Bhuvaneshwar Krithika, Madhavan Subha, Gusev Yuriy
Georgetown-Innovation Center for Biomedical Informatics (Georgetown-ICBI), Georgetown University Medical Center, Washington DC, 20007, USA.
Heliyon. 2024 Jun 10;10(12):e32772. doi: 10.1016/j.heliyon.2024.e32772. eCollection 2024 Jun 30.
The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 virus has affected over 700 million people, and caused over 7 million deaths throughout the world as of April 2024, and continues to affect people through seasonal waves. While over 675 million people have recovered from this disease globally, the lingering effects of the disease are still under study. Long term effects of SARS-CoV-2 infection, known as 'long COVID,' include a wide range of symptoms including fatigue, chest pain, cellular damage, along with a strong innate immune response characterized by inflammatory cytokine production. Three years after the pandemic, data about long covid studies are finally emerging. More clinical studies and clinical trials are needed to understand and determine the factors that predispose individuals to these long-term side effects. In this methodology paper, our goal was to apply data driven approaches in order to explore the multidimensional landscape of infected lung tissue microenvironment to better understand complex interactions between viral infection, immune response and the lung microbiome of patients with (a) SARS-CoV-2 virus and (b) NL63 coronavirus. The samples were analyzed with several machine learning tools allowing simultaneous detection and quantification of viral RNA amount at genome and gene level; human gene expression and fractions of major types of immune cells, as well as metagenomic analysis of bacterial and viral abundance. To contrast and compare specific viral response to SARS-COV-2, we analyzed deep sequencing data from additional cohort of patients infected with NL63 strain of corona virus. Our correlation analysis of three types of RNA-seq based measurements in patients i.e. fraction of viral RNA (at genome and gene level), Human RNA (transcripts and gene level) and bacterial RNA (metagenomic analysis), showed significant correlation between viral load as well as level of specific viral gene expression with the fractions of immune cells present in lung lavage as well as with abundance of major fractions of lung microbiome in COVID-19 patients. Our methodology-based proof-of-concept study has provided novel insights into complex regulatory signaling interactions and correlative patterns between the viral infection, inhibition of innate and adaptive immune response as well as microbiome landscape of the lung tissue. These initial findings could provide better understanding of the diverse dynamics of immune response and the side effects of the SARS-CoV-2 infection and demonstrates the possibilities of the various types of analyses that could be performed from this type of data.
由严重急性呼吸综合征冠状病毒2型（SARS-CoV-2）引起的2019冠状病毒病（COVID-19）大流行已影响超过7亿人，截至2024年4月，在全球范围内造成超过700万人死亡，并且季节性疫情仍在持续影响着人们。虽然全球已有超过6.75亿人从这种疾病中康复，但该疾病的长期影响仍在研究中。SARS-CoV-2感染的长期影响，即“长新冠”，包括一系列症状，如疲劳、胸痛、细胞损伤，以及以炎性细胞因子产生为特征的强烈先天性免疫反应。大流行三年后，关于长新冠研究的数据终于开始出现。需要更多的临床研究和临床试验来了解和确定使个体易出现这些长期副作用的因素。在这篇方法学论文中，我们的目标是应用数据驱动方法，探索受感染肺组织微环境的多维格局，以更好地理解（a）SARS-CoV-2病毒和（b）NL63冠状病毒患者的病毒感染、免疫反应和肺部微生物群之间的复杂相互作用。使用多种机器学习工具对样本进行分析，可同时在基因组和基因水平检测和定量病毒RNA量；检测人类基因表达、主要免疫细胞类型的比例，以及对细菌和病毒丰度进行宏基因组分析。为了对比和比较对SARS-CoV-2的特异性病毒反应，我们分析了感染冠状病毒NL63株的另一组患者的深度测序数据。我们对患者基于三种RNA测序测量的相关性分析，即病毒RNA比例（在基因组和基因水平）、人类RNA（转录本和基因水平）和细菌RNA（宏基因组分析），显示COVID-19患者的病毒载量以及特定病毒基因表达水平与肺灌洗中存在的免疫细胞比例以及肺部微生物群主要成分的丰度之间存在显著相关性。我们基于方法的概念验证研究为病毒感染、先天性和适应性免疫反应的抑制以及肺组织微生物群格局之间的复杂调节信号相互作用和相关模式提供了新的见解。这些初步发现有助于更好地理解免疫反应的多样动态以及SARS-CoV-2感染的副作用，并展示了从这类数据中可进行的各种分析的可能性。
