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Front Microbiol. 2012 Sep 20;3:310. eCollection 2012.

Non-Biased Enrichment Does Not Improve Quantitative Proteomic Delineation of Reovirus T3D-Infected HeLa Cell Protein Alterations.

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1
Department of Medical Microbiology, Faculty of Medicine, University of Manitoba Winnipeg, MB, Canada.

Abstract

Mass spectrometry-based methods have allowed elucidation of alterations in complex proteomes, such as eukaryotic cells. Such studies have identified and measured relative abundances of thousands of host proteins after cells are infected with a virus. One of the potential limitations in such studies is that generally only the most abundant proteins are identified, leaving the deep richness of the cellular proteome largely unexplored. We differentially labeled HeLa cells with light and heavy stable isotopic forms of lysine and arginine and infected cells with reovirus strain T3D. Cells were harvested at 24 h post-infection. Heavy-labeled infected and light-labeled mock-infected cells were mixed together 1:1. Cells were then divided into cytosol and nuclear fractions and each fraction analyzed, both by standard 2D-HPLC/MS, and also after each fraction had been reacted with a random hexapeptide library (Proteominer(®) beads) to attempt to enrich for low-abundance cellular proteins. A total of 2,736 proteins were identified by two or more peptides at >99% confidence, of which 66 were significantly up-regulated and 67 were significantly down-regulated. Up-regulated proteins included those involved in antimicrobial and antiviral responses, GTPase activity, nucleotide binding, interferon signaling, and enzymes associated with energy generation. Down-regulated proteins included those involved in cell and biological adhesion, regulation of cell proliferation, structural molecule activity, and numerous molecular binding activities. Comparisons of the r(2) correlations, degree of dataset overlap, and numbers of peptides detected suggest that non-biased enrichment approaches may not provide additional data to allow deeper quantitative and comparative mining of complex proteomes.

KEYWORDS:

RNA virus; bioinformatics; host cell alterations; liquid chromatography; mass spectrometry; virus infection

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