Bottom-up and top-down computational modeling approaches to study CMV retinitis

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PROJECT SUMMARY Human cytomegalovirus (HCMV) is widespread and infection results in syndromes ranging from asymptomatic latency to life-threatening sepsis due to reactivation of a latent infection in immunocompromised individuals. One major, yet often overlooked, consequence of HCMV infection in immunosuppressed patients is CMV retinitis (CMVR). Given its ubiquitous nature, risk of visual complications including blindness, and cost associated with diagnosis and therapy, HCMV, the causative agent of CMVR, is a crucial target for investigation to advance knowledge and develop effective therapeutic strategies. The retinal pigment epithelium (RPE) has been impli- cated as a point of entry for viruses, including HCMV, into the neural layer of the retina. The single-step HCMV lytic replication cycle has an approximately 96 h duration in vitro in fibroblasts that culminates in production of infectious virions and destruction of the infected cell. It is known that HCMV infection of adult RPE (ARPE19) cells leads to a slower, less destructive persistent infection and multi-step replication. Computational modeling is a methodological advancement that has only been recently applied to the study of the dynamic profiling of some viral proteins in single-step replication, and there are very few multi-step replication models that exist, likely due to a lack of a readily testable in vitro persistence-like system, thus creating two related knowledge gaps. The objective of this fellowship is to provide training that integrates wet-lab and computational strategies to study complex visual pathologies using HCMV lytic replication kinetics and CMVR as a model system. The goals of this fellowship are to: (1) identify and computationally model the effects of varying multiplicity of infection (MOI) and cell type (i.e., fibroblast vs. ARPE19) on HCMV replication kinetics during single-step replication; (2) develop an in vitro model system similar to a persistent HCMV infection employing multi-step replication in ARPE19 cells; and (3) experimentally investigate and computationally simulate the effects of the anti-HCMV drug ganciclovir (GCV) on the novel persistence-like in vitro system using ARPE19 cells. In Aim 1, I will use a bottom-up, bio- chemically-based, MOI-dependent computational model to describe the protein interactions and regulation in single-step lytic HCMV replication. I will investigate cell type-dependent variability by comparing models param- eterized using MRC5 fibroblast-derived data and ARPE19 cell-derived data. In Aim 2, I will use a top-down approach to computationally model the effects of multi-step HCMV replication in ARPE19 cells at the cellular level using a novel, in vitro persistence-like system and a target-cell limited model. I will generate an in vitro persistence-like system using ARPE19 cells infected at a low MOI, allowing for multi-step replication, to achieve a steady-state level of viral DNA (vDNA). I will correlate this system’s response to the anti-HCMV drug GCV with the predictions from extant target-cell limited models that were parameterized using patient data. Finally, I will use the target-cell limited model to predict the optimal in vitro GCV treatment duration causing vDNA levels to decay below the qPCR limit of detection and then test this using the novel persistence-like system.