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

Aaron L Oom,Charlotte A Stoneham,Mary K Lewinski,Alicia Richards,Jacob M Wozniak,Km Shams-Ud-Doha,David J Gonzalez,Nevan J Krogan,John Guatelli

doi:10.1016/j.mcpro.2022.100194

Abstract

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.

Highlights

Spatial proteomics is a methodologically diverse and rapidly growing field within mass spectrometry (MS) that aims to understand the subcellular localization of the human proteome[1,2,3,4,5,6,7]
Due to the high induction rates of HIV-1 expression and the scalability of this culture system, we reasoned that it would be amenable to subcellular fractionation by differential centrifugation with subsequent MS analysis (Fig. 1A)
To determine the optimal time-point for analysis following induction of HIV-1 expression, cells were treated with doxycycline for 0, 4, 8, 12, 16, and 18 hours, and the expression of HIV-1 proteins was detected by western blotting and flow cytometry (Fig. 1B and C)

Summary

Introduction

Spatial proteomics is a methodologically diverse and rapidly growing field within mass spectrometry (MS) that aims to understand the subcellular localization of the human proteome[1,2,3,4,5,6,7]. To model HIV expression, we used a Jurkat T cell line that harbors a doxycycline-regulated HIV-1 genome These cells were previously developed by our group to generate nearly homogenous HIVpositive cell populations for MS analysis[13]. Nef increases viral growth-rate and infectivity[16], and it dysregulates the trafficking of cellular membrane proteins such as CD4, class I MHC, and proteins involved in T cell activation such as CD2817 and p56-Lck[18]. Some of these activities enable the virus to evade immune detection[19,20]. We used a modified version of the Dynamic Organellar Mapping protocol[5,6] with additional centrifugation steps[4] to enhance organellar resolution, analyzed the fractions by MS using TMT multiplexing

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Results

Conclusion