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Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression

As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectr...

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Autores principales: Oom, Aaron L., Stoneham, Charlotte A., Lewinski, Mary K., Richards, Alicia, Wozniak, Jacob M., Shams-Ud-Doha, Km, Gonzalez, David J., Krogan, Nevan J., Guatelli, John
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Society for Biochemistry and Molecular Biology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956815/
https://www.ncbi.nlm.nih.gov/pubmed/35017099
http://dx.doi.org/10.1016/j.mcpro.2022.100194
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author Oom, Aaron L.
Stoneham, Charlotte A.
Lewinski, Mary K.
Richards, Alicia
Wozniak, Jacob M.
Shams-Ud-Doha, Km
Gonzalez, David J.
Krogan, Nevan J.
Guatelli, John
author_facet Oom, Aaron L.
Stoneham, Charlotte A.
Lewinski, Mary K.
Richards, Alicia
Wozniak, Jacob M.
Shams-Ud-Doha, Km
Gonzalez, David J.
Krogan, Nevan J.
Guatelli, John
author_sort Oom, Aaron L.
collection PubMed
description As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses.
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spelling pubmed-89568152022-03-29 Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression Oom, Aaron L. Stoneham, Charlotte A. Lewinski, Mary K. Richards, Alicia Wozniak, Jacob M. Shams-Ud-Doha, Km Gonzalez, David J. Krogan, Nevan J. Guatelli, John Mol Cell Proteomics Research As systems biology approaches to virology have become more tractable, highly studied viruses such as HIV can now be analyzed in new unbiased ways, including spatial proteomics. We employed here a differential centrifugation protocol to fractionate Jurkat T cells for proteomic analysis by mass spectrometry; these cells contain inducible HIV-1 genomes, enabling us to look for changes in the spatial proteome induced by viral gene expression. Using these proteomics data, we evaluated the merits of several reported machine learning pipelines for classification of the spatial proteome and identification of protein translocations. From these analyses, we found that classifier performance in this system was organelle dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine learning for mitochondrial and endoplasmic reticulum proteins but underperforming on cytosolic, nuclear, and plasma membrane proteins by QSep analysis. We also observed a generally higher performance for protein translocation identification using a Bayesian model, Bayesian analysis of differential localization experiments, on row-normalized data. Comparative Bayesian analysis of differential localization experiment analysis of cells induced to express the WT viral genome versus cells induced to express a genome unable to express the accessory protein Nef identified known Nef-dependent interactors such as T-cell receptor signaling components and coatomer complex. Finally, we found that support vector machine classification showed higher consistency and was less sensitive to HIV-dependent noise. These findings illustrate important considerations for studies of the spatial proteome following viral infection or viral gene expression and provide a reference for future studies of HIV-gene-dropout viruses. American Society for Biochemistry and Molecular Biology 2022-01-08 /pmc/articles/PMC8956815/ /pubmed/35017099 http://dx.doi.org/10.1016/j.mcpro.2022.100194 Text en © 2022 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Research
Oom, Aaron L.
Stoneham, Charlotte A.
Lewinski, Mary K.
Richards, Alicia
Wozniak, Jacob M.
Shams-Ud-Doha, Km
Gonzalez, David J.
Krogan, Nevan J.
Guatelli, John
Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression
title Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression
title_full Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression
title_fullStr Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression
title_full_unstemmed Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression
title_short Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression
title_sort comparative analysis of t-cell spatial proteomics and the influence of hiv expression
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8956815/
https://www.ncbi.nlm.nih.gov/pubmed/35017099
http://dx.doi.org/10.1016/j.mcpro.2022.100194
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