1
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Deutsch EW, Kok LW, Mudge JM, Ruiz-Orera J, Fierro-Monti I, Sun Z, Abelin JG, Alba MM, Aspden JL, Bazzini AA, Bruford EA, Brunet MA, Calviello L, Carr SA, Carvunis AR, Chothani S, Clauwaert J, Dean K, Faridi P, Frankish A, Hubner N, Ingolia NT, Magrane M, Martin MJ, Martinez TF, Menschaert G, Ohler U, Orchard S, Rackham O, Roucou X, Slavoff SA, Valen E, Wacholder A, Weissman JS, Wu W, Xie Z, Choudhary J, Bassani-Sternberg M, Vizcaíno JA, Ternette N, Moritz RL, Prensner JR, van Heesch S. High-quality peptide evidence for annotating non-canonical open reading frames as human proteins. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.09.612016. [PMID: 39314370 PMCID: PMC11419116 DOI: 10.1101/2024.09.09.612016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.
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2
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van Wijk KJ, Leppert T, Sun Z, Guzchenko I, Debley E, Sauermann G, Routray P, Mendoza L, Sun Q, Deutsch EW. The Zea mays PeptideAtlas: A New Maize Community Resource. J Proteome Res 2024; 23:3984-4004. [PMID: 39101213 DOI: 10.1021/acs.jproteome.4c00320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/06/2024]
Abstract
This study presents the Maize PeptideAtlas resource (www.peptideatlas.org/builds/maize) to help solve questions about the maize proteome. Publicly available raw tandem mass spectrometry (MS/MS) data for maize collected from ProteomeXchange were reanalyzed through a uniform processing and metadata annotation pipeline. These data are from a wide range of genetic backgrounds and many sample types and experimental conditions. The protein search space included different maize genome annotations for the B73 inbred line from MaizeGDB, UniProtKB, NCBI RefSeq, and for the W22 inbred line. 445 million MS/MS spectra were searched, of which 120 million were matched to 0.37 million distinct peptides. Peptides were matched to 66.2% of proteins in the most recent B73 nuclear genome annotation. Furthermore, most conserved plastid- and mitochondrial-encoded proteins (NCBI RefSeq annotations) were identified. Peptides and proteins identified in the other B73 genome annotations will improve maize genome annotation. We also illustrate the high-confidence detection of unique W22 proteins. N-terminal acetylation, phosphorylation, ubiquitination, and three lysine acylations (K-acetyl, K-malonyl, and K-hydroxyisobutyryl) were identified and can be inspected through a PTM viewer in PeptideAtlas. All matched MS/MS-derived peptide data are linked to spectral, technical, and biological metadata. This new PeptideAtlas is integrated in MaizeGDB with a peptide track in JBrowse.
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Affiliation(s)
- Klaas J van Wijk
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Tami Leppert
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Zhi Sun
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Isabell Guzchenko
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Erica Debley
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Georgia Sauermann
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Pratyush Routray
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Luis Mendoza
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Qi Sun
- Computational Biology Service Unit, Cornell University, Ithaca, New York 14853, United States
| | - Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
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3
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Jiang Y, Rex DA, Schuster D, Neely BA, Rosano GL, Volkmar N, Momenzadeh A, Peters-Clarke TM, Egbert SB, Kreimer S, Doud EH, Crook OM, Yadav AK, Vanuopadath M, Hegeman AD, Mayta M, Duboff AG, Riley NM, Moritz RL, Meyer JG. Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry. ACS MEASUREMENT SCIENCE AU 2024; 4:338-417. [PMID: 39193565 PMCID: PMC11348894 DOI: 10.1021/acsmeasuresciau.3c00068] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Revised: 05/03/2024] [Accepted: 05/03/2024] [Indexed: 08/29/2024]
Abstract
Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.
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Affiliation(s)
- Yuming Jiang
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Devasahayam Arokia
Balaya Rex
- Center for
Systems Biology and Molecular Medicine, Yenepoya Research Centre, Yenepoya (Deemed to be University), Mangalore 575018, India
| | - Dina Schuster
- Department
of Biology, Institute of Molecular Systems
Biology, ETH Zurich, Zurich 8093, Switzerland
- Department
of Biology, Institute of Molecular Biology
and Biophysics, ETH Zurich, Zurich 8093, Switzerland
- Laboratory
of Biomolecular Research, Division of Biology and Chemistry, Paul Scherrer Institute, Villigen 5232, Switzerland
| | - Benjamin A. Neely
- Chemical
Sciences Division, National Institute of
Standards and Technology, NIST, Charleston, South Carolina 29412, United States
| | - Germán L. Rosano
- Mass
Spectrometry
Unit, Institute of Molecular and Cellular
Biology of Rosario, Rosario, 2000 Argentina
| | - Norbert Volkmar
- Department
of Biology, Institute of Molecular Systems
Biology, ETH Zurich, Zurich 8093, Switzerland
| | - Amanda Momenzadeh
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Trenton M. Peters-Clarke
- Department
of Pharmaceutical Chemistry, University
of California—San Francisco, San Francisco, California, 94158, United States
| | - Susan B. Egbert
- Department
of Chemistry, University of Manitoba, Winnipeg, Manitoba, R3T 2N2 Canada
| | - Simion Kreimer
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Emma H. Doud
- Center
for Proteome Analysis, Indiana University
School of Medicine, Indianapolis, Indiana, 46202-3082, United States
| | - Oliver M. Crook
- Oxford
Protein Informatics Group, Department of Statistics, University of Oxford, Oxford OX1 3LB, United
Kingdom
| | - Amit Kumar Yadav
- Translational
Health Science and Technology Institute, NCR Biotech Science Cluster 3rd Milestone Faridabad-Gurgaon
Expressway, Faridabad, Haryana 121001, India
| | | | - Adrian D. Hegeman
- Departments
of Horticultural Science and Plant and Microbial Biology, University of Minnesota, Twin Cities, Minnesota 55108, United States
| | - Martín
L. Mayta
- School
of Medicine and Health Sciences, Center for Health Sciences Research, Universidad Adventista del Plata, Libertador San Martin 3103, Argentina
- Molecular
Biology Department, School of Pharmacy and Biochemistry, Universidad Nacional de Rosario, Rosario 2000, Argentina
| | - Anna G. Duboff
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Nicholas M. Riley
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Robert L. Moritz
- Institute
for Systems biology, Seattle, Washington 98109, United States
| | - Jesse G. Meyer
- Department
of Computational Biomedicine, Cedars Sinai
Medical Center, Los Angeles, California 90048, United States
- Smidt Heart
Institute, Cedars Sinai Medical Center, Los Angeles, California 90048, United States
- Advanced
Clinical Biosystems Research Institute, Cedars Sinai Medical Center, Los
Angeles, California 90048, United States
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4
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Cai J, Yun Q, Zhang CY, Wang Z, Hinshaw SM, Zhou H, Suhandynata RT. Concatemer Assisted Stoichiometry Analysis (CASA): targeted mass spectrometry for protein quantification. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.26.605382. [PMID: 39091769 PMCID: PMC11291133 DOI: 10.1101/2024.07.26.605382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Large multi-protein machines are central to multiple biological processes. However, stoichiometric determination of protein complex subunits in their native states presents a significant challenge. This study addresses the limitations of current tools in accuracy and precision by introducing concatemer-assisted stoichiometry analysis (CASA). CASA leverages stable isotope-labeled concatemers and liquid chromatography parallel reaction monitoring mass spectrometry (LC-PRM-MS) to achieve robust quantification of proteins with sub-femtomole sensitivity. As a proof-of-concept, CASA was applied to study budding yeast kinetochores. Stoichiometries were determined for ex vivo reconstituted kinetochore components, including the canonical H3 nucleosomes, centromeric (Cse4CENP-A) nucleosomes, centromere proximal factors (Cbf1 and CBF3 complex), inner kinetochore proteins (Mif2CENP-C, Ctf19CCAN complex), and outer kinetochore proteins (KMN network). Absolute quantification by CASA revealed Cse4CENP-A as a cell-cycle controlled limiting factor for kinetochore assembly. These findings demonstrate that CASA is applicable for stoichiometry analysis of multi-protein assemblies.
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Affiliation(s)
- Jiaxi Cai
- Department of Cellular and Molecular Medicine, University of California, San Diego, California
- Department of Bioengineering, University of California, San Diego, California
| | - Quan Yun
- Department of Cellular and Molecular Medicine, University of California, San Diego, California
| | - Cindy Yuxuan Zhang
- Department of Cellular and Molecular Medicine, University of California, San Diego, California
| | - Ziyi Wang
- Department of Cellular and Molecular Medicine, University of California, San Diego, California
| | - Stephen M. Hinshaw
- Department of Chemical and Systems Biology, Stanford University, Palo Alto, California
| | - Huilin Zhou
- Department of Cellular and Molecular Medicine, University of California, San Diego, California
- Department of Bioengineering, University of California, San Diego, California
- Moores Cancer Center, University of California, San Diego, California
| | - Raymond T. Suhandynata
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, California
- Department of Pathology, University of California, San Diego, California
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5
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Hansen J, Kunert C, Münstermann H, Raezke KP, Seifert S. Application of untargeted liquid chromatography-mass spectrometry to routine analysis of food using three-dimensional bucketing and machine learning. Sci Rep 2024; 14:16594. [PMID: 39026016 PMCID: PMC11258308 DOI: 10.1038/s41598-024-67459-y] [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: 11/27/2023] [Accepted: 07/11/2024] [Indexed: 07/20/2024] Open
Abstract
For the detection of food adulteration, sensitive and reproducible analytical methods are required. Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is a highly sensitive method that can be used to obtain analytical fingerprints consisting of a variety of different components. Since the comparability of measurements carried out with different devices and at different times is not given, specific adulterants are usually detected in targeted analyses instead of analyzing the entire fingerprint. However, this comprehensive analysis is desirable in order to stay ahead in the race against food fraudsters, who are constantly adapting their adulterations to the latest state of the art in analytics. We have developed and optimized an approach that enables the separate processing of untargeted LC‑HRMS data obtained from different devices and at different times. We demonstrate this by the successful determination of the geographical origin of honey samples using a random forest model. We then show that this approach can be applied to develop a continuously learning classification model and our final model, based on data from 835 samples, achieves a classification accuracy of 94% for 126 test samples from 6 different countries.
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Affiliation(s)
- Jule Hansen
- Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146, Hamburg, Germany
| | - Christof Kunert
- Eurofins Food Integrity Control Services GmbH, Berliner Str. 2, 27721, Ritterhude, Germany
| | - Hella Münstermann
- Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146, Hamburg, Germany
| | - Kurt-Peter Raezke
- Eurofins Food Integrity Control Services GmbH, Berliner Str. 2, 27721, Ritterhude, Germany
| | - Stephan Seifert
- Institute of Food Chemistry, Hamburg School of Food Science, University of Hamburg, Grindelallee 117, 20146, Hamburg, Germany.
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6
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Omenn GS, Lane L, Overall CM, Lindskog C, Pineau C, Packer NH, Cristea IM, Weintraub ST, Orchard S, Roehrl MHA, Nice E, Guo T, Van Eyk JE, Liu S, Bandeira N, Aebersold R, Moritz RL, Deutsch EW. The 2023 Report on the Proteome from the HUPO Human Proteome Project. J Proteome Res 2024; 23:532-549. [PMID: 38232391 PMCID: PMC11026053 DOI: 10.1021/acs.jproteome.3c00591] [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] [Indexed: 01/19/2024]
Abstract
Since 2010, the Human Proteome Project (HPP), the flagship initiative of the Human Proteome Organization (HUPO), has pursued two goals: (1) to credibly identify the protein parts list and (2) to make proteomics an integral part of multiomics studies of human health and disease. The HPP relies on international collaboration, data sharing, standardized reanalysis of MS data sets by PeptideAtlas and MassIVE-KB using HPP Guidelines for quality assurance, integration and curation of MS and non-MS protein data by neXtProt, plus extensive use of antibody profiling carried out by the Human Protein Atlas. According to the neXtProt release 2023-04-18, protein expression has now been credibly detected (PE1) for 18,397 of the 19,778 neXtProt predicted proteins coded in the human genome (93%). Of these PE1 proteins, 17,453 were detected with mass spectrometry (MS) in accordance with HPP Guidelines and 944 by a variety of non-MS methods. The number of neXtProt PE2, PE3, and PE4 missing proteins now stands at 1381. Achieving the unambiguous identification of 93% of predicted proteins encoded from across all chromosomes represents remarkable experimental progress on the Human Proteome parts list. Meanwhile, there are several categories of predicted proteins that have proved resistant to detection regardless of protein-based methods used. Additionally there are some PE1-4 proteins that probably should be reclassified to PE5, specifically 21 LINC entries and ∼30 HERV entries; these are being addressed in the present year. Applying proteomics in a wide array of biological and clinical studies ensures integration with other omics platforms as reported by the Biology and Disease-driven HPP teams and the antibody and pathology resource pillars. Current progress has positioned the HPP to transition to its Grand Challenge Project focused on determining the primary function(s) of every protein itself and in networks and pathways within the context of human health and disease.
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Affiliation(s)
- Gilbert S. Omenn
- University of Michigan, Ann Arbor, Michigan 48109, United States
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics and University of Geneva, 1015 Lausanne, Switzerland
| | - Christopher M. Overall
- University of British Columbia, Vancouver, BC V6T 1Z4, Canada, Yonsei University Republic of Korea
| | | | - Charles Pineau
- University Rennes, Inserm U1085, Irset, 35042 Rennes, France
| | | | | | - Susan T. Weintraub
- University of Texas Health Science Center-San Antonio, San Antonio, Texas 78229-3900, United States
| | | | - Michael H. A. Roehrl
- Department of Pathology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, United States
| | | | - Tiannan Guo
- Westlake Center for Intelligent Proteomics, Westlake Laboratory, Westlake University, Hangzhou 310024, Zhejiang Province, China
| | - Jennifer E. Van Eyk
- Advanced Clinical Biosystems Research Institute, Smidt Heart Institute, Cedars-Sinai Medical Center, 127 South San Vicente Boulevard, Pavilion, 9th Floor, Los Angeles, CA, 90048, United States
| | - Siqi Liu
- BGI Group, Shenzhen 518083, China
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, CA, 92093, United States
| | - Ruedi Aebersold
- Institute of Molecular Systems Biology in ETH Zurich, 8092 Zurich, Switzerland
- University of Zurich, 8092 Zurich, Switzerland
| | - Robert L. Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W. Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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7
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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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8
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Lee KT, Pranoto IKA, Kim SY, Choi HJ, To NB, Chae H, Lee JY, Kim JE, Kwon YV, Nam JW. Comparative interactome analysis of α-arrestin families in human and Drosophila. eLife 2024; 12:RP88328. [PMID: 38270169 PMCID: PMC10945707 DOI: 10.7554/elife.88328] [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] [Indexed: 01/26/2024] Open
Abstract
The α-arrestins form a large family of evolutionally conserved modulators that control diverse signaling pathways, including both G-protein-coupled receptor (GPCR)-mediated and non-GPCR-mediated pathways, across eukaryotes. However, unlike β-arrestins, only a few α-arrestin targets and functions have been characterized. Here, using affinity purification and mass spectrometry, we constructed interactomes for 6 human and 12 Drosophila α-arrestins. The resulting high-confidence interactomes comprised 307 and 467 prey proteins in human and Drosophila, respectively. A comparative analysis of these interactomes predicted not only conserved binding partners, such as motor proteins, proteases, ubiquitin ligases, RNA splicing factors, and GTPase-activating proteins, but also those specific to mammals, such as histone modifiers and the subunits of V-type ATPase. Given the manifestation of the interaction between the human α-arrestin, TXNIP, and the histone-modifying enzymes, including HDAC2, we undertook a global analysis of transcription signals and chromatin structures that were affected by TXNIP knockdown. We found that TXNIP activated targets by blocking HDAC2 recruitment to targets, a result that was validated by chromatin immunoprecipitation assays. Additionally, the interactome for an uncharacterized human α-arrestin ARRDC5 uncovered multiple components in the V-type ATPase, which plays a key role in bone resorption by osteoclasts. Our study presents conserved and species-specific protein-protein interaction maps for α-arrestins, which provide a valuable resource for interrogating their cellular functions for both basic and clinical research.
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Affiliation(s)
- Kyung-Tae Lee
- Department of Life Science, College of Natural Sciences, Hanyang UniversitySeoulRepublic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang UniversitySeoulRepublic of Korea
| | - Inez KA Pranoto
- Department of Biochemistry, University of WashingtonSeattleUnited States
| | - Soon-Young Kim
- Department of Molecular Medicine, Cell and Matrix Research Institute, School of Medicine, Kyungpook National UniversityDaeguRepublic of Korea
| | - Hee-Joo Choi
- Bio-BigData Center, Hanyang Institute for Bioscience and Biotechnology, Hanyang UniversitySeoulRepublic of Korea
- Department of Pathology, College of Medicine, Hanyang UniversitySeoulRepublic of Korea
- Hanyang Biomedical Research Institute, Hanyang UniversitySeoulRepublic of Korea
| | - Ngoc Bao To
- Department of Life Science, College of Natural Sciences, Hanyang UniversitySeoulRepublic of Korea
| | - Hansong Chae
- Department of Life Science, College of Natural Sciences, Hanyang UniversitySeoulRepublic of Korea
| | - Jeong-Yeon Lee
- Bio-BigData Center, Hanyang Institute for Bioscience and Biotechnology, Hanyang UniversitySeoulRepublic of Korea
- Department of Pathology, College of Medicine, Hanyang UniversitySeoulRepublic of Korea
| | - Jung-Eun Kim
- Department of Molecular Medicine, Cell and Matrix Research Institute, School of Medicine, Kyungpook National UniversityDaeguRepublic of Korea
| | - Young V Kwon
- Department of Biochemistry, University of WashingtonSeattleUnited States
| | - Jin-Wu Nam
- Department of Life Science, College of Natural Sciences, Hanyang UniversitySeoulRepublic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang UniversitySeoulRepublic of Korea
- Bio-BigData Center, Hanyang Institute for Bioscience and Biotechnology, Hanyang UniversitySeoulRepublic of Korea
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9
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van Wijk KJ, Leppert T, Sun Z, Kearly A, Li M, Mendoza L, Guzchenko I, Debley E, Sauermann G, Routray P, Malhotra S, Nelson A, Sun Q, Deutsch EW. Detection of the Arabidopsis Proteome and Its Post-translational Modifications and the Nature of the Unobserved (Dark) Proteome in PeptideAtlas. J Proteome Res 2024; 23:185-214. [PMID: 38104260 DOI: 10.1021/acs.jproteome.3c00536] [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] [Indexed: 12/19/2023]
Abstract
This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource (build 2023-10) providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected post-translational modifications (PTMs), and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18,267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins, and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome: the "dark" proteome. This dark proteome is highly enriched for E3 ligases, transcription factors, and for certain (e.g., CLE, IDA, PSY) but not other (e.g., THIONIN, CAP) signaling peptides families. A machine learning model trained on RNA expression data and protein properties predicts the probability that proteins will be detected. The model aids in discovery of proteins with short half-life (e.g., SIG1,3 and ERF-VII TFs) and for developing strategies to identify the missing proteins. PeptideAtlas is linked to TAIR, tracks in JBrowse, and several other community proteomics resources.
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Affiliation(s)
- Klaas J van Wijk
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Tami Leppert
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Zhi Sun
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Alyssa Kearly
- Boyce Thompson Institute, Ithaca, New York 14853, United States
| | - Margaret Li
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Luis Mendoza
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Isabell Guzchenko
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Erica Debley
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Georgia Sauermann
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Pratyush Routray
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, United States
| | - Sagunya Malhotra
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
| | - Andrew Nelson
- Boyce Thompson Institute, Ithaca, New York 14853, United States
| | - Qi Sun
- Computational Biology Service Unit, Cornell University, Ithaca, New York 14853, United States
| | - Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington 98109, United States
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10
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Mandal K, Wicaksono G, Yu C, Adams JJ, Hoopmann MR, Temple WC, Izgutdina A, Escobar BP, Gorelik M, Ihling CH, Nix MA, Naik A, Xie WH, Hübner J, Rollins LA, Reid SM, Ramos E, Kasap C, Steri V, Serrano JAC, Salangsang F, Phojanakong P, McMillan M, Gavallos V, Leavitt AD, Logan AC, Rooney CM, Eyquem J, Sinz A, Huang BJ, Stieglitz E, Smith CC, Moritz RL, Sidhu SS, Huang L, Wiita AP. Structural surfaceomics reveals an AML-specific conformation of integrin β 2 as a CAR T cellular therapy target. NATURE CANCER 2023; 4:1592-1609. [PMID: 37904046 PMCID: PMC10663162 DOI: 10.1038/s43018-023-00652-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 09/12/2023] [Indexed: 11/01/2023]
Abstract
Safely expanding indications for cellular therapies has been challenging given a lack of highly cancer-specific surface markers. Here we explore the hypothesis that tumor cells express cancer-specific surface protein conformations that are invisible to standard target discovery pipelines evaluating gene or protein expression, and these conformations can be identified and immunotherapeutically targeted. We term this strategy integrating cross-linking mass spectrometry with glycoprotein surface capture 'structural surfaceomics'. As a proof of principle, we apply this technology to acute myeloid leukemia (AML), a hematologic malignancy with dismal outcomes and no known optimal immunotherapy target. We identify the activated conformation of integrin β2 as a structurally defined, widely expressed AML-specific target. We develop and characterize recombinant antibodies to this protein conformation and show that chimeric antigen receptor T cells eliminate AML cells and patient-derived xenografts without notable toxicity toward normal hematopoietic cells. Our findings validate an AML conformation-specific target antigen and demonstrate a tool kit for applying these strategies more broadly.
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Affiliation(s)
- Kamal Mandal
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Gianina Wicaksono
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Clinton Yu
- Department of Physiology and Biophysics, University of California Irvine, Irvine, CA, USA
| | - Jarrett J Adams
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
| | | | - William C Temple
- Department of Pediatrics, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
- Department of Pediatrics, Division of Allergy, Immunology, and Bone Marrow Transplantation, University of California San Francisco, San Francisco, CA, USA
| | - Adila Izgutdina
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Bonell Patiño Escobar
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Maryna Gorelik
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
| | - Christian H Ihling
- Department of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Martin-Luther University Halle-Wittenberg, Halle, Germany
| | - Matthew A Nix
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Akul Naik
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - William H Xie
- UCSF/Gladstone Institute for Genomic Immunology, San Francisco, CA, USA
| | - Juwita Hübner
- Department of Pediatrics, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Lisa A Rollins
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston Methodist Hospital-Texas Children's Hospital, Houston, TX, USA
| | - Sandy M Reid
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston Methodist Hospital-Texas Children's Hospital, Houston, TX, USA
| | - Emilio Ramos
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Corynn Kasap
- Department of Medicine, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Veronica Steri
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Juan Antonio Camara Serrano
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Fernando Salangsang
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Paul Phojanakong
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Melanie McMillan
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Victor Gavallos
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Andrew D Leavitt
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA
| | - Aaron C Logan
- Department of Medicine, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Cliona M Rooney
- Center for Cell and Gene Therapy, Baylor College of Medicine, Houston Methodist Hospital-Texas Children's Hospital, Houston, TX, USA
| | - Justin Eyquem
- UCSF/Gladstone Institute for Genomic Immunology, San Francisco, CA, USA
- Department of Medicine, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Andrea Sinz
- Department of Pharmaceutical Chemistry and Bioanalytics, Institute of Pharmacy, Martin-Luther University Halle-Wittenberg, Halle, Germany
| | - Benjamin J Huang
- Department of Pediatrics, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
| | - Elliot Stieglitz
- Department of Pediatrics, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA
| | - Catherine C Smith
- Department of Medicine, Division of Hematology/Oncology, University of California San Francisco, San Francisco, CA, USA
| | | | - Sachdev S Sidhu
- The Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- School of Pharmacy, University of Waterloo, Kitchener, Ontario, Canada
| | - Lan Huang
- Department of Physiology and Biophysics, University of California Irvine, Irvine, CA, USA
| | - Arun P Wiita
- Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA.
- Helen Diller Family Comprehensive Cancer Center, University of California San Francisco, San Francisco, CA, USA.
- Department of Bioengineering and Therapeutic Sciences, University of California San Francisco, San Francisco, CA, USA.
- Chan Zuckerberg Biohub San Francisco, San Francisco, CA, USA.
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11
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van Wijk KJ, Leppert T, Sun Z, Kearly A, Li M, Mendoza L, Guzchenko I, Debley E, Sauermann G, Routray P, Malhotra S, Nelson A, Sun Q, Deutsch EW. Mapping the Arabidopsis thaliana proteome in PeptideAtlas and the nature of the unobserved (dark) proteome; strategies towards a complete proteome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.06.01.543322. [PMID: 37333403 PMCID: PMC10274743 DOI: 10.1101/2023.06.01.543322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
This study describes a new release of the Arabidopsis thaliana PeptideAtlas proteomics resource providing protein sequence coverage, matched mass spectrometry (MS) spectra, selected PTMs, and metadata. 70 million MS/MS spectra were matched to the Araport11 annotation, identifying ∼0.6 million unique peptides and 18267 proteins at the highest confidence level and 3396 lower confidence proteins, together representing 78.6% of the predicted proteome. Additional identified proteins not predicted in Araport11 should be considered for building the next Arabidopsis genome annotation. This release identified 5198 phosphorylated proteins, 668 ubiquitinated proteins, 3050 N-terminally acetylated proteins and 864 lysine-acetylated proteins and mapped their PTM sites. MS support was lacking for 21.4% (5896 proteins) of the predicted Araport11 proteome - the 'dark' proteome. This dark proteome is highly enriched for certain ( e.g. CLE, CEP, IDA, PSY) but not other ( e.g. THIONIN, CAP,) signaling peptides families, E3 ligases, TFs, and other proteins with unfavorable physicochemical properties. A machine learning model trained on RNA expression data and protein properties predicts the probability for proteins to be detected. The model aids in discovery of proteins with short-half life ( e.g. SIG1,3 and ERF-VII TFs) and completing the proteome. PeptideAtlas is linked to TAIR, JBrowse, PPDB, SUBA, UniProtKB and Plant PTM Viewer.
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12
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Peng J, Chan C, Meng F, Hu Y, Chen L, Lin G, Zhang S, Wheeler AR. Comparison of Database Searching Programs for the Analysis of Single-Cell Proteomics Data. J Proteome Res 2023; 22:1298-1308. [PMID: 36892105 DOI: 10.1021/acs.jproteome.2c00821] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/10/2023]
Abstract
Single-cell proteomics is emerging as an important subfield in the proteomics and mass spectrometry communities, with potential to reshape our understanding of cell development, cell differentiation, disease diagnosis, and the development of new therapies. Compared with significant advancements in the "hardware" that is used in single-cell proteomics, there has been little work comparing the effects of using different "software" packages to analyze single-cell proteomics datasets. To this end, seven popular proteomics programs were compared here, applying them to search three single-cell proteomics datasets generated by three different platforms. The results suggest that MSGF+, MSFragger, and Proteome Discoverer are generally more efficient in maximizing protein identifications, that MaxQuant is better suited for the identification of low-abundance proteins, that MSFragger is superior in elucidating peptide modifications, and that Mascot and X!Tandem are better for analyzing long peptides. Furthermore, an experiment with different loading amounts was carried out to investigate changes in identification results and to explore areas in which single-cell proteomics data analysis may be improved in the future. We propose that this comparative study may provide insight for experts and beginners alike operating in the emerging subfield of single-cell proteomics.
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Affiliation(s)
- Jiaxi Peng
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
| | - Calvin Chan
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada
| | - Fei Meng
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, Hunan 410000, China
| | - Yechen Hu
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
| | - Lingfan Chen
- Fujian Province New Drug Safety Evaluation Centre, Fujian Medical University, Fuzhou Fujian 350108, China
| | - Ge Lin
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, Hunan 410000, China.,Laboratory of Reproductive and Stem Cell Engineering, NHC Key Laboratory of Human Stem Cell and Reproductive Engineering, Central South University, Changsha, Hunan 410075, China
| | - Shen Zhang
- Clinical Research Center for Reproduction and Genetics in Hunan Province, Reproductive and Genetic Hospital of CITIC-XIANGYA, Changsha, Hunan 410000, China
| | - Aaron R Wheeler
- Department of Chemistry, University of Toronto, Toronto, Ontario M5S 3H6, Canada.,Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada.,Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario M5S 3G9, Canada
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13
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Deutsch EW, Mendoza L, Shteynberg DD, Hoopmann MR, Sun Z, Eng JK, Moritz RL. Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite. J Proteome Res 2023; 22:615-624. [PMID: 36648445 PMCID: PMC10166710 DOI: 10.1021/acs.jproteome.2c00624] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.
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Affiliation(s)
- Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Luis Mendoza
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | | | | | - Zhi Sun
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Jimmy K Eng
- Proteomics Resource, University of Washington, Seattle, Washington 98195, United States
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
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14
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Matlock AD, Vaibhav V, Holewinski R, Venkatraman V, Dardov V, Manalo DM, Shelley B, Ornelas L, Banuelos M, Mandefro B, Escalante-Chong R, Li J, Finkbeiner S, Fraenkel E, Rothstein J, Thompson L, Sareen D, Svendsen CN, Van Eyk JE. NeuroLINCS Proteomics: Defining human-derived iPSC proteomes and protein signatures of pluripotency. Sci Data 2023; 10:24. [PMID: 36631473 PMCID: PMC9834231 DOI: 10.1038/s41597-022-01687-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2021] [Accepted: 09/07/2022] [Indexed: 01/13/2023] Open
Abstract
The National Institute of Health (NIH) Library of integrated network-based cellular signatures (LINCS) program is premised on the generation of a publicly available data resource of cell-based biochemical responses or "signatures" to genetic or environmental perturbations. NeuroLINCS uses human inducible pluripotent stem cells (hiPSCs), derived from patients and healthy controls, and differentiated into motor neuron cell cultures. This multi-laboratory effort strives to establish i) robust multi-omic workflows for hiPSC and differentiated neuronal cultures, ii) public annotated data sets and iii) relevant and targetable biological pathways of spinal muscular atrophy (SMA) and amyotrophic lateral sclerosis (ALS). Here, we focus on the proteomics and the quality of the developed workflow of hiPSC lines from 6 individuals, though epigenomics and transcriptomics data are also publicly available. Known and commonly used markers representing 73 proteins were reproducibly quantified with consistent expression levels across all hiPSC lines. Data quality assessments, data levels and metadata of all 6 genetically diverse human iPSCs analysed by DIA-MS are parsable and available as a high-quality resource to the public.
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Affiliation(s)
- Andrea D Matlock
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Vineet Vaibhav
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Ronald Holewinski
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Vidya Venkatraman
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Victoria Dardov
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Danica-Mae Manalo
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Brandon Shelley
- NeuroLINCS, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Loren Ornelas
- NeuroLINCS, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Maria Banuelos
- NeuroLINCS, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Berhan Mandefro
- NeuroLINCS, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | | | - Jonathan Li
- NeuroLINCS, Department of Biological Engineering, MIT, Cambridge, MA, 02142, USA
| | - Steve Finkbeiner
- NeuroLINCS, Gladstone Institute of Neurological Disease and the Departments of Neurology and Physiology, University of California San Francisco, San Francisco, CA, 94158, USA
| | - Ernest Fraenkel
- NeuroLINCS, Department of Biological Engineering, MIT, Cambridge, MA, 02142, USA
| | - Jeffrey Rothstein
- NeuroLINCS, Department of Neuroscience, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Leslie Thompson
- NeuroLINCS, Departments of Psychiatry and Human Behaviour, Neurobiology and Behaviour and UCI MIND, University of California Irvine, Irvine, CA, 92697, USA
| | - Dhruv Sareen
- NeuroLINCS, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Clive N Svendsen
- NeuroLINCS, Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA
| | - Jennifer E Van Eyk
- NeuroLINCS, Advanced Clinical Biosystems Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, 90048, USA.
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15
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Contreras D, Garcia G, Jones MK, Martinez LE, Jayakarunakaran A, Gangalapudi V, Tang J, Wu Y, Zhao JJ, Chen Z, Ramaiah A, Tsui I, Kumar A, Nielsen-Saines K, Wang S, Arumugaswami V. Differential Susceptibility of Fetal Retinal Pigment Epithelial Cells, hiPSC- Retinal Stem Cells, and Retinal Organoids to Zika Virus Infection. Viruses 2023; 15:142. [PMID: 36680182 PMCID: PMC9864143 DOI: 10.3390/v15010142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 12/28/2022] [Accepted: 12/30/2022] [Indexed: 01/03/2023] Open
Abstract
Zika virus (ZIKV) causes microcephaly and congenital eye disease. The cellular and molecular basis of congenital ZIKV infection are not well understood. Here, we utilized a biologically relevant cell-based system of human fetal retinal pigment epithelial cells (FRPEs), hiPSC-derived retinal stem cells (iRSCs), and retinal organoids to investigate ZIKV-mediated ocular cell injury processes. Our data show that FRPEs were highly susceptible to ZIKV infection exhibiting increased apoptosis, whereas iRSCs showed reduced susceptibility. Detailed transcriptomics and proteomics analyses of infected FRPEs were performed. Nucleoside analogue drug treatment inhibited ZIKV replication. Retinal organoids were susceptible to ZIKV infection. The Asian genotype ZIKV exhibited higher infectivity, induced profound inflammatory response, and dysregulated transcription factors involved in retinal organoid differentiation. Collectively, our study shows that ZIKV affects ocular cells at different developmental stages resulting in cellular injury and death, further providing molecular insight into the pathogenesis of congenital eye disease.
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Affiliation(s)
- Deisy Contreras
- Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Gustavo Garcia
- Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA
| | - Melissa Kaye Jones
- Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Laura E. Martinez
- Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA
| | - Akshaya Jayakarunakaran
- Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA
| | | | - Jie Tang
- Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA
| | - Ying Wu
- Alpine BioTherapeutics Corporation, 11107 Roselle Street, Suite 210, San Diego, CA 92121, USA
| | - Jiagang J. Zhao
- Alpine BioTherapeutics Corporation, 11107 Roselle Street, Suite 210, San Diego, CA 92121, USA
| | - Zhaohui Chen
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Arunachalam Ramaiah
- Tata Institute for Genetics and Society, Center at inStem, Bangalore 560065, India
| | - Irena Tsui
- Retina Division, Department of Ophthalmology, University of California at Los Angeles, Los Angeles, CA 90095, USA
| | - Ashok Kumar
- Department of Ophthalmology, Visual and Anatomical Sciences, Wayne State University, Detroit, MI 48201, USA
| | | | - Shaomei Wang
- Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Vaithilingaraja Arumugaswami
- Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
- Department of Molecular and Medical Pharmacology, University of California, Los Angeles, CA 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, CA 90095, USA
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16
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Arendse LB, Murithi JM, Qahash T, Pasaje CFA, Godoy LC, Dey S, Gibhard L, Ghidelli-Disse S, Drewes G, Bantscheff M, Lafuente-Monasterio MJ, Fienberg S, Wambua L, Gachuhi S, Coertzen D, van der Watt M, Reader J, Aswat AS, Erlank E, Venter N, Mittal N, Luth MR, Ottilie S, Winzeler EA, Koekemoer LL, Birkholtz LM, Niles JC, Llinás M, Fidock DA, Chibale K. The anticancer human mTOR inhibitor sapanisertib potently inhibits multiple Plasmodium kinases and life cycle stages. Sci Transl Med 2022; 14:eabo7219. [PMID: 36260689 PMCID: PMC9951552 DOI: 10.1126/scitranslmed.abo7219] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Compounds acting on multiple targets are critical to combating antimalarial drug resistance. Here, we report that the human "mammalian target of rapamycin" (mTOR) inhibitor sapanisertib has potent prophylactic liver stage activity, in vitro and in vivo asexual blood stage (ABS) activity, and transmission-blocking activity against the protozoan parasite Plasmodium spp. Chemoproteomics studies revealed multiple potential Plasmodium kinase targets, and potent inhibition of Plasmodium phosphatidylinositol 4-kinase type III beta (PI4Kβ) and cyclic guanosine monophosphate-dependent protein kinase (PKG) was confirmed in vitro. Conditional knockdown of PI4Kβ in ABS cultures modulated parasite sensitivity to sapanisertib, and laboratory-generated P. falciparum sapanisertib resistance was mediated by mutations in PI4Kβ. Parasite metabolomic perturbation profiles associated with sapanisertib and other known PI4Kβ and/or PKG inhibitors revealed similarities and differences between chemotypes, potentially caused by sapanisertib targeting multiple parasite kinases. The multistage activity of sapanisertib and its in vivo antimalarial efficacy, coupled with potent inhibition of at least two promising drug targets, provides an opportunity to reposition this pyrazolopyrimidine for malaria.
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Affiliation(s)
- Lauren B. Arendse
- Drug Discovery and Development Centre (H3D), University of Cape Town, Rondebosch, Cape Town 7701, South Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Observatory, Cape Town 7925, South Africa
- South African Medical Research Council Drug Discovery and Development Research Unit, University of Cape Town, Rondebosch, Cape Town 7701, South Africa
| | - James M. Murithi
- Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Tarrick Qahash
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
- Huck Center for Malaria Research, Pennsylvania State University, University Park, PA 16802, USA
| | | | - Luiz C. Godoy
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sumanta Dey
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Liezl Gibhard
- Drug Discovery and Development Centre (H3D), University of Cape Town, Rondebosch, Cape Town 7701, South Africa
| | | | - Gerard Drewes
- Cellzome GmbH, a GSK Company, Heidelberg 69117, Germany
| | | | - Maria J. Lafuente-Monasterio
- Tres Cantos Medicines Development Campus-Diseases of the Developing World, GlaxoSmithKline, Tres Cantos, Madrid 28760, Spain
| | - Stephen Fienberg
- Drug Discovery and Development Centre (H3D), University of Cape Town, Rondebosch, Cape Town 7701, South Africa
- Department of Chemistry, University of Cape Town, Rondebosch, Cape Town 7701, South Africa
| | - Lynn Wambua
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Observatory, Cape Town 7925, South Africa
- Department of Chemistry, University of Cape Town, Rondebosch, Cape Town 7701, South Africa
| | - Samuel Gachuhi
- Department of Chemistry, University of Cape Town, Rondebosch, Cape Town 7701, South Africa
| | - Dina Coertzen
- Department of Biochemistry, Genetics and Microbiology, Institute for Sustainable Malaria Control, University of Pretoria, Hatfield 0028, South Africa
| | - Mariëtte van der Watt
- Department of Biochemistry, Genetics and Microbiology, Institute for Sustainable Malaria Control, University of Pretoria, Hatfield 0028, South Africa
| | - Janette Reader
- Department of Biochemistry, Genetics and Microbiology, Institute for Sustainable Malaria Control, University of Pretoria, Hatfield 0028, South Africa
| | - Ayesha S. Aswat
- Wits Research Institute for Malaria, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa
- Centre for Emerging Zoonotic and Parasitic Diseases, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg 2193, South Africa
| | - Erica Erlank
- Wits Research Institute for Malaria, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa
- Centre for Emerging Zoonotic and Parasitic Diseases, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg 2193, South Africa
| | - Nelius Venter
- Wits Research Institute for Malaria, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa
- Centre for Emerging Zoonotic and Parasitic Diseases, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg 2193, South Africa
| | - Nimisha Mittal
- School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Madeline R. Luth
- School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sabine Ottilie
- School of Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | | | - Lizette L. Koekemoer
- Wits Research Institute for Malaria, School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg 2193, South Africa
- Centre for Emerging Zoonotic and Parasitic Diseases, National Institute for Communicable Diseases of the National Health Laboratory Service, Johannesburg 2193, South Africa
| | - Lyn-Marie Birkholtz
- Department of Biochemistry, Genetics and Microbiology, Institute for Sustainable Malaria Control, University of Pretoria, Hatfield 0028, South Africa
| | - Jacquin C. Niles
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Manuel Llinás
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
- Huck Center for Malaria Research, Pennsylvania State University, University Park, PA 16802, USA
- Department of Chemistry, Pennsylvania State University, University Park, PA 16802, USA
| | - David A. Fidock
- Department of Microbiology and Immunology, Columbia University Irving Medical Center, New York, NY 10032, USA
- Center for Malaria Therapeutics and Antimicrobial Resistance, Division of Infectious Diseases, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Kelly Chibale
- Drug Discovery and Development Centre (H3D), University of Cape Town, Rondebosch, Cape Town 7701, South Africa
- Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Observatory, Cape Town 7925, South Africa
- South African Medical Research Council Drug Discovery and Development Research Unit, University of Cape Town, Rondebosch, Cape Town 7701, South Africa
- Department of Chemistry, University of Cape Town, Rondebosch, Cape Town 7701, South Africa
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17
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Caudal A, Tang X, Chavez JD, Keller A, Mohr JP, Bakhtina AA, Villet O, Chen H, Zhou B, Walker MA, Tian R, Bruce JE. Mitochondrial interactome quantitation reveals structural changes in metabolic machinery in the failing murine heart. NATURE CARDIOVASCULAR RESEARCH 2022; 1:855-866. [PMID: 36405497 PMCID: PMC9667921 DOI: 10.1038/s44161-022-00127-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 08/02/2022] [Indexed: 11/09/2022]
Abstract
Advancements in cross-linking mass spectrometry (XL-MS) bridge the gap between purified systems and native tissue environments, allowing the detection of protein structural interactions in their native state. Here we use isobaric quantitative protein interaction reporter technology (iqPIR) to compare the mitochondria protein interactomes in healthy and hypertrophic murine hearts, 4 weeks post-transaortic constriction. The failing heart interactome includes 588 statistically significant cross-linked peptide pairs altered in the disease condition. We observed an increase in the assembly of ketone oxidation oligomers corresponding to an increase in ketone metabolic utilization; remodeling of NDUA4 interaction in Complex IV, likely contributing to impaired mitochondria respiration; and conformational enrichment of ADP/ATP carrier ADT1, which is non-functional for ADP/ATP translocation but likely possesses non-selective conductivity. Our application of quantitative cross-linking technology in cardiac tissue provides molecular-level insights into the complex mitochondria remodeling in heart failure while bringing forth new hypotheses for pathological mechanisms.
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Affiliation(s)
- Arianne Caudal
- Department of Biochemistry, Department of Anesthesiology & Pain Medicine, University of Washington
- Mitochondria and Metabolism Center, Department of Anesthesiology & Pain Medicine, University of Washington
- These authors contributed equally
| | - Xiaoting Tang
- Department of Genome Sciences, University of Washington, Seattle, WA 98105, USA
- These authors contributed equally
| | - Juan D. Chavez
- Department of Genome Sciences, University of Washington, Seattle, WA 98105, USA
| | - Andrew Keller
- Department of Genome Sciences, University of Washington, Seattle, WA 98105, USA
| | - Jared P. Mohr
- Department of Genome Sciences, University of Washington, Seattle, WA 98105, USA
| | - Anna A. Bakhtina
- Department of Genome Sciences, University of Washington, Seattle, WA 98105, USA
| | - Outi Villet
- Mitochondria and Metabolism Center, Department of Anesthesiology & Pain Medicine, University of Washington
| | - Hongye Chen
- Mitochondria and Metabolism Center, Department of Anesthesiology & Pain Medicine, University of Washington
| | - Bo Zhou
- Mitochondria and Metabolism Center, Department of Anesthesiology & Pain Medicine, University of Washington
| | - Matthew A. Walker
- Mitochondria and Metabolism Center, Department of Anesthesiology & Pain Medicine, University of Washington
| | - Rong Tian
- Department of Biochemistry, Department of Anesthesiology & Pain Medicine, University of Washington
- Mitochondria and Metabolism Center, Department of Anesthesiology & Pain Medicine, University of Washington
- These authors jointly supervised this work
| | - James E. Bruce
- Department of Genome Sciences, University of Washington, Seattle, WA 98105, USA
- These authors jointly supervised this work
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18
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Torres-Sangiao E, Giddey AD, Leal Rodriguez C, Tang Z, Liu X, Soares NC. Proteomic Approaches to Unravel Mechanisms of Antibiotic Resistance and Immune Evasion of Bacterial Pathogens. Front Med (Lausanne) 2022; 9:850374. [PMID: 35586072 PMCID: PMC9108449 DOI: 10.3389/fmed.2022.850374] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 03/31/2022] [Indexed: 11/13/2022] Open
Abstract
The profound effects of and distress caused by the global COVID-19 pandemic highlighted what has been known in the health sciences a long time ago: that bacteria, fungi, viruses, and parasites continue to present a major threat to human health. Infectious diseases remain the leading cause of death worldwide, with antibiotic resistance increasing exponentially due to a lack of new treatments. In addition to this, many pathogens share the common trait of having the ability to modulate, and escape from, the host immune response. The challenge in medical microbiology is to develop and apply new experimental approaches that allow for the identification of both the microbe and its drug susceptibility profile in a time-sensitive manner, as well as to elucidate their molecular mechanisms of survival and immunomodulation. Over the last three decades, proteomics has contributed to a better understanding of the underlying molecular mechanisms responsible for microbial drug resistance and pathogenicity. Proteomics has gained new momentum as a result of recent advances in mass spectrometry. Indeed, mass spectrometry-based biomedical research has been made possible thanks to technological advances in instrumentation capability and the continuous improvement of sample processing and workflows. For example, high-throughput applications such as SWATH or Trapped ion mobility enable the identification of thousands of proteins in a matter of minutes. This type of rapid, in-depth analysis, combined with other advanced, supportive applications such as data processing and artificial intelligence, presents a unique opportunity to translate knowledge-based findings into measurable impacts like new antimicrobial biomarkers and drug targets. In relation to the Research Topic “Proteomic Approaches to Unravel Mechanisms of Resistance and Immune Evasion of Bacterial Pathogens,” this review specifically seeks to highlight the synergies between the powerful fields of modern proteomics and microbiology, as well as bridging translational opportunities from biomedical research to clinical practice.
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Affiliation(s)
- Eva Torres-Sangiao
- Clinical Microbiology Lab, University Hospital Marqués de Valdecilla, Santander, Spain
- Instituto de Investigación Sanitaria Marqués de Valdecilla (IDIVAL), Santander, Spain
- *Correspondence: Eva Torres-Sangiao,
| | - Alexander Dyason Giddey
- Sharjah Institute of Medical Research, University of Sharjah, Sharjah, United Arab Emirates
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah, United Arab Emirates
- Division of Chemical and Systems Biology, Department of Integrative Biomedical Sciences, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Cristina Leal Rodriguez
- Copenhagen Prospectives Studies on Asthma in Childhood, COPSAC, Copenhagen University Hospital, Herlev-Gentofte, Denmark
| | - Zhiheng Tang
- Department of Microbiology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Xiaoyun Liu
- Department of Microbiology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing, China
| | - Nelson C. Soares
- Sharjah Institute of Medical Research, University of Sharjah, Sharjah, United Arab Emirates
- Department of Medicinal Chemistry, College of Pharmacy, University of Sharjah, Sharjah, United Arab Emirates
- Nelson C. Soares,
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19
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Simple, efficient and thorough shotgun proteomic analysis with PatternLab V. Nat Protoc 2022; 17:1553-1578. [DOI: 10.1038/s41596-022-00690-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 02/08/2022] [Indexed: 11/08/2022]
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20
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Riffle M, Hoopmann MR, Jaschob D, Zhong G, Moritz RL, MacCoss MJ, Davis TN, Isoherranen N, Zelter A. Discovery and Visualization of Uncharacterized Drug-Protein Adducts Using Mass Spectrometry. Anal Chem 2022; 94:3501-3509. [PMID: 35184559 PMCID: PMC8892443 DOI: 10.1021/acs.analchem.1c04101] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
![]()
Drugs are often metabolized
to reactive intermediates that form
protein adducts. Adducts can inhibit protein activity, elicit immune
responses, and cause life-threatening adverse drug reactions. The
masses of reactive metabolites are frequently unknown, rendering traditional
mass spectrometry-based proteomics approaches incapable of adduct
identification. Here, we present Magnum, an open-mass search algorithm
optimized for adduct identification, and Limelight, a web-based data
processing package for analysis and visualization of data from all
existing algorithms. Limelight incorporates tools for sample comparisons
and xenobiotic-adduct discovery. We validate our tools with three
drug/protein combinations and apply our label-free workflow to identify
novel xenobiotic-protein adducts in CYP3A4. Our new methods and software
enable accurate identification of xenobiotic-protein adducts with
no prior knowledge of adduct masses or protein targets. Magnum outperforms
existing label-free tools in xenobiotic-protein adduct discovery,
while Limelight fulfills a major need in the rapidly developing field
of open-mass searching, which until now lacked comprehensive data
visualization tools.
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Affiliation(s)
- Michael Riffle
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
| | | | - Daniel Jaschob
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
| | - Guo Zhong
- Department of Pharmaceutics, University of Washington, Seattle, Washington 98195, United States
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Trisha N Davis
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
| | - Nina Isoherranen
- Department of Pharmaceutics, University of Washington, Seattle, Washington 98195, United States
| | - Alex Zelter
- Department of Biochemistry, University of Washington, Seattle, Washington 98195, United States
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21
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A Bioinformatics Approach to Mine the Microbial Proteomic Profile of COVID-19 Mass Spectrometry Data. Appl Microbiol 2022. [DOI: 10.3390/applmicrobiol2010010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Mass spectrometry (MS) is one of the key technologies used in proteomics. The majority of studies carried out using proteomics have focused on identifying proteins in biological samples such as human plasma to pin down prognostic or diagnostic biomarkers associated with particular conditions or diseases. This study aims to quantify microbial (viral and bacterial) proteins in healthy human plasma. MS data of healthy human plasma were searched against the complete proteomes of all available viruses and bacteria. With this baseline established, the same strategy was applied to characterize the metaproteomic profile of different SARS-CoV-2 disease stages in the plasma of patients. Two SARS-CoV-2 proteins were detected with a high confidence and could serve as the early markers of SARS-CoV-2 infection. The complete bacterial and viral protein content in SARS-CoV-2 samples was compared for the different disease stages. The number of viral proteins was found to increase significantly with the progression of the infection, at the expense of bacterial proteins. This strategy can be extended to aid in the development of early diagnostic tests for other infectious diseases based on the presence of microbial biomarkers in human plasma samples.
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22
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Tsai J, Wang S, Chang C, Chen C, Wen C, Chen G, Kuo C, Tseng YJ, Chen C. Identification of traumatic acid as a potential plasma biomarker for sarcopenia using a metabolomics-based approach. J Cachexia Sarcopenia Muscle 2022; 13:276-286. [PMID: 34939349 PMCID: PMC8818620 DOI: 10.1002/jcsm.12895] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2020] [Revised: 08/30/2021] [Accepted: 11/21/2021] [Indexed: 01/08/2023] Open
Abstract
BACKGROUND The pathogenesis of sarcopenia is complex and has not been well explored. Identifying biomarkers is a promising strategy for exploring the mechanism of sarcopenia. This study aimed to identify potential biomarkers of sarcopenia through a metabolomic analysis of plasma metabolites in elderly subjects (≥65 years of age) vs. younger adults (<65 years of age). METHODS Of the 168 candidates in the Comprehensive Geriatric Assessment and Frailty Study of Elderly Outpatients, 24 elderly subjects (≥65 years of age) with sarcopenia were age and sex matched with 24 elderly subjects without sarcopenia. In addition, 24 younger adults were recruited for comparison. Muscle strength, gait speed, and metabolic and inflammatory parameters, including plasma tumour necrosis factor-α, C-reactive protein, irisin, and growth differentiation factor 15 (GDF-15) levels were assessed. Metabolomic analysis was carried out using the plasma metabolites. RESULTS Seventy-two participants were enrolled, including 10 (41.6%) men and 14 (58.3%) women in both groups of elderly subjects. The median ages of elderly subjects with and without sarcopenia were 82 (range: 67-88) and 81.5 (range: 67-87) years, respectively. Among the 242 plasma metabolic peaks analysed among these three groups, traumatic acid was considered as a sarcopenia-related metabolite. The plasma traumatic acid signal intensity level was significantly higher in elderly subjects with sarcopenia than in elderly subjects without sarcopenia [591.5 (inter-quartile range, IQR: 491.5-664.5) vs. 430.0 (IQR: 261.0-599.5), P = 0.0063]. The plasma concentrations of traumatic acid were 15.8 (IQR: 11.5-21.7), 21.1 (IQR: 16.0-25.8), and 24.3 (IQR: 18.0-29.5) ppb in younger adults [age range: 23-37 years, 12 (50%) men], elderly subjects without sarcopenia, and elderly subjects with sarcopenia, respectively, thereby depicting an increasing tendency (P for trend = 0.034). This pattern was similar to that of GDF-15, a recognized sarcopenia-related factor. Plasma traumatic acid concentrations were also positively correlated with the presence of hypertension (r = 0.25, P = 0.034), glucose AC (r = 0.34, P = 0.0035), creatinine (r = 0.40, P = 0.0006), and GDF-15 levels (r = 0.25, P = 0.0376), but negatively correlated with the Modification of Diet in Renal Disease-simplify-glomerular filtration rate (r = -0.50, P < 0.0001). Similarly, plasma GDF-15 concentrations were associated with these factors. CONCLUSIONS Traumatic acid might represent a potential plasma biomarker of sarcopenia. However, further studies are needed to validate the results and investigate the underlying mechanisms.
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Affiliation(s)
- Jaw‐Shiun Tsai
- Department of Family MedicineNational Taiwan University Hospital, National Taiwan UniversityTaipeiTaiwan
- Department of Family Medicine, College of MedicineNational Taiwan UniversityTaipeiTaiwan
| | - San‐Yuan Wang
- Master Program in Clinical Genomics and Proteomics, College of PharmacyTaipei Medical UniversityTaipeiTaiwan
| | - Chin‐Hao Chang
- Department of Medical ResearchNational Taiwan University HospitalTaipeiTaiwan
| | - Chin‐Ying Chen
- Department of Family MedicineNational Taiwan University Hospital, National Taiwan UniversityTaipeiTaiwan
- Department of Family Medicine, College of MedicineNational Taiwan UniversityTaipeiTaiwan
| | - Chiung‐Jung Wen
- Department of Family Medicine, College of MedicineNational Taiwan UniversityTaipeiTaiwan
- Department of Geriatrics and GerontologyNational Taiwan University HospitalTaipeiTaiwan
| | - Guan‐Yuan Chen
- Department and Graduate Institute of Forensic Medicine, College of MedicineNational Taiwan UniversityTaipeiTaiwan
| | - Ching‐Hua Kuo
- The Metabolomics Core Laboratory, Center of Genomic MedicineNational Taiwan UniversityTaipeiTaiwan
- School of Pharmacy, College of MedicineNational Taiwan UniversityTaipeiTaiwan
- Department of PharmacyNational Taiwan University Hospital, National Taiwan UniversityTaipeiTaiwan
| | - Y. Jane Tseng
- The Metabolomics Core Laboratory, Center of Genomic MedicineNational Taiwan UniversityTaipeiTaiwan
- School of Pharmacy, College of MedicineNational Taiwan UniversityTaipeiTaiwan
- Department of Computer Science and Information EngineeringNational Taiwan UniversityTaipeiTaiwan
- Graduate Institute of Biomedical Electronics and BioinformaticsNational Taiwan UniversityTaipeiTaiwan
| | - Ching‐Yu Chen
- Department of Family MedicineNational Taiwan University Hospital, National Taiwan UniversityTaipeiTaiwan
- Department of Family Medicine, College of MedicineNational Taiwan UniversityTaipeiTaiwan
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23
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Vertical Inheritance Facilitates Interspecies Diversification in Biosynthetic Gene Clusters and Specialized Metabolites. mBio 2021; 12:e0270021. [PMID: 34809466 PMCID: PMC8609351 DOI: 10.1128/mbio.02700-21] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
While specialized metabolites are thought to mediate ecological interactions, the evolutionary processes driving chemical diversification, particularly among closely related lineages, remain poorly understood. Here, we examine the evolutionary dynamics governing the distribution of natural product biosynthetic gene clusters (BGCs) among 118 strains representing all nine currently named species of the marine actinobacterial genus Salinispora. While much attention has been given to the role of horizontal gene transfer (HGT) in structuring BGC distributions, we find that vertical descent facilitates interspecies BGC diversification over evolutionary timescales. Moreover, we identified a distinct phylogenetic signal among Salinispora species at both the BGC and metabolite level, indicating that specialized metabolism represents a conserved phylogenetic trait. Using a combination of genomic analyses and liquid chromatography–high-resolution tandem mass spectrometry (LC-MS/MS) targeting nine experimentally characterized BGCs and their small molecule products, we identified gene gain/loss events, constrained interspecies recombination, and other evolutionary processes associated with vertical inheritance as major contributors to BGC diversification. These evolutionary dynamics had direct consequences for the compounds produced, as exemplified by species-level differences in salinosporamide production. Together, our results support the concept that specialized metabolites, and their cognate BGCs, can represent phylogenetically conserved functional traits with chemical diversification proceeding in species-specific patterns over evolutionary time frames.
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24
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Stachowicz A, Sundararaman N, Venkatraman V, Van Eyk J, Fert-Bober J. pH/Acetonitrile-Gradient Reversed-Phase Fractionation of Enriched Hyper-Citrullinated Library in Combination with LC-MS/MS Analysis for Confident Identification of Citrullinated Peptides. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2021; 2420:107-126. [PMID: 34905169 DOI: 10.1007/978-1-0716-1936-0_9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Citrullination, the Ca2+-driven enzymatic conversion of arginine residues to citrulline, is a posttranslational modification, implicated in several physiological and pathological processes. Several methods to detect citrullinated proteins have been developed, including color development reagent, fluorescence, phenylglyoxal, and antibody-based methods. These methods yet suffer from limitations in sensitivity, specificity, or citrullinated site determination. Mass spectrometry (MS)-based proteomic analysis has emerged as a promising method to resolve these problems. However, due to low abundance of citrullinated proteins and similar MS features to deamidation of asparagine and glutamine, confident identification of citrullinated proteome is challenging. Here, we present a systematic approach to identify a compendium of steps to enhance the number of detected citrullinated residue and implement diagnostic MS feature that allow the confidence of MS-based identifications. Our method is based on the concept of generation of hyper-citrullinated library with high-pH reversed-phase peptide fractionation that allows to enrich in low abundance citrullinated peptides and amplify the effect of charge loss upon citrullination. Application of our approach to complex global citrullino-proteome datasets demonstrates the confident assessment of citrullinated peptides, thereby enhancing the size and functional interpretation of citrullinated proteomes.
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Affiliation(s)
- Aneta Stachowicz
- Cedars-Sinai Medical Center, Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Los Angeles, CA, USA
- Chair of Pharmacology, Jagiellonian University Medical College, Institute of Pharmacology, Krakow, Poland
| | - Niveda Sundararaman
- Cedars-Sinai Medical Center, Advanced Clinical Biosystems Research Institute, Precision Biomarker Laboratories, Los Angeles, CA, USA
| | - Vidya Venkatraman
- Cedars-Sinai Medical Center, Advanced Clinical Biosystems Research Institute, Precision Biomarker Laboratories, Los Angeles, CA, USA
| | - Jennifer Van Eyk
- Cedars-Sinai Medical Center, Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Los Angeles, CA, USA
- Cedars-Sinai Medical Center, Advanced Clinical Biosystems Research Institute, Precision Biomarker Laboratories, Los Angeles, CA, USA
| | - Justyna Fert-Bober
- Cedars-Sinai Medical Center, Smidt Heart Institute, Advanced Clinical Biosystems Research Institute, Los Angeles, CA, USA.
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25
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Samanipour S, Choi P, O'Brien JW, Pirok BWJ, Reid MJ, Thomas KV. From Centroided to Profile Mode: Machine Learning for Prediction of Peak Width in HRMS Data. Anal Chem 2021; 93:16562-16570. [PMID: 34843646 PMCID: PMC8674881 DOI: 10.1021/acs.analchem.1c03755] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Centroiding is one of the major approaches used for size reduction of the data generated by high-resolution mass spectrometry. During centroiding, performed either during acquisition or as a pre-processing step, the mass profiles are represented by a single value (i.e., the centroid). While being effective in reducing the data size, centroiding also reduces the level of information density present in the mass peak profile. Moreover, each step of the centroiding process and their consequences on the final results may not be completely clear. Here, we present Cent2Prof, a package containing two algorithms that enables the conversion of the centroided data to mass peak profile data and vice versa. The centroiding algorithm uses the resolution-based mass peak width parameter as the first guess and self-adjusts to fit the data. In addition to the m/z values, the centroiding algorithm also generates the measured mass peak widths at half-height, which can be used during the feature detection and identification. The mass peak profile prediction algorithm employs a random-forest model for the prediction of mass peak widths, which is consequently used for mass profile reconstruction. The centroiding results were compared to the outputs of the MZmine-implemented centroiding algorithm. Our algorithm resulted in rates of false detection ≤5% while the MZmine algorithm resulted in 30% rate of false positive and 3% rate of false negative. The error in profile prediction was ≤56% independent of the mass, ionization mode, and intensity, which was 6 times more accurate than the resolution-based estimated values.
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Affiliation(s)
- Saer Samanipour
- Van't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands.,Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, Queensland 4102, Australia.,Norwegian Institute for Water Research (NIVA), Økernveien 94, Oslo 0579, Norway
| | - Phil Choi
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, Queensland 4102, Australia.,Water Unit, Health Protection Branch, Prevention Division, Queensland Department of Health, Brisbane, Queensland 4000, Australia
| | - Jake W O'Brien
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, Queensland 4102, Australia
| | - Bob W J Pirok
- Van't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Science Park 904, Amsterdam 1098 XH, The Netherlands
| | - Malcolm J Reid
- Norwegian Institute for Water Research (NIVA), Økernveien 94, Oslo 0579, Norway
| | - Kevin V Thomas
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, Queensland 4102, Australia
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26
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Sun B, Lorang C, Qin S, Zhang Y, Liu K, Li G, Sun Z, Francke A, Utleg AG, Hu Z, Wang K, Moritz RL, Hood L. Mouse Organ-Specific Proteins and Functions. Cells 2021; 10:cells10123449. [PMID: 34943957 PMCID: PMC8700158 DOI: 10.3390/cells10123449] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 11/29/2021] [Accepted: 12/04/2021] [Indexed: 11/19/2022] Open
Abstract
Organ-specific proteins (OSPs) possess great medical potential both in clinics and in biomedical research. Applications of them—such as alanine transaminase, aspartate transaminase, and troponins—in clinics have raised certain concerns of their organ specificity. The dynamics and diversity of protein expression in heterogeneous human populations are well known, yet their effects on OSPs are less addressed. Here, we used mice as a model and implemented a breadth study to examine the panorgan proteome for potential variations in organ specificity in different genetic backgrounds. Using reasonable resources, we generated panorgan proteomes of four in-bred mouse strains. The results revealed a large diversity that was more profound among OSPs than among proteomes overall. We defined a robustness score to quantify such variation and derived three sets of OSPs with different stringencies. In the meantime, we found that the enriched biological functions of OSPs are also organ-specific and are sensitive and useful to assess the quality of OSPs. We hope our breadth study can open doors to explore the molecular diversity and dynamics of organ specificity at the protein level.
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Affiliation(s)
- Bingyun Sun
- Departments of Chemistry, Simon Fraser University, Burnaby, BC V5A1S6, Canada; (Y.Z.); (K.L.)
- Departments of Molecular Biology and Biochemistry, Simon Fraser University, Burnaby, BC V5A1S6, Canada
- Correspondence: (B.S.); (L.H.)
| | - Cynthia Lorang
- Institute for Systems Biology, Seattle, WA 98109, USA; (C.L.); (S.Q.); (G.L.); (Z.S.); (A.G.U.); (Z.H.); (K.W.); (R.L.M.)
| | - Shizhen Qin
- Institute for Systems Biology, Seattle, WA 98109, USA; (C.L.); (S.Q.); (G.L.); (Z.S.); (A.G.U.); (Z.H.); (K.W.); (R.L.M.)
| | - Yijuan Zhang
- Departments of Chemistry, Simon Fraser University, Burnaby, BC V5A1S6, Canada; (Y.Z.); (K.L.)
| | - Ken Liu
- Departments of Chemistry, Simon Fraser University, Burnaby, BC V5A1S6, Canada; (Y.Z.); (K.L.)
| | - Gray Li
- Institute for Systems Biology, Seattle, WA 98109, USA; (C.L.); (S.Q.); (G.L.); (Z.S.); (A.G.U.); (Z.H.); (K.W.); (R.L.M.)
| | - Zhi Sun
- Institute for Systems Biology, Seattle, WA 98109, USA; (C.L.); (S.Q.); (G.L.); (Z.S.); (A.G.U.); (Z.H.); (K.W.); (R.L.M.)
| | - Ashley Francke
- Departments of Computing Science, Simon Fraser University, Burnaby, BC V5A1S6, Canada;
| | - Angelita G. Utleg
- Institute for Systems Biology, Seattle, WA 98109, USA; (C.L.); (S.Q.); (G.L.); (Z.S.); (A.G.U.); (Z.H.); (K.W.); (R.L.M.)
| | - Zhiyuan Hu
- Institute for Systems Biology, Seattle, WA 98109, USA; (C.L.); (S.Q.); (G.L.); (Z.S.); (A.G.U.); (Z.H.); (K.W.); (R.L.M.)
| | - Kai Wang
- Institute for Systems Biology, Seattle, WA 98109, USA; (C.L.); (S.Q.); (G.L.); (Z.S.); (A.G.U.); (Z.H.); (K.W.); (R.L.M.)
| | - Robert L. Moritz
- Institute for Systems Biology, Seattle, WA 98109, USA; (C.L.); (S.Q.); (G.L.); (Z.S.); (A.G.U.); (Z.H.); (K.W.); (R.L.M.)
| | - Leroy Hood
- Institute for Systems Biology, Seattle, WA 98109, USA; (C.L.); (S.Q.); (G.L.); (Z.S.); (A.G.U.); (Z.H.); (K.W.); (R.L.M.)
- Correspondence: (B.S.); (L.H.)
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27
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Omenn GS, Lane L, Overall CM, Paik YK, Cristea IM, Corrales FJ, Lindskog C, Weintraub S, Roehrl MHA, Liu S, Bandeira N, Srivastava S, Chen YJ, Aebersold R, Moritz RL, Deutsch EW. Progress Identifying and Analyzing the Human Proteome: 2021 Metrics from the HUPO Human Proteome Project. J Proteome Res 2021; 20:5227-5240. [PMID: 34670092 PMCID: PMC9340669 DOI: 10.1021/acs.jproteome.1c00590] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The 2021 Metrics of the HUPO Human Proteome Project (HPP) show that protein expression has now been credibly detected (neXtProt PE1 level) for 18 357 (92.8%) of the 19 778 predicted proteins coded in the human genome, a gain of 483 since 2020 from reports throughout the world reanalyzed by the HPP. Conversely, the number of neXtProt PE2, PE3, and PE4 missing proteins has been reduced by 478 to 1421. This represents remarkable progress on the proteome parts list. The utilization of proteomics in a broad array of biological and clinical studies likewise continues to expand with many important findings and effective integration with other omics platforms. We present highlights from the Immunopeptidomics, Glycoproteomics, Infectious Disease, Cardiovascular, Musculo-Skeletal, Liver, and Cancers B/D-HPP teams and from the Knowledgebase, Mass Spectrometry, Antibody Profiling, and Pathology resource pillars, as well as ethical considerations important to the clinical utilization of proteomics and protein biomarkers.
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Affiliation(s)
- Gilbert S Omenn
- University of Michigan, Ann Arbor, Michigan 48109, United States
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | | | - Young-Ki Paik
- Yonsei Proteome Research Center and Yonsei University, Seoul 03722, Korea
| | - Ileana M Cristea
- Princeton University, Princeton, New Jersey 08544, United States
| | | | | | - Susan Weintraub
- University of Texas Health, San Antonio, San Antonio, Texas 78229-3900, United States
| | - Michael H A Roehrl
- Memorial Sloan Kettering Cancer Center, New York, New York 10065, United States
| | - Siqi Liu
- BGI Group, Shenzhen 518083, China
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, California 92093, United States
| | | | - Yu-Ju Chen
- National Taiwan University, Academia Sinica, Nankang, Taipei 11529, Taiwan
| | - Ruedi Aebersold
- ETH-Zurich and University of Zurich, 8092 Zurich, Switzerland
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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28
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Helness A, Fraszczak J, Joly-Beauparlant C, Bagci H, Trahan C, Arman K, Shooshtarizadeh P, Chen R, Ayoub M, Côté JF, Oeffinger M, Droit A, Möröy T. GFI1 tethers the NuRD complex to open and transcriptionally active chromatin in myeloid progenitors. Commun Biol 2021; 4:1356. [PMID: 34857890 PMCID: PMC8639993 DOI: 10.1038/s42003-021-02889-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 11/11/2021] [Indexed: 12/27/2022] Open
Abstract
Growth factor indepdendent 1 (GFI1) is a SNAG-domain, DNA binding transcriptional repressor which controls myeloid differentiation through molecular mechanisms and co-factors that still remain to be clearly identified. Here we show that GFI1 associates with the chromodomain helicase DNA binding protein 4 (CHD4) and other components of the Nucleosome remodeling and deacetylase (NuRD) complex. In granulo-monocytic precursors, GFI1, CHD4 or GFI1/CHD4 complexes occupy sites enriched for histone marks associated with active transcription suggesting that GFI1 recruits the NuRD complex to target genes regulated by active or bivalent promoters and enhancers. GFI1 and GFI1/CHD4 complexes occupy promoters that are either enriched for IRF1 or SPI1 consensus binding sites, respectively. During neutrophil differentiation, chromatin closure and depletion of H3K4me2 occurs at different degrees depending on whether GFI1, CHD4 or both are present, indicating that GFI1 is more efficient in depleting of H3K4me2 and -me1 marks when associated with CHD4. Our data suggest that GFI1/CHD4 complexes regulate histone modifications differentially to enable regulation of target genes affecting immune response, nucleosome organization or cellular metabolic processes and that both the target gene specificity and the activity of GFI1 during myeloid differentiation depends on the presence of chromatin remodeling complexes. Helness et al. show that GFI1/CHD4 complexes critically regulate chromatin accessibility and histone modifications to regulate target genes affecting diverse cellular processes in neutrophils. Their results provide further insight into the molecular network operated by GFI1 for neutrophil differentiation programs.
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Affiliation(s)
- Anne Helness
- Institut de recherches cliniques de Montréal, Montréal, QC, H2W 1R7, Canada
| | - Jennifer Fraszczak
- Institut de recherches cliniques de Montréal, Montréal, QC, H2W 1R7, Canada
| | | | - Halil Bagci
- Institut de recherches cliniques de Montréal, Montréal, QC, H2W 1R7, Canada.,Institute for Biochemistry, ETH Zürich, Zürich, Switzerland
| | - Christian Trahan
- Institut de recherches cliniques de Montréal, Montréal, QC, H2W 1R7, Canada
| | - Kaifee Arman
- Institut de recherches cliniques de Montréal, Montréal, QC, H2W 1R7, Canada
| | | | - Riyan Chen
- Institut de recherches cliniques de Montréal, Montréal, QC, H2W 1R7, Canada
| | - Marina Ayoub
- Institut de recherches cliniques de Montréal, Montréal, QC, H2W 1R7, Canada.,Hôpital pour Enfants, Ste Justine, Montreal, QC, Canada
| | - Jean-François Côté
- Institut de recherches cliniques de Montréal, Montréal, QC, H2W 1R7, Canada.,Department of Anatomy and Cell Biology, McGill University, Montréal, QC, H3A 0C7, Canada.,Département de Biochimie, Université de Montréal, Montréal, QC, H3C 3J7, Canada.,Division of Experimental Medicine, McGill University, Montreal, QC, Canada
| | - Marlene Oeffinger
- Institut de recherches cliniques de Montréal, Montréal, QC, H2W 1R7, Canada.,Département de Biochimie, Université de Montréal, Montréal, QC, H3C 3J7, Canada
| | - Arnaud Droit
- Département de Médecine Moléculaire, Faculté de Médecine, Université Laval, Québec, QC, Canada
| | - Tarik Möröy
- Institut de recherches cliniques de Montréal, Montréal, QC, H2W 1R7, Canada. .,Division of Experimental Medicine, McGill University, Montreal, QC, Canada. .,Département de Microbiologie, Infectiologie et Immunologie, Université de Montréal, Montréal, QC, Canada.
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29
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Li J, Zhao T, Li J, Shen J, Jia L, Zhu B, Dang L, Ma C, Liu D, Mu F, Hu L, Sun S. Precision N-glycoproteomics reveals elevated LacdiNAc as a novel signature of intrahepatic cholangiocarcinoma. Mol Oncol 2021; 16:2135-2152. [PMID: 34855283 PMCID: PMC9168967 DOI: 10.1002/1878-0261.13147] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 11/02/2021] [Accepted: 11/30/2021] [Indexed: 12/09/2022] Open
Abstract
Primary liver cancer, mainly comprising hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), remains a major global health problem. Although ICC is clinically different from HCC, their molecular differences are still largely unclear. In this study, precision N‐glycoproteomic analysis was performed on both ICC and HCC tumors as well as paracancer tissues to investigate their aberrant site‐specific N‐glycosylation. By using our newly developed glycoproteomic methods and novel algorithm, termed ‘StrucGP’, a total of 486 N‐glycan structures attached on 1235 glycosites were identified from 894 glycoproteins in ICC and HCC tumors. Notably, glycans with uncommon LacdiNAc (GalNAcβ1‐4GlcNAc) structures were distinguished from their isomeric glycans. In addition to several bi‐antennary and/or bisecting glycans that were commonly elevated in ICC and HCC, a number of LacdiNAc‐containing, tri‐antennary, and core‐fucosylated glycans were uniquely increased in ICC. More interestingly, almost all LacdiNAc‐containing N‐glycopeptides were enhanced in ICC tumor but not in HCC tumor, and this phenomenon was further confirmed by lectin histochemistry and the high expression of β1‐4 GalNAc transferases in ICC at both mRNA and protein expression levels. The novel N‐glycan alterations uniquely detected in ICC provide a valuable resource for future studies regarding to the discovery of ICC diagnostic biomarkers, therapeutic targets, and mechanism investigations.
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Affiliation(s)
- Jun Li
- College of Life ScienceNorthwest UniversityXi'anChina
| | - Ting Zhao
- College of Life ScienceNorthwest UniversityXi'anChina
| | - Jing Li
- College of Life ScienceNorthwest UniversityXi'anChina
| | - Jiechen Shen
- College of Life ScienceNorthwest UniversityXi'anChina
| | - Li Jia
- College of Life ScienceNorthwest UniversityXi'anChina
| | - Bojing Zhu
- College of Life ScienceNorthwest UniversityXi'anChina
| | - Liuyi Dang
- College of Life ScienceNorthwest UniversityXi'anChina
| | - Chen Ma
- College of Life ScienceNorthwest UniversityXi'anChina
| | - Didi Liu
- College of Life ScienceNorthwest UniversityXi'anChina
| | - Fan Mu
- Department of Hepatobiliary SurgeryInstitute of Advanced Surgical Technology and EngineeringThe First Affiliated Hospital of Xi'an Jiaotong UniversityChina
| | - Liangshuo Hu
- Department of Hepatobiliary SurgeryInstitute of Advanced Surgical Technology and EngineeringThe First Affiliated Hospital of Xi'an Jiaotong UniversityChina
| | - Shisheng Sun
- College of Life ScienceNorthwest UniversityXi'anChina
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30
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Hruska M, Holub D. Evaluation of an integrative Bayesian peptide detection approach on a combinatorial peptide library. EUROPEAN JOURNAL OF MASS SPECTROMETRY (CHICHESTER, ENGLAND) 2021; 27:217-234. [PMID: 34989269 DOI: 10.1177/14690667211066725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Detection of peptides lies at the core of bottom-up proteomics analyses. We examined a Bayesian approach to peptide detection, integrating match-based models (fragments, retention time, isotopic distribution, and precursor mass) and peptide prior probability models under a unified probabilistic framework. To assess the relevance of these models and their various combinations, we employed a complete- and a tail-complete search of a low-precursor-mass synthetic peptide library based on oncogenic KRAS peptides. The fragment match was by far the most informative match-based model, while the retention time match was the only remaining such model with an appreciable impact--increasing correct detections by around 8 %. A peptide prior probability model built from a reference proteome greatly improved the detection over a uniform prior, essentially transforming de novo sequencing into a reference-guided search. The knowledge of a correct sequence tag in advance to peptide-spectrum matching had only a moderate impact on peptide detection unless the tag was long and of high certainty. The approach also derived more precise error rates on the analyzed combinatorial peptide library than those estimated using PeptideProphet and Percolator, showing its potential applicability for the detection of homologous peptides. Although the approach requires further computational developments for routine data analysis, it illustrates the value of peptide prior probabilities and presents a Bayesian approach for their incorporation into peptide detection.
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Affiliation(s)
- Miroslav Hruska
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, 98735Palacky University, Olomouc, Czech Republic
- Department of Computer Science, Faculty of Science, 98735Palacky University, Olomouc, Czech Republic
| | - Dusan Holub
- Institute of Molecular and Translational Medicine, Faculty of Medicine and Dentistry, 98735Palacky University, Olomouc, Czech Republic
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31
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Li J, Lim RG, Kaye JA, Dardov V, Coyne AN, Wu J, Milani P, Cheng A, Thompson TG, Ornelas L, Frank A, Adam M, Banuelos MG, Casale M, Cox V, Escalante-Chong R, Daigle JG, Gomez E, Hayes L, Holewenski R, Lei S, Lenail A, Lima L, Mandefro B, Matlock A, Panther L, Patel-Murray NL, Pham J, Ramamoorthy D, Sachs K, Shelley B, Stocksdale J, Trost H, Wilhelm M, Venkatraman V, Wassie BT, Wyman S, Yang S, Van Eyk JE, Lloyd TE, Finkbeiner S, Fraenkel E, Rothstein JD, Sareen D, Svendsen CN, Thompson LM. An integrated multi-omic analysis of iPSC-derived motor neurons from C9ORF72 ALS patients. iScience 2021; 24:103221. [PMID: 34746695 PMCID: PMC8554488 DOI: 10.1016/j.isci.2021.103221] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 06/29/2021] [Accepted: 09/30/2021] [Indexed: 12/13/2022] Open
Abstract
Neurodegenerative diseases are challenging for systems biology because of the lack of reliable animal models or patient samples at early disease stages. Induced pluripotent stem cells (iPSCs) could address these challenges. We investigated DNA, RNA, epigenetics, and proteins in iPSC-derived motor neurons from patients with ALS carrying hexanucleotide expansions in C9ORF72. Using integrative computational methods combining all omics datasets, we identified novel and known dysregulated pathways. We used a C9ORF72 Drosophila model to distinguish pathways contributing to disease phenotypes from compensatory ones and confirmed alterations in some pathways in postmortem spinal cord tissue of patients with ALS. A different differentiation protocol was used to derive a separate set of C9ORF72 and control motor neurons. Many individual -omics differed by protocol, but some core dysregulated pathways were consistent. This strategy of analyzing patient-specific neurons provides disease-related outcomes with small numbers of heterogeneous lines and reduces variation from single-omics to elucidate network-based signatures.
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Affiliation(s)
- Jonathan Li
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ryan G. Lim
- UCI MIND, University of California, Irvine, CA 92697, USA
| | - Julia A. Kaye
- Center for Systems and Therapeutics and the Taube/Koret Center for Neurodegenerative Disease, Gladstone Institutes, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Victoria Dardov
- The Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA
- Advanced Clinical Biosystems Research Institute, The Barbra Streisand Heart Center, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Alyssa N. Coyne
- Brain Science Institute, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
- Department of Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
| | - Jie Wu
- Department of Biological Chemistry, University of California, Irvine, CA 92697, USA
| | - Pamela Milani
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Andrew Cheng
- Cellular and Molecular Medicine Program, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
| | | | - Loren Ornelas
- The Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA
| | - Aaron Frank
- The Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA
| | - Miriam Adam
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Maria G. Banuelos
- The Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA
| | - Malcolm Casale
- UCI MIND, University of California, Irvine, CA 92697, USA
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA
| | - Veerle Cox
- Cellular and Molecular Medicine Program, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
| | - Renan Escalante-Chong
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - J. Gavin Daigle
- Brain Science Institute, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
- Department of Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
| | - Emilda Gomez
- The Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA
| | - Lindsey Hayes
- Department of Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
| | - Ronald Holewenski
- Advanced Clinical Biosystems Research Institute, The Barbra Streisand Heart Center, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Susan Lei
- The Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA
| | - Alex Lenail
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Leandro Lima
- Center for Systems and Therapeutics and the Taube/Koret Center for Neurodegenerative Disease, Gladstone Institutes, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Berhan Mandefro
- The Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA
| | - Andrea Matlock
- Advanced Clinical Biosystems Research Institute, The Barbra Streisand Heart Center, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Lindsay Panther
- The Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA
| | | | - Jacqueline Pham
- Department of Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
| | - Divya Ramamoorthy
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Karen Sachs
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Brandon Shelley
- The Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA
| | - Jennifer Stocksdale
- UCI MIND, University of California, Irvine, CA 92697, USA
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA
| | - Hannah Trost
- The Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA
| | - Mark Wilhelm
- Brain Science Institute, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
| | - Vidya Venkatraman
- Advanced Clinical Biosystems Research Institute, The Barbra Streisand Heart Center, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Brook T. Wassie
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Stacia Wyman
- Sue and Bill Gross Stem Cell Center, University of California, Irvine, CA 92697, USA
| | - Stephanie Yang
- Brain Science Institute, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
| | | | - Jennifer E. Van Eyk
- Advanced Clinical Biosystems Research Institute, The Barbra Streisand Heart Center, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Thomas E. Lloyd
- Department of Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
| | - Steven Finkbeiner
- Center for Systems and Therapeutics and the Taube/Koret Center for Neurodegenerative Disease, Gladstone Institutes, University of California, San Francisco, San Francisco, CA 94158, USA
- Departments of Neurology and Physiology, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Ernest Fraenkel
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Jeffrey D. Rothstein
- Brain Science Institute, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
- Department of Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
- Cellular and Molecular Medicine Program, Johns Hopkins University School of Medicine, Baltimore, MA 212056, USA
| | - Dhruv Sareen
- The Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA
| | - Clive N. Svendsen
- The Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, 8700 Beverly Boulevard, Los Angeles, CA 90048, USA
| | - Leslie M. Thompson
- UCI MIND, University of California, Irvine, CA 92697, USA
- Department of Biological Chemistry, University of California, Irvine, CA 92697, USA
- Department of Neurobiology and Behavior, University of California, Irvine, CA 92697, USA
- Department of Psychiatry and Human Behavior, University of California, Irvine, CA 92697, USA
- Sue and Bill Gross Stem Cell Center, University of California, Irvine, CA 92697, USA
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32
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van Wijk KJ, Leppert T, Sun Q, Boguraev SS, Sun Z, Mendoza L, Deutsch EW. The Arabidopsis PeptideAtlas: Harnessing worldwide proteomics data to create a comprehensive community proteomics resource. THE PLANT CELL 2021; 33:3421-3453. [PMID: 34411258 PMCID: PMC8566204 DOI: 10.1093/plcell/koab211] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 08/13/2021] [Indexed: 05/02/2023]
Abstract
We developed a resource, the Arabidopsis PeptideAtlas (www.peptideatlas.org/builds/arabidopsis/), to solve central questions about the Arabidopsis thaliana proteome, such as the significance of protein splice forms and post-translational modifications (PTMs), or simply to obtain reliable information about specific proteins. PeptideAtlas is based on published mass spectrometry (MS) data collected through ProteomeXchange and reanalyzed through a uniform processing and metadata annotation pipeline. All matched MS-derived peptide data are linked to spectral, technical, and biological metadata. Nearly 40 million out of ∼143 million MS/MS (tandem MS) spectra were matched to the reference genome Araport11, identifying ∼0.5 million unique peptides and 17,858 uniquely identified proteins (only isoform per gene) at the highest confidence level (false discovery rate 0.0004; 2 non-nested peptides ≥9 amino acid each), assigned canonical proteins, and 3,543 lower-confidence proteins. Physicochemical protein properties were evaluated for targeted identification of unobserved proteins. Additional proteins and isoforms currently not in Araport11 were identified that were generated from pseudogenes, alternative start, stops, and/or splice variants, and small Open Reading Frames; these features should be considered when updating the Arabidopsis genome. Phosphorylation can be inspected through a sophisticated PTM viewer. PeptideAtlas is integrated with community resources including TAIR, tracks in JBrowse, PPDB, and UniProtKB. Subsequent PeptideAtlas builds will incorporate millions more MS/MS data.
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Affiliation(s)
- Klaas J van Wijk
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, USA
- Authors for correspondence: (K.J.V.W.), (E.W.D.)
| | - Tami Leppert
- Institute for Systems Biology (ISB), Seattle, Washington 98109, USA
| | - Qi Sun
- Computational Biology Service Unit, Cornell University, Ithaca, New York 14853, USA
| | - Sascha S Boguraev
- Section of Plant Biology, School of Integrative Plant Sciences (SIPS), Cornell University, Ithaca, New York 14853, USA
| | - Zhi Sun
- Institute for Systems Biology (ISB), Seattle, Washington 98109, USA
| | - Luis Mendoza
- Institute for Systems Biology (ISB), Seattle, Washington 98109, USA
| | - Eric W Deutsch
- Institute for Systems Biology (ISB), Seattle, Washington 98109, USA
- Authors for correspondence: (K.J.V.W.), (E.W.D.)
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33
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Deutsch EW, Omenn GS, Sun Z, Maes M, Pernemalm M, Palaniappan KK, Letunica N, Vandenbrouck Y, Brun V, Tao SC, Yu X, Geyer PE, Ignjatovic V, Moritz RL, Schwenk JM. Advances and Utility of the Human Plasma Proteome. J Proteome Res 2021; 20:5241-5263. [PMID: 34672606 PMCID: PMC9469506 DOI: 10.1021/acs.jproteome.1c00657] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.
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Affiliation(s)
- Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Gilbert S Omenn
- Institute for Systems Biology, Seattle, Washington 98109, United States.,Departments of Computational Medicine & Bioinformatics, Internal Medicine, and Human Genetics and School of Public Health, University of Michigan, Ann Arbor, Michigan 48109-2218, United States
| | - Zhi Sun
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Michal Maes
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Maria Pernemalm
- Department of Oncology and Pathology/Science for Life Laboratory, Karolinska Institutet, 171 65 Stockholm, Sweden
| | | | - Natasha Letunica
- Murdoch Children's Research Institute, 50 Flemington Road, Parkville 3052, Victoria, Australia
| | - Yves Vandenbrouck
- Université Grenoble Alpes, CEA, Inserm U1292, Grenoble 38000, France
| | - Virginie Brun
- Université Grenoble Alpes, CEA, Inserm U1292, Grenoble 38000, France
| | - Sheng-Ce Tao
- Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Center for Systems Biomedicine, Shanghai Jiao Tong University, B207 SCSB Building, 800 Dongchuan Road, Shanghai 200240, China
| | - Xiaobo Yu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences-Beijing (PHOENIX Center), Beijing Institute of Lifeomics, Beijing 102206, China
| | - Philipp E Geyer
- OmicEra Diagnostics GmbH, Behringstr. 6, 82152 Planegg, Germany
| | - Vera Ignjatovic
- Murdoch Children's Research Institute, 50 Flemington Road, Parkville 3052, Victoria, Australia.,Department of Paediatrics, The University of Melbourne, 50 Flemington Road, Parkville 3052, Victoria, Australia
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Jochen M Schwenk
- Affinity Proteomics, Science for Life Laboratory, Department of Protein Science, KTH Royal Institute of Technology, Tomtebodavägen 23, SE-171 65 Solna, Sweden
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34
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Deep representation features from DreamDIA XMBD improve the analysis of data-independent acquisition proteomics. Commun Biol 2021; 4:1190. [PMID: 34650228 PMCID: PMC8517002 DOI: 10.1038/s42003-021-02726-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 09/27/2021] [Indexed: 12/24/2022] Open
Abstract
We developed DreamDIAXMBD (denoted as DreamDIA), a software suite based on a deep representation model for data-independent acquisition (DIA) data analysis. DreamDIA adopts a data-driven strategy to capture comprehensive information from elution patterns of peptides in DIA data and achieves considerable improvements on both identification and quantification performance compared with other state-of-the-art methods such as OpenSWATH, Skyline and DIA-NN. Specifically, in contrast to existing methods which use only 6 to 10 selected fragment ions from spectral libraries, DreamDIA extracts additional features from hundreds of theoretical elution profiles originated from different ions of each precursor using a deep representation network. To achieve higher coverage of target peptides without sacrificing specificity, the extracted features are further processed by nonlinear discriminative models under the framework of positive-unlabeled learning with decoy peptides as affirmative negative controls. DreamDIA is publicly available at https://github.com/xmuyulab/DreamDIA-XMBD for high coverage and accuracy DIA data analysis.
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Atkinson SC, Heaton SM, Audsley MD, Kleifeld O, Borg NA. TRIM25 and DEAD-Box RNA Helicase DDX3X Cooperate to Regulate RIG-I-Mediated Antiviral Immunity. Int J Mol Sci 2021; 22:9094. [PMID: 34445801 PMCID: PMC8396550 DOI: 10.3390/ijms22169094] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/18/2021] [Accepted: 08/18/2021] [Indexed: 12/25/2022] Open
Abstract
The cytoplasmic retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs) initiate interferon (IFN) production and antiviral gene expression in response to RNA virus infection. Consequently, RLR signalling is tightly regulated by both host and viral factors. Tripartite motif protein 25 (TRIM25) is an E3 ligase that ubiquitinates multiple substrates within the RLR signalling cascade, playing both ubiquitination-dependent and -independent roles in RIG-I-mediated IFN induction. However, additional regulatory roles are emerging. Here, we show a novel interaction between TRIM25 and another protein in the RLR pathway that is essential for type I IFN induction, DEAD-box helicase 3X (DDX3X). In vitro assays and knockdown studies reveal that TRIM25 ubiquitinates DDX3X at lysine 55 (K55) and that TRIM25 and DDX3X cooperatively enhance IFNB1 induction following RIG-I activation, but the latter is independent of TRIM25's catalytic activity. Furthermore, we found that the influenza A virus non-structural protein 1 (NS1) disrupts the TRIM25:DDX3X interaction, abrogating both TRIM25-mediated ubiquitination of DDX3X and cooperative activation of the IFNB1 promoter. Thus, our results reveal a new interplay between two RLR-host proteins that cooperatively enhance IFN-β production. We also uncover a new and further mechanism by which influenza A virus NS1 suppresses host antiviral defence.
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Affiliation(s)
- Sarah C. Atkinson
- Immunity and Immune Evasion Laboratory, Chronic Infectious and Inflammatory Diseases Research, School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC 3083, Australia; (S.C.A.); (M.D.A.)
- Infection & Immunity Program, Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia;
| | - Steven M. Heaton
- Infection & Immunity Program, Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia;
| | - Michelle D. Audsley
- Immunity and Immune Evasion Laboratory, Chronic Infectious and Inflammatory Diseases Research, School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC 3083, Australia; (S.C.A.); (M.D.A.)
- Infection & Immunity Program, Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia;
| | - Oded Kleifeld
- Faculty of Biology, Technion-Israel Institute of Technology, Haifa 32000, Israel;
| | - Natalie A. Borg
- Immunity and Immune Evasion Laboratory, Chronic Infectious and Inflammatory Diseases Research, School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC 3083, Australia; (S.C.A.); (M.D.A.)
- Infection & Immunity Program, Monash Biomedicine Discovery Institute and Department of Biochemistry and Molecular Biology, Monash University, Clayton, VIC 3800, Australia;
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Kammers K, Taub MA, Mathias RA, Yanek LR, Kanchan K, Venkatraman V, Sundararaman N, Martin J, Liu S, Hoyle D, Raedschelders K, Holewinski R, Parker S, Dardov V, Faraday N, Becker DM, Cheng L, Wang ZZ, Leek JT, Van Eyk JE, Becker LC. Gene and protein expression in human megakaryocytes derived from induced pluripotent stem cells. J Thromb Haemost 2021; 19:1783-1799. [PMID: 33829634 DOI: 10.1111/jth.15334] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 01/25/2021] [Accepted: 02/19/2021] [Indexed: 01/26/2023]
Abstract
BACKGROUND There is interest in deriving megakaryocytes (MKs) from pluripotent stem cells (iPSC) for biological studies. We previously found that genomic structural integrity and genotype concordance is maintained in iPSC-derived MKs. OBJECTIVE To establish a comprehensive dataset of genes and proteins expressed in iPSC-derived MKs. METHODS iPSCs were reprogrammed from peripheral blood mononuclear cells (MNCs) and MKs were derived from the iPSCs in 194 healthy European American and African American subjects. mRNA was isolated and gene expression measured by RNA sequencing. Protein expression was measured in 62 of the subjects using mass spectrometry. RESULTS AND CONCLUSIONS MKs expressed genes and proteins known to be important in MK and platelet function and demonstrated good agreement with previous studies in human MKs derived from CD34+ progenitor cells. The percent of cells expressing the MK markers CD41 and CD42a was consistent in biological replicates, but variable across subjects, suggesting that unidentified subject-specific factors determine differentiation of MKs from iPSCs. Gene and protein sets important in platelet function were associated with increasing expression of CD41/42a, while those related to more basic cellular functions were associated with lower CD41/42a expression. There was differential gene expression by the sex and race (but not age) of the subject. Numerous genes and proteins were highly expressed in MKs but not known to play a role in MK or platelet function; these represent excellent candidates for future study of hematopoiesis, platelet formation, and/or platelet function.
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Affiliation(s)
- Kai Kammers
- Division of Biostatistics and Bioinformatics, Department of Oncology, Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Margaret A Taub
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Rasika A Mathias
- The GeneSTAR Program, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Division of Allergy and Clinical Immunology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Lisa R Yanek
- The GeneSTAR Program, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kanika Kanchan
- Division of Allergy and Clinical Immunology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Vidya Venkatraman
- Advanced Clinical Biosystems Research Institute, Barbra Streisand Woman's Heart Center, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Niveda Sundararaman
- Advanced Clinical Biosystems Research Institute, Barbra Streisand Woman's Heart Center, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Joshua Martin
- The GeneSTAR Program, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Senquan Liu
- Division of Hematology and Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Dixie Hoyle
- Division of Hematology and Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Koen Raedschelders
- Advanced Clinical Biosystems Research Institute, Barbra Streisand Woman's Heart Center, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Ronald Holewinski
- Advanced Clinical Biosystems Research Institute, Barbra Streisand Woman's Heart Center, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Sarah Parker
- Advanced Clinical Biosystems Research Institute, Barbra Streisand Woman's Heart Center, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Victoria Dardov
- Advanced Clinical Biosystems Research Institute, Barbra Streisand Woman's Heart Center, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Nauder Faraday
- The GeneSTAR Program, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Diane M Becker
- The GeneSTAR Program, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Linzhao Cheng
- Division of Hematology and Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Zack Z Wang
- Division of Hematology and Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jeffrey T Leek
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Jennifer E Van Eyk
- Advanced Clinical Biosystems Research Institute, Barbra Streisand Woman's Heart Center, The Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Lewis C Becker
- The GeneSTAR Program, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Jia L, Li J, Li P, Liu D, Li J, Shen J, Zhu B, Ma C, Zhao T, Lan R, Dang L, Li W, Sun S. Site-specific glycoproteomic analysis revealing increased core-fucosylation on FOLR1 enhances folate uptake capacity of HCC cells to promote EMT. Am J Cancer Res 2021; 11:6905-6921. [PMID: 34093861 PMCID: PMC8171077 DOI: 10.7150/thno.56882] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 04/14/2021] [Indexed: 12/24/2022] Open
Abstract
Rationale: Epithelial-mesenchymal transition (EMT) has been recognized as an important step toward high invasion and metastasis of many cancers including hepatocellular carcinoma (HCC), while the mechanism for EMT promotion is still ambiguous. Methods: The dynamic alterations of site-specific glycosylation during HGF/TGF-β1-induced EMT process of three HCC cell lines were systematically investigated using precision glycoproteomic methods. The possible roles of EMT-related glycoproteins and site-specific glycans were further confirmed by various molecular biological approaches. Results: Using mass spectrometry-based glycoproteomic methods, we totally identified 2306 unique intact glycopeptides from SMMC-7721 and HepG2 cell lines, and found that core-fucosylated glycans were accounted for the largest proportion of complex N-glycans. Through quantification analysis of intact glycopeptides, we found that the majority of core-fucosylated intact glycopeptides from folate receptor α (FOLR1) were up-regulated in the three HGF-treated cell lines. Similarly, core-fucosylation of FOLR1 were up-regulated in SMMC-7721 and Hep3B cells with TGF-β1 treatment. Using molecular approaches, we further demonstrated that FUT8 was a driver for HGF/TGF-β1-induced EMT. The silencing of FUT8 reduced core-fucosylation and partially blocked the progress of HGF-induced EMT. Finally, we confirmed that the level of core-fucosylation on FOLR1 especially at the glycosite Asn-201 positively regulated the cellular uptake capacity of folates, and enhanced uptake of folates could promote the EMT of HCC cells. Conclusions: Based on the results, we proposed a potential pathway for HGF or TGF-β1-induced EMT of HCC cells: HGF or TGF-β1 treatment of HCC cells can increase the expression of glycosyltransferase FUT8 to up-regulate the core-fucosylation of N-glycans on glycoproteins including the FOLR1; core-fucosylation on FOLR1 can then enhance the folate uptake capacity to finally promote the EMT progress of HCC cells.
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Finamore F, Cecchettini A, Ceccherini E, Signore G, Ferro F, Rocchiccioli S, Baldini C. Characterization of Extracellular Vesicle Cargo in Sjögren's Syndrome through a SWATH-MS Proteomics Approach. Int J Mol Sci 2021; 22:ijms22094864. [PMID: 34064456 PMCID: PMC8124455 DOI: 10.3390/ijms22094864] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 04/23/2021] [Accepted: 04/24/2021] [Indexed: 12/19/2022] Open
Abstract
Primary Sjögren’s syndrome (pSS) is a complex heterogeneous disease characterized by a wide spectrum of glandular and extra-glandular manifestations. In this pilot study, a SWATH-MS approach was used to monitor extracellular vesicles-enriched saliva (EVs) sub-proteome in pSS patients, to compare it with whole saliva (WS) proteome, and assess differential expressed proteins between pSS and healthy control EVs samples. Comparison between EVs and WS led to the characterization of compartment-specific proteins with a moderate degree of overlap. A total of 290 proteins were identified and quantified in EVs from healthy and pSS patients. Among those, 121 proteins were found to be differentially expressed in pSS, 82% were found to be upregulated, and 18% downregulated in pSS samples. The most representative functional pathways associated to the protein networks were related to immune-innate response, including several members of S100 protein family, annexin A2, resistin, serpin peptidase inhibitors, azurocidin, and CD14 monocyte differentiation antigen. Our results highlight the usefulness of EVs for the discovery of novel salivary-omic biomarkers and open novel perspectives in pSS for the identification of proteins of clinical relevance that could be used not only for the disease diagnosis but also to improve patients’ stratification and treatment-monitoring. Data are available via ProteomeXchange with identifier PXD025649.
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Affiliation(s)
- Francesco Finamore
- Clinical Phisiology Institute-CNR, 56124 Pisa, Italy; (F.F.); (E.C.); (S.R.)
| | - Antonella Cecchettini
- Department of Clinical and Experimental Medicine, Rheumatology Unit, University of Pisa, 56126 Pisa, Italy; (F.F.); (C.B.)
- Correspondence:
| | - Elisa Ceccherini
- Clinical Phisiology Institute-CNR, 56124 Pisa, Italy; (F.F.); (E.C.); (S.R.)
| | - Giovanni Signore
- Fondazione Pisana per la Scienza, S Giuliano Terme, 56017 Pisa, Italy;
| | - Francesco Ferro
- Department of Clinical and Experimental Medicine, Rheumatology Unit, University of Pisa, 56126 Pisa, Italy; (F.F.); (C.B.)
| | - Silvia Rocchiccioli
- Clinical Phisiology Institute-CNR, 56124 Pisa, Italy; (F.F.); (E.C.); (S.R.)
| | - Chiara Baldini
- Department of Clinical and Experimental Medicine, Rheumatology Unit, University of Pisa, 56126 Pisa, Italy; (F.F.); (C.B.)
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Neely BA. Cloudy with a Chance of Peptides: Accessibility, Scalability, and Reproducibility with Cloud-Hosted Environments. J Proteome Res 2021; 20:2076-2082. [PMID: 33513299 PMCID: PMC8637422 DOI: 10.1021/acs.jproteome.0c00920] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Cloud-hosted environments offer known benefits when computational needs outstrip affordable local workstations, enabling high-performance computation without a physical cluster. What has been less apparent, especially to novice users, is the transformative potential for cloud-hosted environments to bridge the digital divide that exists between poorly funded and well-resourced laboratories, and to empower modern research groups with remote personnel and trainees. Using cloud-based proteomic bioinformatic pipelines is not predicated on analyzing thousands of files, but instead can be used to improve accessibility during remote work, extreme weather, or working with under-resourced remote trainees. The general benefits of cloud-hosted environments also allow for scalability and encourage reproducibility. Since one possible hurdle to adoption is awareness, this paper is written with the nonexpert in mind. The benefits and possibilities of using a cloud-hosted environment are emphasized by describing how to setup an example workflow to analyze a previously published label-free data-dependent acquisition mass spectrometry data set of mammalian urine. Cost and time of analysis are compared using different computational tiers, and important practical considerations are described. Overall, cloud-hosted environments offer the potential to solve large computational problems, but more importantly can enable and accelerate research in smaller research groups with inadequate infrastructure and suboptimal local computational resources.
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Affiliation(s)
- Benjamin A Neely
- Chemical Sciences Division, National Institute of Standards and Technology, Charleston, South Carolina 29412, United States
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40
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McCabe A, Jones AR. lcmsWorld: High-Performance 3D Visualization Software for Mass Spectrometry. J Proteome Res 2021; 20:1981-1985. [PMID: 33710902 DOI: 10.1021/acs.jproteome.0c00618] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Complex biological samples, in particular, in proteomics and metabolomics research, are often analyzed using mass spectrometry paired with liquid chromatography or gas chromatography. The chromatography stage adds a third dimension (retention time) to the usual 2D mass spectrometry output (mass/charge, detected ion counts). Experimental results are often discovered by complex computational analysis, but it is not always possible to know if the data has been correctly interpreted. To perform quality-control checks, it can often be helpful to verify the results by manually examining the raw data, and it is typically easier to understand the data in a graphical, rather than numerical, form. 3D graphics hardware is present in most modern computers but is rarely utilized by bioinformatics software, even when the data to be viewed are naturally 3D. lcmsWorld is new software that uses graphics hardware to quickly and smoothly examine and compare LC-MS data. A preprocessing step allows the software to subsequently access any area of the data instantly at multiple levels of detail. The data can then be freely navigated while the software automatically selects, loads, and displays the most appropriate detail. lcmsWorld is open source. Releases, source code, and example data files are available via https://github.com/PGB-LIV/lcmsWorld.
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Affiliation(s)
- Antony McCabe
- Computational Biology Facility, University of Liverpool, Liverpool L69 7ZB, United Kingdom
| | - Andrew R Jones
- Computational Biology Facility, University of Liverpool, Liverpool L69 7ZB, United Kingdom.,Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L69 7ZB, United Kingdom
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41
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Concepcion FA, Khan MN, Ju Wang JD, Wei AD, Ojemann JG, Ko AL, Shi Y, Eng JK, Ramirez JM, Poolos NP. HCN Channel Phosphorylation Sites Mapped by Mass Spectrometry in Human Epilepsy Patients and in an Animal Model of Temporal Lobe Epilepsy. Neuroscience 2021; 460:13-30. [PMID: 33571596 DOI: 10.1016/j.neuroscience.2021.01.038] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/07/2021] [Accepted: 01/26/2021] [Indexed: 10/22/2022]
Abstract
Because hyperpolarization-activated cyclic nucleotide-gated (HCN) ion channels modulate the excitability of cortical and hippocampal principal neurons, these channels play a key role in the hyperexcitability that occurs during the development of epilepsy after a brain insult, or epileptogenesis. In epileptic rats generated by pilocarpine-induced status epilepticus, HCN channel activity is downregulated by two main mechanisms: a hyperpolarizing shift in gating and a decrease in amplitude of the current mediated by HCN channels, Ih. Because these mechanisms are modulated by various phosphorylation signaling pathways, we hypothesized that phosphorylation changes occur at individual HCN channel amino acid residues (phosphosites) during epileptogenesis. We collected CA1 hippocampal tissue from male Sprague Dawley rats made epileptic by pilocarpine-induced status epilepticus, and age-matched naïve controls. We also included resected human brain tissue containing epileptogenic zones (EZs) where seizures arise for comparison to our chronically epileptic rats. After enrichment for HCN1 and HCN2 isoforms by immunoprecipitation and trypsin in-gel digestion, the samples were analyzed by mass spectrometry. We identified numerous phosphosites from HCN1 and HCN2 channels, representing a novel survey of phosphorylation sites within HCN channels. We found high levels of HCN channel phosphosite homology between humans and rats. We also identified a novel HCN1 channel phosphosite S791, which underwent significantly increased phosphorylation during the chronic epilepsy stage. Heterologous expression of a phosphomimetic mutant, S791D, replicated a hyperpolarizing shift in Ih gating seen in neurons from chronically epileptic rats. These results show that HCN1 channel phosphorylation is altered in epilepsy and may be of pathogenic importance.
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Affiliation(s)
- F A Concepcion
- Department of Neurology and Regional Epilepsy Center, University of Washington, Seattle, WA, United States
| | - M N Khan
- Department of Neurology and Regional Epilepsy Center, University of Washington, Seattle, WA, United States
| | - J-D Ju Wang
- Seattle Children's Research Institute, Center for Integrative Brain Research, Seattle, WA, United States
| | - A D Wei
- Seattle Children's Research Institute, Center for Integrative Brain Research, Seattle, WA, United States
| | - J G Ojemann
- Seattle Children's Research Institute, Center for Integrative Brain Research, Seattle, WA, United States; Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - A L Ko
- Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - Y Shi
- Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, United States
| | - J K Eng
- Proteomics Resource, University of Washington, Seattle, WA, United States
| | - J-M Ramirez
- Seattle Children's Research Institute, Center for Integrative Brain Research, Seattle, WA, United States; Department of Neurological Surgery, University of Washington, Seattle, WA, United States
| | - N P Poolos
- Department of Neurology and Regional Epilepsy Center, University of Washington, Seattle, WA, United States.
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42
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Rabe A, Gesell Salazar M, Völker U. Bottom-Up Community Proteome Analysis of Saliva Samples and Tongue Swabs by Data-Dependent Acquisition Nano LC-MS/MS Mass Spectrometry. Methods Mol Biol 2021; 2327:221-238. [PMID: 34410648 DOI: 10.1007/978-1-0716-1518-8_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Analysis using mass spectrometry enables the characterization of metaproteomes in their native environments and overcomes the limitation of proteomics of pure cultures. Metaproteomics is a promising approach to link functions of currently actively expressed genes to the phylogenetic composition of the microbiome in their habitat. In this chapter, we describe the preparation of saliva samples and tongue swabs for nLC-MS/MS measurements and their bioinformatic analysis based on the Trans-Proteomic Pipeline and Prophane to study the oral microbiome .
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Affiliation(s)
- Alexander Rabe
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany.
| | - Manuela Gesell Salazar
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
| | - Uwe Völker
- Department of Functional Genomics, Interfaculty Institute for Genetics and Functional Genomics, University Medicine Greifswald, Greifswald, Germany
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43
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Eng JK, Deutsch EW. Extending Comet for Global Amino Acid Variant and Post-Translational Modification Analysis Using the PSI Extended FASTA Format. Proteomics 2020; 20:e1900362. [PMID: 32106352 PMCID: PMC7483226 DOI: 10.1002/pmic.201900362] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 02/21/2020] [Indexed: 01/15/2023]
Abstract
Protein identification by tandem mass spectrometry sequence database searching is a standard practice in many proteomics laboratories. The de facto standard for the representation of sequence databases used as input to sequence database search tools is the FASTA format. The Human Proteome Organization's Proteomics Standards Initiative has developed an extension to the FASTA format termed the proteomics standards initiative extended FASTA format or PSI extended FASTA format (PEFF) where additional information such as structural annotations are encoded in the protein description lines. Comet has been extended to automatically analyze the post translational modifications and amino acid substitutions encoded in PEFF databases. Comet's PEFF implementation and example analysis results searching a HEK293 dataset against the neXtProt PEFF database are presented.
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Affiliation(s)
- Jimmy K Eng
- Proteomics Resource, University of Washington, Seattle, WA, 98195, USA
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44
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Takan S, Allmer J. DNMSO; an ontology for representing de novo sequencing results from Tandem-MS data. PeerJ 2020; 8:e10216. [PMID: 33150092 PMCID: PMC7585381 DOI: 10.7717/peerj.10216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 09/28/2020] [Indexed: 11/20/2022] Open
Abstract
For the identification and sequencing of proteins, mass spectrometry (MS) has become the tool of choice and, as such, drives proteomics. MS/MS spectra need to be assigned a peptide sequence for which two strategies exist. Either database search or de novo sequencing can be employed to establish peptide spectrum matches. For database search, mzIdentML is the current community standard for data representation. There is no community standard for representing de novo sequencing results, but we previously proposed the de novo markup language (DNML). At the moment, each de novo sequencing solution uses different data representation, complicating downstream data integration, which is crucial since ensemble predictions may be more useful than predictions of a single tool. We here propose the de novo MS Ontology (DNMSO), which can, for example, provide many-to-many mappings between spectra and peptide predictions. Additionally, an application programming interface (API) that supports any file operation necessary for de novo sequencing from spectra input to reading, writing, creating, of the DNMSO format, as well as conversion from many other file formats, has been implemented. This API removes all overhead from the production of de novo sequencing tools and allows developers to concentrate on algorithm development completely. We make the API and formal descriptions of the format freely available at https://github.com/savastakan/dnmso.
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Affiliation(s)
- Savaş Takan
- Department of Computer Engineering, Faculty of Engineering, Izmir Institute of Technology, Izmir, Turkey
| | - Jens Allmer
- Hochschule Ruhr West, University of Applied Sciences, Medical Informatics and Bioinformatics, Institute for Measurement Engineering and Sensor Technology, Mülheim an der Ruhr, Germany
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45
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Omenn GS, Lane L, Overall CM, Cristea IM, Corrales FJ, Lindskog C, Paik YK, Van Eyk JE, Liu S, Pennington SR, Snyder MP, Baker MS, Bandeira N, Aebersold R, Moritz RL, Deutsch EW. Research on the Human Proteome Reaches a Major Milestone: >90% of Predicted Human Proteins Now Credibly Detected, According to the HUPO Human Proteome Project. J Proteome Res 2020; 19:4735-4746. [PMID: 32931287 DOI: 10.1021/acs.jproteome.0c00485] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
According to the 2020 Metrics of the HUPO Human Proteome Project (HPP), expression has now been detected at the protein level for >90% of the 19 773 predicted proteins coded in the human genome. The HPP annually reports on progress made throughout the world toward credibly identifying and characterizing the complete human protein parts list and promoting proteomics as an integral part of multiomics studies in medicine and the life sciences. NeXtProt release 2020-01 classified 17 874 proteins as PE1, having strong protein-level evidence, up 180 from 17 694 one year earlier. These represent 90.4% of the 19 773 predicted coding genes (all PE1,2,3,4 proteins in neXtProt). Conversely, the number of neXtProt PE2,3,4 proteins, termed the "missing proteins" (MPs), was reduced by 230 from 2129 to 1899 since the neXtProt 2019-01 release. PeptideAtlas is the primary source of uniform reanalysis of raw mass spectrometry data for neXtProt, supplemented this year with extensive data from MassIVE. PeptideAtlas 2020-01 added 362 canonical proteins between 2019 and 2020 and MassIVE contributed 84 more, many of which converted PE1 entries based on non-MS evidence to the MS-based subgroup. The 19 Biology and Disease-driven B/D-HPP teams continue to pursue the identification of driver proteins that underlie disease states, the characterization of regulatory mechanisms controlling the functions of these proteins, their proteoforms, and their interactions, and the progression of transitions from correlation to coexpression to causal networks after system perturbations. And the Human Protein Atlas published Blood, Brain, and Metabolic Atlases.
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Affiliation(s)
- Gilbert S Omenn
- University of Michigan, Ann Arbor, Michigan 48109, United States.,Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Lydie Lane
- CALIPHO Group, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | | | - Ileana M Cristea
- Princeton University, Princeton, New Jersey 08544, United States
| | | | | | | | | | - Siqi Liu
- BGI Group, Shenzhen 518083, China
| | | | | | - Mark S Baker
- Macquarie University, Macquarie Park, NSW 2109, Australia
| | - Nuno Bandeira
- University of California, San Diego, La Jolla, California 92093, United States
| | - Ruedi Aebersold
- ETH-Zurich and University of Zurich, 8092 Zurich, Switzerland
| | - Robert L Moritz
- Institute for Systems Biology, Seattle, Washington 98109, United States
| | - Eric W Deutsch
- Institute for Systems Biology, Seattle, Washington 98109, United States
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46
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Burleigh K, Maltbaek JH, Cambier S, Green R, Gale M, James RC, Stetson DB. Human DNA-PK activates a STING-independent DNA sensing pathway. Sci Immunol 2020; 5:5/43/eaba4219. [PMID: 31980485 DOI: 10.1126/sciimmunol.aba4219] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Accepted: 12/19/2019] [Indexed: 12/20/2022]
Abstract
Detection of intracellular DNA by the cGAS-STING pathway activates a type I interferon-mediated innate immune response that protects from virus infection. Whether there are additional DNA sensing pathways, and how such pathways might function, remains controversial. We show here that humans-but not laboratory mice-have a second, potent, STING-independent DNA sensing pathway (SIDSP). We identify human DNA-dependent protein kinase (DNA-PK) as the sensor of this pathway and demonstrate that DNA-PK activity drives a robust and broad antiviral response. We show that the E1A oncoprotein of human adenovirus 5 and the ICP0 protein of herpes simplex virus 1 block this response. We found heat shock protein HSPA8/HSC70 as a target for inducible phosphorylation in the DNA-PK antiviral pathway. Last, we demonstrate that DNA damage and detection of foreign DNA trigger distinct modalities of DNA-PK activity. These findings reveal the existence, sensor, a specific downstream target, and viral antagonists of a SIDSP in human cells.
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Affiliation(s)
- Katelyn Burleigh
- Department of Immunology, University of Washington School of Medicine, 750 Republican St., Seattle, WA 98109, USA
| | - Joanna H Maltbaek
- Department of Immunology, University of Washington School of Medicine, 750 Republican St., Seattle, WA 98109, USA
| | - Stephanie Cambier
- Department of Immunology, University of Washington School of Medicine, 750 Republican St., Seattle, WA 98109, USA
| | - Richard Green
- Department of Immunology, University of Washington School of Medicine, 750 Republican St., Seattle, WA 98109, USA
| | - Michael Gale
- Department of Immunology, University of Washington School of Medicine, 750 Republican St., Seattle, WA 98109, USA.,Center for Innate Immunity and Immune Disease, University of Washington School of Medicine, 750 Republican St., Seattle, WA 98109, USA
| | - Richard C James
- Center for Immunity and Immunotherapies, Seattle Children's Research Institute, 1900 9th Avenue, Seattle, WA 98101, USA.,Department of Pediatrics, University of Washington School of Medicine, 1959 NE Pacific Street, Seattle, WA 98195, USA
| | - Daniel B Stetson
- Department of Immunology, University of Washington School of Medicine, 750 Republican St., Seattle, WA 98109, USA. .,Center for Innate Immunity and Immune Disease, University of Washington School of Medicine, 750 Republican St., Seattle, WA 98109, USA
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47
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Iacobucci I, Monaco V, Cozzolino F, Monti M. From classical to new generation approaches: An excursus of -omics methods for investigation of protein-protein interaction networks. J Proteomics 2020; 230:103990. [PMID: 32961344 DOI: 10.1016/j.jprot.2020.103990] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/11/2020] [Accepted: 08/31/2020] [Indexed: 01/24/2023]
Abstract
Functional Proteomics aims to the identification of in vivo protein-protein interaction (PPI) in order to piece together protein complexes, and therefore, cell pathways involved in biological processes of interest. Over the years, proteomic approaches used for protein-protein interaction investigation have relied on classical biochemical protocols adapted to a global overview of protein-protein interactions, within so-called "interactomics" investigation. In particular, their coupling with advanced mass spectrometry instruments and innovative analytical methods led to make great strides in the PPIs investigation in proteomics. In this review, an overview of protein complexes purification strategies, from affinity purification approaches, including proximity-dependent labeling techniques and cross-linking strategy for the identification of transient interactions, to Blue Native Gel Electrophoresis (BN-PAGE) and Size Exclusion Chromatography (SEC) employed in the "complexome profiling", has been reported, giving a look to their developments, strengths and weakness and providing to readers several recent applications of each strategy. Moreover, a section dedicated to bioinformatic databases and platforms employed for protein networks analyses was also included.
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Affiliation(s)
- Ilaria Iacobucci
- Department of Chemical Sciences, University Federico II of Naples, Strada Comunale Cinthia, 26, 80126 Naples, Italy; CEINGE Advanced Biotechnologies, Via G. Salvatore 486, 80145 Naples, Italy
| | - Vittoria Monaco
- CEINGE Advanced Biotechnologies, Via G. Salvatore 486, 80145 Naples, Italy
| | - Flora Cozzolino
- Department of Chemical Sciences, University Federico II of Naples, Strada Comunale Cinthia, 26, 80126 Naples, Italy; CEINGE Advanced Biotechnologies, Via G. Salvatore 486, 80145 Naples, Italy.
| | - Maria Monti
- Department of Chemical Sciences, University Federico II of Naples, Strada Comunale Cinthia, 26, 80126 Naples, Italy; CEINGE Advanced Biotechnologies, Via G. Salvatore 486, 80145 Naples, Italy.
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48
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Winkler R. ProtyQuant: Comparing label-free shotgun proteomics datasets using accumulated peptide probabilities. J Proteomics 2020; 230:103985. [PMID: 32956841 DOI: 10.1016/j.jprot.2020.103985] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 08/07/2020] [Accepted: 09/10/2020] [Indexed: 11/20/2022]
Abstract
Comparing multiple label-free shotgun proteomics datasets requires various data processing and formatting steps, including peptide-spectrum matching, protein inference, and quantification. Finally, the compilation of results files into a format that allows for downstream analyses. ProtyQuant performs protein inference and quantification calculations, and combines the results of individual datasets into plain text tables. These are lightweight, human-readable, and easy to import into databases or statistical software. ProtyQuant reads validated pepXML from proteomic workflows such as the Trans-Proteomic Pipeline (TPP), which makes it compatible with many commercial and free search engines. For protein inference and quantification, a modified version of the PIPQ program (He et al. 2016) was integrated. In contrast to simple spectral-counting, PIPQ sums up peptide probabilities. For assigning peptides to proteins, three algorithms are available: Multiple Counting, Equal Division, and Linear Programming. The accumulated peptide probabilities (app) are used for both tasks, protein probability estimation, and quantification. ProtyQuant was tested using a reference dataset for label-free shotgun proteomics, obtained from different concentrations of 48 human UPS proteins spiked into yeast lysate. Compared to ProteinProphet, ProtyQuant detected up to 126 (15%) more proteins in the mixture, applying an equal false positive rate (FPR). Using the app values for label-free quantification showed suitable sensitivity and linearity. Strikingly, the app values represent a realistic measure of 'Protein Presence,' an integral concept of protein probability and quantity. ProtyQuant provides a graphical user interface (GUI) and scripts for console-based processing. It is available (GNU GLP v3) for Windows, Linux, and Docker from https://bitbucket.org/lababi/protyquant/. SIGNIFICANCE: Integrating data from multiple shot-gun proteomics experiments overwhelms non-expert researchers. ProtyQuant complements well-established workflows by aiding the comparison of proteins across samples. Importantly, the probability and abundance of proteins are seen from a holistic point of view. The accumulated peptide probability (app) as an integral measure of 'Protein Presence' demonstrated reliable performance for both protein identification and quantification. Using the app as a single measure facilitates the compilation of reports in comparative proteomics.
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Affiliation(s)
- Robert Winkler
- Center for Research and Advanced Studies (CINVESTAV) Irapuato, Department of Biochemistry and Biotechnology, Km. 9.6 Libramiento Norte Carr. Irapuato-León, 36824 Irapuato, GTO, Mexico.
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49
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Krasny L, Bland P, Burns J, Lima NC, Harrison PT, Pacini L, Elms ML, Ning J, Martinez VG, Yu YR, Acton SE, Ho PC, Calvo F, Swain A, Howard BA, Natrajan RC, Huang PH. A mouse SWATH-mass spectrometry reference spectral library enables deconvolution of species-specific proteomic alterations in human tumour xenografts. Dis Model Mech 2020; 13:dmm044586. [PMID: 32493768 PMCID: PMC7375474 DOI: 10.1242/dmm.044586] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Accepted: 05/20/2020] [Indexed: 12/11/2022] Open
Abstract
SWATH-mass spectrometry (MS) enables accurate and reproducible proteomic profiling in multiple model organisms including the mouse. Here, we present a comprehensive mouse reference spectral library (MouseRefSWATH) that permits quantification of up to 10,597 proteins (62.2% of the mouse proteome) by SWATH-MS. We exploit MouseRefSWATH to develop an analytical pipeline for species-specific deconvolution of proteomic alterations in human tumour xenografts (XenoSWATH). This method overcomes the challenge of high sequence similarity between mouse and human proteins, facilitating the study of host microenvironment-tumour interactions from 'bulk tumour' measurements. We apply the XenoSWATH pipeline to characterize an intraductal xenograft model of breast ductal carcinoma in situ and uncover complex regulation consistent with stromal reprogramming, where the modulation of cell migration pathways is not restricted to tumour cells but also operates in the mouse stroma upon progression to invasive disease. MouseRefSWATH and XenoSWATH open new opportunities for in-depth and reproducible proteomic assessment to address wide-ranging biological questions involving this important model organism.
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MESH Headings
- Animals
- Breast Neoplasms/metabolism
- Breast Neoplasms/pathology
- Carcinoma, Intraductal, Noninfiltrating/metabolism
- Carcinoma, Intraductal, Noninfiltrating/pathology
- Cell Communication
- Cell Line, Tumor
- Chromatography, Liquid
- Databases, Protein
- Female
- Heterografts
- Humans
- Male
- Mice
- Mice, Inbred C57BL
- Mice, Nude
- Mice, SCID
- NIH 3T3 Cells
- Neoplasm Proteins/metabolism
- Neoplasm Transplantation
- Proteome
- Proteomics
- Species Specificity
- Stromal Cells/metabolism
- Stromal Cells/pathology
- Tandem Mass Spectrometry
- Tumor Microenvironment
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Affiliation(s)
- Lukas Krasny
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Philip Bland
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK
| | - Jessica Burns
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Nadia Carvalho Lima
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Peter T Harrison
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Laura Pacini
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Mark L Elms
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
| | - Jian Ning
- Tumour Profiling Unit, The Institute of Cancer Research, London SW3 6JB, UK
| | - Victor Garcia Martinez
- Stromal Immunology Group, MRC Laboratory for Molecular Cell Biology, University College London WC1E 6BT, London, UK
| | - Yi-Ru Yu
- Department of Oncology, University of Lausanne, Lausanne CH-1066, Switzerland
- Ludwig Institute for Cancer Research, Lausanne CH-1066, Switzerland
| | - Sophie E Acton
- Stromal Immunology Group, MRC Laboratory for Molecular Cell Biology, University College London WC1E 6BT, London, UK
| | - Ping-Chih Ho
- Department of Oncology, University of Lausanne, Lausanne CH-1066, Switzerland
- Ludwig Institute for Cancer Research, Lausanne CH-1066, Switzerland
| | - Fernando Calvo
- The Tumour Microenvironment Team, Institute of Biomedicine and Biotechnology of Cantabria, Santander 39011, Spain
| | - Amanda Swain
- Tumour Profiling Unit, The Institute of Cancer Research, London SW3 6JB, UK
| | - Beatrice A Howard
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK
| | - Rachael C Natrajan
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London SW3 6JB, UK
| | - Paul H Huang
- Division of Molecular Pathology, The Institute of Cancer Research, London SW3 6JB, UK
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50
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Chavez JD, Tang X, Campbell MD, Reyes G, Kramer PA, Stuppard R, Keller A, Zhang H, Rabinovitch PS, Marcinek DJ, Bruce JE. Mitochondrial protein interaction landscape of SS-31. Proc Natl Acad Sci U S A 2020; 117:15363-15373. [PMID: 32554501 PMCID: PMC7334473 DOI: 10.1073/pnas.2002250117] [Citation(s) in RCA: 87] [Impact Index Per Article: 21.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Mitochondrial dysfunction underlies the etiology of a broad spectrum of diseases including heart disease, cancer, neurodegenerative diseases, and the general aging process. Therapeutics that restore healthy mitochondrial function hold promise for treatment of these conditions. The synthetic tetrapeptide, elamipretide (SS-31), improves mitochondrial function, but mechanistic details of its pharmacological effects are unknown. Reportedly, SS-31 primarily interacts with the phospholipid cardiolipin in the inner mitochondrial membrane. Here we utilize chemical cross-linking with mass spectrometry to identify protein interactors of SS-31 in mitochondria. The SS-31-interacting proteins, all known cardiolipin binders, fall into two groups, those involved in ATP production through the oxidative phosphorylation pathway and those involved in 2-oxoglutarate metabolic processes. Residues cross-linked with SS-31 reveal binding regions that in many cases, are proximal to cardiolipin-protein interacting regions. These results offer a glimpse of the protein interaction landscape of SS-31 and provide mechanistic insight relevant to SS-31 mitochondrial therapy.
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Affiliation(s)
- Juan D Chavez
- Department of Genome Sciences, University of Washington, Seattle, WA 98105
| | - Xiaoting Tang
- Department of Genome Sciences, University of Washington, Seattle, WA 98105
| | | | - Gustavo Reyes
- Department of Radiology, University of Washington, Seattle, WA 98105
| | - Philip A Kramer
- Department of Radiology, University of Washington, Seattle, WA 98105
| | - Rudy Stuppard
- Department of Radiology, University of Washington, Seattle, WA 98105
| | - Andrew Keller
- Department of Genome Sciences, University of Washington, Seattle, WA 98105
| | - Huiliang Zhang
- Department of Pathology, University of Washington, Seattle, WA 98195
| | | | - David J Marcinek
- Department of Radiology, University of Washington, Seattle, WA 98105
| | - James E Bruce
- Department of Genome Sciences, University of Washington, Seattle, WA 98105;
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