Dataset Information

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

ABSTRACT: As systems biology approaches to virology have become more tractable, it has become possible to analyze highly studied viruses such as HIV in new, unbiased ways, including spatial proteomics. We have employed here a differential centrifugation protocol to fractionate an inducible model of HIV-expression in Jurkat T cells for proteomic analysis by mass spectrometry. 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 was organelle-dependent, with Bayesian t-augmented Gaussian mixture modeling outperforming support vector machine (SVM) learning for mitochondrial and ER 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, BANDLE, on SVM-classified data. Comparative BANDLE analysis of WT and ΔNef models also identified known Nef-dependent interactors such as TCR signaling and coatomer complex. Lastly, we found that SVM classification showed higher consistency and was less sensitive to HIV-dependent noise in our data. These findings illustrate important considerations for future studies of the spatial proteome following viral infection or expression where their generalizability can be further assessed.

INSTRUMENT(S): Orbitrap Fusion Lumos

ORGANISM(S): Homo Sapiens (human)

SUBMITTER: Alicia Richards

LAB HEAD: Dr. Nevan Krogan

PROVIDER: PXD029956 | Pride | 2022-01-16

REPOSITORIES: Pride

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	20190207_alr_Guatelli_P828_IndA_fr1.raw	Raw
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Comparative Analysis of T-Cell Spatial Proteomics and the Influence of HIV Expression.

Oom Aaron L AL Stoneham Charlotte A CA Lewinski Mary K MK Richards Alicia A Wozniak Jacob M JM Shams-Ud-Doha Km K Gonzalez David J DJ Krogan Nevan J NJ Guatelli John J

Molecular & cellular proteomics : MCP 20220108 3

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 s ...[more]

PMID: 35017099

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