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data independent acquisition (DIA) is an emerging technology with deep proteome coverage and accurate quantitative capability in proteomics studies, but is still in the early stage of development in the field of glycoproteomics. We propose GproDIA, a framework for the proteome-wide characterization of intact glycopeptides from DIA data with comprehensive statistical control by a 2-dimentional false discovery rate approach and a glycoform inference algorithm, enabling accurate identification of intact glycopeptides using wide isolation windows. We further utilize a semi-empirical spectrum prediction strategy to expand the coverage of spectral libraries of glycopeptides. We benchmark our method for N-glycopeptide profiling on DIA data of yeast and human serum samples, demonstrating that DIA with GproDIA outperforms the data-dependent acquisition-based methods for glycoproteomics in terms of capacity and data completeness of identification, as well as accuracy and precision of quantification. We expect that this work can provide a powerful tool for glycoproteomic studies.
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Affiliation(s)
- Yi Yang
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China
| | - Guoquan Yan
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China
| | - Siyuan Kong
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China
| | - Mengxi Wu
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China
| | - Pengyuan Yang
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China
- The Shanghai Key Laboratory of Medical Epigenetics, Fudan University, Shanghai, 200000, China
- The International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Fudan University, Shanghai, 200000, China
- NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, 200000, China
| | - Weiqian Cao
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China.
- The Shanghai Key Laboratory of Medical Epigenetics, Fudan University, Shanghai, 200000, China.
- The International Co-laboratory of Medical Epigenetics and Metabolism (Ministry of Science and Technology), Fudan University, Shanghai, 200000, China.
- NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, 200000, China.
| | - Liang Qiao
- Department of Chemistry and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China.
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32
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Goecker ZC, Legg KM, Salemi MR, Herren AW, Phinney BS, McKiernan HE, Parker GJ. Alternative LC-MS/MS Platforms and Data Acquisition Strategies for Proteomic Genotyping of Human Hair Shafts. J Proteome Res 2021; 20:4655-4666. [PMID: 34491751 DOI: 10.1021/acs.jproteome.1c00209] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Protein is a major component of all biological evidence. Proteomic genotyping is the use of genetically variant peptides (GVPs) that contain single-amino-acid polymorphisms to infer the genotype of matching nonsynonymous single-nucleotide polymorphisms for the individual from whom the protein sample originated. This can be used to statistically associate an individual to evidence found at a crime scene. The utility of the inferred genotype increases as the detection of GVPs increases, which is the direct result of technology transfer to mass spectrometry platforms typically available. Digests of single (2 cm) human hair shafts from three European and two African subjects were analyzed using data-dependent acquisition on a Q-Exactive Plus Hybrid Quadrupole-Orbitrap system, data-independent acquisition and a variant of parallel reaction monitoring (PRM) on an Orbitrap Fusion Lumos Tribrid system, and multiple reaction monitoring (MRM) on an Agilent 6495 triple quadrupole system. In our hands, average GVP detection from a selected panel of 24 GVPs increased from 6.5 ± 1.1 and 3.1 ± 0.8 using data-dependent and -independent acquisition to 9.5 ± 0.7 and 11.7 ± 1.7 using PRM and MRM (p < 0.05), respectively. PRM resulted in a 1.3-fold increase in detection sensitivity, and MRM resulted in a 1.6-fold increase in detection sensitivity. This increase in biomarker detection has a functional impact on the statistical association of a protein sample and an individual. Increased biomarker sensitivity, using Markov Chain Monte Carlo modeling, produced a median-estimated random match probability of over 1 in 10 trillion from a single hair using targeted proteomics. For PRM and MRM, detected GVPs were validated by the inclusion of stable isotope-labeled peptides in each sample, which served also as a detection trigger. This research accomplishes two aims: the demonstration of utility for alternative analytical platforms in proteomic genotyping and the establishment of validation methods for the evaluation of inferred genotypes.
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Affiliation(s)
- Zachary C Goecker
- Department of Environmental Toxicology, University of California, Davis, California 95616, United States
| | - Kevin M Legg
- The Center for Forensic Science Research and Education, Willow Grove, Pennsylvania 19090, United States
| | - Michelle R Salemi
- Proteomics Core Facility, University of California, Davis, California 95616, United States
| | - Anthony W Herren
- Proteomics Core Facility, University of California, Davis, California 95616, United States
| | - Brett S Phinney
- Proteomics Core Facility, University of California, Davis, California 95616, United States
| | - Heather E McKiernan
- The Center for Forensic Science Research and Education, Willow Grove, Pennsylvania 19090, United States
| | - Glendon J Parker
- Department of Environmental Toxicology, University of California, Davis, California 95616, United States
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Cao X, Sandberg A, Araújo JE, Cvetkovski F, Berglund E, Eriksson LE, Pernemalm M. Evaluation of Spin Columns for Human Plasma Depletion to Facilitate MS-Based Proteomics Analysis of Plasma. J Proteome Res 2021; 20:4610-4620. [PMID: 34320313 PMCID: PMC8419864 DOI: 10.1021/acs.jproteome.1c00378] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
High abundant protein depletion is a common strategy applied to increase analytical depth in global plasma proteomics experiment setups. The standard strategies for depletion of the highest abundant proteins currently rely on multiple-use HPLC columns or multiple-use spin columns. Here we evaluate the performance of single-use spin columns for plasma depletion and show that the single-use spin reduces handling time by allowing parallelization and is easily adapted to a nonspecialized lab environment without reducing the high plasma proteome coverage and reproducibility. In addition, we evaluate the effect of viral heat inactivation on the plasma proteome, an additional step in the plasma preparation workflow that allows the sample preparation of SARS-Cov2-infected samples to be performed in a BSL3 laboratory, and report the advantage of performing the heat inactivation postdepletion. We further show the possibility of expanding the use of the depletion column cross-species to macaque plasma samples. In conclusion, we report that single-use spin columns for high abundant protein depletion meet the requirements for reproducibly in in-depth plasma proteomics and can be applied on a common animal model while also reducing the sample handling time.
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Affiliation(s)
- Xiaofang Cao
- Cancer Proteomics Mass Spectrometry, Scilifelab, Department of Oncology and Pathology, Karolinska Institutet, SE-141 86 Stockholm, Sweden
| | - AnnSofi Sandberg
- Cancer Proteomics Mass Spectrometry, Scilifelab, Department of Oncology and Pathology, Karolinska Institutet, SE-141 86 Stockholm, Sweden
| | - José Eduardo Araújo
- Cancer Proteomics Mass Spectrometry, Scilifelab, Department of Oncology and Pathology, Karolinska Institutet, SE-141 86 Stockholm, Sweden
| | - Filip Cvetkovski
- Research and Development, ITB-Med AB, SE-113 66 Stockholm, Sweden
| | - Erik Berglund
- Section of Endocrine and Sarcoma Surgery, Department of Molecular Medicine and Surgery; Department of Clinical Science, Intervention and Technology (CLINTEC), Division of Transplantation, Surgery, Karolinska Institute, SE-141 86 Stockholm, Sweden
| | - Lars E Eriksson
- Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, SE-171 77 Stockholm, Sweden.,Medical Unit Infectious Diseases, Karolinska University Hospital, SE-141 86 Huddinge, Sweden.,School of Health Sciences, City University of London, London EC1 V 0HB, United Kingdom
| | - Maria Pernemalm
- Cancer Proteomics Mass Spectrometry, Scilifelab, Department of Oncology and Pathology, Karolinska Institutet, SE-141 86 Stockholm, Sweden
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34
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Bludau I, Frank M, Dörig C, Cai Y, Heusel M, Rosenberger G, Picotti P, Collins BC, Röst H, Aebersold R. Systematic detection of functional proteoform groups from bottom-up proteomic datasets. Nat Commun 2021; 12:3810. [PMID: 34155216 PMCID: PMC8217233 DOI: 10.1038/s41467-021-24030-x] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 05/25/2021] [Indexed: 02/05/2023] Open
Abstract
To a large extent functional diversity in cells is achieved by the expansion of molecular complexity beyond that of the coding genome. Various processes create multiple distinct but related proteins per coding gene - so-called proteoforms - that expand the functional capacity of a cell. Evaluating proteoforms from classical bottom-up proteomics datasets, where peptides instead of intact proteoforms are measured, has remained difficult. Here we present COPF, a tool for COrrelation-based functional ProteoForm assessment in bottom-up proteomics data. It leverages the concept of peptide correlation analysis to systematically assign peptides to co-varying proteoform groups. We show applications of COPF to protein complex co-fractionation data as well as to more typical protein abundance vs. sample data matrices, demonstrating the systematic detection of assembly- and tissue-specific proteoform groups, respectively, in either dataset. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets.
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Affiliation(s)
- Isabell Bludau
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Max Frank
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Christian Dörig
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Yujia Cai
- Department of Molecular Genetics, University of Toronto, Toronto, Canada
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
| | - Moritz Heusel
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Division of Infection Medicine (BMC), Department of Clinical Sciences, Lund University, Lund, Sweden
| | - George Rosenberger
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- Columbia University, New York, NY, USA
| | - Paola Picotti
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Ben C Collins
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
- School of Biological Sciences, Queen's University Belfast, Belfast, UK
| | - Hannes Röst
- Department of Molecular Genetics, University of Toronto, Toronto, Canada.
- The Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada.
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
- Faculty of Science, University of Zurich, Zurich, Switzerland.
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35
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Gao E, Li W, Wu C, Shao W, Di Y, Liu Y. Data-independent acquisition-based proteome and phosphoproteome profiling across six melanoma cell lines reveals determinants of proteotypes. Mol Omics 2021; 17:413-425. [PMID: 33728422 PMCID: PMC8205956 DOI: 10.1039/d0mo00188k] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Human cancer cell lines are widely used in pharmacological and systems biological studies. The rapid documentation of the steady-state gene expression landscape of the cells used in a particular experiment may help to improve the reproducibility of scientific research. Here we applied a data-independent acquisition mass spectrometry (DIA-MS) method, coupled with a peptide spectral-library-free data analysis workflow, to measure both the proteome and phosphoproteome of a melanoma cell line panel with different metastatic properties. For each cell line, the single-shot DIA-MS detected 8100 proteins and almost 40 000 phosphopeptides in the respective measurements of two hours. Benchmarking the DIA-MS data towards the RNA-seq data and tandem mass tag (TMT)-MS results from the same set of cell lines demonstrated comparable qualitative coverage and quantitative reproducibility. Our data confirmed the high but complex mRNA-protein and protein-phospsite correlations. The results successfully established DIA-MS as a strong and competitive proteotyping approach for cell lines. The data further showed that all subunits of the glycosylphosphatidylinositol (GPI)-anchor transamidase complex were overexpressed in metastatic melanoma cells and identified altered phosphoprotein modules such as the BAF complex and mRNA splicing between metastatic and primary cells. This study provides a high-quality resource for calibrating DIA-MS performance, benchmarking DIA bioinformatic algorithms, and exploring the metastatic proteotypes in melanoma cells.
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Affiliation(s)
- Erli Gao
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA.
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Kitata RB, Choong WK, Tsai CF, Lin PY, Chen BS, Chang YC, Nesvizhskii AI, Sung TY, Chen YJ. A data-independent acquisition-based global phosphoproteomics system enables deep profiling. Nat Commun 2021; 12:2539. [PMID: 33953186 PMCID: PMC8099862 DOI: 10.1038/s41467-021-22759-z] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 03/29/2021] [Indexed: 01/07/2023] Open
Abstract
Phosphoproteomics can provide insights into cellular signaling dynamics. To achieve deep and robust quantitative phosphoproteomics profiling for minute amounts of sample, we here develop a global phosphoproteomics strategy based on data-independent acquisition (DIA) mass spectrometry and hybrid spectral libraries derived from data-dependent acquisition (DDA) and DIA data. Benchmarking the method using 166 synthetic phosphopeptides shows high sensitivity (<0.1 ng), accurate site localization and reproducible quantification (~5% median coefficient of variation). As a proof-of-concept, we use lung cancer cell lines and patient-derived tissue to construct a hybrid phosphoproteome spectral library covering 159,524 phosphopeptides (88,107 phosphosites). Based on this library, our single-shot streamlined DIA workflow quantifies 36,350 phosphosites (19,755 class 1) in cell line samples within two hours. Application to drug-resistant cells and patient-derived lung cancer tissues delineates site-specific phosphorylation events associated with resistance and tumor progression, showing that our workflow enables the characterization of phosphorylation signaling with deep coverage, high sensitivity and low between-run missing values.
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Affiliation(s)
| | - Wai-Kok Choong
- Institute of Information Science, Academia Sinica, Taipei, 11529, Taiwan
| | - Chia-Feng Tsai
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, 99354, USA
| | - Pei-Yi Lin
- Institute of Chemistry, Academia Sinica, Taipei, 11529, Taiwan
| | - Bo-Shiun Chen
- Institute of Chemistry, Academia Sinica, Taipei, 11529, Taiwan
- Department of Chemistry, National Taiwan University, Taipei, 10617, Taiwan
| | - Yun-Chien Chang
- Institute of Chemistry, Academia Sinica, Taipei, 11529, Taiwan
- Department of Chemistry, National Taiwan University, Taipei, 10617, Taiwan
| | - Alexey I Nesvizhskii
- Department of Computational Medicine and Bioinformatics, and Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, 48109, USA
| | - Ting-Yi Sung
- Institute of Information Science, Academia Sinica, Taipei, 11529, Taiwan
| | - Yu-Ju Chen
- Institute of Chemistry, Academia Sinica, Taipei, 11529, Taiwan.
- Department of Chemistry, National Taiwan University, Taipei, 10617, Taiwan.
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37
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Karaduta O, Dvanajscak Z, Zybailov B. Metaproteomics-An Advantageous Option in Studies of Host-Microbiota Interaction. Microorganisms 2021; 9:microorganisms9050980. [PMID: 33946610 PMCID: PMC8147213 DOI: 10.3390/microorganisms9050980] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 04/29/2021] [Accepted: 04/30/2021] [Indexed: 12/20/2022] Open
Abstract
Gut microbiome contributes to host health by maintaining homeostasis, increasing digestive efficiency, and facilitating the development of the immune system. Manipulating gut microbiota is being recognized as a therapeutic target to manage various chronic diseases. The therapeutic manipulation of the intestinal microbiome is achieved through diet modification, the administration of prebiotics, probiotics, or antibiotics, and more recently, fecal microbiome transplantation (FMT). In this opinion paper, we give a perspective on the current status of application of multi-omics technologies in the analysis of host-microbiota interactions. The aim of this paper was to highlight the strengths of metaproteomics, which integrates with and often relies on other approaches.
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Affiliation(s)
- Oleg Karaduta
- Department of Biochemistry and Molecular Biology, UAMS, Little Rock, AR 72205, USA;
- Correspondence: ; Tel.: +1-501-251-5381
| | | | - Boris Zybailov
- Department of Biochemistry and Molecular Biology, UAMS, Little Rock, AR 72205, USA;
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38
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Bakochi A, Mohanty T, Pyl PT, Gueto-Tettay CA, Malmström L, Linder A, Malmström J. Cerebrospinal fluid proteome maps detect pathogen-specific host response patterns in meningitis. eLife 2021; 10:64159. [PMID: 33821792 PMCID: PMC8043743 DOI: 10.7554/elife.64159] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 04/04/2021] [Indexed: 12/21/2022] Open
Abstract
Meningitis is a potentially life-threatening infection characterized by the inflammation of the leptomeningeal membranes. Many different viral and bacterial pathogens can cause meningitis, with differences in mortality rates, risk of developing neurological sequelae, and treatment options. Here, we constructed a compendium of digital cerebrospinal fluid (CSF) proteome maps to define pathogen-specific host response patterns in meningitis. The results revealed a drastic and pathogen-type specific influx of tissue-, cell-, and plasma proteins in the CSF, where, in particular, a large increase of neutrophil-derived proteins in the CSF correlated with acute bacterial meningitis. Additionally, both acute bacterial and viral meningitis result in marked reduction of brain-enriched proteins. Generation of a multiprotein LASSO regression model resulted in an 18-protein panel of cell- and tissue-associated proteins capable of classifying acute bacterial meningitis and viral meningitis. The same protein panel also enabled classification of tick-borne encephalitis, a subgroup of viral meningitis, with high sensitivity and specificity. The work provides insights into pathogen-specific host response patterns in CSF from different disease etiologies to support future classification of pathogen type based on host response patterns in meningitis.
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Affiliation(s)
- Anahita Bakochi
- Department of Clinical Sciences, Division of Infection Medicine, Lund University, Lund, Sweden
| | - Tirthankar Mohanty
- Department of Clinical Sciences, Division of Infection Medicine, Lund University, Lund, Sweden
| | - Paul Theodor Pyl
- Division of Surgery, Oncology, and Pathology, Department of Clinical Sciences, Biomedical Center, Lund University, Lund, Sweden
| | | | - Lars Malmström
- Department of Clinical Sciences, Division of Infection Medicine, Lund University, Lund, Sweden
| | - Adam Linder
- Department of Clinical Sciences, Division of Infection Medicine, Lund University, Lund, Sweden
| | - Johan Malmström
- Department of Clinical Sciences, Division of Infection Medicine, Lund University, Lund, Sweden
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39
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Ahn SB, Kamath KS, Mohamedali A, Noor Z, Wu JX, Pascovici D, Adhikari S, Cheruku HR, Guillemin GJ, McKay MJ, Nice EC, Baker MS. Use of a Recombinant Biomarker Protein DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins. J Proteome Res 2021; 20:2374-2389. [PMID: 33752330 DOI: 10.1021/acs.jproteome.0c00898] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Credible detection and quantification of low abundance proteins from human blood plasma is a major challenge in precision medicine biomarker discovery when using mass spectrometry (MS). In this proof-of-concept study, we employed a mixture of selected recombinant proteins in DDA libraries to subsequently identify (not quantify) cancer-associated low abundance plasma proteins using SWATH/DIA. The exemplar DDA recombinant protein spectral library (rPSL) was derived from tryptic digestion of 36 recombinant human proteins that had been previously implicated as possible cancer biomarkers from both our own and other studies. The rPSL was then used to identify proteins from nondepleted colorectal cancer (CRC) EDTA plasmas by SWATH-MS. Most (32/36) of the proteins used in the rPSL were reliably identified from CRC plasma samples, including 8 proteins (i.e., BTC, CXCL10, IL1B, IL6, ITGB6, TGFα, TNF, TP53) not previously detected using high-stringency protein inference MS according to PeptideAtlas. The rPSL SWATH-MS protocol was compared to DDA-MS using MARS-depleted and postdigestion peptide fractionated plasmas (here referred to as a human plasma DDA library). Of the 32 proteins identified using rPSL SWATH, only 12 could be identified using DDA-MS. The 20 additional proteins exclusively identified using the rPSL SWATH approach were almost exclusively lower abundance (i.e., <10 ng/mL) proteins. To mitigate justified FDR concerns, and to replicate a more typical library creation approach, the DDA rPSL library was merged with a human plasma DDA library and SWATH identification repeated using such a merged library. The majority (33/36) of the low abundance plasma proteins added from the rPSL were still able to be identified using such a merged library when high-stringency HPP Guidelines v3.0 protein inference criteria were applied to our data set. The MS data set has been deposited to ProteomeXchange Consortium via the PRIDE partner repository (PXD022361).
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Affiliation(s)
- Seong Beom Ahn
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Karthik S Kamath
- Australian Proteome Analysis Facility (APAF), Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Abidali Mohamedali
- Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Zainab Noor
- ProCan, Children's Medical Research Institute, The University of Sydney, Westmead, Newtown, NSW 2042, Australia
| | - Jemma X Wu
- Australian Proteome Analysis Facility (APAF), Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Dana Pascovici
- Australian Proteome Analysis Facility (APAF), Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Subash Adhikari
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Harish R Cheruku
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Gilles J Guillemin
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Matthew J McKay
- Australian Proteome Analysis Facility (APAF), Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Edouard C Nice
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, VIC 3800, Australia
| | - Mark S Baker
- Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Macquarie University, Macquarie Park, NSW 2109, Australia
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40
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Moyer TB, Parsley NC, Sadecki PW, Schug WJ, Hicks LM. Leveraging orthogonal mass spectrometry based strategies for comprehensive sequencing and characterization of ribosomal antimicrobial peptide natural products. Nat Prod Rep 2021; 38:489-509. [PMID: 32929442 PMCID: PMC7956910 DOI: 10.1039/d0np00046a] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Covering: Up to July 2020Ribosomal antimicrobial peptide (AMP) natural products, also known as ribosomally synthesized and post-translationally modified peptides (RiPPs) or host defense peptides, demonstrate potent bioactivities and impressive complexity that complicate molecular and biological characterization. Tandem mass spectrometry (MS) has rapidly accelerated bioactive peptide sequencing efforts, yet standard workflows insufficiently address intrinsic AMP diversity. Herein, orthogonal approaches to accelerate comprehensive and accurate molecular characterization without the need for prior isolation are reviewed. Chemical derivatization, proteolysis (enzymatic and chemical cleavage), multistage MS fragmentation, and separation (liquid chromatography and ion mobility) strategies can provide complementary amino acid composition and post-translational modification data to constrain sequence solutions. Examination of two complex case studies, gomesin and styelin D, highlights the practical implementation of the proposed approaches. Finally, we emphasize the importance of heterogeneous AMP peptidoforms that confer varying biological function, an area that warrants significant further development.
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Affiliation(s)
- Tessa B Moyer
- Department of Chemistry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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41
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Li J, Zhan X. Mass spectrometry-based proteomics analyses of post-translational modifications and proteoforms in human pituitary adenomas. BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS 2020; 1869:140584. [PMID: 33321259 DOI: 10.1016/j.bbapap.2020.140584] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 12/06/2020] [Accepted: 12/08/2020] [Indexed: 12/13/2022]
Abstract
Pituitary adenoma (PA) is a common intracranial neoplasm, which affects the hypothalamus-pituitary-target organ axis systems, and is hazardous to human health. Post-translational modifications (PTMs), including phosphorylation, ubiquitination, nitration, and sumoylation, are vitally important in the PA pathogenesis. The large-scale analysis of PTMs could provide a global view of molecular mechanisms for PA. Proteoforms, which are used to define various protein structural and functional forms originated from the same gene, are the future direction of proteomics research. The global studies of different proteoforms and PTMs of hypophyseal hormones such as growth hormone (GH) and prolactin (PRL) and the proportion change of different GH proteoforms or PRL proteoforms in human pituitary tissue could provide new insights into the clinical value of pituitary hormones in PAs. Multiple quantitative proteomics methods, including mass spectrometry (MS)-based label-free and stable isotope-labeled strategies in combination with different PTM-peptide enrichment methods such as TiO2 enrichment of tryptic phosphopeptides and antibody enrichment of other PTM-peptides increase the feasibility for researchers to study PA proteomes. This article reviews the research status of PTMs and proteoforms in PAs, including the enrichment method, technical limitation, quantitative proteomics strategies, and the future perspectives, to achieve the goals of in-depth understanding its molecular pathogenesis, and discovering effective biomarkers and clinical therapeutic targets for predictive, preventive, and personalized treatment of PA patients.
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Affiliation(s)
- Jiajia Li
- University Creative Research Initiatives Center, Shandong First Medical University, 6699 Qingdao Road, Jinan, Shandong 250117, P. R. China; Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 P. R. China; State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, PR China
| | - Xianquan Zhan
- University Creative Research Initiatives Center, Shandong First Medical University, 6699 Qingdao Road, Jinan, Shandong 250117, P. R. China; Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008 P. R. China; State Local Joint Engineering Laboratory for Anticancer Drugs, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, PR China; Department of Oncology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, PR China; National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan 410008, PR China.
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42
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Buric F, Zrimec J, Zelezniak A. Parallel Factor Analysis Enables Quantification and Identification of Highly Convolved Data-Independent-Acquired Protein Spectra. PATTERNS (NEW YORK, N.Y.) 2020; 1:100137. [PMID: 33336195 PMCID: PMC7733873 DOI: 10.1016/j.patter.2020.100137] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 09/14/2020] [Accepted: 10/12/2020] [Indexed: 11/26/2022]
Abstract
High-throughput data-independent acquisition (DIA) is the method of choice for quantitative proteomics, combining the best practices of targeted and shotgun approaches. The resultant DIA spectra are, however, highly convolved and with no direct precursor-fragment correspondence, complicating biological sample analysis. Here, we present CANDIA (canonical decomposition of data-independent-acquired spectra), a GPU-powered unsupervised multiway factor analysis framework that deconvolves multispectral scans to individual analyte spectra, chromatographic profiles, and sample abundances, using parallel factor analysis. The deconvolved spectra can be annotated with traditional database search engines or used as high-quality input for de novo sequencing methods. We demonstrate that spectral libraries generated with CANDIA substantially reduce the false discovery rate underlying the validation of spectral quantification. CANDIA covers up to 33 times more total ion current than library-based approaches, which typically use less than 5% of total recorded ions, thus allowing quantification and identification of signals from unexplored DIA spectra. Conventional DIA spectral libraries cover less than 3% of a scan's total ion count CANDIA deconvolves peptide signals by leveraging all scan data CANDIA uses GPUs to enable tensor algebra on massive DIA mass spectrometry data CANDIA output enables high-confidence and precise quantitative proteomics
The latest high-throughput mass spectrometry-based technologies can record virtually all molecules from complex biological samples, providing a holistic picture of proteomes in cells and tissues and enabling an evaluation of the overall status of a person's health. However, current best practices are still only scratching the surface of the wealth of available information obtained from the massive proteome datasets, and efficient novel data-driven strategies are needed. Powered by advances in GPU hardware and open-source machine-learning frameworks, we developed a data-driven approach, CANDIA, which disassembles highly complex proteomics data into the elementary molecular signatures of the proteins in biological samples. Our work provides a performant and adaptable solution that complements existing mass spectrometry techniques. As the central mathematical methods are generic, other scientific fields that are dealing with highly convolved datasets will benefit from this work.
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Affiliation(s)
- Filip Buric
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Gothenburg 412 96, Sweden
| | - Jan Zrimec
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Gothenburg 412 96, Sweden
| | - Aleksej Zelezniak
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, Gothenburg 412 96, Sweden.,Science for Life Laboratory, Tomtebodavägen 23a, Stockholm 171 65, Sweden
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43
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Wu C, Ba Q, Lu D, Li W, Salovska B, Hou P, Mueller T, Rosenberger G, Gao E, Di Y, Zhou H, Fornasiero EF, Liu Y. Global and Site-Specific Effect of Phosphorylation on Protein Turnover. Dev Cell 2020; 56:111-124.e6. [PMID: 33238149 DOI: 10.1016/j.devcel.2020.10.025] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 10/05/2020] [Accepted: 10/30/2020] [Indexed: 02/02/2023]
Abstract
To date, the effects of specific modification types and sites on protein lifetime have not been systematically illustrated. Here, we describe a proteomic method, DeltaSILAC, to quantitatively assess the impact of site-specific phosphorylation on the turnover of thousands of proteins in live cells. Based on the accurate and reproducible mass spectrometry-based method, a pulse labeling approach using stable isotope-labeled amino acids in cells (pSILAC), phosphoproteomics, and a unique peptide-level matching strategy, our DeltaSILAC profiling revealed a global, unexpected delaying effect of many phosphosites on protein turnover. We further found that phosphorylated sites accelerating protein turnover are functionally selected for cell fitness, enriched in Cyclin-dependent kinase substrates, and evolutionarily conserved, whereas the glutamic acids surrounding phosphosites significantly delay protein turnover. Our method represents a generalizable approach and provides a rich resource for prioritizing the effects of phosphorylation sites on protein lifetime in the context of cell signaling and disease biology.
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Affiliation(s)
- Chongde Wu
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Qian Ba
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Dayun Lu
- Department of Analytical Chemistry and CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Barbora Salovska
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA; Department of Genome Integrity, Institute of Molecular Genetics of the Czech Academy of Sciences, Prague, Czech Republic
| | - Pingfu Hou
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Torsten Mueller
- German Cancer Research Center, DKFZ, 69120 Heidelberg, Germany
| | | | - Erli Gao
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Yi Di
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Hu Zhou
- Department of Analytical Chemistry and CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Eugenio F Fornasiero
- Department of Neuro- and Sensory Physiology, University Medical Center Göttingen, 37073 Göttingen, Germany
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA; Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA.
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44
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Whetton AD, Preston GW, Abubeker S, Geifman N. Proteomics and Informatics for Understanding Phases and Identifying Biomarkers in COVID-19 Disease. J Proteome Res 2020; 19:4219-4232. [PMID: 32657586 PMCID: PMC7384384 DOI: 10.1021/acs.jproteome.0c00326] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Indexed: 02/07/2023]
Abstract
The emergence of novel coronavirus disease 2019 (COVID-19), caused by the SARS-CoV-2 coronavirus, has necessitated the urgent development of new diagnostic and therapeutic strategies. Rapid research and development, on an international scale, has already generated assays for detecting SARS-CoV-2 RNA and host immunoglobulins. However, the complexities of COVID-19 are such that fuller definitions of patient status, trajectory, sequelae, and responses to therapy are now required. There is accumulating evidence-from studies of both COVID-19 and the related disease SARS-that protein biomarkers could help to provide this definition. Proteins associated with blood coagulation (D-dimer), cell damage (lactate dehydrogenase), and the inflammatory response (e.g., C-reactive protein) have already been identified as possible predictors of COVID-19 severity or mortality. Proteomics technologies, with their ability to detect many proteins per analysis, have begun to extend these early findings. To be effective, proteomics strategies must include not only methods for comprehensive data acquisition (e.g., using mass spectrometry) but also informatics approaches via which to derive actionable information from large data sets. Here we review applications of proteomics to COVID-19 and SARS and outline how pipelines involving technologies such as artificial intelligence could be of value for research on these diseases.
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Affiliation(s)
- Anthony D. Whetton
- Stoller
Biomarker Discovery Centre, Faculty of Biology Medicine and Health
(FBMH), University of Manchester, Manchester M20 4GJ, United Kingdom
- Stem
Cell and Leukaemia Proteomics Laboratory, Manchester Cancer Research
Centre, University of Manchester, Manchester M13 9PL, United Kingdom
- Manchester
National Institute for Health Biomedical Research Centre, Manchester M13 9WL, United Kingdom
| | - George W. Preston
- Stoller
Biomarker Discovery Centre, Faculty of Biology Medicine and Health
(FBMH), University of Manchester, Manchester M20 4GJ, United Kingdom
- Stem
Cell and Leukaemia Proteomics Laboratory, Manchester Cancer Research
Centre, University of Manchester, Manchester M13 9PL, United Kingdom
| | - Semira Abubeker
- Stoller
Biomarker Discovery Centre, Faculty of Biology Medicine and Health
(FBMH), University of Manchester, Manchester M20 4GJ, United Kingdom
- Stem
Cell and Leukaemia Proteomics Laboratory, Manchester Cancer Research
Centre, University of Manchester, Manchester M13 9PL, United Kingdom
| | - Nophar Geifman
- Centre
for Health Informatics, FBMH, University
of Manchester, Manchester M13 9PL, United Kingdom
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45
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A comprehensive overview of proteomics approach for COVID 19: new perspectives in target therapy strategies. ACTA ACUST UNITED AC 2020; 11:223-232. [PMID: 33162722 PMCID: PMC7605460 DOI: 10.1007/s42485-020-00052-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Revised: 10/09/2020] [Accepted: 10/17/2020] [Indexed: 12/24/2022]
Abstract
World Health Organisation declared COVID-19 a pandemic on March 11, 2020. It was temporarily named as 2019-nCoV then subsequently named as COVID-19 virus. A coronavirus is a group of viruses, known to be zoonotic, causing illness ranging from acute to mild respiratory infections. These are spherical or pleomorphic enveloped particles containing positive sense RNA. The virus enters host cells, its uncoated genetic material transcribes, and translates. Since it has started spreading rapidly, protective measures have been taken all over the world. However, its transmission has been proved to be unstoppable and the absence of an effective drug makes the situation worse. The scientific community has gone all-out to discover and develop a possible vaccine or a competent antiviral drug. Other domains of biological sciences that promise effective results and target somewhat stable entities that are proteins, could be very useful in this time of crisis. Proteomics and metabolomics are the vast fields that are equipped with sufficient technologies to face this challenge. Various protein separation and identification techniques are available which facilitates the analysis of various types of interactions among proteins and their evolutionary lineages. The presented review aims at confronting the question: 'how proteomics can help in tackling SARS-CoV-2?' It deals with the role of upcoming proteome technology in these pandemic situations and discusses the proteomics approach towards the COVID-19 dilemma.
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46
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Zhu FY, Song YC, Zhang KL, Chen X, Chen MX. Quantifying Plant Dynamic Proteomes by SWATH-based Mass Spectrometry. TRENDS IN PLANT SCIENCE 2020; 25:1171-1172. [PMID: 32891562 DOI: 10.1016/j.tplants.2020.07.014] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 07/30/2020] [Indexed: 05/20/2023]
Affiliation(s)
- Fu-Yuan Zhu
- Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, Jiangsu Province, China.
| | - Yu-Chen Song
- Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, Jiangsu Province, China
| | - Kai-Lu Zhang
- Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, Jiangsu Province, China
| | - Xi Chen
- SpecAlly Life Technology Co., Ltd, Wuhan, China; Wuhan Institute of Biotechnology, Wuhan, China
| | - Mo-Xian Chen
- Co-Innovation Center for Sustainable Forestry in Southern China, College of Biology and the Environment, Nanjing Forestry University, Nanjing 210037, Jiangsu Province, China; Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
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47
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Wang D, Gan G, Chen X, Zhong CQ. QuantPipe: A User-Friendly Pipeline Software Tool for DIA Data Analysis Based on the OpenSWATH-PyProphet-TRIC Workflow. J Proteome Res 2020; 20:1096-1102. [PMID: 33091296 DOI: 10.1021/acs.jproteome.0c00704] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Targeted analysis of data-independent acquisition (DIA) mass spectrometry data requires elegant software tools and strict statistical control. OpenSWATH-PyProphet-TRIC is a widely used DIA data analysis workflow. The OpenSWATH-PyProphet-TRIC workflow is typically executed by running command lines. Here, we present QuantPipe, which is a graphic interface software tool based on the OpenSWATH-PyProphet-TRIC workflow. In addition to OpenSWATH-PyProphet-TRIC functions, QuantPipe can convert the spectral library to the assay library and output peptides and protein intensities. We demonstrated that QuantPipe can be used to analyze SWATH-MS data from TripleTOF 5600 and TripleTOF 6600, phospho-SWATH-MS data, DIA data from Orbitrap instrument, and diaPASEF data from TimsTOF Pro instrument. The executable files, user manual, and source code of QuantPipe are freely available at https://github.com/tachengxmu/QuantPipe/releases.
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Affiliation(s)
- Dazheng Wang
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen, P. R. China
| | - Guohong Gan
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen, P. R. China
| | - Xi Chen
- SpecAlly Life Technology Co., Ltd., Wuhan, P. R. China.,Medical Research Institute, Wuhan University, Wuhan, P. R. China
| | - Chuan-Qi Zhong
- State Key Laboratory of Cellular Stress Biology, Innovation Center for Cellular Signaling Network, School of Life Sciences, Xiamen University, Xiamen, P. R. China
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48
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Robinson AE, Binek A, Venkatraman V, Searle BC, Holewinski RJ, Rosenberger G, Parker SJ, Basisty N, Xie X, Lund PJ, Saxena G, Mato JM, Garcia BA, Schilling B, Lu SC, Van Eyk JE. Lysine and Arginine Protein Post-translational Modifications by Enhanced DIA Libraries: Quantification in Murine Liver Disease. J Proteome Res 2020; 19:4163-4178. [PMID: 32966080 DOI: 10.1021/acs.jproteome.0c00685] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Proteoforms containing post-translational modifications (PTMs) represent a degree of functional diversity only harnessed through analytically precise simultaneous quantification of multiple PTMs. Here we present a method to accurately differentiate an unmodified peptide from its PTM-containing counterpart through data-independent acquisition-mass spectrometry, leveraging small precursor mass windows to physically separate modified peptidoforms from each other during MS2 acquisition. We utilize a lysine and arginine PTM-enriched peptide assay library and site localization algorithm to simultaneously localize and quantify seven PTMs including mono-, di-, and trimethylation, acetylation, and succinylation in addition to total protein quantification in a single MS run without the need to enrich experimental samples. To evaluate biological relevance, this method was applied to liver lysate from differentially methylated nonalcoholic steatohepatitis (NASH) mouse models. We report that altered methylation and acetylation together with total protein changes drive the novel hypothesis of a regulatory function of PTMs in protein synthesis and mRNA stability in NASH.
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Affiliation(s)
- Aaron E Robinson
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
| | - Aleksandra Binek
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
| | - Vidya Venkatraman
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
| | - Brian C Searle
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Ronald J Holewinski
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
| | - George Rosenberger
- Department of Systems Biology, Columbia University, New York, New York 10027, United States
| | - Sarah J Parker
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
| | - Nathan Basisty
- Buck Institute for Research on Aging, Novato, California 94945, United States
| | - Xueshu Xie
- Buck Institute for Research on Aging, Novato, California 94945, United States
| | - Peder J Lund
- Department of Biochemistry and Biophysics, Epigenetics Institute, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104, United States
| | | | - José M Mato
- CIC bioGUNE, Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (Ciberehd), Technology Park of Bizkaia, 48160 Derio, Bizkaia, Spain
| | - Benjamin A Garcia
- Department of Biochemistry and Biophysics, Epigenetics Institute, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104, United States
| | - Birgit Schilling
- Buck Institute for Research on Aging, Novato, California 94945, United States
| | - Shelly C Lu
- Division of Digestive and Liver Diseases, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Jennifer E Van Eyk
- Advanced Clinical Biosystems Research Institute, The Smidt Heart Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
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49
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Wang S, Li W, Hu L, Cheng J, Yang H, Liu Y. NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses. Nucleic Acids Res 2020; 48:e83. [PMID: 32526036 PMCID: PMC7641313 DOI: 10.1093/nar/gkaa498] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Revised: 04/20/2020] [Accepted: 06/08/2020] [Indexed: 02/05/2023] Open
Abstract
Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.
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Affiliation(s)
- Shisheng Wang
- West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Wenxue Li
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA
| | - Liqiang Hu
- West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Jingqiu Cheng
- West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Hao Yang
- West China-Washington Mitochondria and Metabolism Research Center; Key Lab of Transplant Engineering and Immunology, MOH, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Yansheng Liu
- Yale Cancer Biology Institute, Yale University, West Haven, CT 06516, USA.,Department of Pharmacology, Yale University School of Medicine, New Haven, CT 06520, USA
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50
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Verhelst S, De Clerck L, Willems S, Van Puyvelde B, Daled S, Deforce D, Dhaenens M. Comprehensive histone epigenetics: A mass spectrometry based screening assay to measure epigenetic toxicity. MethodsX 2020; 7:101055. [PMID: 32995308 PMCID: PMC7508989 DOI: 10.1016/j.mex.2020.101055] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/02/2020] [Indexed: 01/23/2023] Open
Abstract
Evidence of the involvement of epigenetics in pathologies such as cancer, diabetes, and neurodegeneration has increased global interest in epigenetic modifications. For nearly thirty years, it has been known that cancer cells exhibit abnormal DNA methylation patterns. In contrast, the large-scale analysis of histone post-translational modifications (hPTMs) has lagged behind because classically, histone modification analysis has relied on site specific antibody-based techniques. Mass spectrometry (MS) is a technique that holds the promise to picture the histone code comprehensively in a single experiment. Therefore, we developed an MS-based method that is capable of tracking all possible hPTMs in an untargeted approach. In this way, trends in single and combinatorial hPTMs can be reported and enable prediction of the epigenetic toxicity of compounds. Moreover, this method is based on the use of human cells to provide preliminary data, thereby omitting the need to sacrifice laboratory animals. Improving the workflow and the user-friendliness in order to become a high throughput, easily applicable, toxicological screening assay is an ongoing effort. Still, this novel toxicoepigenetic assay and the data it generates holds great potential for, among others, pharmaceutical industry, food science, clinical diagnostics and, environmental toxicity screening. •There is a growing interest in epigenetic modifications, and more specifically in histone post-translational modifications (hPTMs).•We describe an MS-based workflow that is capable of tracking all possible hPTMs in an untargeted approach that makes use of human cells.•Improving the workflow and the user-friendliness in order to become a high throughput, easily applicable, toxicological screening assay is an ongoing effort.
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Key Words
- AUC, area under the curve
- DDA, data-dependent acquisition
- DIA, data-independent acquisition
- DTT, dithiothreitol
- Drug safety
- FA, formic acid
- FDR, false discovery rate
- GABA, gamma-aminobutyric acid
- GRX, gingisrex
- HAT, histone acetyltransferase
- HDACi, histone deacetylase inhibitor
- HLB, hypotonic lysis buffer
- HPLC, high-performance liquid chromatography
- Histone post-translational modifications
- K, Lysine
- LC-MS/MS
- M, Methionine
- MS, Mass spectrometry
- MS/MS, tandem mass spectrometry
- N, asparagine
- PBS, phosphate buffered saline
- Pharmacoepigenetics
- Proteomics
- Q, glutamine
- R, arginine
- RA, relative abundance
- RP, reversed phase
- RT, room temperature
- S, serine
- SWATH, sequential window acquisition of all theoretical fragment ion spectra
- T, threonine
- TEAB, triethylammonium bicarbonate
- Toxicoepigenetics
- VPA, valproic acid
- Y, tyrosine
- hESC, human embryonic stem cell
- hPTM, histone post-translational modification
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Affiliation(s)
- Sigrid Verhelst
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium
| | - Laura De Clerck
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium
| | - Sander Willems
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium
| | - Bart Van Puyvelde
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium
| | - Simon Daled
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium
| | - Dieter Deforce
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium
| | - Maarten Dhaenens
- ProGenTomics, Laboratory of Pharmaceutical Biotechnology, Ghent University, Ottergemsesteenweg 460, 9000, Ghent, Belgium
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