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Marek's disease virus (MDV), an alphaherpesvirus of poultry, causes Marek's disease and is characterized by visceral CD4+TCRαβ+ T-cell lymphomas in susceptible hosts. Immortal cell lines harbouring the viral genome have been generated from ex vivo cultures of MD tumours. As readily available sources of large numbers of cells, MDV-transformed lymphoblastoid cell lines (LCLs) are extremely valuable for studies of virus-host interaction. While the viral genome in most cells is held in a latent state, minor populations of cells display spontaneous reactivation identifiable by the expression of lytic viral genes. Spontaneous reactivation in these cells presents an opportunity to investigate the biological processes involved in the virus reactivation. For detailed characterization of the molecular events associated with reactivation, we used two lymphoblastoid cell lines derived from lymphomas induced by pRB1B-UL47eGFP, a recombinant MDV engineered to express enhanced green fluorescent protein (EGFP) fused with the UL47. We used fluorescence-activated cell sorting to purify the low-frequency EGFP-positive cells with a spontaneously activating viral genome from the majority EGFP-negative cells and analysed their gene expression profiles by RNA-seq using Illumina HiSeq2500. Ingenuity pathway analysis on more than 2000 differentially expressed genes between the lytically infected (EGFP-positive) and latently infected (EGFP-negative) cell populations identified the biological pathways involved in the reactivation. Virus-reactivating cells exhibited differential expression of a significant number of viral genes, with hierarchical differences in expression levels. Downregulation of a number of host genes including those directly involved in T-cell activation, such as CD3, CD28, ICOS and phospholipase C, was also noticed in the LCL undergoing lytic switch.

journal of general virology

Differentially expressed genes during spontaneous lytic switch of Marek's disease virus in lymphoblastoid cell lines determined by global gene expression profiling