Centre for Health Informatics, Division of Informatics, Imaging and Data Sciences, Faculty of Biology, Medicine and Health (FBMH), University of Manchester, Vaughan House, Portsmouth Street, Manchester, M13 9GB, UK.
Stoller Biomarker Discovery Centre, Division of Cancer Sciences, FBMH, University of Manchester, Manchester, UK.
J Transl Med. 2020 Aug 3;18(1):297. doi: 10.1186/s12967-020-02472-z.
The severe acute respiratory syndrome virus SARS-CoV-2, a close relative of the SARS-CoV virus, is the cause of the recent COVID-19 pandemic affecting, to date, over 14 million individuals across the globe and demonstrating relatively high rates of infection and mortality. A third virus, the H5N1, responsible for avian influenza, has caused infection with some clinical similarities to those in COVID-19 infections. Cytokines, small proteins that modulate immune responses, have been directly implicated in some of the severe responses seen in COVID-19 patients, e.g. cytokine storms. Understanding the immune processes related to COVID-19, and other similar infections, could help identify diagnostic markers and therapeutic targets.
Here we examine data of cytokine, immune cell types, and disease associations captured from biomedical literature associated with COVID-19, Coronavirus in general, SARS, and H5N1 influenza, with the objective of identifying potentially useful relationships and areas for future research.
Cytokine and cell-type associations captured from Medical Subject Heading (MeSH) terms linked to thousands of PubMed records, has identified differing patterns of associations between the four corpuses of publications (COVID-19, Coronavirus, SARS, or H5N1 influenza). Clustering of cytokine-disease co-occurrences in the context of Coronavirus has identified compelling clusters of co-morbidities and symptoms, some of which already known to be linked to COVID-19. Finally, network analysis identified sub-networks of cytokines and immune cell types associated with different manifestations, co-morbidities and symptoms of Coronavirus, SARS, and H5N1.
Systematic review of research in medicine is essential to facilitate evidence-based choices about health interventions. In a fast moving pandemic the approach taken here will identify trends and enable rapid comparison to the literature of related diseases.
严重急性呼吸系统综合症病毒 SARS-CoV-2 是 SARS 病毒的近亲,是导致最近 COVID-19 大流行的原因,迄今为止,该病毒已在全球范围内感染了超过 1400 万人,并显示出相对较高的感染率和死亡率。第三种病毒 H5N1 是导致禽流感的病原体,其感染的某些临床特征与 COVID-19 感染相似。细胞因子是调节免疫反应的小分子蛋白,已被直接牵连到 COVID-19 患者的一些严重反应中,例如细胞因子风暴。了解与 COVID-19 及其他类似感染相关的免疫过程,有助于识别诊断标志物和治疗靶点。
在这里,我们检查了与 COVID-19、一般冠状病毒、SARS 和 H5N1 流感相关的生物医学文献中捕获的细胞因子、免疫细胞类型和疾病关联的数据,目的是确定潜在的有用关系和未来研究领域。
从与数千篇 PubMed 记录相关的医学主题词 (MeSH) 术语中捕获的细胞因子和细胞类型关联,已经确定了四个出版物语料库(COVID-19、冠状病毒、SARS 或 H5N1 流感)之间不同的关联模式。在冠状病毒背景下对细胞因子-疾病共现的聚类,已经确定了令人信服的共病和症状集群,其中一些已经与 COVID-19 相关。最后,网络分析确定了与冠状病毒、SARS 和 H5N1 的不同表现、共病和症状相关的细胞因子和免疫细胞类型的子网络。
对医学研究进行系统综述对于促进基于证据的健康干预选择至关重要。在快速发展的大流行中,这里采用的方法将确定趋势,并能够快速将其与相关疾病的文献进行比较。