Using a comparative One Health approach to investigate the structural basis of antigenic variation among human and avian influenza viruses

使用比较 One Health 方法研究人类和禽流感病毒抗原变异的结构基础

• 批准号: MR/R024758/1
• 负责人: William Harvey
• 金额: $ 37.04万
• 依托单位: University of Glasgow
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FR024758%2F1
• 关键词: Using; comparative; Health; approach; investigate

Human influenza viruses are estimated to cause 3-5 million cases of severe illness globally each year resulting in 250,000 to 500,000 deaths. Avian influenza viruses (AIVs) represent a threat to global poultry production and to human health through both zoonotic infection and the pandemic potential of reassortant viruses. The WHO's Global Influenza Surveillance and Response System has conducted virological surveillance for over half a century, is responsible for genetic and antigenic characterisation of circulating, and supports the selection of influenza viruses for human influenza vaccine production. Vaccination of poultry has become one of the principal practices for control of the endemic disease in many countries and human vaccination would be a vital component of the response in a pandemic situation. The effectiveness of vaccines that exist to protect against human and avian influenza viruses are threatened by the emergence of antigenic variants. Suboptimal vaccine matching due to antigenic drift can result in vaccine failure burdening human and animal health, causing significant economic losses, and threatening food security. Influenza vaccine efficacy is highly dependent on antigenic matching of vaccine seed to circulating strains, and influenza strains are characterized by antigenic drift over time, structural changes in B-cell epitopes facilitate escape from pre-existing immunity. There is, therefore, a pressing need to better understand the molecular basis of antigenicity of the major influenza antigen, haemagglutinin. This is particularly true for AIVs which typically have received less attention and consequently about which less is known. Meanwhile, extensive resources have been invested in the antigenic characterisation of human seasonal influenza viruses, in particular influenza A H1N1 and H3N2, to guide decisions on human seasonal influenza vaccine composition. The extensive knowledge and data from human viruses can inform more rigorous and biologically informed models to apply to AIVs where there is an opportunity for significant advances to be made using a comparative One Health approach. A biophysically-informed structural model of the immunological reactivity and receptor binding avidity of these human and avian pathogens based on the structures and amino-acid sequences of their antigenic proteins will be built. This will be used to identify the underlying factors that affect virus cross-reactivity, and hence vaccine efficacy. This model will extend previous work through greater integration of information on 3-D protein structures and the biophysics determining antibody-antigen interactions. The extensive data already generated for human influenza A viruses will be collated and used to refine this model. This refined model will then be applied to AIV, transferring knowledge from the medical field to the veterinary field. Laboratory work will be used to experimentally validate the structural model of HA phenotypic variation. Studies of AIVs will offer a provide a better understanding of the influence of changes in receptor-binding avidity on cross-reactivity, which can in turn benefit the medical field, since this has become very difficult to measure for human influenza A H3N2 viruses.We will then use these results to predict cross-reactivity for newly emerging viruses and thereby the likelihood of vaccine escape, and to propose vaccine seed strains most likely to be effective for control of novel AIV threats benefitting veterinary vaccinology. The improved knowledge of the structural basis of variation in HA phenotype will also benefit existing methods used to predict evolutionary trajectories of influenza viruses, aiding decision makers concerned with the selection of human influenza viruses. Additionally, the biophysical model will allow predictions of previously unobserved changes to viral proteins to be made.

据估计，人类流感病毒每年在全球引起 3-500 万例严重疾病，导致 25 万至 50 万人死亡。禽流感病毒（AIV）通过人畜共患感染和重配病毒的大流行潜力对全球家禽生产和人类健康构成威胁。世卫组织的全球流感监测和应对系统已进行病毒学监测半个多世纪，负责流行的遗传和抗原特征分析，并支持选择用于人类流感疫苗生产的流感病毒。家禽疫苗接种已成为许多国家控制地方性疾病的主要做法之一，而人类疫苗接种将是大流行情况下应对措施的重要组成部分。现有的预防人类和禽流感病毒的疫苗的有效性受到抗原变体的出现的威胁。由于抗原漂移导致的疫苗匹配不理想可能会导致疫苗失败，给人类和动物健康带来负担，造成重大经济损失，并威胁粮食安全。流感疫苗的功效高度依赖于疫苗种子与流行毒株的抗原匹配，而流感毒株的特点是随着时间的推移抗原漂移，B细胞表位的结构变化有助于逃避预先存在的免疫。因此，迫切需要更好地了解主要流感抗原血凝素抗原性的分子基础。对于 AIV 来说尤其如此，它们通常受到的关注较少，因此人们对其知之甚少。与此同时，人们在人类季节性流感病毒（特别是甲型H1N1和H3N2流感）的抗原表征方面投入了大量资源，以指导人类季节性流感疫苗成分的决策。来自人类病毒的广泛知识和数据可以为更严格和生物学信息丰富的模型提供信息，以应用于AIV，其中使用比较One Health方法有机会取得重大进展。将根据这些人类和禽类病原体的抗原蛋白的结构和氨基酸序列，建立一个基于生物物理的结构模型，描述这些人类和禽类病原体的免疫反应性和受体结合亲合力。这将用于确定影响病毒交叉反应的潜在因素，从而确定疫苗功效。该模型将通过更好地整合 3D 蛋白质结构和确定抗体-抗原相互作用的生物物理学信息来扩展之前的工作。已经生成的人类甲型流感病毒的大量数据将被整理并用于完善该模型。然后，这个改进的模型将应用于AIV，将知识从医学领域转移到兽医领域。实验室工作将用于通过实验验证 HA 表型变异的结构模型。对AIV的研究将有助于更好地了解受体结合亲合力变化对交叉反应性的影响，这反过来又可以使医学领域受益，因为这对于人类甲型流感H3N2病毒来说已经变得非常难以测量。然后，我们将利用这些结果来预测新出现的病毒的交叉反应性，从而预测疫苗逃逸的可能性，并提出最有可能有效控制新型病毒的疫苗种子株。 AIV 威胁有利于兽医疫苗学。对HA表型变异结构基础的进一步了解也将有利于用于预测流感病毒进化轨迹的现有方法，帮助决策者选择人类流感病毒。此外，生物物理模型将允许预测以前未观察到的病毒蛋白质的变化。

项目成果

Recurrent independent emergence and transmission of SARS-CoV-2 Spike amino acid H69/V70 deletions

• DOI: 10.21203/rs.3.rs-136937/v1
• 发表时间: 2021-01
• 作者: Ravindra K. Gupta;S. Kemp;William T. Harvey;Spyros Lytras;A. Carabelli;D. Robertson

Reduced neutralisation of the Delta (B.1.617.2) SARS-CoV-2 variant of concern following vaccination.

• DOI: 10.1371/journal.ppat.1010022
• 发表时间: 2021-12
• 期刊: PLoS pathogens
• 影响因子: 6.7
• 作者: Davis C;Logan N;Tyson G;Orton R;Harvey WT;Perkins JS;Mollett G;Blacow RM;COVID-19 Genomics UK (COG-UK) Consortium;Peacock TP;Barclay WS;Cherepanov P;Palmarini M;Murcia PR;Patel AH;Robertson DL;Haughney J;Thomson EC;Willett BJ;COVID-19 DeplOyed VaccinE (DOVE) Cohort Study investigators
• 通讯作者: COVID-19 DeplOyed VaccinE (DOVE) Cohort Study investigators

Integrating patient and whole genome sequencing data to provide insights into the epidemiology of seasonal influenza A(H3N2) viruses

整合患者和全基因组测序数据，深入了解季节性甲型流感 (H3N2) 病毒的流行病学

• DOI: 10.1101/121434
• 发表时间: 2017
• 作者: Goldstein E

A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses

贝叶斯方法将结构数据纳入甲型流感 (H3N2) 病毒基因型与抗原表型的映射中

• DOI: 10.1101/2022.03.26.485931
• 发表时间: 2022
• 作者: Harvey W

A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses.

• DOI: 10.1371/journal.pcbi.1010885
• 发表时间: 2023-03
• 期刊: PLoS computational biology
• 影响因子: 4.3