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Melendez C, Sanders J, Yilmaz M, Bittremieux W, Fondrie WE, Oh S, Noble WS. Accounting for Digestion Enzyme Bias in Casanovo. J Proteome Res 2024; 23:4761-4769. [PMID: 39213590 DOI: 10.1021/acs.jproteome.4c00422] [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: 09/04/2024]
Abstract
A key parameter of any bottom-up proteomics mass spectrometry experiment is the identity of the enzyme that is used to digest proteins in the sample into peptides. The Casanovo de novo sequencing model was trained using data that was generated with trypsin digestion; consequently, the model prefers to predict peptides that end with the amino acids "K" or "R". This bias is desirable when Casanovo is used to analyze data that was also generated using trypsin but can be problematic if the data was generated using some other digestion enzyme. In this work, we modify Casanovo to take as input the identity of the digestion enzyme alongside each observed spectrum. We then train Casanovo with data generated by using several different enzymes, and we demonstrate that the resulting model successfully learns to capture enzyme-specific behavior. However, we find, surprisingly, that this new model does not yield a significant improvement in sequencing accuracy relative to a model trained without enzyme information but using the same training set. This observation may have important implications for future attempts to make use of experimental metadata in de novo sequencing models.
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Affiliation(s)
- Carlo Melendez
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Justin Sanders
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Melih Yilmaz
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Wout Bittremieux
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium
| | | | - Sewoong Oh
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - William Stafford Noble
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
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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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Fomenkov A, Weigele P, McClung C, Madinger C, Roberts RJ. Complete genome assembly and methylome dissection of Methanococcus aeolicus PL15/H p. Front Microbiol 2023; 14:1112734. [PMID: 37089567 PMCID: PMC10113651 DOI: 10.3389/fmicb.2023.1112734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 03/21/2023] [Indexed: 04/25/2023] Open
Abstract
Although restriction-modification systems are found in both Eubacterial and Archaeal kingdoms, comparatively less is known about patterns of DNA methylation and genome defense systems in archaea. Here we report the complete closed genome sequence and methylome analysis of Methanococcus aeolicus PL15/H p , a strain of the CO2-reducing methanogenic archaeon and a commercial source for MaeI, MaeII, and MaeIII restriction endonucleases. The M. aeolicus PL15/H p genome consists of a 1.68 megabase circular chromosome predicted to contain 1,615 protein coding genes and 38 tRNAs. A combination of methylome sequencing, homology-based genome annotation, and recombinant gene expression identified five restriction-modification systems encoded by this organism, including the methyltransferase and site-specific endonuclease of MaeIII. The MaeIII restriction endonuclease was recombinantly expressed, purified and shown to have site-specific DNA cleavage activity in vitro.
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4
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Sharma S, Singh DK, Mettu VS, Yue G, Ahire D, Basit A, Heyward S, Prasad B. Quantitative Characterization of Clinically Relevant Drug-Metabolizing Enzymes and Transporters in Rat Liver and Intestinal Segments for Applications in PBPK Modeling. Mol Pharm 2023; 20:1737-1749. [PMID: 36791335 DOI: 10.1021/acs.molpharmaceut.2c00950] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Rats are extensively used as a preclinical model for assessing drug pharmacokinetics (PK) and tissue distribution; however, successful translation of the rat data requires information on the differences in drug metabolism and transport mechanisms between rats and humans. To partly fill this knowledge gap, we quantified clinically relevant drug-metabolizing enzymes and transporters (DMETs) in the liver and different intestinal segments of Sprague-Dawley rats. The levels of DMET proteins in rats were quantified using the global proteomics-based total protein approach (TPA) and targeted proteomics. The abundance of the major DMET proteins was largely comparable using quantitative global and targeted proteomics. However, global proteomics-based TPA was able to detect and quantify a comprehensive list of 66 DMET proteins in the liver and 37 DMET proteins in the intestinal segments of SD rats without the need for peptide standards. Cytochrome P450 (Cyp) and UDP-glycosyltransferase (Ugt) enzymes were mainly detected in the liver with the abundance ranging from 8 to 6502 and 74 to 2558 pmol/g tissue. P-gp abundance was higher in the intestine (124.1 pmol/g) as compared to that in the liver (26.6 pmol/g) using the targeted analysis. Breast cancer resistance protein (Bcrp) was most abundant in the intestinal segments, whereas organic anion transporting polypeptides (Oatp) 1a1, 1a4, 1b2, and 2a1 and multidrug resistance proteins (Mrp) 2 and 6 were predominantly detected in the liver. To demonstrate the utility of these data, we modeled digoxin PK by integrating protein abundance of P-gp and Cyp3a2 into a physiologically based PK (PBPK) model constructed using PK-Sim software. The model was able to reliably predict the systemic as well as tissue concentrations of digoxin in rats. These findings suggest that proteomics-informed PBPK models in preclinical species can allow mechanistic PK predictions in animal models including tissue drug concentrations.
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Affiliation(s)
- Sheena Sharma
- College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington 99202, United States
| | - Dilip K Singh
- College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington 99202, United States
| | - Vijay S Mettu
- College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington 99202, United States
| | - Guihua Yue
- College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington 99202, United States
| | - Deepak Ahire
- College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington 99202, United States
| | - Abdul Basit
- College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington 99202, United States
| | | | - Bhagwat Prasad
- College of Pharmacy and Pharmaceutical Sciences, Washington State University, Spokane, Washington 99202, United States
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5
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Comprehensive proteomic analysis of murine terminal erythroid differentiation. Blood Adv 2021; 4:1464-1477. [PMID: 32282884 DOI: 10.1182/bloodadvances.2020001652] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 03/04/2020] [Indexed: 12/12/2022] Open
Abstract
Murine-based cellular models have provided and continue to provide many useful insights into the fundamental mechanisms of erythropoiesis, as well as insights into the pathophysiology of inherited and acquired red cell disorders. Although detailed information on many aspects of these cell models is available, comprehensive proteomic data are lacking. This is a critical knowledge gap, as proteins are effectors of most biologic processes. To address this critical unmet need, proteomes of the murine cell lines Friend erythroleukemia (MEL), GATA1 erythroid (G1ER), and embryonic stem cell-derived erythroid progenitor (MEDEP) and proteomes of cultured murine marrow-derived erythroblasts at different stages of terminal erythroid differentiation were analyzed. The proteomes of MEDEP cells and primary murine erythroid cells were most similar, whereas those of MEL and G1ER cells were more distantly related. We demonstrated that the overall cellular content of histones does not decrease during terminal differentiation, despite strong chromatin condensation. Comparison of murine and human proteomes throughout terminal erythroid differentiation revealed that many noted transcriptomic changes were significantly dampened at the proteome level, especially at the end of the terminal differentiation process. Analysis of the early events associated with induction of terminal differentiation in MEDEP cells revealed divergent alterations in associated transcriptomes and proteomes. These proteomic data are powerful and valuable tools for the study of fundamental mechanisms of normal and disordered erythropoiesis and will be of broad interest to a wide range of investigators for making the appropriate choice of various cell lines to study inherited and acquired diseases of the erythrocyte.
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6
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Tang L, Wu Z, Wang J, Zhang X. Formaldehyde Derivatization: An Unexpected Side Reaction During Filter-Aided Sample Preparation. Anal Chem 2020; 92:12120-12125. [PMID: 32786431 DOI: 10.1021/acs.analchem.0c01981] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
The filter-aided sample preparation (FASP) method has been commonly used for proteomic sample preparation due to its high efficiency in removing impurities. Herein, we report an overlooked +12 Da side modification during FASP method using Microcon spin filters. We confirmed that the side modification is caused by formaldehyde released from the spin filter and found that the side modification leads to 10.5% and 9.5% loss in proteome-level peptide and protein identification, respectively. We evaluated different pretreatment procedures to reduce the side reaction. Furthermore, on the basis of the evaluation results of different brands of spin filters, we recommend Nanosep spin filters for different proteomic studies, especially for amine-labeling proteomic studies. Our results would benefit researchers employing the spin filters to improve their results and also help spin filter manufacturers to improve the product quality. Data are available via ProteomeXchange with identifier PXD018737.
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Affiliation(s)
- Langlang Tang
- State Key Laboratory of Genetic Engineering, Department of Biochemistry, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Zhen Wu
- State Key Laboratory of Genetic Engineering, Department of Biochemistry, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Jie Wang
- State Key Laboratory of Genetic Engineering, Department of Biochemistry, School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Xumin Zhang
- State Key Laboratory of Genetic Engineering, Department of Biochemistry, School of Life Sciences, Fudan University, Shanghai 200438, China
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7
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Abstract
Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported1, advances in mass-spectrometry-based proteomics2 have enabled increasingly comprehensive identification and quantification of the human proteome3-6. However, there have been few comparisons across species7,8, in stark contrast with genomics initiatives9. Here we use an advanced proteomics workflow-in which the peptide separation step is performed by a microstructured and extremely reproducible chromatographic system-for the in-depth study of 100 taxonomically diverse organisms. With two million peptide and 340,000 stringent protein identifications obtained in a standardized manner, we double the number of proteins with solid experimental evidence known to the scientific community. The data also provide a large-scale case study for sequence-based machine learning, as we demonstrate by experimentally confirming the predicted properties of peptides from Bacteroides uniformis. Our results offer a comparative view of the functional organization of organisms across the entire evolutionary range. A remarkably high fraction of the total proteome mass in all kingdoms is dedicated to protein homeostasis and folding, highlighting the biological challenge of maintaining protein structure in all branches of life. Likewise, a universally high fraction is involved in supplying energy resources, although these pathways range from photosynthesis through iron sulfur metabolism to carbohydrate metabolism. Generally, however, proteins and proteomes are remarkably diverse between organisms, and they can readily be explored and functionally compared at www.proteomesoflife.org.
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8
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Wegler C, Ölander M, Wiśniewski JR, Lundquist P, Zettl K, Åsberg A, Hjelmesæth J, Andersson TB, Artursson P. Global variability analysis of mRNA and protein concentrations across and within human tissues. NAR Genom Bioinform 2020; 2:lqz010. [PMID: 33575562 PMCID: PMC7671341 DOI: 10.1093/nargab/lqz010] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2019] [Revised: 08/27/2019] [Accepted: 10/06/2019] [Indexed: 12/25/2022] Open
Abstract
Genes and proteins show variable expression patterns throughout the human body. However, it is not clear whether relative differences in mRNA concentrations are retained on the protein level. Furthermore, inter-individual protein concentration variability within single tissue types has not been comprehensively explored. Here, we used the Gini index for in-depth concentration variability analysis of publicly available transcriptomics and proteomics data, and of an in-house proteomics dataset of human liver and jejunum from 38 donors. We found that the transfer of concentration variability from mRNA to protein is limited, that established 'reference genes' for data normalization vary markedly at the protein level, that protein concentrations cover a wide variability spectrum within single tissue types, and that concentration variability analysis can be a convenient starting point for identifying disease-associated proteins and novel biomarkers. Our results emphasize the importance of considering individual concentration levels, as opposed to population averages, for personalized systems biology analysis.
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Affiliation(s)
- Christine Wegler
- Department of Pharmacy, Uppsala University, Uppsala SE-75123, Sweden
- DMPK, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Magnus Ölander
- Department of Pharmacy, Uppsala University, Uppsala SE-75123, Sweden
| | - Jacek R Wiśniewski
- Biochemical Proteomics Group, Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried D-82152, Germany
| | - Patrik Lundquist
- Department of Pharmacy, Uppsala University, Uppsala SE-75123, Sweden
| | - Katharina Zettl
- Biochemical Proteomics Group, Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried D-82152, Germany
| | - Anders Åsberg
- Department of Pharmacy, University of Oslo, Oslo NO-0316, Norway
- Department of Transplantation Medicine, Oslo University Hospital-Rikshospitalet, Oslo NO-0316, Norway
| | - Jøran Hjelmesæth
- Morbid Obesity Centre, Department of Medicine, Vestfold Hospital Trust, Tønsberg NO-3103, Norway
- Department of Endocrinology, Morbid Obesity and Preventive Medicine, Institute of Clinical Medicine, University of Oslo, Oslo NO-0316, Norway
| | - Tommy B Andersson
- DMPK, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg SE-43183, Sweden
| | - Per Artursson
- Department of Pharmacy and Science for Life Laboratory, Uppsala University, Uppsala SE-75123, Sweden
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9
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Wiśniewski JR, Zettl K. Datasets: Sensitivity and protein digestion course of proteomic Filter Aided Sample Preparation. Data Brief 2019; 26:104530. [PMID: 31667293 PMCID: PMC6811893 DOI: 10.1016/j.dib.2019.104530] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2019] [Revised: 09/02/2019] [Accepted: 09/09/2019] [Indexed: 12/14/2022] Open
Abstract
Sensitivity of FASP was tested using SDS lysates from HeLa cells and mouse brain. Peptides were analyzed using a QExactive HF-X instrument. Whole cell lysates of Hela cells were processed with FASP using single or double, consecutive or successive, digestion with LysC or trypsin. The generated peptides were analyzed using a LTQ-Orbitrap mass spectrometer. These datasets accompany “Filter Aided Sample Preparation – A Tutorial” (Wiśniewski, 2019).
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Affiliation(s)
| | - Katharina Zettl
- Max Planck Institute of Biochemistry, 82152, Martinsried, Germany
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10
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Prasad B, Achour B, Artursson P, Hop CECA, Lai Y, Smith PC, Barber J, Wisniewski JR, Spellman D, Uchida Y, Zientek M, Unadkat JD, Rostami-Hodjegan A. Toward a Consensus on Applying Quantitative Liquid Chromatography-Tandem Mass Spectrometry Proteomics in Translational Pharmacology Research: A White Paper. Clin Pharmacol Ther 2019; 106:525-543. [PMID: 31175671 PMCID: PMC6692196 DOI: 10.1002/cpt.1537] [Citation(s) in RCA: 78] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 05/22/2019] [Indexed: 12/18/2022]
Abstract
Quantitative translation of information on drug absorption, disposition, receptor engagement, and drug-drug interactions from bench to bedside requires models informed by physiological parameters that link in vitro studies to in vivo outcomes. To predict in vivo outcomes, biochemical data from experimental systems are routinely scaled using protein quantity in these systems and relevant tissues. Although several laboratories have generated useful quantitative proteomic data using state-of-the-art mass spectrometry, no harmonized guidelines exit for sample analysis and data integration to in vivo translation practices. To address this gap, a workshop was held on September 27 and 28, 2018, in Cambridge, MA, with 100 experts attending from academia, the pharmaceutical industry, and regulators. Various aspects of quantitative proteomics and its applications in translational pharmacology were debated. A summary of discussions and best practices identified by this expert panel are presented in this "White Paper" alongside unresolved issues that were outlined for future debates.
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Affiliation(s)
- Bhagwat Prasad
- Department of Pharmaceutics, University of Washington, Seattle, WA
| | - Brahim Achour
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
| | - Per Artursson
- Department of Pharmacy, Uppsala University, Uppsala, Sweden
| | | | | | - Philip C Smith
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC
| | - Jill Barber
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
| | - Jacek R Wisniewski
- Biochemical Proteomics Group, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Daniel Spellman
- Pharmacokinetics, Pharmacodynamics & Drug Metabolism, Merck & Co., Inc., West Point, PA
| | - Yasuo Uchida
- Graduate School of Pharmaceutical Sciences, Tohoku University, Sendai, Japan
| | | | | | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, University of Manchester, Manchester, UK
- Certara UK Ltd. (Simcyp Division), 1 Concourse Way, Sheffield, UK
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11
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Couto N, Al-Majdoub ZM, Achour B, Wright PC, Rostami-Hodjegan A, Barber J. Quantification of Proteins Involved in Drug Metabolism and Disposition in the Human Liver Using Label-Free Global Proteomics. Mol Pharm 2019; 16:632-647. [DOI: 10.1021/acs.molpharmaceut.8b00941] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Narciso Couto
- Centre for Applied Pharmacokinetic Research, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT, U.K
- Department of Chemical and Biological Engineering, ChELSI Institute (Chemical Engineering at the Life Science Interface), University of Sheffield, Sir Robert Hadfield Building, Mappin Street, Sheffield S1 3JD, U.K
| | - Zubida M. Al-Majdoub
- Centre for Applied Pharmacokinetic Research, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT, U.K
| | - Brahim Achour
- Centre for Applied Pharmacokinetic Research, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT, U.K
| | - Phillip C. Wright
- Department of Chemical and Biological Engineering, ChELSI Institute (Chemical Engineering at the Life Science Interface), University of Sheffield, Sir Robert Hadfield Building, Mappin Street, Sheffield S1 3JD, U.K
| | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT, U.K
- Simcyp Ltd. (a Certara company), 1 Concourse Way, Sheffield S1 2BJ, U.K
| | - Jill Barber
- Centre for Applied Pharmacokinetic Research, University of Manchester, Stopford Building, Oxford Road, Manchester M13 9PT, U.K
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