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Proteomics. 2012 Feb;12(4-5):666-72. doi: 10.1002/pmic.201100488. Epub 2012 Jan 19.

Using SILAC and quantitative proteomics to investigate the interactions between viral and host proteomes.

Author information

1
Faculty of Biological Sciences, and Astbury Centre for StructuralMolecular Biology, Institute of Molecular and Cellular Biology, University of Leeds, Leeds, UK. j.a.hiscox@leeds.ac.uk

Abstract

Viruses continue to pose some of the greatest threats to human and animal health, and food security worldwide. Therefore, new approaches are required to increase our understanding of virus-host cell interactions and subsequently design more effective therapeutic countermeasures. Quantitative proteomics based on stable isotope labeling by amino acids in cell culture (SILAC), coupled to LC-MS/MS and bioinformatic analysis, is providing an excellent resource for studying host cell proteomes and can readily be applied for the study of virus infection. Here, we review this approach and discuss how virus-host cell interactions can best be studied, what is realistically feasible, and the potential limitations. For example, sub-cellular fractionation can reduce sample complexity for LC-MS/MS, increase data return and provide information regarding protein trafficking between different cellular compartments. The key to successful quantitative proteomics combines good experimental design and appropriate sample preparation with statistical analysis and validation of the MS data through the use of independent techniques and functional analysis. The annotation of the human genome and the increasing availability of biological reagents such as antibodies, provide the optimum parameters for studying viruses that infect humans, in human cell lines. SILAC-based quantitative proteomics can also be used to study the interactome of viral proteins with the host cell. Coupling proteomic studies with global transcriptomic and RNA depletion experiments will provide great insights into the complexity of the infection process, and potentially reveal new antiviral targets.

PMID:
22246955
DOI:
10.1002/pmic.201100488
[Indexed for MEDLINE]

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