Genetic grouping of SARS-CoV-2 coronavirus sequences using informative subtype markers for pandemic spread visualization
Genetic grouping of SARS-CoV-2 coronavirus sequences using informative subtype markers for pandemic spread visualization
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DOI：
10.1101/2020.04.07.030759
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发表时间：
2020-04
影响因子：
4.3
通讯作者：
Zhengqiao Zhao;B. Sokhansanj;C. Malhotra;Kitty Zheng;G. Rosen
中科院分区：
文献类型：
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作者：
Zhengqiao Zhao;B. Sokhansanj;C. Malhotra;Kitty Zheng;G. Rosen
我们提出了一个有效的框架，用于SARS-CoV-2的遗传亚型，SARS-CoV-2是导致COVID-19大流行的新型冠状病毒。有效的病毒亚型分型使疾病传播的地理分布和时间动态可视化和建模成为可能。因此，分型促进了有效遏制策略的发展，并可能促进治疗和疫苗策略的发展。然而，实时识别病毒亚型具有挑战性：SARS-CoV-2是一种新型病毒，而且大流行正在迅速扩大。病毒亚型可能难以检测，因为快速进化;创始人效应比选择压力更重要;亚型的聚类阈值没有标准化。我们建议使用基于人群的方法来识别现有SARS-CoV-2序列的突变特征：熵测量，然后进行频率分析。这些特征，信息亚型标记（ISM），定义了一组紧凑的核苷酸位点，这些位点表征了从不同个体测序的病毒基因组中最可变（因此最具信息性）的位置。通过ISM压缩，我们发现，某些遥远的核苷酸变异协变，包括非编码和ORF 1ab位点与D 614 G刺突蛋白突变，这已成为越来越普遍的大流行的蔓延。ISM也可用于下游分析，例如病毒动态的时空可视化。通过分析GISAID数据库中的序列数据，我们通过比较使用ISM的时空分析与亚洲、欧洲和美国病毒传播的流行病学研究，验证了基于ISM的亚型分型的实用性。此外，我们显示了ISMs与SARS-CoV-2进化的系统发育重建的关系，因此，ISMs可以对基于系统发育树的分析发挥重要的补充作用，例如Nextstrain [1]项目中所做的。开发的管道为新添加的SARS-CoV-2序列动态生成ISM，并更新流行病时空动态的可视化，并可在Github上https://github.com/EESI/ISM和通过互动网站https://covid19-ism.coe.drexel.edu/获得。截至2020年6月21日，据报道，导致COVID-19的新型冠状病毒SARS-CoV-2在全球范围内扩大到870万例确诊病例。全球SARS-CoV-2大流行凸显了实时跟踪病毒传播动态的重要性。截至2020年6月，研究人员已经从全球感染者的47，000多个样本中获得了SARS-CoV-2的基因序列。由于病毒很容易变异，感染个体的每个序列都包含与个体暴露地点和样本日期相关的有用信息。但是，在SARS-CoV-2的整个基因组中有超过30，000个碱基，因此在整个序列的基础上跟踪遗传变异变得很难。我们描述了一种方法，而不是有效地识别和标记SARS-CoV-2的遗传变异或“亚型”。应用这种方法会产生一个紧凑的、11个碱基长的压缩标签，称为信息亚型标记或“ISM”。我们为每个ISM定义病毒亚型，并显示亚型的区域分布如何跟踪大流行的进展。主要发现包括（1）与迅速传播的刺突蛋白共变的核苷酸，以及（2）追踪美国各地与亚洲有关的地方亚型的出现，并与纽约的爆发不同，后者被发现与欧洲有关。
We propose an efficient framework for genetic subtyping of SARS-CoV-2, the novel coronavirus that causes the COVID-19 pandemic. Efficient viral subtyping enables visualization and modeling of the geographic distribution and temporal dynamics of disease spread. Subtyping thereby advances the development of effective containment strategies and, potentially, therapeutic and vaccine strategies. However, identifying viral subtypes in real-time is challenging: SARS-CoV-2 is a novel virus, and the pandemic is rapidly expanding. Viral subtypes may be difficult to detect due to rapid evolution; founder effects are more significant than selection pressure; and the clustering threshold for subtyping is not standardized. We propose to identify mutational signatures of available SARS-CoV-2 sequences using a population-based approach: an entropy measure followed by frequency analysis. These signatures, Informative Subtype Markers (ISMs), define a compact set of nucleotide sites that characterize the most variable (and thus most informative) positions in the viral genomes sequenced from different individuals. Through ISM compression, we find that certain distant nucleotide variants covary, including non-coding and ORF1ab sites covarying with the D614G spike protein mutation which has become increasingly prevalent as the pandemic has spread. ISMs are also useful for downstream analyses, such as spatiotemporal visualization of viral dynamics. By analyzing sequence data available in the GISAID database, we validate the utility of ISM-based subtyping by comparing spatiotemporal analyses using ISMs to epidemiological studies of viral transmission in Asia, Europe, and the United States. In addition, we show the relationship of ISMs to phylogenetic reconstructions of SARS-CoV-2 evolution, and therefore, ISMs can play an important complementary role to phylogenetic tree-based analysis, such as is done in the Nextstrain [1] project. The developed pipeline dynamically generates ISMs for newly added SARS-CoV-2 sequences and updates the visualization of pandemic spatiotemporal dynamics, and is available on Github at https://github.com/EESI/ISM and via an interactive website at https://covid19-ism.coe.drexel.edu/. Author Summary The novel coronavirus responsible for COVID-19, SARS-CoV-2, expanded to reportedly 8.7 million confirmed cases worldwide by June 21, 2020. The global SARS-CoV-2 pandemic highlights the importance of tracking viral transmission dynamics in real-time. Through June 2020, researchers have obtained genetic sequences of SARS-CoV-2 from over 47,000 samples from infected individuals worldwide. Since the virus readily mutates, each sequence of an infected individual contains useful information linked to the individual’s exposure location and sample date. But, there are over 30,000 bases in the full SARS-CoV-2 genome—so tracking genetic variants on a whole-sequence basis becomes unwieldy. We describe a method to instead efficiently identify and label genetic variants, or “subtypes” of SARS-CoV-2. Applying this method results in a compact, 11 base-long compressed label, called an Informative Subtype Marker or “ISM”. We define viral subtypes for each ISM, and show how regional distribution of subtypes track the progress of the pandemic. Major findings include (1) covarying nucleotides with the spike protein which has spread rapidly and (2) tracking emergence of a local subtype across the United States connected to Asia and distinct from the outbreak in New York, which is found to be connected to Europe.