Cargando…
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...
Autores principales: | , , , , , , , , |
---|---|
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 |
_version_ | 1784676635800240128 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8956815 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | American Society for Biochemistry and Molecular Biology |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT oomaaronl comparativeanalysisoftcellspatialproteomicsandtheinfluenceofhivexpression AT stonehamcharlottea comparativeanalysisoftcellspatialproteomicsandtheinfluenceofhivexpression AT lewinskimaryk comparativeanalysisoftcellspatialproteomicsandtheinfluenceofhivexpression AT richardsalicia comparativeanalysisoftcellspatialproteomicsandtheinfluenceofhivexpression AT wozniakjacobm comparativeanalysisoftcellspatialproteomicsandtheinfluenceofhivexpression AT shamsuddohakm comparativeanalysisoftcellspatialproteomicsandtheinfluenceofhivexpression AT gonzalezdavidj comparativeanalysisoftcellspatialproteomicsandtheinfluenceofhivexpression AT krogannevanj comparativeanalysisoftcellspatialproteomicsandtheinfluenceofhivexpression AT guatellijohn comparativeanalysisoftcellspatialproteomicsandtheinfluenceofhivexpression |