1
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Lin A, See D, Fondrie WE, Keich U, Noble WS. Target-decoy false discovery rate estimation using Crema. Proteomics 2024; 24:e2300084. [PMID: 38380501 DOI: 10.1002/pmic.202300084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2023] [Revised: 01/06/2024] [Accepted: 01/16/2024] [Indexed: 02/22/2024]
Abstract
Assigning statistical confidence estimates to discoveries produced by a tandem mass spectrometry proteomics experiment is critical to enabling principled interpretation of the results and assessing the cost/benefit ratio of experimental follow-up. The most common technique for computing such estimates is to use target-decoy competition (TDC), in which observed spectra are searched against a database of real (target) peptides and a database of shuffled or reversed (decoy) peptides. TDC procedures for estimating the false discovery rate (FDR) at a given score threshold have been developed for application at the level of spectra, peptides, or proteins. Although these techniques are relatively straightforward to implement, it is common in the literature to skip over the implementation details or even to make mistakes in how the TDC procedures are applied in practice. Here we present Crema, an open-source Python tool that implements several TDC methods of spectrum-, peptide- and protein-level FDR estimation. Crema is compatible with a variety of existing database search tools and provides a straightforward way to obtain robust FDR estimates.
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Affiliation(s)
- Andy Lin
- Chemical and Biological Signatures, Pacific Northwest National Laboratory, Seattle, Washington, USA
| | - Donavan See
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
| | | | - Uri Keich
- School of Mathematics and Statistics, University of Sydney, Sydney, Australia
| | - William Stafford Noble
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, USA
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
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2
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Harris L, Fondrie WE, Oh S, Noble WS. Evaluating Proteomics Imputation Methods with Improved Criteria. J Proteome Res 2023; 22:3427-3438. [PMID: 37861703 PMCID: PMC10949645 DOI: 10.1021/acs.jproteome.3c00205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Abstract
Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. Missing values hinder reproducibility, reduce statistical power, and make it difficult to compare across samples or experiments. Although many methods exist for imputing missing values, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error such as the mean-squared error between imputed and held-out values. Here we evaluate the performance of commonly used imputation methods using three practical, "downstream-centric" criteria. These criteria measure the ability to identify differentially expressed peptides, generate new quantitative peptides, and improve the peptide lower limit of quantification. Our evaluation comprises several experiment types and acquisition strategies, including data-dependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides but that it can identify new quantitative peptides and improve the peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that existing imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.
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Affiliation(s)
- Lincoln Harris
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195, United States
| | | | - Sewoong Oh
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195, United States
| | - William S 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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3
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Allen C, Meinl R, Paez JS, Searle BC, Just S, Pino LK, Fondrie WE. nf-encyclopedia: A Cloud-Ready Pipeline for Chromatogram Library Data-Independent Acquisition Proteomics Workflows. J Proteome Res 2023; 22:2743-2749. [PMID: 37417926 DOI: 10.1021/acs.jproteome.2c00613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/08/2023]
Abstract
Data-independent acquisition (DIA) mass spectrometry methods provide systematic and comprehensive quantification of the proteome; yet, relatively few open-source tools are available to analyze DIA proteomics experiments. Fewer still are tools that can leverage gas phase fractionated (GPF) chromatogram libraries to enhance the detection and quantification of peptides in these experiments. Here, we present nf-encyclopedia, an open-source NextFlow pipeline that connects three open-source tools, MSConvert, EncyclopeDIA, and MSstats, to analyze DIA proteomics experiments with or without chromatogram libraries. We demonstrate that nf-encyclopedia is reproducible when run on either a cloud platform or a local workstation and provides robust peptide and protein quantification. Additionally, we found that MSstats enhances protein-level quantitative performance over EncyclopeDIA alone. Finally, we benchmarked the ability of nf-encyclopedia to scale to large experiments in the cloud by leveraging the parallelization of compute resources. The nf-encyclopedia pipeline is available under a permissive Apache 2.0 license; run it on your desktop, cluster, or in the cloud: https://github.com/TalusBio/nf-encyclopedia.
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Affiliation(s)
- Carolyn Allen
- Talus Bioscience, Seattle, Washington 98122, United States
| | - Rico Meinl
- Talus Bioscience, Seattle, Washington 98122, United States
| | | | - Brian C Searle
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, United States
- Pelotonia Institute for Immuno-Oncology, The Ohio State University Comprehensive Cancer Center, Columbus, Ohio 43210, United States
- Proteome Software, Inc., Portland, Oregon 97219, United States
| | - Seth Just
- Proteome Software, Inc., Portland, Oregon 97219, United States
| | - Lindsay K Pino
- Talus Bioscience, Seattle, Washington 98122, United States
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4
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Kertesz-Farkas A, Nii Adoquaye Acquaye FL, Bhimani K, Eng JK, Fondrie WE, Grant C, Hoopmann MR, Lin A, Lu YY, Moritz RL, MacCoss MJ, Noble WS. The Crux Toolkit for Analysis of Bottom-Up Tandem Mass Spectrometry Proteomics Data. J Proteome Res 2023; 22:561-569. [PMID: 36598107 DOI: 10.1021/acs.jproteome.2c00615] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The Crux tandem mass spectrometry data analysis toolkit provides a collection of algorithms for analyzing bottom-up proteomics tandem mass spectrometry data. Many publications have described various individual components of Crux, but a comprehensive summary has not been published since 2014. The goal of this work is to summarize the functionality of Crux, focusing on developments since 2014. We begin with empirical results demonstrating our recently implemented speedups to the Tide search engine. Other new features include a new score function in Tide, two new confidence estimation procedures, as well as three new tools: Param-medic for estimating search parameters directly from mass spectrometry data, Kojak for searching cross-linked mass spectra, and DIAmeter for searching data independent acquisition data against a sequence database.
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Affiliation(s)
- Attila Kertesz-Farkas
- Department of Data Analysis and Artificial Intelligence and Laboratory on AI for Computational Biology, Faculty of Computer Science, HSE University, 20 Myasnitskaya ulitsa, Moscow 101000, Russia
| | - Frank Lawrence Nii Adoquaye Acquaye
- Department of Data Analysis and Artificial Intelligence and Laboratory on AI for Computational Biology, Faculty of Computer Science, HSE University, 20 Myasnitskaya ulitsa, Moscow 101000, Russia
| | - Kishankumar Bhimani
- Department of Data Analysis and Artificial Intelligence and Laboratory on AI for Computational Biology, Faculty of Computer Science, HSE University, 20 Myasnitskaya ulitsa, Moscow 101000, Russia
| | - Jimmy K Eng
- Proteomics Resource, University of Washington, 850 Republican Street, Seattle, Washington 98109-4725, United States
| | - William E Fondrie
- Talus Bioscience550 17th Avenue, Seattle, Washington 98122, United States
| | - Charles Grant
- Department of Genome Sciences, University of Washington3720 15th Avenue NE, Seattle, Washington 98195, United States
| | - Michael R Hoopmann
- Insititute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - Andy Lin
- Department of Genome Sciences, University of Washington3720 15th Avenue NE, Seattle, Washington 98195, United States
| | - Yang Y Lu
- Department of Genome Sciences, University of Washington3720 15th Avenue NE, Seattle, Washington 98195, United States
| | - Robert L Moritz
- Insititute for Systems Biology, 401 Terry Avenue N, Seattle, Washington 98109, United States
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington3720 15th Avenue NE, Seattle, Washington 98195, United States
| | - William Stafford Noble
- Department of Genome Sciences, University of Washington3720 15th Avenue NE, Seattle, Washington 98195, United States.,Paul G. Allen School of Computer Science and Engineering, University of Washington185 E Stevens Way NE, Seattle, Washington 98195-2350, United States
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5
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Arab I, Fondrie WE, Laukens K, Bittremieux W. Semisupervised Machine Learning for Sensitive Open Modification Spectral Library Searching. J Proteome Res 2023; 22:585-593. [PMID: 36688569 DOI: 10.1021/acs.jproteome.2c00616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
A key analysis task in mass spectrometry proteomics is matching the acquired tandem mass spectra to their originating peptides by sequence database searching or spectral library searching. Machine learning is an increasingly popular postprocessing approach to maximize the number of confident spectrum identifications that can be obtained at a given false discovery rate threshold. Here, we have integrated semisupervised machine learning in the ANN-SoLo tool, an efficient spectral library search engine that is optimized for open modification searching to identify peptides with any type of post-translational modification. We show that machine learning rescoring boosts the number of spectra that can be identified for both standard searching and open searching, and we provide insights into relevant spectrum characteristics harnessed by the machine learning model. The semisupervised machine learning functionality has now been fully integrated into ANN-SoLo, which is available as open source under the permissive Apache 2.0 license on GitHub at https://github.com/bittremieux/ANN-SoLo.
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Affiliation(s)
- Issar Arab
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium.,Biomedical Informatics Network Antwerpen (biomina), 2020 Antwerp, Belgium
| | | | - Kris Laukens
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium.,Biomedical Informatics Network Antwerpen (biomina), 2020 Antwerp, Belgium
| | - Wout Bittremieux
- Department of Computer Science, University of Antwerp, 2020 Antwerp, Belgium.,Biomedical Informatics Network Antwerpen (biomina), 2020 Antwerp, Belgium
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6
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Heil LR, Fondrie WE, McGann CD, Federation AJ, Noble WS, MacCoss MJ, Keich U. Building Spectral Libraries from Narrow-Window Data-Independent Acquisition Mass Spectrometry Data. J Proteome Res 2022; 21:1382-1391. [PMID: 35549345 DOI: 10.1021/acs.jproteome.1c00895] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Advances in library-based methods for peptide detection from data-independent acquisition (DIA) mass spectrometry have made it possible to detect and quantify tens of thousands of peptides in a single mass spectrometry run. However, many of these methods rely on a comprehensive, high-quality spectral library containing information about the expected retention time and fragmentation patterns of peptides in the sample. Empirical spectral libraries are often generated through data-dependent acquisition and may suffer from biases as a result. Spectral libraries can be generated in silico, but these models are not trained to handle all possible post-translational modifications. Here, we propose a false discovery rate-controlled spectrum-centric search workflow to generate spectral libraries directly from gas-phase fractionated DIA tandem mass spectrometry data. We demonstrate that this strategy is able to detect phosphorylated peptides and can be used to generate a spectral library for accurate peptide detection and quantitation in wide-window DIA data. We compare the results of this search workflow to other library-free approaches and demonstrate that our search is competitive in terms of accuracy and sensitivity. These results demonstrate that the proposed workflow has the capacity to generate spectral libraries while avoiding the limitations of other methods.
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Affiliation(s)
- Lilian R Heil
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
| | - William E Fondrie
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
| | - Christopher D McGann
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
| | - Alexander J Federation
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
| | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States.,Paul G. Allen School for Computer Science and Engineering, University of Washington, Seattle, Washington 98105, United States
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington 98105, United States
| | - Uri Keich
- School of Mathematics and Statistics, University of Sydney, Sydney, NSW 2006, Australia
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7
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Phipps WS, Smith KD, Yang HY, Henderson CM, Pflaum H, Lerch ML, Fondrie WE, Emrick MA, Wu CC, MacCoss MJ, Noble WS, Hoofnagle AN. Tandem Mass Spectrometry-Based Amyloid Typing Using Manual Microdissection and Open-Source Data Processing. Am J Clin Pathol 2022; 157:748-757. [PMID: 35512256 PMCID: PMC9071319 DOI: 10.1093/ajcp/aqab185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Accepted: 09/20/2021] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVES Standard implementations of amyloid typing by liquid chromatography-tandem mass spectrometry use capabilities unavailable to most clinical laboratories. To improve accessibility of this testing, we explored easier approaches to tissue sampling and data processing. METHODS We validated a typing method using manual sampling in place of laser microdissection, pairing the technique with a semiquantitative measure of sampling adequacy. In addition, we created an open-source data processing workflow (Crux Pipeline) for clinical users. RESULTS Cases of amyloidosis spanning the major types were distinguishable with 100% specificity using measurements of individual amyloidogenic proteins or in combination with the ratio of λ and κ constant regions. Crux Pipeline allowed for rapid, batched data processing, integrating the steps of peptide identification, statistical confidence estimation, and label-free protein quantification. CONCLUSIONS Accurate mass spectrometry-based amyloid typing is possible without laser microdissection. To facilitate entry into solid tissue proteomics, newcomers can leverage manual sampling approaches in combination with Crux Pipeline and related tools.
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Affiliation(s)
- William S Phipps
- Department of Laboratory Medicine and Pathology, Seattle, WA, USA
| | - Kelly D Smith
- Department of Laboratory Medicine and Pathology, Seattle, WA, USA
- Department of Medicine, Seattle, WA, USA
| | - Han-Yin Yang
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Clark M Henderson
- Department of Laboratory Medicine and Pathology, Seattle, WA, USA
- Seagen, Bothel, WA, USA
| | - Hannah Pflaum
- Department of Laboratory Medicine and Pathology, Seattle, WA, USA
- Seattle Children’s Hospital, Seattle, WA, USA
| | - Melissa L Lerch
- Department of Laboratory Medicine and Pathology, Seattle, WA, USA
| | - William E Fondrie
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Christine C Wu
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Andrew N Hoofnagle
- Department of Laboratory Medicine and Pathology, Seattle, WA, USA
- Department of Medicine, Seattle, WA, USA
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8
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Abstract
The volume of proteomics and mass spectrometry data available in public repositories continues to grow at a rapid pace as more researchers embrace open science practices. Open access to the data behind scientific discoveries has become critical to validate published findings and develop new computational tools. Here, we present ppx, a Python package that provides easy, programmatic access to the data stored in ProteomeXchange repositories, such as PRIDE and MassIVE. The ppx package can be used as either a command line tool or a Python package to retrieve the files and metadata associated with a project when provided its identifier. To demonstrate how ppx enhances reproducible research, we used ppx within a Snakemake workflow to reanalyze a published data set with the open modification search tool ANN-SoLo and compared our reanalysis to the original results. We show that ppx readily integrates into workflows, and our reanalysis produced results consistent with the original analysis. We envision that ppx will be a valuable tool for creating reproducible analyses, providing tool developers easy access to data for development, testing, and benchmarking, and enabling the use of mass spectrometry data in data-intensive analyses. The ppx package is freely available and open source under the MIT license at https://github.com/wfondrie/ppx.
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Affiliation(s)
- William E Fondrie
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Wout Bittremieux
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA, USA
- Department of Computer Science, University of Antwerp, Antwerp, Belgium
| | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
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9
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Mudge MC, Nunn BL, Firth E, Ewert M, Hales K, Fondrie WE, Noble WS, Toner J, Light B, Junge KA. Subzero, saline incubations of
Colwellia psychrerythraea
reveal strategies and biomarkers for sustained life in extreme icy environments. Environ Microbiol 2021; 23:3840-3866. [DOI: 10.1111/1462-2920.15485] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2020] [Accepted: 03/22/2021] [Indexed: 11/26/2022]
Affiliation(s)
- Miranda C. Mudge
- Department of Genome Sciences University of Washington Seattle WA USA
- Department of Molecular and Cellular Biology University of Washington Seattle WA USA
| | - Brook L. Nunn
- Department of Genome Sciences University of Washington Seattle WA USA
- Astrobiology Program University of Washington Seattle WA USA
| | - Erin Firth
- Applied Physics Lab, Polar Science Center University of Washington Seattle WA USA
| | - Marcela Ewert
- Applied Physics Lab, Polar Science Center University of Washington Seattle WA USA
| | - Kianna Hales
- Department of Genome Sciences University of Washington Seattle WA USA
| | | | - William S. Noble
- Department of Genome Sciences University of Washington Seattle WA USA
- Paul G. Allen School of Computer Science and Engineering University of Washington Seattle WA USA
| | - Jonathan Toner
- Department of Earth and Space Sciences University of Washington Seattle WA USA
| | - Bonnie Light
- Applied Physics Lab, Polar Science Center University of Washington Seattle WA USA
| | - Karen A. Junge
- Applied Physics Lab, Polar Science Center University of Washington Seattle WA USA
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10
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Abstract
Machine learning methods have proven invaluable for increasing the sensitivity of peptide detection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, use semi-supervised algorithms to learn models directly from the datasets that they analyze. Although these methods are effective for many proteomics experiments, we suspected that they may be suboptimal for experiments of smaller scale. In this work, we found that the power and consistency of Percolator results was reduced as the size of the experiment was decreased. As an alternative, we propose a different operating mode for Percolator: learn a model with Percolator from a large dataset and use the learned model to evaluate the small-scale experiment. We call this a “static modeling” approach, in contrast to Percolator’s usual “dynamic model” that is trained anew for each dataset. We applied this static modeling approach to two settings: small, gel-based experiments and single-cell proteomics. In both cases, static models increased the yield of detected peptides and eliminated the model-induced variability of the standard dynamic approach. These results suggest that static models are a powerful tool for bringing the full benefits of Percolator and other semi-supervised algorithms to small-scale experiments.
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Affiliation(s)
- William E Fondrie
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195-5065, United States
| | - William S Noble
- Department of Genome Sciences, University of Washington, Seattle, Washington 98195-5065, United States.,Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington 98195-5065, United States
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11
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Au DT, Arai AL, Fondrie WE, Muratoglu SC, Strickland DK. Role of the LDL Receptor-Related Protein 1 in Regulating Protease Activity and Signaling Pathways in the Vasculature. Curr Drug Targets 2019; 19:1276-1288. [PMID: 29749311 DOI: 10.2174/1389450119666180511162048] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Revised: 04/24/2018] [Accepted: 04/24/2018] [Indexed: 12/22/2022]
Abstract
Aortic aneurysms represent a significant clinical problem as they largely go undetected until a rupture occurs. Currently, an understanding of mechanisms leading to aneurysm formation is limited. Numerous studies clearly indicate that vascular smooth muscle cells play a major role in the development and response of the vasculature to hemodynamic changes and defects in these responses can lead to aneurysm formation. The LDL receptor-related protein 1 (LRP1) is major smooth muscle cell receptor that has the capacity to mediate the endocytosis of numerous ligands and to initiate and regulate signaling pathways. Genetic evidence in humans and mouse models reveal a critical role for LRP1 in maintaining the integrity of the vasculature. Understanding the mechanisms by which this is accomplished represents an important area of research, and likely involves LRP1's ability to regulate levels of proteases known to degrade the extracellular matrix as well as its ability to modulate signaling events.
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Affiliation(s)
- Dianaly T Au
- Center for Vascular and Inflammatory Diseases, Biopark I, R213, 800 W. Baltimore Street, Baltimore, Maryland 21201, MD, United States
| | - Allison L Arai
- Center for Vascular and Inflammatory Diseases, Biopark I, R213, 800 W. Baltimore Street, Baltimore, Maryland 21201, MD, United States
| | - William E Fondrie
- Center for Vascular and Inflammatory Diseases, Biopark I, R213, 800 W. Baltimore Street, Baltimore, Maryland 21201, MD, United States
| | - Selen C Muratoglu
- Center for Vascular and Inflammatory Diseases, Biopark I, R213, 800 W. Baltimore Street, Baltimore, Maryland 21201, MD, United States.,Department of Physiology, University of Maryland School of Medicine, Baltimore, Maryland 21201, MD, United States
| | - Dudley K Strickland
- Center for Vascular and Inflammatory Diseases, Biopark I, R213, 800 W. Baltimore Street, Baltimore, Maryland 21201, MD, United States.,Department of Physiology, University of Maryland School of Medicine, Baltimore, Maryland 21201, MD, United States.,Department of Surgery, University of Maryland School of Medicine, Baltimore, Maryland 21201, MD, United States
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12
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Liang T, Leung LM, Opene B, Fondrie WE, Lee YI, Chandler CE, Yoon SH, Doi Y, Ernst RK, Goodlett DR. Rapid Microbial Identification and Antibiotic Resistance Detection by Mass Spectrometric Analysis of Membrane Lipids. Anal Chem 2019; 91:1286-1294. [PMID: 30571097 DOI: 10.1021/acs.analchem.8b02611] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
Infectious diseases have a substantial global health impact. Clinicians need rapid and accurate diagnoses of infections to direct patient treatment and improve antibiotic stewardship. Current technologies employed in routine diagnostics are based on bacterial culture followed by morphological trait differentiation and biochemical testing, which can be time-consuming and labor-intensive. With advances in mass spectrometry (MS) for clinical diagnostics, the U.S. Food and Drug Administration has approved two microbial identification platforms based on matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS analysis of microbial proteins. We recently reported a novel and complementary approach by comparing MALDI-TOF mass spectra of microbial membrane lipid fingerprints to identify ESKAPE pathogens. However, this lipid-based approach used a sample preparation method that required more than a working day from sample collection to identification. Here, we report a new method that extracts lipids efficiently and rapidly from microbial membranes using an aqueous sodium acetate (SA) buffer that can be used to identify clinically relevant Gram-positive and -negative pathogens and fungal species in less than an hour. The SA method also has the ability to differentiate antibiotic-susceptible and antibiotic-resistant strains, directly identify microbes from biological specimens, and detect multiple pathogens in a mixed sample. These results should have positive implications for the manner in which bacteria and fungi are identified in general hospital settings and intensive care units.
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Affiliation(s)
- Tao Liang
- Department of Pharmaceutical Sciences, School of Pharmacy , University of Maryland , Baltimore , Maryland 20742 , United States
| | - Lisa M Leung
- Department of Microbial Pathogenesis, School of Dentistry , University of Maryland , Baltimore , Maryland 20742 , United States.,Divisions of Microbiology and Molecular Biology, Laboratories Administration , Maryland Department of Health , Baltimore , Maryland 21215 , United States
| | - Belita Opene
- Department of Microbial Pathogenesis, School of Dentistry , University of Maryland , Baltimore , Maryland 20742 , United States
| | - William E Fondrie
- Center for Vascular and Inflammatory Diseases , University of Maryland , Baltimore , Maryland 20742 , United States
| | - Young In Lee
- Department of Microbial Pathogenesis, School of Dentistry , University of Maryland , Baltimore , Maryland 20742 , United States
| | - Courtney E Chandler
- Department of Microbial Pathogenesis, School of Dentistry , University of Maryland , Baltimore , Maryland 20742 , United States
| | - Sung Hwan Yoon
- Department of Microbial Pathogenesis, School of Dentistry , University of Maryland , Baltimore , Maryland 20742 , United States
| | - Yohei Doi
- Division of Infectious Diseases, School of Medicine , University of Pittsburgh , Pittsburgh , Pennsylvania 15260 , United States
| | - Robert K Ernst
- Department of Microbial Pathogenesis, School of Dentistry , University of Maryland , Baltimore , Maryland 20742 , United States
| | - David R Goodlett
- Department of Pharmaceutical Sciences, School of Pharmacy , University of Maryland , Baltimore , Maryland 20742 , United States
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13
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Au DT, Ying Z, Hernández-Ochoa EO, Fondrie WE, Hampton B, Migliorini M, Galisteo R, Schneider MF, Daugherty A, Rateri DL, Strickland DK, Muratoglu SC. LRP1 (Low-Density Lipoprotein Receptor-Related Protein 1) Regulates Smooth Muscle Contractility by Modulating Ca 2+ Signaling and Expression of Cytoskeleton-Related Proteins. Arterioscler Thromb Vasc Biol 2018; 38:2651-2664. [PMID: 30354243 PMCID: PMC6214382 DOI: 10.1161/atvbaha.118.311197] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Objective- Mutations affecting contractile-related proteins in the ECM (extracellular matrix), microfibrils, or vascular smooth muscle cells can predispose the aorta to aneurysms. We reported previously that the LRP1 (low-density lipoprotein receptor-related protein 1) maintains vessel wall integrity, and smLRP1-/- mice exhibited aortic dilatation. The current study focused on defining the mechanisms by which LRP1 regulates vessel wall function and integrity. Approach and Results- Isometric contraction assays demonstrated that vasoreactivity of LRP1-deficient aortic rings was significantly attenuated when stimulated with vasoconstrictors, including phenylephrine, thromboxane receptor agonist U-46619, increased potassium, and L-type Ca2+ channel ligand FPL-64176. Quantitative proteomics revealed proteins involved in actin polymerization and contraction were significantly downregulated in aortas of smLRP1-/- mice. However, studies with calyculin A indicated that although aortic muscle from smLRP1-/- mice can contract in response to calyculin A, a role for LRP1 in regulating the contractile machinery is not revealed. Furthermore, intracellular calcium imaging experiments identified defects in calcium release in response to a RyR (ryanodine receptor) agonist in smLRP1-/- aortic rings and cultured vascular smooth muscle cells. Conclusions- These results identify a critical role for LRP1 in modulating vascular smooth muscle cell contraction by regulating calcium signaling events that potentially protect against aneurysm development.
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MESH Headings
- Actin Cytoskeleton/drug effects
- Actin Cytoskeleton/genetics
- Actin Cytoskeleton/metabolism
- Actin Cytoskeleton/ultrastructure
- Animals
- Aorta/metabolism
- Calcium Channels/genetics
- Calcium Channels/metabolism
- Calcium Signaling/drug effects
- Cytoskeletal Proteins/genetics
- Cytoskeletal Proteins/metabolism
- Female
- Gene Expression Regulation
- Low Density Lipoprotein Receptor-Related Protein-1
- Male
- Mice, Knockout
- Muscle, Smooth, Vascular/drug effects
- Muscle, Smooth, Vascular/metabolism
- Muscle, Smooth, Vascular/ultrastructure
- Receptors, LDL/deficiency
- Receptors, LDL/genetics
- Receptors, LDL/metabolism
- Ryanodine Receptor Calcium Release Channel/genetics
- Ryanodine Receptor Calcium Release Channel/metabolism
- Tissue Culture Techniques
- Tumor Suppressor Proteins/deficiency
- Tumor Suppressor Proteins/genetics
- Tumor Suppressor Proteins/metabolism
- Vasoconstriction/drug effects
- Vasoconstrictor Agents/pharmacology
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Affiliation(s)
- Dianaly T. Au
- Center for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Zhekang Ying
- Department of Medicine Cardiology Division, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Erick O. Hernández-Ochoa
- Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - William E. Fondrie
- Center for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Brian Hampton
- Center for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Mary Migliorini
- Center for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- Saha Cardiovascular Research Center and Department of Physiology, University of Kentucky, Lexington, KY 40536, USA
| | - Rebeca Galisteo
- Center for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Martin F. Schneider
- Department of Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Alan Daugherty
- Saha Cardiovascular Research Center and Department of Physiology, University of Kentucky, Lexington, KY 40536, USA
| | - Debra L. Rateri
- Saha Cardiovascular Research Center and Department of Physiology, University of Kentucky, Lexington, KY 40536, USA
| | - Dudley K. Strickland
- Center for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- Department of Surgery, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- Department of Physiology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Selen C. Muratoglu
- Center for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD 21201, USA
- Department of Physiology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
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14
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Fondrie WE, Liang T, Oyler BL, Leung LM, Ernst RK, Strickland DK, Goodlett DR. Pathogen Identification Direct From Polymicrobial Specimens Using Membrane Glycolipids. Sci Rep 2018; 8:15857. [PMID: 30367087 PMCID: PMC6203844 DOI: 10.1038/s41598-018-33681-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 08/20/2018] [Indexed: 12/31/2022] Open
Abstract
With the increased prevalence of multidrug-resistant Gram-negative bacteria, the use of colistin and other last-line antimicrobials is being revisited clinically. As a result, there has been an emergence of colistin-resistant bacterial species, including Acinetobacter baumannii and Klebsiella pneumoniae. The rapid identification of such pathogens is vitally important for the effective treatment of patients. We previously demonstrated that mass spectrometry of bacterial glycolipids has the capacity to identify and detect colistin resistance in a variety of bacterial species. In this study, we present a machine learning paradigm that is capable of identifying A. baumannii, K. pneumoniae and their colistin-resistant forms using a manually curated dataset of lipid mass spectra from 48 additional Gram-positive and -negative organisms. We demonstrate that these classifiers detect A. baumannii and K. pneumoniae in isolate and polymicrobial specimens, establishing a framework to translate glycolipid mass spectra into pathogen identifications.
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Affiliation(s)
- William E Fondrie
- Center for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Tao Liang
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD, 21201, USA
| | - Benjamin L Oyler
- Toxicology and Pharmacology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - Lisa M Leung
- Department of Microbial Pathogenesis, University of Maryland School of Dentistry, Baltimore, MD, 21201, USA.,Divisions of Microbiology and Molecular Biology, Laboratories Administration, Maryland Department of Health, Baltimore, Maryland, 21205, USA
| | - Robert K Ernst
- Department of Microbial Pathogenesis, University of Maryland School of Dentistry, Baltimore, MD, 21201, USA
| | - Dudley K Strickland
- Center for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.,Department of Surgery, University of Maryland School of Medicine, Baltimore, MD, 21201, USA.,Department of Physiology, University of Maryland School of Medicine, Baltimore, MD, 21201, USA
| | - David R Goodlett
- Department of Pharmaceutical Sciences, University of Maryland School of Pharmacy, Baltimore, MD, 21201, USA.
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15
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Muratoglu SC, Au DT, Ying Z, Hernandez-Ochoa E, Fondrie WE, Hampton B, Migliorini M, Galisteo R, Schneider MF, Daugherty A, Rateri DL, Strickland D. Abstract 284: Lrp1 Regulates Smooth Muscle Contractility by Modulating Cytoskeletal Dynamics and Ca
2+
Signaling. Arterioscler Thromb Vasc Biol 2018. [DOI: 10.1161/atvb.38.suppl_1.284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Objective:
Mutations affecting proteins in the extracellular matrix (ECM), microfibrils, or vascular smooth muscle cells (VSMCs) that impact contractility can predispose individuals to thoracic aortic aneurysms. We reported previously that the low-density lipoprotein receptor-related protein 1 (LRP1) maintains vessel wall integrity, and smooth muscle LRP1-deficient (
smLRP1
-/-
) mice exhibited aortic dilatation. The current study focused on the descending thoracic aorta (DTA) and examined the role of LRP1 in VSMC contractility and its potential effect on the vascular ECM.
Approach and Results:
LRP1-deficient VSMCs exhibited a synthetic phenotype characterized by higher proliferation rates and an increase in synthetic organelles, mitochondria, multivesicular bodies, and macropinocytotic vesicles. LRP1-deficient VSMCs also displayed changes in their microfilament and actin structure that result in an inadequate interaction with the ECM. Quantitative proteomics identified proteins involved in actin polymerization and contraction that were downregulated significantly in the DTA of
smLRP1
-/-
mice. Further analysis by qRT-PCR revealed attenuated mRNA levels for α-1D adrenergic receptor (
adra1d
) and calcium voltage-gated channel subunit α1 C (
cacna1c
) in
smLRP1
-/-
aortas. Isometric contraction assays confirmed aberrant contraction of
smLRP1
-/-
aortic rings when stimulated with vasoconstrictors. Furthermore, intracellular calcium imaging identified defects in response to a ryanodine receptor agonist in
smLRP1
-/-
aortic rings.
Conclusions:
These results suggest that LRP1 is required for maintaining the VSMC contractile phenotype and identifies a novel role for LRP1 in calcium homeostasis that potentially protects against aneurysm development.
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16
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Khan MM, Tran BQ, Jang YJ, Park SH, Fondrie WE, Chowdhury K, Yoon SH, Goodlett DR, Chae SW, Chae HJ, Seo SY, Goo YA. Assessment of the Therapeutic Potential of Persimmon Leaf Extract on Prediabetic Subjects. Mol Cells 2017; 40:466-475. [PMID: 28681595 PMCID: PMC5547216 DOI: 10.14348/molcells.2017.2298] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Revised: 05/12/2017] [Accepted: 05/15/2017] [Indexed: 12/12/2022] Open
Abstract
Dietary supplements have exhibited myriads of positive health effects on human health conditions and with the advent of new technological advances, including in the fields of proteomics, genomics, and metabolomics, biological and pharmacological activities of dietary supplements are being evaluated for their ameliorative effects in human ailments. Recent interests in understanding and discovering the molecular targets of phytochemical-gene-protein-metabolite dynamics resulted in discovery of a few protein signature candidates that could potentially be used to assess the effects of dietary supplements on human health. Persimmon (Diospyros kaki) is a folk medicine, commonly used as dietary supplement in China, Japan, and South Korea, owing to its different beneficial health effects including anti-diabetic implications. However, neither mechanism of action nor molecular biomarkers have been discovered that could either validate or be used to evaluate effects of persimmon on human health. In present study, Mass Spectrometry (MS)-based proteomic studies were accomplished to discover proteomic molecular signatures that could be used to understand therapeutic potentials of persimmon leaf extract (PLE) in diabetes amelioration. Saliva, serum, and urine samples were analyzed and we propose that salivary proteins can be used for evaluating treatment effectiveness and in improving patient compliance. The present discovery proteomics study demonstrates that salivary proteomic profile changes were found as a result of PLE treatment in prediabetic subjects that could specifically be used as potential protein signature candidates.
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Affiliation(s)
- Mohd M. Khan
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201,
USA
- Present address: University of Maryland School of Medicine, Baltimore, MD 21201,
USA
| | - Bao Quoc Tran
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201,
USA
| | - Yoon-Jin Jang
- Department of Pharmacology, Chonbuk National University Medical School, Jeonju 54907,
Korea
| | - Soo-Hyun Park
- Clinical Trial Center for Functional Foods, Chonbuk National University Hospital, Jeonju 54907,
Korea
| | | | | | - Sung Hwan Yoon
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201,
USA
| | - David R. Goodlett
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201,
USA
| | - Soo-Wan Chae
- Department of Pharmacology, Chonbuk National University Medical School, Jeonju 54907,
Korea
- Clinical Trial Center for Functional Foods, Chonbuk National University Hospital, Jeonju 54907,
Korea
| | - Han-Jung Chae
- Department of Pharmacology, Chonbuk National University Medical School, Jeonju 54907,
Korea
| | - Seung-Young Seo
- Department of Internal Medicine, Research Institute of Clinical Medicine, Chonbuk National University Medical School and Hospital, Jeonju 54907,
Korea
| | - Young Ah Goo
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, MD 21201,
USA
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17
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Clark DJ, Fondrie WE, Yang A, Mao L. Triple SILAC quantitative proteomic analysis reveals differential abundance of cell signaling proteins between normal and lung cancer-derived exosomes. J Proteomics 2016; 133:161-169. [DOI: 10.1016/j.jprot.2015.12.023] [Citation(s) in RCA: 100] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2015] [Revised: 12/07/2015] [Accepted: 12/17/2015] [Indexed: 01/06/2023]
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18
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Clark DJ, Fondrie WE, Liao Z, Hanson PI, Fulton A, Mao L, Yang AJ. Redefining the Breast Cancer Exosome Proteome by Tandem Mass Tag Quantitative Proteomics and Multivariate Cluster Analysis. Anal Chem 2015; 87:10462-9. [PMID: 26378940 PMCID: PMC7389820 DOI: 10.1021/acs.analchem.5b02586] [Citation(s) in RCA: 55] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Exosomes are microvesicles of endocytic origin constitutively released by multiple cell types into the extracellular environment. With evidence that exosomes can be detected in the blood of patients with various malignancies, the development of a platform that uses exosomes as a diagnostic tool has been proposed. However, it has been difficult to truly define the exosome proteome due to the challenge of discerning contaminant proteins that may be identified via mass spectrometry using various exosome enrichment strategies. To better define the exosome proteome in breast cancer, we incorporated a combination of Tandem-Mass-Tag (TMT) quantitative proteomics approach and Support Vector Machine (SVM) cluster analysis of three conditioned media derived fractions corresponding to a 10 000g cellular debris pellet, a 100 000g crude exosome pellet, and an Optiprep enriched exosome pellet. The quantitative analysis identified 2 179 proteins in all three fractions, with known exosomal cargo proteins displaying at least a 2-fold enrichment in the exosome fraction based on the TMT protein ratios. Employing SVM cluster analysis allowed for the classification 251 proteins as "true" exosomal cargo proteins. This study provides a robust and vigorous framework for the future development of using exosomes as a potential multiprotein marker phenotyping tool that could be useful in breast cancer diagnosis and monitoring disease progression.
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Affiliation(s)
- David J. Clark
- Department of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, Baltimore, Maryland 21201, United States
- Marlene and Stewart Greenebaum Cancer Center, University of Maryland, Baltimore, Maryland 21201, United States
| | - William E. Fondrie
- Marlene and Stewart Greenebaum Cancer Center, University of Maryland, Baltimore, Maryland 21201, United States
- Center for Vascular and Inflammatory Diseases, University of Maryland School of Medicine, Baltimore, Maryland 21201, United States
| | - Zhongping Liao
- Lily Research Laboratory, Eli Lily and Company, Indianapolis, Indiana 46285, United States
| | - Phyllis I. Hanson
- Department of Cell Biology and Physiology, Washington University School of Medicine, St. Louis, Missouri 63110, United States
| | - Amy Fulton
- Marlene and Stewart Greenebaum Cancer Center, University of Maryland, Baltimore, Maryland 21201, United States
| | - Li Mao
- Department of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, Baltimore, Maryland 21201, United States
- Marlene and Stewart Greenebaum Cancer Center, University of Maryland, Baltimore, Maryland 21201, United States
| | - Austin J. Yang
- Marlene and Stewart Greenebaum Cancer Center, University of Maryland, Baltimore, Maryland 21201, United States
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