Abstract
Extensive reorganization of infected cells and the formation of large structures known as the nuclear replication compartment (RC) and cytoplasmic assembly compartment (AC) is a hallmark of beta-herpesvirus infection. These restructurings rely on extensive compartmentalization of the processes that make up the virus manufacturing chain. Compartmentalization of the nuclear processes during murine cytomegalovirus (MCMV) infection is not well described. In this study, we visualized five viral proteins (pIE1, pE1, pM25, pm48.2, and pM57) and replicated viral DNA to reveal the nuclear events during MCMV infection. As expected, these events can be matched with those described for other beta and alpha herpesviruses and contribute to the overall picture of herpesvirus assembly. Imaging showed that four viral proteins (pE1, pM25, pm48.2, and pM57) and replicated viral DNA condense in the nucleus into membraneless assemblies (MLAs) that undergo a maturation sequence to form the RC. One of these proteins (pM25), which is also expressed in a cytoplasmic form (pM25l), showed similar MLAs in the AC. Bioinformatics tools for predicting biomolecular condensates showed that four of the five proteins had a high propensity for liquid-liquid phase separation (LLPS), suggesting that LLPS may be a mechanism for compartmentalization within RC and AC. Examination of the physical properties of MLAs formed during the early phase of infection by 1,6-hexanediol treatment in vivo revealed liquid-like properties of pE1 MLAs and more solid-like properties of pM25 MLAs, indicating heterogeneity of mechanisms in the formation of virus-induced MLAs. Analysis of the five viral proteins and replicated viral DNA shows that the maturation sequence of RC and AC is not completed in many cells, suggesting that virus production and release is carried out by a rather limited number of cells. This study thus lays the groundwork for further investigation of the replication cycle of beta-herpesviruses, and the results should be incorporated into plans for high-throughput and single-cell analytic approaches.