Real-time tracking of virus evolution for vaccine strain selection and epidemiological investigation

实时跟踪病毒进化，用于疫苗株选择和流行病学调查

• 批准号: 10687985
• 负责人: Trevor BC Bedford
• 金额: $ 41.81万
• 依托单位: FRED HUTCHINSON CANCER CENTER
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-23 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10687985
• 关键词: 2019-nCoV; African; American; COVID-19 pandemic; Communicable Diseases; Contact Tracing; Data; Data Set; Dengue Virus; Disease Outbreaks; Early Diagnosis; Ebola; Ebola virus; Epidemic; Epidemiologist; Epidemiology; Evolution; Future; Genes; Genetic; Genomics; Geography; Goals; Human; Immunity; Influenza; Methodology; Methods; Modeling; Monitor; Mumps virus; Pattern; Population; Predisposition; Process; Public Health; Time; Tuberculosis; United States; Update; Vaccines; Variant; Viral; Viral Genome; Viral Load result; Virus; Work; ZIKA; Zika Virus; bioinformatics pipeline; epidemiology study; genomic data; genomic epidemiology; improved; influenza virus vaccine; influenzavirus; innovation; novel; outbreak response; pandemic disease; pathogen; pathogenic virus; public health intervention; seasonal influenza; tool; transmission process; vaccine efficacy; viral genomics; viral outbreak; web site

Project Summary Viral pathogens are an enduring threat to global public health. This project aims to use viral genomic data to improve understanding of ongoing virus evolution and to make actionable inferences to reduce the global burden of viral infectious disease. In order to be relevant for public health interventions, analyses of viral sequence data need to be incredibly rapid, both in terms of computation and in terms of dissemination. To accomplish these goals, this project will create novel methodological tools to analyze evolutionary dynamics from influenza genetic sequence data and to analyze transmission patterns from outbreak sequence data. Over the current project period (2016-2021), we developed a real-time analysis platform called Nextstrain, which provides up-to-date analyses for a variety of pathogens including influenza virus, Ebola virus, Zika virus, dengue virus, mumps virus, tuberculosis and SARS-CoV-2. Bioinformatic pipelines developed through Nextstrain are reusable by academic groups and public health labs and resulting analyses are shareable via the website nextstrain.org. In the upcoming project period (2021-2026), we will refine methods for forecasting strain dynamics of influenza virus. Monitoring and forecasting evolution of viral strains is of paramount importance. New antigenic variants of influenza that partially escape from prior human immunity emerge and rapidly sweep through the viral population. Such strains are less susceptible to vaccine-derived immunity and so antigenic evolution results in the need to frequently update the seasonal influenza vaccine. This project aims to refine methods to forecast strain dynamics and predict the makeup of the future influenza population. This forecasting is especially relevant to influenza vaccine strain selection, as a vaccine strain is chosen for the Northern Hemisphere in February for deployment the following winter. Accurate projections will aid in vaccine match for seasonal influenza viruses and result in improved vaccine efficacy. Technical innovations focus on extending models to work across different viruses, different gene segments and to incorporate spatial dynamics. In an outbreak scenario such as the West African Ebola epidemic, the American Zika epidemic or the SARS- CoV-2 pandemic, the focus of public health interventions focus on early diagnosis, contact tracing, isolation and treatment. Epidemiological understanding of transmission dynamics is of paramount importance to outbreak response. Viral genomic data can reveal otherwise hidden transmission patterns and aid in efficient contact tracing. Geographic spread is especially amenable to genomic inferences. This project will develop tools to make epidemiological inferences from outbreak sequence data. These methods will continue to be deployed via the Nextstrain platform, allowing epidemiologists throughout the world to analyze their own datasets. Genomic epidemiology has the potential to truly inform outbreak response. Nextstrain has been instrumental to SARS-CoV-2 genomic epidemiology in the United States and world. Improvements to the accuracy and capabilities of the platform would be well placed.

项目摘要 病毒病原体是对全球公共卫生的持久威胁。该项目旨在利用病毒基因组数据 提高对正在进行的病毒演变的理解，并做出可行的推断，以减少全球 病毒性传染病的负担。为了与公共卫生干预措施相关， 序列数据在计算和传播方面都需要令人难以置信的快速。到 为了实现这些目标，该项目将创建新颖的方法工具来分析进化动态 并分析疫情序列数据的传播模式。 在当前项目期间（2016-2021年），我们开发了一个名为Nextstrain的实时分析平台， 它提供了各种病原体的最新分析，包括埃博拉病毒，寨卡病毒， 病毒、登革热病毒、腮腺炎病毒、结核病和SARS-CoV-2。生物信息学管道通过 Nextstrain可由学术团体和公共卫生实验室重复使用，结果分析可通过 网址：nextstrain.org。 在即将到来的项目期间（2021-2026年），我们将改进预测伊瓜苏应变动态的方法。 病毒监测和预测病毒株的演变至关重要。新抗原变体 部分逃脱先前人类免疫力的病毒出现并迅速席卷病毒 人口这些菌株对疫苗衍生的免疫力较不敏感，因此抗原进化导致 需要经常更新季节性流感疫苗。该项目旨在改进预测方法， 应变动力学和预测未来的组成在Eschuenza人口。这一预测尤其 与流感疫苗株选择相关，因为选择了北方半球的疫苗株， 2月部署第二个冬天。准确的预测将有助于季节性疫苗匹配 从而提高疫苗的有效性。技术创新的重点是将模型扩展到 在不同的病毒，不同的基因片段中工作，并结合空间动力学。 在西非埃博拉疫情、美国寨卡疫情或SARS等疫情爆发的情况下， 在CoV-2大流行期间，公共卫生干预措施的重点是早期诊断、接触者追踪、隔离和 治疗对传播动力学的流行病学了解对暴发至关重要 反应病毒基因组数据可以揭示隐藏的传播模式，并有助于有效接触 追踪地理传播特别容易受到基因组推断的影响。该项目将开发工具， 根据爆发序列数据进行流行病学推断。这些方法将继续部署 通过Nextstrain平台，让世界各地的流行病学家分析他们自己的数据集。 基因组流行病学有可能真正为疫情应对提供信息。Nextstrain一直在帮助 SARS-CoV-2基因组流行病学在美国和世界。提高了准确性， 该平台的能力将得到很好的发挥。

项目成果

Predictive Modeling of Influenza Shows the Promise of Applied Evolutionary Biology.

• DOI: 10.1016/j.tim.2017.09.004
• 发表时间: 2018-03
• 期刊: Trends in microbiology
• 影响因子: 15.9
• 作者: Morris DH;Gostic KM;Pompei S;Bedford T;Łuksza M;Neher RA;Grenfell BT;Lässig M;McCauley JW
• 通讯作者: McCauley JW

Evidence for adaptive evolution in the receptor-binding domain of seasonal coronaviruses OC43 and 229e.

季节性冠状病毒OC43和229E的受体结合结构域的适应性进化的证据。

• DOI: 10.7554/elife.64509
• 发表时间: 2021-01-19
• 期刊: eLife
• 影响因子: 7.7
• 作者: Kistler KE;Bedford T
• 通讯作者: Bedford T

Emergence and expansion of SARS-CoV-2 B.1.526 after identification in New York.

• DOI: 10.1038/s41586-021-03908-2
• 发表时间: 2021-09
• 期刊: Nature
• 影响因子: 64.8
• 作者: Annavajhala MK;Mohri H;Wang P;Nair M;Zucker JE;Sheng Z;Gomez-Simmonds A;Kelley AL;Tagliavia M;Huang Y;Bedford T;Ho DD;Uhlemann AC
• 通讯作者: Uhlemann AC

Viral genomes reveal patterns of the SARS-CoV-2 outbreak in Washington State.

• DOI: 10.1126/scitranslmed.abf0202
• 发表时间: 2021-05-26
• 期刊: Science translational medicine
• 影响因子: 17.1
• 作者: Müller NF;Wagner C;Frazar CD;Roychoudhury P;Lee J;Moncla LH;Pelle B;Richardson M;Ryke E;Xie H;Shrestha L;Addetia A;Rachleff VM;Lieberman NAP;Huang ML;Gautom R;Melly G;Hiatt B;Dykema P;Adler A;Brandstetter E;Han PD;Fay K;Ilcisin M;Lacombe K;Sibley TR;Truong M;Wolf CR;Boeckh M;Englund JA;Famulare M;Lutz BR;Rieder MJ;Thompson M;Duchin JS;Starita LM;Chu HY;Shendure J;Jerome KR;Lindquist S;Greninger AL;Nickerson DA;Bedford T
• 通讯作者: Bedford T

Evolution and rapid spread of a reassortant A(H3N2) virus that predominated the 2017-2018 influenza season.

主导 2017-2018 流感季节的重配 A(H3N2) 病毒的进化和快速传播。

• DOI: 10.1093/ve/vez046
• 发表时间: 2019
• 期刊: Virus evolution
• 影响因子: 5.3
• 作者: Potter,BarneyI;Kondor,Rebecca;Hadfield,James;Huddleston,John;Barnes,John;Rowe,Thomas;Guo,Lizheng;Xu,Xiyan;Neher,RichardA;Bedford,Trevor;Wentworth,DavidE
• 通讯作者: Wentworth,DavidE