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

项目总结 人巨细胞病毒(HCMV)很普遍，感染后会导致各种症状，包括无症状 在免疫受损的个体中，由于潜伏感染重新激活而导致危及生命的败血症的潜伏期。一 免疫抑制患者感染巨细胞病毒的主要后果是巨细胞病毒视网膜炎，但常常被忽视。 (CMVR)。鉴于其无处不在的性质，包括失明在内的视觉并发症的风险以及与以下方面相关的成本 诊断和治疗，巨细胞病毒是CMVR的病原体，是研究和推进的重要靶点。 了解并制定有效的治疗策略。视网膜色素上皮(RPE)被认为是视网膜色素上皮(RPE)。 作为包括巨细胞病毒在内的病毒进入视网膜神经层的入口点。一步法人巨细胞病毒 在体外成纤维细胞中，裂解复制周期大约持续96小时，最终产生 具有感染性的病毒粒子和受感染细胞的破坏。已知人巨细胞病毒感染成人RPE(ARPE19) 细胞导致较慢的、破坏性较小的持续感染和多步骤复制。计算建模 是一种方法学上的进步，只是最近才被应用于动态侧写的研究 一些病毒蛋白是单步复制的，而存在的多步复制模型很少，很可能 由于缺乏一个易于测试的体外持久类系统，从而造成了两个相关的知识空白。这个 该奖学金的目标是提供将湿法实验室和计算策略相结合的培训，以研究 使用HCMV裂解复制动力学和CMVR作为模型系统的复杂视觉病理。的目标 这项研究的目的是：(1)识别和计算不同多发性感染(MOI)的影响。 和细胞类型(即成纤维细胞与ARPE19)在单步复制过程中对HCMV复制动力学的影响；(2)发展 在ARPE19细胞中采用多步骤复制的类似于持续性HCMV感染的体外模型系统； (3)抗人巨细胞病毒药物更昔洛韦的实验研究和数值模拟 (GCV)在使用ARPE19细胞的新型持续体外系统上。在目标1中，我将使用自下而上的生物- 描述蛋白质相互作用和调控的基于化学的、依赖于MOI的计算模型 一步裂解复制巨细胞病毒。我将通过比较模型参数来研究细胞类型相关的可变性。 使用MRC5成纤维细胞来源的数据和ARPE19细胞来源的数据进行标准化。在目标2中，我将使用自上而下的 在ARPE19细胞中计算模拟多步复制巨细胞病毒的效果的方法 水平使用一种新的体外持续系统和靶细胞受限模型。我将在体外产生一个 持久系统使用ARPE19细胞在低MOI下感染，允许多步复制，实现 病毒DNA(VDNA)的稳定水平。我会将这个系统对抗人巨细胞病毒药物GCV的反应与 现有的靶细胞预测限制了使用患者数据进行参数化的模型。最后，我会 使用靶细胞有限模型预测导致VDNA水平下降的最佳GCV体外治疗时间 衰减到qPCR检测极限以下，然后使用新的类似持久性的系统进行测试。

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.