The Respiratory Microbiome in COVID-19: Associations with Severity, Risk Factors, and Host Pathways

COVID-19自2019年出现以来，已造成前所未有的生命损失及全球混乱。造成 由SARS CoV-2病毒，这种感染表现出极端的异质性，从完全无症状， 致命的。与COVID-19严重程度相关的一个因素是上呼吸道的微生物组 特别是口咽部较低的相对丰度的口腔粘膜分类群，如嗜血杆菌， 奈瑟菌属、普雷沃菌属和放线菌属，在严重的COVID-19患者中观察到较低的α多样性 与中度疾病的人相比。这种关联的机制仍然未知， 不清楚因果关系发生在哪个方向。我们建议进一步研究 呼吸道微生物组和COVID-19通过1）增加这些关联对物种，菌株， 和基因水平，2）确定合并症如何在SARS CoV-2之前塑造呼吸道微生物组 感染，和3）识别可能参与这些关联的宿主途径。为了实现第一个目标，我们将 利用200多名住院COVID-19患者的队列（之前入组，样本已在 hand），使用深度宏基因组测序进行分类和功能注释。增加特异性 这一目标所提供的将为体外或动物模型实验铺平道路，这些实验需要物种或菌株 水平关联，以进行适当的实验设计。第二个目标将集中在呼吸道微生物组 肥胖、糖尿病或老年人（没有和没有患过COVID-19）的个人特征，三个 与严重COVID-19风险升高密切相关的疾病。这些影响 呼吸道微生物组是未知的，但一个尚未测试的可能性是，微生物组介导一些 这些条件对疾病严重程度的影响。通过研究这些疾病中的微生物组改变， 在SARS CoV-2感染之前，我们可以识别出严重COVID之前的潜在高风险微生物组， 19.最后，第三个目标是从不同的数据库中提取数据，以创建微生物的知识图谱。 疾病基因关联使用知识图完成，我们将预测宿主基因， 与COVID-19的严重程度，并与上呼吸道的细菌相互作用。有了这些数据，我们可以提出 介导微生物组-COVID-19关联的宿主机制，允许体外随访从 因果关系的相关性。最终，这项工作是现有的高级别协会之间的桥梁， COVID-19和上呼吸道微生物组，以及针对特定机制和因果关系的未来工作 链接.我们最近发表了一篇关于COVID-19气道微生物组所有研究的综述，我们认为， 这些目标解决了目前文献中最关键的理解差距。

COVID-19 has caused unprecedented loss of life and global disruption since its emergence in 2019. Caused by the SARS CoV-2 virus, this infection shows extreme heterogeneity, ranging from completely asymptomatic to deadly. One factor that has been linked to COVID-19 severity is the microbiome of the upper respiratory tract, specifically the oropharynx. Lower relative abundance of oral commensal taxa, such as Haemophilus, Neisseria, Prevotella, and Actinomyces, and lower alpha diversity are seen in severe COVID-19 patients compared to individuals with more moderate disease. The mechanism of this association is still unknown, and it is unclear in which direction causation occurs. We propose to further examine the association between the respiratory microbiome and COVID-19 by 1) increasing specificity of these associations to the species, strain, and gene level, 2) identifying how comorbidities shape the respiratory microbiome prior to SARS CoV-2 infection, and 3) identifying host pathways that may be involved in these associations. For this first aim, we will leverage a cohort of over 200 hospitalized COVID-19 patients (previously enrolled and specimens already in hand), using deep metagenomic sequencing for taxonomic and functional annotation. The increased specificity provided by this aim will pave the way for in vitro or animal model experiments, which require species or strain level associations for proper experimental design. The second aim will focus on respiratory tract microbiome profiles in individuals with obesity, diabetes, or old age (who do not and have not had COVID-19), three conditions that are strongly associated with elevated risk of severe COVID-19. The effect that these have on the respiratory microbiome is unknown, but one still untested possibility is that the microbiome mediates some of the effects of these conditions on disease severity. By studying microbiome alterations in these diseases prior to SARS CoV-2 infection we could identify a potential high risk microbiome that precedes severe COVID- 19. Finally, the third aim pulls data from a diverse set of databases to create a knowledge graph of microbe- disease-gene associations. Using knowledge graph completion, we will predict host genes that both associate with COVID-19 severity, and interact with bacteria in the upper airway. With this data, we can propose possible host mechanisms that mediate microbiome-COVID-19 associations, allowing for in vitro follow-up to move from correlation to causation. Ultimately, this work is a bridge between existing high level associations between COVID-19 and the upper respiratory microbiome, and future work targeting specific mechanisms and causal links. Having recently published a review on all studies of the airway microbiome in COVID-19, we believe that these aims address the most critical gaps in understanding currently in the literature.