Assessing SARS-CoV-2 Variant Evolution in Patients

引起COVID-19大流行的SARS-CoV-2从中国武汉出现并迅速传播 世界各地大流行的一个特点是SARS-CoV-2分支的反复出现， 一些令人关切的变体，其中一些已被证明具有更强的可传播性。的其他方面 然而，这些血统仍然不清楚。在SARS-CoV-2造成的375万例死亡中， 是严重肺炎的结果在这些患者中，肺中持续的SARS-CoV-2病毒复制导致 缓慢进展的肺损伤和随后的呼吸衰竭。然而我们对基因的理解 由于肺泡腔取样困难，SARS-CoV-2在肺中的演变有限 并将病毒样本与可靠和全面的临床数据联系起来。在这方面，成功的临床 肺炎治疗反应（CNOT）系统生物学中心提供了理想的基础设施， 来自COVID患者的深肺病毒样本和相应的免疫应答和临床元数据- 19.我们建议利用临床和研究基础设施的研究SARS-CoV-2变异 和宿主内适应。我们将扩展病毒表型检测技术，将患者表型与病毒基因型联系起来。我们 假设SARS-CoV-2分支影响COVID-19肺炎的严重程度， 在经历严重肺炎的患者的肺中的多样性演变。为了验证我们的假设，我们将 实现以下具体目标：目标1。我们将确定特定的SARS-CoV-2分支是否 与更严重的疾病或改变宿主反应有关。我们将对SARS冠状病毒2型进行测序 从我们机构的COVID-19患者的一般池的生物库中分离的分离株，以及从 严重COVID-19肺炎的插管患者，以确定他们的基因型。之间的关联 将在两个人群中寻找特定的SARS-CoV-2分支和疾病严重程度和结果。目标二。我们 将研究宿主内SARS-CoV-2病毒序列在肺中随时间变化的演变， 严重的COVID-19肺炎患者。在长期呼吸衰竭的患者中，我们将 对病毒分离株进行测序，并使用纵向收集的系列BAL检查宿主免疫应答 样品这些数据将用于量化肺中的病毒动力学，绘制宿主内出现的 病毒准种，以表征由这些变化引起的宿主免疫应答，并将 这些特征与患者的临床状况有关。目标3：我们将生成一个计算模型 将SARS-CoV-2进化枝基因组信息与临床和宿主免疫反应相结合 预测COVID-19感染严重程度的功能。病毒进化枝数据将与以下措施相结合： 宿主免疫应答（BAL液流式细胞术和细胞因子水平）和患者临床元数据， 开发一个综合模型，预测哪些患者会患上特别严重的COVID-19疾病。

SARS-CoV-2, the cause of the COVID-19 pandemic, emerged from Wuhan, China, and rapidly spread around the world. A feature of the pandemic has been the repeated emergence of SARS-CoV-2 clades and variants of concern, some of which have been shown to have enhanced transmissibility. Other aspects of these lineages, however, remain unclear. The vast majority of the 3.75 million deaths caused by SARS-CoV-2 are the result of severe pneumonia. In these patients, ongoing SARS-CoV-2 viral replication in the lungs leads to slowly progressing pulmonary injury and subsequent respiratory failure. Yet our understanding of the genetic evolution of SARS-CoV-2 in the lungs is limited because of difficulties sampling the pulmonary alveolar space and in linking viral samples to robust and comprehensive clinical data. In this regard, the Successful Clinical Response in Pneumonia Therapy (SCRIPT) Systems Biology Center provides the ideal infrastructure to collect deep-lung viral samples and corresponding immune response and clinical metadata from patients with COVID- 19. We propose to leverage the clinical and research infrastructure of SCRIPT to study SARS-CoV-2 variants and intra-host adaptation. We will expand SCRIPT to link patient phenotypes with virus genotypes. Our hypothesis is that SARS-CoV-2 clades influence the severity of COVID-19 pneumonia and that viral diversity evolves in the lungs of patients experiencing severe pneumonia. To test our hypotheses, we will perform the following specific aims: Aim 1. We will determine whether specific SARS-CoV-2 clades are associated with greater disease severity or altered host response. We will sequence SARS-CoV-2 isolates from a biobank of a general pool of COVID-19 patients at our institution and from BAL samples of intubated patients with severe COVID-19 pneumonia to establish their genotypes. Associations between specific SARS-CoV-2 clades and disease severity and outcomes in both populations will be sought. Aim 2. We will examine the evolution of intra-host SARS-CoV-2 viral sequence changes over time in the lungs of patients with severe COVID-19 pneumonia. In a subset of patients with prolonged respiratory failure, we will sequence viral isolates and examine the host immune response using longitudinally collected serial BAL samples. These data will be used to quantify viral dynamics in the lung, to map the intra-host emergence of viral quasi-species, to characterize the host immune responses elicited by these changes, and to correlate these features with the clinical conditions of the patients. Aim 3. We will generate a computational model that integrates SARS-CoV-2 clade genome information with clinical and host immune response features to predict the severity of COVID-19 infections. Viral clade data will be integrated with measures of the host immune response (BAL fluid flow cytometry and cytokine levels) and patient clinical metadata to develop a comprehensive model that predicts which patients will develop especially severe COVID-19 disease.