In conclusion, we identified peptides "K 93 FITSRCRL" and "F 57 GYMTFVHF" from the M protein of PRSSV as H-2K d and H-2D d restricted CTL epitopes, respectively. In this study, we also developed a mouse model of PRRSV infection and this will undoubtedly contribute to our understanding of the cell-mediated immune responses to PRRSV. Network Clustering Revealed the Systemic Alterations of Mitochondrial Protein Expression The mitochondrial protein repertoire varies depending on the cellular state. Protein component modifications caused by mitochondrial DNA (mtDNA) depletion are related to a wide range of human diseases; however, little is known about how nuclear-encoded mitochondrial proteins (mt proteome) changes under such dysfunctional states. In this study, we investigated the systemic alterations of mtDNA-depleted (ρ(0)) mitochondria by using network analysis of gene expression data. By modularizing the quantified proteomics data into protein functional networks, systemic properties of mitochondrial dysfunction were analyzed. We discovered that up-regulated and down-regulated proteins were organized into two predominant subnetworks that exhibited distinct biological processes. The down-regulated network modules are involved in typical mitochondrial functions, while up-regulated proteins are responsible for mtDNA repair and regulation of mt protein expression and transport. Furthermore, comparisons of proteome and transcriptome data revealed that ρ(0) cells attempted to compensate for mtDNA depletion by modulating the coordinated expression/transport of mt proteins. Our results demonstrate that mt protein composition changed to remodel the functional organization of mitochondrial protein networks in response to dysfunctional cellular states. Human mt protein functional networks provide a framework for understanding how cells respond to mitochondrial dysfunctions. Proteomics and expression profiling have been widely applied to understand the cellular processes [1] . Proteins with significant changes in expression have particular interest as markers for various diseases and cellular phenotypes [2] . While there are useful information encoded in the list of differentially expressed proteins, identifying the molecular mechanism of cellular processes from the long list of candidate proteins is challenging [3] . Data-integrative approaches have been successfully applied to address the challenge of interpreting lists of differentially expressed proteins by mapping them onto the protein functional network [4, 5] .