1
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Pietz T, Gupta S, Schlaffner CN, Ahmed S, Steen H, Renard BY, Baum K. PEPerMINT: peptide abundance imputation in mass spectrometry-based proteomics using graph neural networks. Bioinformatics 2024; 40:ii70-ii78. [PMID: 39230699 PMCID: PMC11373339 DOI: 10.1093/bioinformatics/btae389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2024] Open
Abstract
MOTIVATION Accurate quantitative information about protein abundance is crucial for understanding a biological system and its dynamics. Protein abundance is commonly estimated using label-free, bottom-up mass spectrometry (MS) protocols. Here, proteins are digested into peptides before quantification via MS. However, missing peptide abundance values, which can make up more than 50% of all abundance values, are a common issue. They result in missing protein abundance values, which then hinder accurate and reliable downstream analyses. RESULTS To impute missing abundance values, we propose PEPerMINT, a graph neural network model working directly on the peptide level that flexibly takes both peptide-to-protein relationships in a graph format as well as amino acid sequence information into account. We benchmark our method against 11 common imputation methods on 6 diverse datasets, including cell lines, tissue, and plasma samples. We observe that PEPerMINT consistently outperforms other imputation methods. Its prediction performance remains high for varying degrees of missingness, different evaluation approaches, and differential expression prediction. As an additional novel feature, PEPerMINT provides meaningful uncertainty estimates and allows for tailoring imputation to the user's needs based on the reliability of imputed values. AVAILABILITY AND IMPLEMENTATION The code is available at https://github.com/DILiS-lab/pepermint.
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Affiliation(s)
- Tobias Pietz
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany
| | - Sukrit Gupta
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany
- Department of Computer Science and Engineering, Indian Institute of Technology, Ropar, Rupnagar, 140001, India
| | - Christoph N Schlaffner
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA, 02115, United States
| | - Saima Ahmed
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA, 02115, United States
| | - Hanno Steen
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA, 02115, United States
| | - Bernhard Y Renard
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany
- Windreich Department for Artificial Intelligence and Human Health and Hasso Plattner Institute at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, 10029, United States
| | - Katharina Baum
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, 14482, Germany
- Windreich Department for Artificial Intelligence and Human Health and Hasso Plattner Institute at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York City, NY, 10029, United States
- Department of Mathematics and Computer Science, Free University Berlin, Berlin, 14195, Germany
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2
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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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3
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Korchak JA, Jeffery ED, Bandyopadhyay S, Jordan BT, Lehe MD, Watts EF, Fenix A, Wilhelm M, Sheynkman GM. IS-PRM-Based Peptide Targeting Informed by Long-Read Sequencing for Alternative Proteome Detection. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024. [PMID: 39012054 DOI: 10.1021/jasms.4c00119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/17/2024]
Abstract
Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of predefined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (lrRNA-seq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNaseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This lrRNA-seq-informed Tomahto targeted approach is a new modality for generating protein-level evidence of alternative isoforms─a critical first step in designing functional studies and eventually clinical assays.
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Affiliation(s)
- Jennifer A Korchak
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Erin D Jeffery
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Saikat Bandyopadhyay
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22903, United States
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Ben T Jordan
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, Maryland 21701, United States
| | - Micah D Lehe
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Emily F Watts
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22903, United States
| | - Aidan Fenix
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, Washington 98195, United States
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich (TUM), D-85354 Freising, Germany
| | - Gloria M Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia 22903, United States
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia 22903, United States
- UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, Virginia 22903, United States
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4
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Peters-Clarke TM, Coon JJ, Riley NM. Instrumentation at the Leading Edge of Proteomics. Anal Chem 2024; 96:7976-8010. [PMID: 38738990 DOI: 10.1021/acs.analchem.3c04497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Affiliation(s)
- Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Morgridge Institute for Research, Madison, Wisconsin 53715, United States
| | - Nicholas M Riley
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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5
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Korchak JA, Jeffery ED, Bandyopadhyay S, Jordan BT, Lehe M, Watts EF, Fenix A, Wilhelm M, Sheynkman GM. IS-PRM-based peptide targeting informed by long-read sequencing for alternative proteome detection. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.01.587549. [PMID: 38617311 PMCID: PMC11014528 DOI: 10.1101/2024.04.01.587549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Alternative splicing is a major contributor of transcriptomic complexity, but the extent to which transcript isoforms are translated into stable, functional protein isoforms is unclear. Furthermore, detection of relatively scarce isoform-specific peptides is challenging, with many protein isoforms remaining uncharted due to technical limitations. Recently, a family of advanced targeted MS strategies, termed internal standard parallel reaction monitoring (IS-PRM), have demonstrated multiplexed, sensitive detection of pre-defined peptides of interest. Such approaches have not yet been used to confirm existence of novel peptides. Here, we present a targeted proteogenomic approach that leverages sample-matched long-read RNA sequencing (LR RNAseq) data to predict potential protein isoforms with prior transcript evidence. Predicted tryptic isoform-specific peptides, which are specific to individual gene product isoforms, serve as "triggers" and "targets" in the IS-PRM method, Tomahto. Using the model human stem cell line WTC11, LR RNAseq data were generated and used to inform the generation of synthetic standards for 192 isoform-specific peptides (114 isoforms from 55 genes). These synthetic "trigger" peptides were labeled with super heavy tandem mass tags (TMT) and spiked into TMT-labeled WTC11 tryptic digest, predicted to contain corresponding endogenous "target" peptides. Compared to DDA mode, Tomahto increased detectability of isoforms by 3.6-fold, resulting in the identification of five previously unannotated isoforms. Our method detected protein isoform expression for 43 out of 55 genes corresponding to 54 resolved isoforms. This LR RNA seq-informed Tomahto targeted approach, called LRP-IS-PRM, is a new modality for generating protein-level evidence of alternative isoforms - a critical first step in designing functional studies and eventually clinical assays.
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Affiliation(s)
- Jennifer A. Korchak
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Erin D. Jeffery
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Saikat Bandyopadhyay
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Ben T. Jordan
- Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD USA
| | - Micah Lehe
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Emily F. Watts
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
| | - Aidan Fenix
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Mathias Wilhelm
- Computational Mass Spectrometry, Technical University of Munich (TUM), D-85354 Freising, Germany
| | - Gloria M. Sheynkman
- Department of Molecular Physiology and Biological Physics, University of Virginia, Charlottesville, Virginia, USA
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA
- UVA Comprehensive Cancer Center, University of Virginia, Charlottesville, VA, USA
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6
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Rebak AS, Hendriks IA, Nielsen ML. Characterizing citrullination by mass spectrometry-based proteomics. Philos Trans R Soc Lond B Biol Sci 2023; 378:20220237. [PMID: 37778389 PMCID: PMC10542455 DOI: 10.1098/rstb.2022.0237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 05/25/2023] [Indexed: 10/03/2023] Open
Abstract
Citrullination is an important post-translational modification (PTM) of arginine, known to play a role in autoimmune disorders, innate immunity response and maintenance of stem cell potency. However, citrullination remains poorly characterized and not as comprehensively understood compared to other PTMs, such as phosphorylation and ubiquitylation. High-resolution mass spectrometry (MS)-based proteomics offers a valuable approach for studying citrullination in an unbiased manner, allowing confident identification of citrullination modification sites and distinction from deamidation events on asparagine and glutamine. MS efforts have already provided valuable insights into peptidyl arginine deaminase targeting along with site-specific information of citrullination in for example synovial fluids derived from rheumatoid arthritis patients. Still, there is unrealized potential for the wider citrullination field by applying MS-based mass spectrometry approaches for proteome-wide investigations. Here we will outline contemporary methods and current challenges for studying citrullination by MS, and discuss how the development of neoteric citrullination-specific proteomics approaches still may improve our understanding of citrullination networks. This article is part of the Theo Murphy meeting issue 'The virtues and vices of protein citrullination'.
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Affiliation(s)
- A. S. Rebak
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - I. A. Hendriks
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark
| | - M. L. Nielsen
- Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, 2200 Copenhagen, Denmark
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7
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Cerullo AR, McDermott MB, Pepi LE, Liu ZL, Barry D, Zhang S, Yang X, Chen X, Azadi P, Holford M, Braunschweig AB. Comparative mucomic analysis of three functionally distinct Cornu aspersum Secretions. Nat Commun 2023; 14:5361. [PMID: 37660066 PMCID: PMC10475054 DOI: 10.1038/s41467-023-41094-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 08/21/2023] [Indexed: 09/04/2023] Open
Abstract
Every animal secretes mucus, placing them among the most diverse biological materials. Mucus hydrogels are complex mixtures of water, ions, carbohydrates, and proteins. Uncertainty surrounding their composition and how interactions between components contribute to mucus function complicates efforts to exploit their properties. There is substantial interest in commercializing mucus from the garden snail, Cornu aspersum, for skincare, drug delivery, tissue engineering, and composite materials. C. aspersum secretes three mucus-one shielding the animal from environmental threats, one adhesive mucus from the pedal surface of the foot, and another pedal mucus that is lubricating. It remains a mystery how compositional differences account for their substantially different properties. Here, we characterize mucus proteins, glycosylation, ion content, and mechanical properties that could be used to provide insight into structure-function relationships through an integrative "mucomics" approach. We identify macromolecular components of these hydrogels, including a previously unreported protein class termed Conserved Anterior Mollusk Proteins (CAMPs). Revealing differences between C. aspersum mucus shows how considering structure at all levels can inform the design of mucus-inspired materials.
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Affiliation(s)
- Antonio R Cerullo
- The Advanced Science Research Center, Graduate Center of the City University of New York, 85 St. Nicholas Terrace, New York, NY, 10031, USA
- The PhD Program in Biochemistry, Graduate Center of the City University of New York, 365 Fifth Avenue, New York, NY, 10016, USA
- Department of Chemistry and Biochemistry, Hunter College, 695 Park Avenue, New York, NY, 10065, USA
| | - Maxwell B McDermott
- Department of Chemistry and Biochemistry, Hunter College, 695 Park Avenue, New York, NY, 10065, USA
| | - Lauren E Pepi
- Complex Carbohydrate Research Center, University of Georgia, 315 Riverbend Road, Athens, GA, 30602, USA
| | - Zhi-Lun Liu
- The Advanced Science Research Center, Graduate Center of the City University of New York, 85 St. Nicholas Terrace, New York, NY, 10031, USA
- Department of Chemical Engineering, The City College of New York, New York, NY, 10031, USA
| | - Diariou Barry
- The Advanced Science Research Center, Graduate Center of the City University of New York, 85 St. Nicholas Terrace, New York, NY, 10031, USA
| | - Sheng Zhang
- The Advanced Science Research Center, Graduate Center of the City University of New York, 85 St. Nicholas Terrace, New York, NY, 10031, USA
| | - Xu Yang
- Complex Carbohydrate Research Center, University of Georgia, 315 Riverbend Road, Athens, GA, 30602, USA
| | - Xi Chen
- The Advanced Science Research Center, Graduate Center of the City University of New York, 85 St. Nicholas Terrace, New York, NY, 10031, USA
- Department of Chemical Engineering, The City College of New York, New York, NY, 10031, USA
- The PhD Program in Chemistry, Graduate Center of the City University of New York, 365 Fifth Avenue, New York, NY, 10016, USA
- The PhD Program in Physics, Graduate Center of the City University of New York, 365 Fifth Avenue, New York, NY, 10016, USA
| | - Parastoo Azadi
- Complex Carbohydrate Research Center, University of Georgia, 315 Riverbend Road, Athens, GA, 30602, USA
| | - Mande Holford
- The PhD Program in Biochemistry, Graduate Center of the City University of New York, 365 Fifth Avenue, New York, NY, 10016, USA
- Department of Chemistry and Biochemistry, Hunter College, 695 Park Avenue, New York, NY, 10065, USA
- The PhD Program in Chemistry, Graduate Center of the City University of New York, 365 Fifth Avenue, New York, NY, 10016, USA
- The PhD Program in Biology, Graduate Center of the City University of New York, 365 Fifth Avenue, New York, NY, 10016, USA
- Department of Invertebrate Zoology, The American Museum of Natural History, New York, NY, 10024, USA
| | - Adam B Braunschweig
- The Advanced Science Research Center, Graduate Center of the City University of New York, 85 St. Nicholas Terrace, New York, NY, 10031, USA.
- The PhD Program in Biochemistry, Graduate Center of the City University of New York, 365 Fifth Avenue, New York, NY, 10016, USA.
- Department of Chemistry and Biochemistry, Hunter College, 695 Park Avenue, New York, NY, 10065, USA.
- The PhD Program in Chemistry, Graduate Center of the City University of New York, 365 Fifth Avenue, New York, NY, 10016, USA.
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8
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Feng Z, Fang P, Zheng H, Zhang X. DEP2: an upgraded comprehensive analysis toolkit for quantitative proteomics data. Bioinformatics 2023; 39:btad526. [PMID: 37624922 PMCID: PMC10466079 DOI: 10.1093/bioinformatics/btad526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 07/14/2023] [Accepted: 08/23/2023] [Indexed: 08/27/2023] Open
Abstract
SUMMARY Mass spectrometry (MS)-based proteomics has become the most powerful approach to study the proteome of given biological and clinical samples. Advancements in sample preparation and MS detection have extended the application of proteomics but have also brought new demands on data analysis. Appropriate proteomics data analysis workflow mainly requires quality control, hypothesis testing, functional mining, and visualization. Although there are numerous tools for each process, an efficient and universal tandem analysis toolkit to obtain a quick overall view of various proteomics data is still urgently needed. Here, we present DEP2, an updated version of DEP we previously established, for proteomics data analysis. We amended the analysis workflow by incorporating alternative approaches to accommodate diverse proteomics data, introducing peptide-protein summarization and coupling biological function exploration. In summary, DEP2 is a well-rounded toolkit designed for protein- and peptide-level quantitative proteomics data. It features a more flexible differential analysis workflow and includes a user-friendly Shiny application to facilitate data analysis. AVAILABILITY AND IMPLEMENTATION DEP2 is available at https://github.com/mildpiggy/DEP2, released under the MIT license. For further information and usage details, please refer to the package website at https://mildpiggy.github.io/DEP2/.
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Affiliation(s)
- Zhenhuan Feng
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Hong Kong Institute of Science & Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Peiyang Fang
- Sanquan College, Xinxiang Medical University, Xinxiang, Henan 453003, China
| | - Hui Zheng
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Hong Kong Institute of Science & Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510530, China
| | - Xiaofei Zhang
- CAS Key Laboratory of Regenerative Biology, Guangdong Provincial Key Laboratory of Stem Cell and Regenerative Medicine, GIBH-HKU Guangdong-Hong Kong Stem Cell and Regenerative Medicine Research Centre, Hong Kong Institute of Science & Innovation, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, Guangdong 510530, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Biological Targeting Diagnosis, Therapy and Rehabilitation of Guangdong Higher Education Institutes, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510530, China
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9
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Slavov N. Single-cell proteomics: quantifying post-transcriptional regulation during development with mass-spectrometry. Development 2023; 150:dev201492. [PMID: 37387573 PMCID: PMC10323229 DOI: 10.1242/dev.201492] [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: 07/01/2023]
Abstract
Many developmental processes are regulated post-transcriptionally. Such post-transcriptional regulatory mechanisms can now be analyzed by robust single-cell mass spectrometry methods that allow accurate quantification of proteins and their modification in single cells. These methods can enable quantitative exploration of protein synthesis and degradation mechanisms that contribute to developmental cell fate specification. Furthermore, they may support functional analysis of protein conformations and activities in single cells, and thus link protein functions to developmental processes. This Spotlight provides an accessible introduction to single-cell mass spectrometry methods and suggests initial biological questions that are ripe for investigation.
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Affiliation(s)
- Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
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10
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Mehta S, Bernt M, Chambers M, Fahrner M, Föll MC, Gruening B, Horro C, Johnson JE, Loux V, Rajczewski AT, Schilling O, Vandenbrouck Y, Gustafsson OJR, Thang WCM, Hyde C, Price G, Jagtap PD, Griffin TJ. A Galaxy of informatics resources for MS-based proteomics. Expert Rev Proteomics 2023; 20:251-266. [PMID: 37787106 DOI: 10.1080/14789450.2023.2265062] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 09/06/2023] [Indexed: 10/04/2023]
Abstract
INTRODUCTION Continuous advances in mass spectrometry (MS) technologies have enabled deeper and more reproducible proteome characterization and a better understanding of biological systems when integrated with other 'omics data. Bioinformatic resources meeting the analysis requirements of increasingly complex MS-based proteomic data and associated multi-omic data are critically needed. These requirements included availability of software that would span diverse types of analyses, scalability for large-scale, compute-intensive applications, and mechanisms to ease adoption of the software. AREAS COVERED The Galaxy ecosystem meets these requirements by offering a multitude of open-source tools for MS-based proteomics analyses and applications, all in an adaptable, scalable, and accessible computing environment. A thriving global community maintains these software and associated training resources to empower researcher-driven analyses. EXPERT OPINION The community-supported Galaxy ecosystem remains a crucial contributor to basic biological and clinical studies using MS-based proteomics. In addition to the current status of Galaxy-based resources, we describe ongoing developments for meeting emerging challenges in MS-based proteomic informatics. We hope this review will catalyze increased use of Galaxy by researchers employing MS-based proteomics and inspire software developers to join the community and implement new tools, workflows, and associated training content that will add further value to this already rich ecosystem.
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Affiliation(s)
- Subina Mehta
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Matthias Bernt
- Helmholtz Centre for Environmental Research - UFZ, Department Computational Biology, Leipzig, Germany
| | | | - Matthias Fahrner
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Melanie Christine Föll
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA
| | - Bjoern Gruening
- Bioinformatics Group, Department of Computer Science, Albert-Ludwigs-University Freiburg, Freiburg, Germany
| | - Carlos Horro
- Proteomics Unit, Department of Biomedicine, University of Bergen, Bergen, Norway
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - James E Johnson
- Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Valentin Loux
- Université Paris-Saclay, INRAE, MaIAGE, Jouy-en-Josas, France
- Université Paris-Saclay, INRAE, BioinfOmics, MIGALE bioinformatics facility, Jouy-en-Josas, France
| | - Andrew T Rajczewski
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Oliver Schilling
- Institute for Surgical Pathology, Medical Center - University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | | | - W C Mike Thang
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Cameron Hyde
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Sippy Downs, University of the Sunshine Coast, Australia
| | - Gareth Price
- Queensland Cyber Infrastructure Foundation (QCIF), Australia
- Institute of Molecular Bioscience, University of Queensland, St Lucia, Australia
| | - Pratik D Jagtap
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Griffin
- Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Minneapolis, MN, USA
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11
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Chang HY, Gui CY, Huang TC, Hung YC, Chen TY. Quantitative Proteomic Analysis on the Slightly Acidic Electrolyzed Water Triggered Viable but Non-Culturable Listeria monocytogenes. Int J Mol Sci 2023; 24:10616. [PMID: 37445793 DOI: 10.3390/ijms241310616] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/16/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
This study undertakes a comprehensive exploration of the impact of slightly acidic electrolyzed water (SAEW) on Listeria monocytogenes, a common foodborne pathogen, with a particular focus on understanding the molecular mechanisms leading to the viable but nonculturable (VBNC) state. Given the widespread application of SAEW as an effective disinfectant in the food industry, uncovering these molecular pathways is crucial for improving food safety measures. We employed tandem mass tags (TMT), labeling proteomic techniques and LC-MS/MS to identify differentially expressed proteins under two doses of SAEW conditions. We indicated 203 differential expressed proteins (DEPs), including 78 up-regulated and 125 down-regulated DEPs. The functional enrichment analysis of these proteins indicated that ribosomes, biosynthesis of secondary metabolites, and aminoacyl-tRNA biosynthesis were enriched functions affected by SAEW. Further, we delved into the role of protein chlorination, a potential consequence of reactive chlorine species generated during the SAEW production process, by identifying 31 chlorinated peptides from 22 proteins, with a dominant sequence motif of Rxxxxx[cY] and functionally enriched in translation. Our findings suggest that SAEW might prompt alterations in the protein translation process and trigger compensatory ribosome biosynthesis. However, an imbalance in the levels of elongation factors and AARSs could hinder recovery, leading to the VBNC state. This research carries substantial implications for food safety and sanitation, as it adds to our understanding of the SAEW-induced VBNC state in L. monocytogenes and offers potential strategies for its control.
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Affiliation(s)
- Hsin-Yi Chang
- Graduate Institute of Medical Sciences, National Defense Medical Center, Taipei 11490, Taiwan
- Department of Research and Development, National Defense Medical Center, Taipei 11490, Taiwan
| | - Chin-Ying Gui
- Department of Food Science, National Taiwan Ocean University, Keelung 20224, Taiwan
| | - Tsui-Chin Huang
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
| | - Yen-Con Hung
- Department of Food Science & Technology, University of Georgia, Griffin, GA 30223-1797, USA
| | - Tai-Yuan Chen
- Department of Food Science, National Taiwan Ocean University, Keelung 20224, Taiwan
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12
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Sharman K, Patterson NH, Weiss A, Neumann EK, Guiberson ER, Ryan DJ, Gutierrez DB, Spraggins JM, Van de Plas R, Skaar EP, Caprioli RM. Rapid Multivariate Analysis Approach to Explore Differential Spatial Protein Profiles in Tissue. J Proteome Res 2023; 22:1394-1405. [PMID: 35849531 PMCID: PMC9845430 DOI: 10.1021/acs.jproteome.2c00206] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Spatially targeted proteomics analyzes the proteome of specific cell types and functional regions within tissue. While spatial context is often essential to understanding biological processes, interpreting sub-region-specific protein profiles can pose a challenge due to the high-dimensional nature of the data. Here, we develop a multivariate approach for rapid exploration of differential protein profiles acquired from distinct tissue regions and apply it to analyze a published spatially targeted proteomics data set collected from Staphylococcus aureus-infected murine kidney, 4 and 10 days postinfection. The data analysis process rapidly filters high-dimensional proteomic data to reveal relevant differentiating species among hundreds to thousands of measured molecules. We employ principal component analysis (PCA) for dimensionality reduction of protein profiles measured by microliquid extraction surface analysis mass spectrometry. Subsequently, k-means clustering of the PCA-processed data groups samples by chemical similarity. Cluster center interpretation revealed a subset of proteins that differentiate between spatial regions of infection over two time points. These proteins appear involved in tricarboxylic acid metabolomic pathways, calcium-dependent processes, and cytoskeletal organization. Gene ontology analysis further uncovered relationships to tissue damage/repair and calcium-related defense mechanisms. Applying our analysis in infectious disease highlighted differential proteomic changes across abscess regions over time, reflecting the dynamic nature of host-pathogen interactions.
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Affiliation(s)
- Kavya Sharman
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Program in Chemical & Physical Biology, Vanderbilt University Medical Center, Nashville, Tennessee 37232, United States
| | - Nathan Heath Patterson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Andy Weiss
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37212, United States
| | - Elizabeth K Neumann
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Emma R Guiberson
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Daniel J Ryan
- Pfizer Inc., Chesterfield, Missouri 63017, United States
| | - Danielle B Gutierrez
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Jeffrey M Spraggins
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Raf Van de Plas
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Delft Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands
| | - Eric P Skaar
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, Tennessee 37212, United States
- Department of Medicine, Vanderbilt University, Nashville, Tennessee 37232, United States
| | - Richard M Caprioli
- Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Biochemistry, Vanderbilt University, Nashville, Tennessee 37232, United States
- Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Department of Pharmacology, Vanderbilt University, Nashville, Tennessee 37232, United States
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13
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Qu P, Rom O, Li K, Jia L, Gao X, Liu Z, Ding S, Zhao M, Wang H, Chen S, Xiong X, Zhao Y, Xue C, Zhao Y, Chu C, Wen B, Finney AC, Zheng Z, Cao W, Zhao J, Bai L, Zhao S, Sun D, Zeng R, Lin J, Liu W, Zheng L, Zhang J, Liu E, Chen YE. DT-109 ameliorates nonalcoholic steatohepatitis in nonhuman primates. Cell Metab 2023; 35:742-757.e10. [PMID: 37040763 DOI: 10.1016/j.cmet.2023.03.013] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 01/03/2023] [Accepted: 03/17/2023] [Indexed: 04/13/2023]
Abstract
Nonalcoholic steatohepatitis (NASH) prevalence is rising with no pharmacotherapy approved. A major hurdle in NASH drug development is the poor translatability of preclinical studies to safe/effective clinical outcomes, and recent failures highlight a need to identify new targetable pathways. Dysregulated glycine metabolism has emerged as a causative factor and therapeutic target in NASH. Here, we report that the tripeptide DT-109 (Gly-Gly-Leu) dose-dependently attenuates steatohepatitis and fibrosis in mice. To enhance the probability of successful translation, we developed a nonhuman primate model that histologically and transcriptionally mimics human NASH. Applying a multiomics approach combining transcriptomics, proteomics, metabolomics, and metagenomics, we found that DT-109 reverses hepatic steatosis and prevents fibrosis progression in nonhuman primates, not only by stimulating fatty acid degradation and glutathione formation, as found in mice, but also by modulating microbial bile acid metabolism. Our studies describe a highly translatable NASH model and highlight the need for clinical evaluation of DT-109.
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Affiliation(s)
- Pengxiang Qu
- Laboratory Animal Center, Xi'an Jiaotong University Health Science Center, 76 Yanta West Road, Xi'an, Shaanxi 710061, China
| | - Oren Rom
- Department of Internal Medicine, Frankel Cardiovascular Center, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48109, USA; Department of Pathology and Translational Pathobiology, Louisiana State University Health Sciences Center-Shreveport, Shreveport, LA 71103, USA; Department of Molecular and Cellular Physiology, Louisiana State University Health Sciences Center-Shreveport, Shreveport, LA 71103, USA; Center for Cardiovascular Diseases and Sciences, Louisiana State University Health Sciences Center-Shreveport, Shreveport, LA 71103, USA
| | - Ke Li
- Beijing Tiantan Hospital, China National Clinical Research Center for Neurological Diseases, Advanced Innovation Center for Human Brain Protection, Capital Medical University, 6 Tiantan Xili, Chongwen District, Beijing 100050, China
| | - Linying Jia
- Laboratory Animal Center, Xi'an Jiaotong University Health Science Center, 76 Yanta West Road, Xi'an, Shaanxi 710061, China
| | - Xiaojing Gao
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Zhipeng Liu
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, IN 47907, USA
| | - Shusi Ding
- Beijing Tiantan Hospital, China National Clinical Research Center for Neurological Diseases, Advanced Innovation Center for Human Brain Protection, Capital Medical University, 6 Tiantan Xili, Chongwen District, Beijing 100050, China
| | - Mingming Zhao
- The Institute of Cardiovascular Sciences, School of Basic Medical Sciences, Key Laboratory of Molecular Cardiovascular Science of Ministry of Education, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Health Science Center, Peking University, 38 Xue Yuan Road, Beijing 100191, China
| | - Huiqing Wang
- The Institute of Cardiovascular Sciences, School of Basic Medical Sciences, Key Laboratory of Molecular Cardiovascular Science of Ministry of Education, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Health Science Center, Peking University, 38 Xue Yuan Road, Beijing 100191, China
| | - Shuangshuang Chen
- Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai 200031, China
| | - Xuelian Xiong
- Department of Endocrinology and Metabolism, Fudan Institute of Metabolic Diseases, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Xuhui District, Shanghai 200031, China
| | - Ying Zhao
- Department of Internal Medicine, Frankel Cardiovascular Center, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48109, USA
| | - Chao Xue
- Department of Internal Medicine, Frankel Cardiovascular Center, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48109, USA
| | - Yang Zhao
- Department of Internal Medicine, Frankel Cardiovascular Center, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48109, USA
| | - Chengshuang Chu
- CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Bo Wen
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alexandra C Finney
- Department of Pathology and Translational Pathobiology, Louisiana State University Health Sciences Center-Shreveport, Shreveport, LA 71103, USA
| | - Zuowen Zheng
- Spring Biological Technology Development Co., Ltd, Fangchenggang, Guangxi 538000, China
| | - Wenbin Cao
- Laboratory Animal Center, Xi'an Jiaotong University Health Science Center, 76 Yanta West Road, Xi'an, Shaanxi 710061, China
| | - Jinpeng Zhao
- Laboratory Animal Center, Xi'an Jiaotong University Health Science Center, 76 Yanta West Road, Xi'an, Shaanxi 710061, China
| | - Liang Bai
- Laboratory Animal Center, Xi'an Jiaotong University Health Science Center, 76 Yanta West Road, Xi'an, Shaanxi 710061, China
| | - Sihai Zhao
- Laboratory Animal Center, Xi'an Jiaotong University Health Science Center, 76 Yanta West Road, Xi'an, Shaanxi 710061, China
| | - Duxin Sun
- Department of Pharmaceutical Sciences, College of Pharmacy, University of Michigan, Ann Arbor, MI 48109, USA
| | - Rong Zeng
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China; CAS Key Laboratory of Systems Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Jiandie Lin
- Life Sciences Institute and Department of Cell & Developmental Biology, University of Michigan Medical Center, Ann Arbor, MI 48109, USA
| | - Wanqing Liu
- Department of Pharmaceutical Sciences and Department of Pharmacology, Wayne State University, Detroit, MI 48201, USA
| | - Lemin Zheng
- Beijing Tiantan Hospital, China National Clinical Research Center for Neurological Diseases, Advanced Innovation Center for Human Brain Protection, Capital Medical University, 6 Tiantan Xili, Chongwen District, Beijing 100050, China; The Institute of Cardiovascular Sciences, School of Basic Medical Sciences, Key Laboratory of Molecular Cardiovascular Science of Ministry of Education, NHC Key Laboratory of Cardiovascular Molecular Biology and Regulatory Peptides, Beijing Key Laboratory of Cardiovascular Receptors Research, Health Science Center, Peking University, 38 Xue Yuan Road, Beijing 100191, China.
| | - Jifeng Zhang
- Department of Internal Medicine, Frankel Cardiovascular Center, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48109, USA.
| | - Enqi Liu
- Laboratory Animal Center, Xi'an Jiaotong University Health Science Center, 76 Yanta West Road, Xi'an, Shaanxi 710061, China.
| | - Y Eugene Chen
- Department of Internal Medicine, Frankel Cardiovascular Center, University of Michigan, 2800 Plymouth Road, Ann Arbor, MI 48109, USA.
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14
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Slavov N. Great Gains in Mass Spectrometry Data Interpretation. J Proteome Res 2023; 22:659. [PMID: 36866536 PMCID: PMC10031382 DOI: 10.1021/acs.jproteome.3c00099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Affiliation(s)
- Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, Massachusetts 02115, United States
- Parallel Squared Technology Institute, Watertown, Massachusetts 02472, United States
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15
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Boekweg H, Payne SH. Challenges and opportunities for single cell computational proteomics. Mol Cell Proteomics 2023; 22:100518. [PMID: 36828128 PMCID: PMC10060113 DOI: 10.1016/j.mcpro.2023.100518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/15/2023] [Accepted: 02/17/2023] [Indexed: 02/25/2023] Open
Abstract
Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it's necessary to realize that current algorithms being employed on single-cell data were designed for bulk data, and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data, and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.
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Affiliation(s)
- Hannah Boekweg
- Biology Department, Brigham Young University, Provo, Utah, USA
| | - Samuel H Payne
- Biology Department, Brigham Young University, Provo, Utah, USA.
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16
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Juan H, Huang H. Quantitative analysis of high‐throughput biological data. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2023. [DOI: 10.1002/wcms.1658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Affiliation(s)
- Hsueh‐Fen Juan
- Department of Life Science, Institute of Biomedical Electronics and Bioinformatics, and Center for Systems Biology National Taiwan University Taipei Taiwan
- Taiwan AI Labs Taipei Taiwan
| | - Hsuan‐Cheng Huang
- Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei Taiwan
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17
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Cox J. Prediction of peptide mass spectral libraries with machine learning. Nat Biotechnol 2023; 41:33-43. [PMID: 36008611 DOI: 10.1038/s41587-022-01424-w] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/11/2022] [Indexed: 01/21/2023]
Abstract
The recent development of machine learning methods to identify peptides in complex mass spectrometric data constitutes a major breakthrough in proteomics. Longstanding methods for peptide identification, such as search engines and experimental spectral libraries, are being superseded by deep learning models that allow the fragmentation spectra of peptides to be predicted from their amino acid sequence. These new approaches, including recurrent neural networks and convolutional neural networks, use predicted in silico spectral libraries rather than experimental libraries to achieve higher sensitivity and/or specificity in the analysis of proteomics data. Machine learning is galvanizing applications that involve large search spaces, such as immunopeptidomics and proteogenomics. Current challenges in the field include the prediction of spectra for peptides with post-translational modifications and for cross-linked pairs of peptides. Permeation of machine-learning-based spectral prediction into search engines and spectrum-centric data-independent acquisition workflows for diverse peptide classes and measurement conditions will continue to push sensitivity and dynamic range in proteomics applications in the coming years.
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Affiliation(s)
- Jürgen Cox
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany.
- Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway.
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18
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Targeted hydrolysis of native potato protein: A novel workflow for obtaining hydrolysates with improved interfacial properties. Food Hydrocoll 2022. [DOI: 10.1016/j.foodhyd.2022.108299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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19
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Beck L, Geiger T. MS-based technologies for untargeted single-cell proteomics. Curr Opin Biotechnol 2022; 76:102736. [DOI: 10.1016/j.copbio.2022.102736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 03/19/2022] [Accepted: 04/24/2022] [Indexed: 11/28/2022]
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20
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Ruhle M, Espinal-Enríquez J, Hernández-Lemus E. The Breast Cancer Protein Co-Expression Landscape. Cancers (Basel) 2022; 14:2957. [PMID: 35740621 PMCID: PMC9221059 DOI: 10.3390/cancers14122957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 06/03/2022] [Accepted: 06/11/2022] [Indexed: 11/16/2022] Open
Abstract
Breast cancer is a complex phenotype (or better yet, several complex phenotypes) characterized by the interplay of a large number of cellular and biomolecular entities. Biological networks have been successfully used to capture some of the heterogeneity of intricate pathophenotypes, including cancer. Gene coexpression networks, in particular, have been used to study large-scale regulatory patterns. Ultimately, biological processes are carried out by proteins and their complexes. However, to date, most of the tumor profiling research has focused on the genomic and transcriptomic information. Here, we tried to expand this profiling through the analysis of open proteomic data via mutual information co-expression networks' analysis. We could observe that there are distinctive biological processes associated with communities of these networks and how some transcriptional co-expression phenomena are lost at the protein level. These kinds of data and network analyses are a broad resource to explore cellular behavior and cancer research.
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Affiliation(s)
- Martín Ruhle
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico; (M.R.); (J.E.-E.)
| | - Jesús Espinal-Enríquez
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico; (M.R.); (J.E.-E.)
- Center for Complexity Sciences, Universidad Nacional Autonoma de Mexico, Mexico City 04510, Mexico
| | - Enrique Hernández-Lemus
- Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico; (M.R.); (J.E.-E.)
- Center for Complexity Sciences, Universidad Nacional Autonoma de Mexico, Mexico City 04510, Mexico
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21
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IntroSpect: Motif-Guided Immunopeptidome Database Building Tool to Improve the Sensitivity of HLA I Binding Peptide Identification by Mass Spectrometry. Biomolecules 2022; 12:biom12040579. [PMID: 35454168 PMCID: PMC9025654 DOI: 10.3390/biom12040579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 04/11/2022] [Accepted: 04/12/2022] [Indexed: 01/02/2023] Open
Abstract
Although database search tools originally developed for shotgun proteome have been widely used in immunopeptidomic mass spectrometry identifications, they have been reported to achieve undesirably low sensitivities or high false positive rates as a result of the hugely inflated search space caused by the lack of specific enzymic digestions in immunopeptidome. To overcome such a problem, we developed a motif-guided immunopeptidome database building tool named IntroSpect, which is designed to first learn the peptide motifs from high confidence hits in the initial search, and then build a targeted database for refined search. Evaluated on 18 representative HLA class I datasets, IntroSpect can improve the sensitivity by an average of 76%, compared to conventional searches with unspecific digestions, while maintaining a very high level of accuracy (~96%), as confirmed by synthetic validation experiments. A distinct advantage of IntroSpect is that it does not depend on any external HLA data, so that it performs equally well on both well-studied and poorly-studied HLA types, unlike the previously developed method SpectMHC. We have also designed IntroSpect to keep a global FDR that can be conveniently controlled, similar to a conventional database search. Finally, we demonstrate the practical value of IntroSpect by discovering neoepitopes from MS data directly, an important application in cancer immunotherapies. IntroSpect is freely available to download and use.
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22
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Becker JP, Riemer AB. The Importance of Being Presented: Target Validation by Immunopeptidomics for Epitope-Specific Immunotherapies. Front Immunol 2022; 13:883989. [PMID: 35464395 PMCID: PMC9018990 DOI: 10.3389/fimmu.2022.883989] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 03/16/2022] [Indexed: 11/26/2022] Open
Abstract
Presentation of tumor-specific or tumor-associated peptides by HLA class I molecules to CD8+ T cells is the foundation of epitope-centric cancer immunotherapies. While often in silico HLA binding predictions or in vitro immunogenicity assays are utilized to select candidates, mass spectrometry-based immunopeptidomics is currently the only method providing a direct proof of actual cell surface presentation. Despite much progress in the last decade, identification of such HLA-presented peptides remains challenging. Here we review typical workflows and current developments in the field of immunopeptidomics, highlight the challenges which remain to be solved and emphasize the importance of direct target validation for clinical immunotherapy development.
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Affiliation(s)
- Jonas P. Becker
- Immunotherapy and Immunoprevention, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Angelika B. Riemer
- Immunotherapy and Immunoprevention, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Molecular Vaccine Design, German Center for Infection Research (DZIF), Partner Site Heidelberg, Heidelberg, Germany
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23
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Hiort P, Schlaffner CN, Steen JA, Renard BY, Steen H. multiFLEX-LF: A Computational Approach to Quantify the Modification Stoichiometries in Label-Free Proteomics Data Sets. J Proteome Res 2022; 21:899-909. [PMID: 35086334 PMCID: PMC9936407 DOI: 10.1021/acs.jproteome.1c00669] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
In liquid-chromatography-tandem-mass-spectrometry-based proteomics, information about the presence and stoichiometry of protein modifications is not readily available. To overcome this problem, we developed multiFLEX-LF, a computational tool that builds upon FLEXIQuant, which detects modified peptide precursors and quantifies their modification extent by monitoring the differences between observed and expected intensities of the unmodified precursors. multiFLEX-LF relies on robust linear regression to calculate the modification extent of a given precursor relative to a within-study reference. multiFLEX-LF can analyze entire label-free discovery proteomics data sets in a precursor-centric manner without preselecting a protein of interest. To analyze modification dynamics and coregulated modifications, we hierarchically clustered the precursors of all proteins based on their computed relative modification scores. We applied multiFLEX-LF to a data-independent-acquisition-based data set acquired using the anaphase-promoting complex/cyclosome (APC/C) isolated at various time points during mitosis. The clustering of the precursors allows for identifying varying modification dynamics and ordering the modification events. Overall, multiFLEX-LF enables the fast identification of potentially differentially modified peptide precursors and the quantification of their differential modification extent in large data sets using a personal computer. Additionally, multiFLEX-LF can drive the large-scale investigation of the modification dynamics of peptide precursors in time-series and case-control studies. multiFLEX-LF is available at https://gitlab.com/SteenOmicsLab/multiflex-lf.
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Affiliation(s)
- Pauline Hiort
- Department of Pathology, Boston Children's Hospital, Boston, Massachusetts 02115, United States.,Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam 14482, Germany
| | - Christoph N Schlaffner
- Department of Pathology, Boston Children's Hospital, Boston, Massachusetts 02115, United States.,Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam 14482, Germany.,F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, Massachusetts 02115, United States.,Department of Neurology, Harvard Medical School, Boston, Massachusetts 02115, United States
| | - Judith A Steen
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Boston, Massachusetts 02115, United States.,Department of Neurology, Harvard Medical School, Boston, Massachusetts 02115, United States.,Neurobiology Program, Boston Children's Hospital, Boston, Massachusetts 02115, United States
| | - Bernhard Y Renard
- Data Analytics and Computational Statistics, Hasso-Plattner-Institute, Faculty of Digital Engineering, University of Potsdam, Potsdam 14482, Germany
| | - Hanno Steen
- Department of Pathology, Boston Children's Hospital, Boston, Massachusetts 02115, United States.,Neurobiology Program, Boston Children's Hospital, Boston, Massachusetts 02115, United States.,Department of Pathology, Harvard Medical School, Boston, Massachusetts 02115, United States.,Precision Vaccines Program, Boston Children's Hospital, Boston, Massachusetts 02115, United States
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24
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The emerging role of mass spectrometry-based proteomics in drug discovery. Nat Rev Drug Discov 2022; 21:637-654. [PMID: 35351998 DOI: 10.1038/s41573-022-00409-3] [Citation(s) in RCA: 109] [Impact Index Per Article: 54.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/19/2022] [Indexed: 12/14/2022]
Abstract
Proteins are the main targets of most drugs; however, system-wide methods to monitor protein activity and function are still underused in drug discovery. Novel biochemical approaches, in combination with recent developments in mass spectrometry-based proteomics instrumentation and data analysis pipelines, have now enabled the dissection of disease phenotypes and their modulation by bioactive molecules at unprecedented resolution and dimensionality. In this Review, we describe proteomics and chemoproteomics approaches for target identification and validation, as well as for identification of safety hazards. We discuss innovative strategies in early-stage drug discovery in which proteomics approaches generate unique insights, such as targeted protein degradation and the use of reactive fragments, and provide guidance for experimental strategies crucial for success.
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25
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Ahn JH, Kang CK, Kim EM, Kim AR, Kim A. Proteomics for Early Detection of Non-Muscle-Invasive Bladder Cancer: Clinically Useful Urine Protein Biomarkers. Life (Basel) 2022; 12:395. [PMID: 35330146 PMCID: PMC8950253 DOI: 10.3390/life12030395] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 02/25/2022] [Accepted: 03/03/2022] [Indexed: 11/25/2022] Open
Abstract
Bladder cancer is the fourth most common cancer in men, and most cases are non-muscle-invasive. A high recurrence rate is a critical problem in non-muscle-invasive bladder cancer. The availability of few urine tests hinders the effective detection of superficial and small bladder tumors. Cystoscopy is the gold standard for diagnosis; however, it is associated with urinary tract infections, hematuria, and pain. Early detection is imperative, as intervention influences recurrence. Therefore, urinary biomarkers need to be developed to detect these bladder cancers. Recently, several protein candidates in the urine have been identified as biomarkers. In the present narrative review, the current status of the development of urinary protein biomarkers, including FDA-approved biomarkers, is summarized. Additionally, contemporary proteomic technologies, such as antibody-based methods, mass-spectrometry-based methods, and machine-learning-based diagnosis, are reported. Furthermore, new strategies for the rapid and correct profiling of potential biomarkers of bladder cancer in urine are introduced, along with their limitations. The advantages of urinary protein biomarkers and the development of several related technologies are highlighted in this review. Moreover, an in-depth understanding of the scientific background and available protocols in research and clinical applications of the surveillance of non-muscle bladder cancer is provided.
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Affiliation(s)
- Jae-Hak Ahn
- Department of Urology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul 05030, Korea;
| | - Chan-Koo Kang
- Department of Advanced Convergence, Handong Global University, Pohang 37554, Gyeongbuk, Korea;
- School of Life Science, Handong Global University, Pohang 37554, Gyungbuk, Korea
| | - Eun-Mee Kim
- Department of Emergency Medical Technology, Korea Nazarene University, Cheonan 31172, Chungcheongnam-do, Korea;
| | - Ah-Ram Kim
- Department of Advanced Convergence, Handong Global University, Pohang 37554, Gyeongbuk, Korea;
- School of Life Science, Handong Global University, Pohang 37554, Gyungbuk, Korea
| | - Aram Kim
- Department of Urology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul 05030, Korea;
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26
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Magalingam KB, Somanath SD, Ramdas P, Haleagrahara N, Radhakrishnan AK. 6-Hydroxydopamine Induces Neurodegeneration in Terminally Differentiated SH-SY5Y Neuroblastoma Cells via Enrichment of the Nucleosomal Degradation Pathway: a Global Proteomics Approach. J Mol Neurosci 2022; 72:1026-1046. [PMID: 35258800 PMCID: PMC9064865 DOI: 10.1007/s12031-021-01962-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 12/22/2021] [Indexed: 01/07/2023]
Abstract
The SH-SY5Y human neuroblastoma cells have been used for decades as a cell-based model of dopaminergic neurons to explore the underlying science of cellular and molecular mechanisms of neurodegeneration in Parkinson’s disease (PD). However, data revealing the protein expression changes in 6-OHDA induced cytotoxicity in differentiated SH-SY5Y cells remain void. Therefore, we investigated the differentially regulated proteins expressed in terminally differentiated SH-SY5Y cells (differ-SH-SY5Y neural cells) exposed to 6-hydroxydopamine (6-OHDA) using the LC–MS/MS technology and construed the data using the online bioinformatics databases such as PANTHER, STRING, and KEGG. Our studies demonstrated that the neuronal development in differ-SH-SY5Y neural cells was indicated by the overexpression of proteins responsible for neurite formations such as calnexin (CANX) and calreticulin (CALR) besides significant downregulation of ribosomal proteins. The enrichment of the KEGG ribosome pathway was detected with significant downregulation (p < 0.05) of all the 21 ribosomal proteins in differ-SH-SY5Y neural cells compared with undifferentiated cells. Whereas in the PD model, the pathological changes induced by 6-OHDA were indicated by the presence of unfolded and misfolded proteins, which triggered the response of 10 kDa heat shock proteins (HSP), namely HSPE1 and HSPA9. Moreover, the 6-OHDA-induced neurodegeneration in differ-SH-SY5Y neural cells also upregulated the voltage-dependent anion-selective channel protein 1 (VDAC1) protein and enriched the KEGG systemic lupus erythematosus (SLE) pathway that was regulated by 17 histone proteins (p < 0.05) in differ-SH-SY5Y neural cells. These results suggest that the nucleosomal degradation pathway may have regulated the 6-OHDA induced neurodegeneration in PD cell-based model, which is reflected by increased apoptosis and histone release in differ-SH-SY5Y neural cells.
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Affiliation(s)
- Kasthuri Bai Magalingam
- School of Postgraduate Studies, International Medical University, Kuala Lumpur, Malaysia
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Malaysia, Bandar Sunway, Malaysia
| | - Sushela Devi Somanath
- Pathology Division, School of Medicine, International Medical University, Kuala Lumpur, Malaysia
| | - Premdass Ramdas
- Division of Applied Biomedical Sciences and Biotechnology, School of Health Sciences, International Medical University, Kuala Lumpur, Malaysia
| | - Nagaraja Haleagrahara
- College of Public Health, Medicine and Veterinary Sciences, James Cook University, Townsville, QLD, 4811, Australia
| | - Ammu Kutty Radhakrishnan
- Jeffrey Cheah School of Medicine and Health Sciences, Monash University, Malaysia, Bandar Sunway, Malaysia.
- Monash-Industry Palm Oil Education and Research Platform (MIPO), Monash University Malaysia, Bandar Sunway, Selangor, Malaysia.
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27
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Brady MM, Meyer AS. Cataloguing the proteome: Current developments in single-molecule protein sequencing. BIOPHYSICS REVIEWS 2022; 3:011304. [PMID: 38505228 PMCID: PMC10903494 DOI: 10.1063/5.0065509] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 01/13/2022] [Indexed: 03/21/2024]
Abstract
The cellular proteome is complex and dynamic, with proteins playing a critical role in cell-level biological processes that contribute to homeostasis, stimuli response, and disease pathology, among others. As such, protein analysis and characterization are of extreme importance in both research and clinical settings. In the last few decades, most proteomics analysis has relied on mass spectrometry, affinity reagents, or some combination thereof. However, these techniques are limited by their requirements for large sample amounts, low resolution, and insufficient dynamic range, making them largely insufficient for the characterization of proteins in low-abundance or single-cell proteomic analysis. Despite unique technical challenges, several single-molecule protein sequencing (SMPS) technologies have been proposed in recent years to address these issues. In this review, we outline several approaches to SMPS technologies and discuss their advantages, limitations, and potential contributions toward an accurate, sensitive, and high-throughput platform.
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Affiliation(s)
- Morgan M. Brady
- Department of Biology, University of Rochester, Rochester, New York 14627, USA
| | - Anne S. Meyer
- Department of Biology, University of Rochester, Rochester, New York 14627, USA
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28
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Affinity Selection from Synthetic Peptide Libraries Enabled by De Novo MS/MS Sequencing. Int J Pept Res Ther 2022. [DOI: 10.1007/s10989-022-10370-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
AbstractRecently, de novo MS/MS peptide sequencing has enabled the application of affinity selections to synthetic peptide mixtures that approach the diversity of phage libraries (> 108 random peptides). In conjunction with ‘split-mix’ solid phase synthesis to access equimolar peptide mixtures, this approach provides a straightforward means to examine synthetic peptide libraries of considerably higher diversity than has been feasible historically. Here, we offer a critical perspective on this work, report emerging data, and highlight opportunities for further methods refinement. With continued development, ‘affinity selection–mass spectrometry’ may become a complimentary approach to phage display, in vitro selection, and DNA-encoded libraries for the discovery of synthetic ligands that modulate protein function.
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29
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Balotf S, Wilson R, Tegg RS, Nichols DS, Wilson CR. Shotgun Proteomics as a Powerful Tool for the Study of the Proteomes of Plants, Their Pathogens, and Plant-Pathogen Interactions. Proteomes 2022; 10:5. [PMID: 35225985 PMCID: PMC8883913 DOI: 10.3390/proteomes10010005] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/12/2022] [Accepted: 01/17/2022] [Indexed: 12/31/2022] Open
Abstract
The interaction between plants and pathogenic microorganisms is a multifaceted process mediated by both plant- and pathogen-derived molecules, including proteins, metabolites, and lipids. Large-scale proteome analysis can quantify the dynamics of proteins, biological pathways, and posttranslational modifications (PTMs) involved in the plant-pathogen interaction. Mass spectrometry (MS)-based proteomics has become the preferred method for characterizing proteins at the proteome and sub-proteome (e.g., the phosphoproteome) levels. MS-based proteomics can reveal changes in the quantitative state of a proteome and provide a foundation for understanding the mechanisms involved in plant-pathogen interactions. This review is intended as a primer for biologists that may be unfamiliar with the diverse range of methodology for MS-based shotgun proteomics, with a focus on techniques that have been used to investigate plant-pathogen interactions. We provide a summary of the essential steps required for shotgun proteomic studies of plants, pathogens and plant-pathogen interactions, including methods for protein digestion, identification, separation, and quantification. Finally, we discuss how protein PTMs may directly participate in the interaction between a pathogen and its host plant.
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Affiliation(s)
- Sadegh Balotf
- New Town Research Laboratories, Tasmanian Institute of Agriculture, University of Tasmania, New Town, TAS 7008, Australia; (S.B.); (R.S.T.)
| | - Richard Wilson
- Central Science Laboratory, University of Tasmania, Hobart, TAS 7001, Australia;
| | - Robert S. Tegg
- New Town Research Laboratories, Tasmanian Institute of Agriculture, University of Tasmania, New Town, TAS 7008, Australia; (S.B.); (R.S.T.)
| | - David S. Nichols
- Central Science Laboratory, University of Tasmania, Hobart, TAS 7001, Australia;
| | - Calum R. Wilson
- New Town Research Laboratories, Tasmanian Institute of Agriculture, University of Tasmania, New Town, TAS 7008, Australia; (S.B.); (R.S.T.)
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30
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Ahrens CH, Wade JT, Champion MM, Langer JD. A Practical Guide to Small Protein Discovery and Characterization Using Mass Spectrometry. J Bacteriol 2022; 204:e0035321. [PMID: 34748388 PMCID: PMC8765459 DOI: 10.1128/jb.00353-21] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Small proteins of up to ∼50 amino acids are an abundant class of biomolecules across all domains of life. Yet due to the challenges inherent in their size, they are often missed in genome annotations, and are difficult to identify and characterize using standard experimental approaches. Consequently, we still know few small proteins even in well-studied prokaryotic model organisms. Mass spectrometry (MS) has great potential for the discovery, validation, and functional characterization of small proteins. However, standard MS approaches are poorly suited to the identification of both known and novel small proteins due to limitations at each step of a typical proteomics workflow, i.e., sample preparation, protease digestion, liquid chromatography, MS data acquisition, and data analysis. Here, we outline the major MS-based workflows and bioinformatic pipelines used for small protein discovery and validation. Special emphasis is placed on highlighting the adjustments required to improve detection and data quality for small proteins. We discuss both the unbiased detection of small proteins and the targeted analysis of small proteins of interest. Finally, we provide guidelines to prioritize novel small proteins, and an outlook on methods with particular potential to further improve comprehensive discovery and characterization of small proteins.
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Affiliation(s)
- Christian H. Ahrens
- Agroscope, Method Development and Analytics & SIB Swiss Institute of Bioinformatics, Wädenswil, Switzerland
| | - Joseph T. Wade
- Wadsworth Center, New York State Department of Health, Albany, New York, USA
- Department of Biomedical Sciences, School of Public Health, University at Albany, Albany, New York, USA
| | - Matthew M. Champion
- Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana, USA
| | - Julian D. Langer
- Mass Spectrometry and Proteomics, Max Planck Institute of Biophysics, Frankfurt am Main, Germany
- Proteomics, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
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31
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Rehfeldt TG, Krawczyk K, Bøgebjerg M, Schwämmle V, Röttger R. MS2AI: automated repurposing of public peptide LC-MS data for machine learning applications. Bioinformatics 2022; 38:875-877. [PMID: 34636883 DOI: 10.1093/bioinformatics/btab701] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 09/09/2021] [Accepted: 10/11/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Liquid-chromatography mass-spectrometry (LC-MS) is the established standard for analyzing the proteome in biological samples by identification and quantification of thousands of proteins. Machine learning (ML) promises to considerably improve the analysis of the resulting data, however, there is yet to be any tool that mediates the path from raw data to modern ML applications. More specifically, ML applications are currently hampered by three major limitations: (i) absence of balanced training data with large sample size; (ii) unclear definition of sufficiently information-rich data representations for e.g. peptide identification; (iii) lack of benchmarking of ML methods on specific LC-MS problems. RESULTS We created the MS2AI pipeline that automates the process of gathering vast quantities of MS data for large-scale ML applications. The software retrieves raw data from either in-house sources or from the proteomics identifications database, PRIDE. Subsequently, the raw data are stored in a standardized format amenable for ML, encompassing MS1/MS2 spectra and peptide identifications. This tool bridges the gap between MS and AI, and to this effect we also present an ML application in the form of a convolutional neural network for the identification of oxidized peptides. AVAILABILITY AND IMPLEMENTATION An open-source implementation of the software can be found at https://gitlab.com/roettgerlab/ms2ai. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | - Konrad Krawczyk
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Mathias Bøgebjerg
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Veit Schwämmle
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Odense, Denmark
| | - Richard Röttger
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
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32
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Simkovich A, Kohalmi SE, Wang A. Purification and Proteomics Analysis of Phloem Tissues from Virus-Infected Plants. Methods Mol Biol 2022; 2400:125-137. [PMID: 34905197 DOI: 10.1007/978-1-0716-1835-6_13] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The plant phloem vasculature is crucial for plant growth and development, and is essential for the systemic movement (SM) of plant viruses. Recent transcriptomic studies of the phloem during virus infection have shown the importance of this tissue, yet transcript levels do not provide definitive answers how virus-host interactions favour successful viral SM. Proteomic analyses have been used to identify host-virus protein interactions, uncovering a variety of ways by which viruses utilize host cellular machinery for completion of the viral infection cycle. Despite this new evidence through proteomics, very few phloem centric studies during viral infection have been performed. Here, we describe a protocol for the isolation of phloem tissues and proteins and the subsequent label-free quantitation (LFQ), for identification of proteomic alterations caused by viral infection.
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Affiliation(s)
- Aaron Simkovich
- London Research and Development Centre, Agriculture and Agri-Food Canada, London, ON, Canada
- Department of Biology, The University of Western Ontario, London, ON, Canada
| | - Susanne E Kohalmi
- Department of Biology, The University of Western Ontario, London, ON, Canada
| | - Aiming Wang
- London Research and Development Centre, Agriculture and Agri-Food Canada, London, ON, Canada.
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33
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Sinitcyn P, Gerwien M, Cox J. MaxQuant Module for the Identification of Genomic Variants Propagated into Peptides. Methods Mol Biol 2022; 2456:339-347. [PMID: 35612753 DOI: 10.1007/978-1-0716-2124-0_23] [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: 10/18/2022]
Abstract
Standard shotgun proteomics data analysis pipelines usually only identify peptides that are encoded in the reference genome. In many situations, it is desirable to identify peptides resulting from non-synonymous variations as well. Here, we present a new module in the MaxQuant software that takes both DNA and RNA based next-generation sequencing (NGS) data as well as raw proteomics data as input. This allows for the identification of variant peptides that are otherwise missed.
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Affiliation(s)
- Pavel Sinitcyn
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Maximilian Gerwien
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Jürgen Cox
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany.
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34
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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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35
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Britt HM, Cragnolini T, Thalassinos K. Integration of Mass Spectrometry Data for Structural Biology. Chem Rev 2021; 122:7952-7986. [PMID: 34506113 DOI: 10.1021/acs.chemrev.1c00356] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Mass spectrometry (MS) is increasingly being used to probe the structure and dynamics of proteins and the complexes they form with other macromolecules. There are now several specialized MS methods, each with unique sample preparation, data acquisition, and data processing protocols. Collectively, these methods are referred to as structural MS and include cross-linking, hydrogen-deuterium exchange, hydroxyl radical footprinting, native, ion mobility, and top-down MS. Each of these provides a unique type of structural information, ranging from composition and stoichiometry through to residue level proximity and solvent accessibility. Structural MS has proved particularly beneficial in studying protein classes for which analysis by classic structural biology techniques proves challenging such as glycosylated or intrinsically disordered proteins. To capture the structural details for a particular system, especially larger multiprotein complexes, more than one structural MS method with other structural and biophysical techniques is often required. Key to integrating these diverse data are computational strategies and software solutions to facilitate this process. We provide a background to the structural MS methods and briefly summarize other structural methods and how these are combined with MS. We then describe current state of the art approaches for the integration of structural MS data for structural biology. We quantify how often these methods are used together and provide examples where such combinations have been fruitful. To illustrate the power of integrative approaches, we discuss progress in solving the structures of the proteasome and the nuclear pore complex. We also discuss how information from structural MS, particularly pertaining to protein dynamics, is not currently utilized in integrative workflows and how such information can provide a more accurate picture of the systems studied. We conclude by discussing new developments in the MS and computational fields that will further enable in-cell structural studies.
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Affiliation(s)
- Hannah M Britt
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom
| | - Tristan Cragnolini
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, United Kingdom
| | - Konstantinos Thalassinos
- Institute of Structural and Molecular Biology, Division of Biosciences, University College London, London WC1E 6BT, United Kingdom.,Institute of Structural and Molecular Biology, Birkbeck College, University of London, London WC1E 7HX, United Kingdom
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36
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Karimi MR, Karimi AH, Abolmaali S, Sadeghi M, Schmitz U. Prospects and challenges of cancer systems medicine: from genes to disease networks. Brief Bioinform 2021; 23:6361045. [PMID: 34471925 PMCID: PMC8769701 DOI: 10.1093/bib/bbab343] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 12/20/2022] Open
Abstract
It is becoming evident that holistic perspectives toward cancer are crucial in deciphering the overwhelming complexity of tumors. Single-layer analysis of genome-wide data has greatly contributed to our understanding of cellular systems and their perturbations. However, fundamental gaps in our knowledge persist and hamper the design of effective interventions. It is becoming more apparent than ever, that cancer should not only be viewed as a disease of the genome but as a disease of the cellular system. Integrative multilayer approaches are emerging as vigorous assets in our endeavors to achieve systemic views on cancer biology. Herein, we provide a comprehensive review of the approaches, methods and technologies that can serve to achieve systemic perspectives of cancer. We start with genome-wide single-layer approaches of omics analyses of cellular systems and move on to multilayer integrative approaches in which in-depth descriptions of proteogenomics and network-based data analysis are provided. Proteogenomics is a remarkable example of how the integration of multiple levels of information can reduce our blind spots and increase the accuracy and reliability of our interpretations and network-based data analysis is a major approach for data interpretation and a robust scaffold for data integration and modeling. Overall, this review aims to increase cross-field awareness of the approaches and challenges regarding the omics-based study of cancer and to facilitate the necessary shift toward holistic approaches.
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Affiliation(s)
| | | | | | - Mehdi Sadeghi
- Department of Cell & Molecular Biology, Semnan University, Semnan, Iran
| | - Ulf Schmitz
- Department of Molecular & Cell Biology, James Cook University, Townsville, QLD 4811, Australia
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Yu SH, Ferretti D, Schessner JP, Rudolph JD, Borner GHH, Cox J. Expanding the Perseus Software for Omics Data Analysis With Custom Plugins. ACTA ACUST UNITED AC 2021; 71:e105. [PMID: 32931150 DOI: 10.1002/cpbi.105] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python. The user-written codes will be fully integrated into the Perseus data analysis workflow as custom activities. This also makes language-specific R and Python libraries from CRAN (cran.r-project.org), Bioconductor (bioconductor.org), PyPI (pypi.org), and Anaconda (anaconda.org) accessible in Perseus. The different available approaches are explained in detail in this article. To facilitate the distribution of user-developed plugins among users, we have created a plugin repository for community sharing and filled it with the examples provided in this article and a collection of already existing and more extensive plugins. © 2020 The Authors. Basic Protocol 1: Basic steps for R plugins Support Protocol 1: R plugins with additional arguments Basic Protocol 2: Basic steps for python plugins Support Protocol 2: Python plugins with additional arguments Basic Protocol 3: Basic steps and construction of C# plugins Basic Protocol 4: Basic steps of construction and connection for R plugins with C# interface Support Protocol 4: Advanced example of R Plugin with C# interface: UMAP Basic Protocol 5: Basic steps of construction and connection for python plugins with C# interface Support Protocol 5: Advanced example of python plugin with C# interface: UMAP Support Protocol 6: A basic workflow for the analysis of label-free quantification proteomics data using perseus.
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Affiliation(s)
- Sung-Huan Yu
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Daniela Ferretti
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Julia P Schessner
- Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Jan Daniel Rudolph
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany.,Bosch Center for Artificial Intelligence, Robert-Bosch-Campus 1, Renningen, Germany
| | - Georg H H Borner
- Systems Biology of Membrane Trafficking Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Jürgen Cox
- Computational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany.,Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
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Missing Value Monitoring to Address Missing Values in Quantitative Proteomics. Methods Mol Biol 2021. [PMID: 33950505 DOI: 10.1007/978-1-0716-1024-4_27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Many classes of key functional proteins such as transcription factors or cell cycle proteins are present in the proteome at a very low concentration. These low-abundance proteins are almost entirely invisible to systematic quantitative analysis by classical data dependent proteomics methods (DDA). Moreover, DDA runs in shotgun proteomics experiments are plenty of missing values among the replicates due to the stochastic nature of the acquisition method, thus hampering the robustness of the quantitative analysis. Here, we have overcome these obstacles designing a robust workflow named missing value monitoring (MvM) in order to follow low abundance proteins dynamics.
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Abstract
Mass-spectrometry-based proteomics enables quantitative analysis of thousands of human proteins. However, experimental and computational challenges restrict progress in the field. This review summarizes the recent flurry of machine-learning strategies using artificial deep neural networks (or "deep learning") that have started to break barriers and accelerate progress in the field of shotgun proteomics. Deep learning now accurately predicts physicochemical properties of peptides from their sequence, including tandem mass spectra and retention time. Furthermore, deep learning methods exist for nearly every aspect of the modern proteomics workflow, enabling improved feature selection, peptide identification, and protein inference.
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Affiliation(s)
- Jesse G. Meyer
- Department of Biochemistry, Medical College of Wisconsin, Milwaukee, WI 53226, USA
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Galindo-Luján R, Pont L, Minic Z, Berezovski MV, Sanz-Nebot V, Benavente F. Characterization and differentiation of quinoa seed proteomes by label-free mass spectrometry-based shotgun proteomics. Food Chem 2021; 363:130250. [PMID: 34120052 DOI: 10.1016/j.foodchem.2021.130250] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 11/19/2022]
Abstract
Quinoa seed proteins are of prime importance in human nutrition and in plant breeding for cultivar identification and improvement. In this study, proteins from seeds of black, red, white quinoa from Peru and white quinoa from Bolivia (also known as royal) were extracted, digested and analyzed by nano-liquid chromatography coupled to Orbitrap tandem mass spectrometry (LC-MS/MS). The raw mass spectra data were processed for identification and label-free quantification (LFQ) using MaxQuant/Andromeda against a specific quinoa database from The National Center for Biotechnology Information (NCBI). In total, 1,211 quinoa proteins (85 were uncharacterized) were identified. Inspection and visualization using Venn diagrams, heat maps and Gene Ontology (GO) graphs revealed proteome similarities and differences between the four varieties. The presented data provides the most comprehensive experimental quinoa seed proteome map existing to date in the literature, as a starting point for more specific characterization and nutritional studies of quinoa and quinoa-containing foodstuff.
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Affiliation(s)
- Rocío Galindo-Luján
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain
| | - Laura Pont
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain
| | - Zoran Minic
- John L. Holmes Mass Spectrometry Facility, Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Maxim V Berezovski
- John L. Holmes Mass Spectrometry Facility, Department of Chemistry and Biomolecular Sciences, University of Ottawa, Ottawa, Ontario K1N 6N5, Canada
| | - Victoria Sanz-Nebot
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain
| | - Fernando Benavente
- Department of Chemical Engineering and Analytical Chemistry, Institute for Research on Nutrition and Food Safety (INSA·UB), University of Barcelona, 08028 Barcelona, Spain.
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Important Issues in Planning a Proteomics Experiment: Statistical Considerations of Quantitative Proteomic Data. Methods Mol Biol 2021; 2228:1-20. [PMID: 33950479 DOI: 10.1007/978-1-0716-1024-4_1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/15/2023]
Abstract
Mass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterward. This chapter thus covers aspects of both statistical planning as well as the actual analysis of quantitative proteomic experiments.
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Aslan JE. Platelet Proteomes, Pathways, and Phenotypes as Informants of Vascular Wellness and Disease. Arterioscler Thromb Vasc Biol 2021; 41:999-1011. [PMID: 33441027 PMCID: PMC7980774 DOI: 10.1161/atvbaha.120.314647] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Platelets rapidly undergo responsive transitions in form and function to repair vascular endothelium and mediate hemostasis. In contrast, heterogeneous platelet subpopulations with a range of primed or refractory phenotypes gradually arise in chronic inflammatory and other conditions in a manner that may indicate or support disease. Qualitatively distinguishable platelet phenotypes are increasingly associated with a variety of physiological and pathological circumstances; however, the origins and significance of platelet phenotypic variation remain unclear and conceptually vague. As changes in platelet function in disease exhibit many similarities to platelets following the activation of platelet agonist receptors, the intracellular responses of platelets common to hemostasis and inflammation may provide insights to the molecular basis of platelet phenotype. Here, we review concepts around how protein-level relations-from platelet receptors through intracellular signaling events-may help to define platelet phenotypes in inflammation, immune responses, aging, and other conditions. We further discuss how representing systems-wide platelet proteomics data profiles as circuit-like networks of causally related intracellular events, or, pathway maps, may inform molecular definitions of platelet phenotype. In addition to offering insights into platelets as druggable targets, maps of causally arranged intracellular relations underlying platelet function can also advance precision and interceptive medicine efforts by leveraging platelets as accessible, dynamic, endogenous, circulating biomarkers of vascular wellness and disease. Graphic Abstract: A graphic abstract is available for this article.
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Affiliation(s)
- Joseph E. Aslan
- Knight Cardiovascular Institute, School of Medicine, Oregon Health & Science University, Portland, Oregon, USA
- Department of Chemical Physiology and Biochemistry and School of Medicine, Oregon Health & Science University, Portland, Oregon, USA
- Department of Biomedical Engineering, School of Medicine, Oregon Health & Science University, Portland, Oregon, USA
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43
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Specht H, Emmott E, Petelski AA, Huffman RG, Perlman DH, Serra M, Kharchenko P, Koller A, Slavov N. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biol 2021; 22:50. [PMID: 33504367 PMCID: PMC7839219 DOI: 10.1186/s13059-021-02267-5] [Citation(s) in RCA: 283] [Impact Index Per Article: 94.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 01/07/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of the limitations of quantitative single-cell protein analysis. RESULTS To overcome this limitation, we develop SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantifies over 3042 proteins in 1490 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. Parallel measurements of transcripts by 10× Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus, SCoPE2 supports quantification with improved count statistics. This allowed exploring regulatory interactions, such as interactions between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53. CONCLUSIONS Even in a homogeneous environment, macrophage proteomes are heterogeneous. This heterogeneity correlates to the inflammatory axis of classically and alternatively activated macrophages. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.
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Affiliation(s)
- Harrison Specht
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA.
| | - Edward Emmott
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
- Present Address: Department of Biochemistry, Centre for Proteome Research, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Aleksandra A Petelski
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
| | - R Gray Huffman
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
| | - David H Perlman
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
- Present Address: Merck Exploratory Sciences Center, Merck Sharp & Dohme Corp., 320 Bent St., Cambridge, MA, 02141, USA
| | - Marco Serra
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Peter Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Antonius Koller
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
| | - Nikolai Slavov
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA.
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44
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Specht H, Emmott E, Petelski AA, Huffman RG, Perlman DH, Serra M, Kharchenko P, Koller A, Slavov N. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biol 2021; 22:50. [PMID: 33504367 DOI: 10.1101/665307] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 01/07/2021] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of the limitations of quantitative single-cell protein analysis. RESULTS To overcome this limitation, we develop SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantifies over 3042 proteins in 1490 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. Parallel measurements of transcripts by 10× Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus, SCoPE2 supports quantification with improved count statistics. This allowed exploring regulatory interactions, such as interactions between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53. CONCLUSIONS Even in a homogeneous environment, macrophage proteomes are heterogeneous. This heterogeneity correlates to the inflammatory axis of classically and alternatively activated macrophages. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.
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Affiliation(s)
- Harrison Specht
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA.
| | - Edward Emmott
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
- Present Address: Department of Biochemistry, Centre for Proteome Research, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Aleksandra A Petelski
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
| | - R Gray Huffman
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
| | - David H Perlman
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
- Present Address: Merck Exploratory Sciences Center, Merck Sharp & Dohme Corp., 320 Bent St., Cambridge, MA, 02141, USA
| | - Marco Serra
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Peter Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Antonius Koller
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
| | - Nikolai Slavov
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA.
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45
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Wei Y, Varanasi RS, Schwarz T, Gomell L, Zhao H, Larson DJ, Sun B, Liu G, Chen H, Raabe D, Gault B. Machine-learning-enhanced time-of-flight mass spectrometry analysis. PATTERNS 2021; 2:100192. [PMID: 33659909 PMCID: PMC7892357 DOI: 10.1016/j.patter.2020.100192] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 11/13/2020] [Accepted: 12/17/2020] [Indexed: 01/06/2023]
Abstract
Mass spectrometry is a widespread approach used to work out what the constituents of a material are. Atoms and molecules are removed from the material and collected, and subsequently, a critical step is to infer their correct identities based on patterns formed in their mass-to-charge ratios and relative isotopic abundances. However, this identification step still mainly relies on individual users' expertise, making its standardization challenging, and hindering efficient data processing. Here, we introduce an approach that leverages modern machine learning technique to identify peak patterns in time-of-flight mass spectra within microseconds, outperforming human users without loss of accuracy. Our approach is cross-validated on mass spectra generated from different time-of-flight mass spectrometry (ToF-MS) techniques, offering the ToF-MS community an open-source, intelligent mass spectra analysis. A machine-learning method provides reliable atomic/molecular labels for ToF-MS No human labeling or prior information required The training dataset is artificially generated based on isotopic abundances Method validated on a variety of materials and two ToF-MS-based techniques
Time-of-flight mass spectrometry (ToF-MS) is a mainstream analytical technique widely used in biology, chemistry, and materials science. ToF-MS provides quantitative compositional analysis with high sensitivity across a wide dynamic range of mass-to-charge ratios. A critical step in ToF-MS is to infer the identity of the detected ions. Here, we introduce a machine-learning-enhanced algorithm to provide a user-independent approach to performing this identification using patterns from the natural isotopic abundances of individual atomic and molecular ions, without human labeling or prior knowledge of composition. Results from several materials and techniques are compared with those obtained by field experts. Our open-source, easy-to-implement, reliable analytic method accelerates this identification process. A wide range of ToF-MS-based applications can benefit from our approach, e.g., hunting for patterns of biomarkers or for contamination on solid surfaces in high-throughput data.
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Affiliation(s)
- Ye Wei
- Max-Planck-Institut für Eisenforschung, Max-Planck-Strasse 1, 40237 Düsseldorf, Germany
| | | | - Torsten Schwarz
- Max-Planck-Institut für Eisenforschung, Max-Planck-Strasse 1, 40237 Düsseldorf, Germany
| | - Leonie Gomell
- Max-Planck-Institut für Eisenforschung, Max-Planck-Strasse 1, 40237 Düsseldorf, Germany
| | - Huan Zhao
- Max-Planck-Institut für Eisenforschung, Max-Planck-Strasse 1, 40237 Düsseldorf, Germany
| | - David J Larson
- CAMECA Instruments, 5470 Nobel Drive, Madison, WI 53711, USA
| | - Binhan Sun
- Max-Planck-Institut für Eisenforschung, Max-Planck-Strasse 1, 40237 Düsseldorf, Germany
| | - Geng Liu
- Key Laboratory for Advanced Materials of Ministry of Education, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Hao Chen
- Key Laboratory for Advanced Materials of Ministry of Education, School of Materials Science and Engineering, Tsinghua University, Beijing 100084, China
| | - Dierk Raabe
- Max-Planck-Institut für Eisenforschung, Max-Planck-Strasse 1, 40237 Düsseldorf, Germany
| | - Baptiste Gault
- Max-Planck-Institut für Eisenforschung, Max-Planck-Strasse 1, 40237 Düsseldorf, Germany.,Department of Materials, Royal School of Mines, Imperial College, London SW7 2AZ, UK
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46
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Jäger N. Bioinformatics workflows for clinical applications in precision oncology. Semin Cancer Biol 2021; 84:103-112. [PMID: 33476720 DOI: 10.1016/j.semcancer.2020.12.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 12/15/2020] [Accepted: 12/28/2020] [Indexed: 12/23/2022]
Abstract
High-throughput molecular profiling of tumors is a fundamental aspect of precision oncology, enabling the identification of genomic alterations that can be targeted therapeutically. In this context, a patient is matched to a specific drug or therapy based on the tumor's underlying genetic driver events rather than the histologic classification. This approach requires extensive bioinformatics methodology and workflows, including raw sequencing data processing and quality control, variant calling and annotation, integration of different molecular data types, visualization and finally reporting the data to physicians, cancer researchers and pharmacologists in a format that is readily interpretable for clinical decision making. This review comprises a broad overview of these bioinformatics aspects and discusses the multiple analytical, technical and interpretational challenges that remain to efficiently translate molecular findings into personalized treatment recommendations.
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Affiliation(s)
- Natalie Jäger
- Hopp Children's Cancer Center Heidelberg (KiTZ) & Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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47
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Sinitcyn P, Hamzeiy H, Salinas Soto F, Itzhak D, McCarthy F, Wichmann C, Steger M, Ohmayer U, Distler U, Kaspar-Schoenefeld S, Prianichnikov N, Yılmaz Ş, Rudolph JD, Tenzer S, Perez-Riverol Y, Nagaraj N, Humphrey SJ, Cox J. MaxDIA enables library-based and library-free data-independent acquisition proteomics. Nat Biotechnol 2021; 39:1563-1573. [PMID: 34239088 PMCID: PMC8668435 DOI: 10.1038/s41587-021-00968-7] [Citation(s) in RCA: 82] [Impact Index Per Article: 27.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 05/27/2021] [Indexed: 02/06/2023]
Abstract
MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA-hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA's bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies-BoxCar acquisition and trapped ion mobility spectrometry-both lead to deep and accurate proteome quantification.
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Affiliation(s)
- Pavel Sinitcyn
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Hamid Hamzeiy
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Favio Salinas Soto
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Daniel Itzhak
- grid.499295.aChan Zuckerberg Biohub, San Francisco, CA USA
| | - Frank McCarthy
- grid.499295.aChan Zuckerberg Biohub, San Francisco, CA USA
| | - Christoph Wichmann
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | | | | | - Ute Distler
- grid.5802.f0000 0001 1941 7111Institute for Immunology, Johannes Gutenberg University, Mainz, Germany
| | | | - Nikita Prianichnikov
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Şule Yılmaz
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany
| | - Jan Daniel Rudolph
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany ,grid.6584.f0000 0004 0553 2276Bosch Center for Artificial Intelligence, Renningen, Germany
| | - Stefan Tenzer
- grid.5802.f0000 0001 1941 7111Institute for Immunology, Johannes Gutenberg University, Mainz, Germany
| | - Yasset Perez-Riverol
- grid.225360.00000 0000 9709 7726European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, UK
| | | | - Sean J. Humphrey
- grid.1013.30000 0004 1936 834XSchool of Life and Environmental Sciences, Charles Perkins Centre, University of Sydney, Camperdown, New South Wales Australia
| | - Jürgen Cox
- grid.418615.f0000 0004 0491 845XComputational Systems Biochemistry Research Group, Max-Planck Institute of Biochemistry, Martinsried, Germany ,grid.7914.b0000 0004 1936 7443Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway
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48
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Development of New Antiproliferative Compound against Human Tumor Cells from the Marine Microalgae Nannochloropsis gaditana by Applied Proteomics. Int J Mol Sci 2020; 22:ijms22010096. [PMID: 33374179 PMCID: PMC7795124 DOI: 10.3390/ijms22010096] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2020] [Revised: 12/15/2020] [Accepted: 12/22/2020] [Indexed: 12/18/2022] Open
Abstract
Proteomics is a crucial tool for unravelling the molecular dynamics of essential biological processes, becoming a pivotal technique for basic and applied research. Diverse bioinformatic tools are required to manage and explore the huge amount of information obtained from a single proteomics experiment. Thus, functional annotation and protein-protein interactions are evaluated in depth leading to the biological conclusions that best fit the proteomic response in the system under study. To gain insight into potential applications of the identified proteins, a novel approach named "Applied Proteomics" has been developed by comparing the obtained protein information with the existing patents database. The development of massive sequencing technology and mass spectrometry (MS/MS) improvements has allowed the application of proteomics nonmodel microorganisms, which have been deeply described as a novel source of metabolites. Between them, Nannochloropsis gaditana has been pointed out as an alternative source of biomolecules. Recently, our research group has reported the first complete proteome analysis of this microalga, which was analysed using the applied proteomics concept with the identification of 488 proteins with potential industrial applications. To validate our approach, we selected the UCA01 protein from the prohibitin family. The recombinant version of this protein showed antiproliferative activity against two tumor cell lines, Caco2 (colon adenocarcinoma) and HepG-2 (hepatocellular carcinoma), proving that proteome data have been transformed into relevant biotechnological information. From Nannochloropsis gaditana has been developed a new tool against cancer-the protein named UCA01. This protein has selective effects inhibiting the growth of tumor cells, but does not show any effect on control cells. This approach describes the first practical approach to transform proteome information in a potential industrial application, named "applied proteomics". It is based on a novel bioalgorithm, which is able to identify proteins with potential industrial applications. From hundreds of proteins described in the proteome of N. gaditana, the bioalgorithm identified over 400 proteins with potential uses; one of them was selected as UCA01, "in vitro" and its potential was demonstrated against cancer. This approach has great potential, but the applications are potentially numerous and undefined.
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49
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Muggia L, Ametrano CG, Sterflinger K, Tesei D. An Overview of Genomics, Phylogenomics and Proteomics Approaches in Ascomycota. Life (Basel) 2020; 10:E356. [PMID: 33348904 PMCID: PMC7765829 DOI: 10.3390/life10120356] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Revised: 12/10/2020] [Accepted: 12/12/2020] [Indexed: 12/26/2022] Open
Abstract
Fungi are among the most successful eukaryotes on Earth: they have evolved strategies to survive in the most diverse environments and stressful conditions and have been selected and exploited for multiple aims by humans. The characteristic features intrinsic of Fungi have required evolutionary changes and adaptations at deep molecular levels. Omics approaches, nowadays including genomics, metagenomics, phylogenomics, transcriptomics, metabolomics, and proteomics have enormously advanced the way to understand fungal diversity at diverse taxonomic levels, under changeable conditions and in still under-investigated environments. These approaches can be applied both on environmental communities and on individual organisms, either in nature or in axenic culture and have led the traditional morphology-based fungal systematic to increasingly implement molecular-based approaches. The advent of next-generation sequencing technologies was key to boost advances in fungal genomics and proteomics research. Much effort has also been directed towards the development of methodologies for optimal genomic DNA and protein extraction and separation. To date, the amount of proteomics investigations in Ascomycetes exceeds those carried out in any other fungal group. This is primarily due to the preponderance of their involvement in plant and animal diseases and multiple industrial applications, and therefore the need to understand the biological basis of the infectious process to develop mechanisms for biologic control, as well as to detect key proteins with roles in stress survival. Here we chose to present an overview as much comprehensive as possible of the major advances, mainly of the past decade, in the fields of genomics (including phylogenomics) and proteomics of Ascomycota, focusing particularly on those reporting on opportunistic pathogenic, extremophilic, polyextremotolerant and lichenized fungi. We also present a review of the mostly used genome sequencing technologies and methods for DNA sequence and protein analyses applied so far for fungi.
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Affiliation(s)
- Lucia Muggia
- Department of Life Sciences, University of Trieste, 34127 Trieste, Italy
| | - Claudio G. Ametrano
- Grainger Bioinformatics Center, Department of Science and Education, The Field Museum, Chicago, IL 60605, USA;
| | - Katja Sterflinger
- Academy of Fine Arts Vienna, Institute of Natual Sciences and Technology in the Arts, 1090 Vienna, Austria;
| | - Donatella Tesei
- Department of Biotechnology, University of Natural Resources and Life Sciences, 1190 Vienna, Austria;
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Cicaloni V, Pecorelli A, Cordone V, Tinti L, Rossi M, Hayek J, Salvini L, Tinti C, Valacchi G. A proteomics approach to further highlight the altered inflammatory condition in Rett syndrome. Arch Biochem Biophys 2020; 696:108660. [PMID: 33159892 DOI: 10.1016/j.abb.2020.108660] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 10/24/2020] [Accepted: 10/28/2020] [Indexed: 12/12/2022]
Abstract
Rett syndrome (RTT) is a progressive neurodevelopmental disorder caused by mutations in the X-linked MECP2 gene. RTT patients show multisystem disturbances associated with perturbed redox homeostasis and inflammation, which appear as possible key factors in RTT pathogenesis. In this study, using primary dermal fibroblasts from control and RTT subjects, we performed a proteomic analysis that, together with data mining approaches, allowed us to carry out a comprehensive characterization of RTT cellular proteome. Functional and pathway enrichment analyses showed that differentially expressed proteins in RTT were mainly enriched in biological processes related to immune/inflammatory responses. Overall, by using proteomic data mining as supportive approach, our results provide a detailed insight into the molecular pathways involved in RTT immune dysfunction that, causing tissue and organ damage, can increase the vulnerability of affected patients to unknown endogenous factors or infections.
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Affiliation(s)
- Vittoria Cicaloni
- Toscana Life Science Foundation, Via Fiorentina 1, 53100, Siena, Italy
| | - Alessandra Pecorelli
- Plants for Human Health Institute, Animal Science Dept., NC Research Campus, NC State University, 600 Laureate Way, Kannapolis, NC, 28081, USA
| | - Valeria Cordone
- Department of Biomedical and Specialist Surgical Sciences, University of Ferrara, Ferrara, Italy
| | - Laura Tinti
- Toscana Life Science Foundation, Via Fiorentina 1, 53100, Siena, Italy
| | - Marco Rossi
- Toscana Life Science Foundation, Via Fiorentina 1, 53100, Siena, Italy
| | - Joussef Hayek
- Toscana Life Science Foundation, Via Fiorentina 1, 53100, Siena, Italy
| | - Laura Salvini
- Toscana Life Science Foundation, Via Fiorentina 1, 53100, Siena, Italy
| | - Cristina Tinti
- Toscana Life Science Foundation, Via Fiorentina 1, 53100, Siena, Italy
| | - Giuseppe Valacchi
- Plants for Human Health Institute, Animal Science Dept., NC Research Campus, NC State University, 600 Laureate Way, Kannapolis, NC, 28081, USA; Department of Biomedical and Specialist Surgical Sciences, University of Ferrara, Ferrara, Italy; Kyung Hee University, Department of Food and Nutrition, Seoul, South Korea.
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