RAPID: Modeling the Host-Microbiome-Virome Interactions and their Impact on COVID-19 Severity.

COVID-19是一种复杂的疾病。虽然有时没有症状，但许多人会出现改变生活的症状和可能导致死亡的副作用。年龄、性别、种族背景、病史和生活方式都会影响感染率及其严重程度。为了侵入人体细胞，病毒附着在某些大分子上，这些大分子在不同的组织和器官中以不同的水平表达。这些大分子的局部表达可以受到微生物组的影响，即生长在组织和器官内部和周围的细菌。该项目将分析人类微生物组如何影响COVID-19的进展。将开发计算模型，使用微生物组组成来预测COVID-19疾病的结果。这些模型将更好地了解SARS-COV-2感染的基本生物学，并改进治疗和预防策略。主要假设是，局部微生物组调节对COVID-19发展至关重要的大分子的利基特异性表达。其中包括ACE2受体、TMPRSS2丝氨酸蛋白酶、与SARS-COV-2入侵和复制相关的未知基因，以及过度细胞因子反应的介质。此外，我们认为这些差异与不同的疾病严重程度有关。目标是开发一个统计模型，用于预测COVID-19感染的疾病进展和严重程度。纳入新冠肺炎住院患者，纵向采集鼻咽、粪便、口腔、尿液和血液样本。对血液样本进行细胞因子分析，并选择剩余样本进行16S rRNA分类分析，以表征当地微生物组，并通过亚转录组测序来表征基因表达谱和病毒基因组序列。测量病毒载量。这些数据将与社区收集的检测样本的亚转录组学数据相结合，这些样本只提供单一的鼻咽样本。该数据集将用于构建旨在预测COVID-19疾病进展和严重程度风险的模型。将使用多因素建模技术分析多组学因素与疾病严重程度的关联，该技术利用数据的时间维度，并纳入种族和人口因素。构建的模型将为筛查中的风险分层提供信息，并增加我们对影响COVID-19疾病轨迹的宿主和微生物组因素的理解。COVID-19疾病模型的开发也可能为其他病毒感染预测模型的未来开发提供信息。该项目由ENG/CBET的细胞和生化工程项目以及MCB/BIO的系统和合成生物学项目共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

COVID-19 is a complex disease. While sometimes there are no symptoms, many people suffer life-altering symptoms and side effects that can led to death. Age, gender, racial background, medical history, and lifestyle all can influence the rate of infection and its severity. To invade human cells, the virus attaches to certain macromolecules that are expressed at different levels in different tissues and organs. The local expression of these macromolecules can be influenced by the microbiome, i.e. the bacteria that grow in and around the tissues and organs. This project will analyze how the human microbiome affects the progression of COVID-19. Computational models will be developed that use microbiome compositions to predict outcomes of COVID-19 disease. Such models will provide a better understanding of the basic biology of SARS-COV-2 infection and lead to improved treatment and prevention strategies. The main hypothesis is that local microbiomes modulate niche-specific expression of macromolecules critical to COVID-19 development. These include the ACE2 receptor, TMPRSS2 serine protease, unidentified genes associated with SARS-COV-2 invasion and replication, and mediators of the excessive cytokine response. Further, we propose that these differences are associated with differential disease severity. The goal is to develop a statistical model for prediction of disease progression and severity of COVID-19 infection. COVID-19 inpatients are enrolled and nasopharyngeal, stool, buccal, urine and blood samples are collected longitudinally. Blood samples are processed for cytokine profiles and a selection of the remaining samples are analyzed by 16S rRNA taxonomic profiling to characterize the local microbiome and by metatranscriptomic sequencing to characterize gene expression profiles and the virus genome sequence. Viral loads are measured. These data will be augmented with metatranscriptomic data from community-collected testing samples which provide only a single nasopharyngeal sample. This dataset will be used to construct models aimed at predicting risk of COVID-19 disease progression and severity. Associations of the multi omic factors with disease severity will be analyzed using multifactorial modeling techniques that leverage the temporal dimension of the data and also incorporate racial and demographic factors. The constructed models will inform risk stratification in screening and increase our understanding of the host-and-microbiome factors impacting the trajectory of COVID-19 disease. Development of the disease model for COVID-19 may also inform the future development of predictive models for other viral infections. This project is being funding jointly between the Cellular and Biochemical Engineering Program in ENG/CBET and the Systems and Synthetic Biology Program in MCB/BIO.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.