A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses
A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses
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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
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Harvey W
中科院分区：
文献类型：
--
作者：
Harvey W
病原体的表面抗原通常被疫苗引发的抗体靶向，但抗原变异性，特别是在RNA病毒如流感病毒、HIV和SARS-CoV-2中，对疫苗接种的控制提出了挑战。例如，甲型流感（H3 N2）于1968年进入人群，引起大流行，并且此后一直通过密集的全球监测和实验室表征来监测抗原漂移变体的出现，沿着其他季节性流感病毒。病毒之间的遗传差异及其抗原相似性之间的关系的统计模型提供了有用的信息，告知疫苗的开发，虽然致病突变的准确识别是复杂的高度相关的遗传信号，由于进化过程中出现。在这里，我们使用经过实验验证的模型的稀疏分层贝叶斯模拟来整合遗传和抗原数据，识别了甲型流感（H3 N2）病毒中支持抗原漂移的遗传变化。我们发现，将蛋白质结构数据纳入变量选择有助于解决由于相关信号而产生的模糊性，代表血凝素位置的变量的比例决定性地包括或排除，从59.8%增加到72.4%。通过与实验确定的抗原位点的接近度来判断变量选择的准确性同时得到提高。因此，结构引导的变量选择提高了识别抗原变异的遗传解释的置信度，并且我们还表明，优先识别致病突变并不会损害分析的预测能力。事实上，将结构信息纳入变量选择导致可以更准确地预测来自遗传序列的表型未表征病毒的抗原测定滴度的模型。结合起来，这些分析有可能为参考病毒的选择、实验室检测的靶向以及不同基因型进化成功的预测提供信息，因此可用于为疫苗选择过程提供信息。
Surface antigens of pathogens are commonly targeted by vaccine-elicited antibodies but antigenic variability, notably in RNA viruses such as influenza, HIV and SARS-CoV-2, pose challenges for control by vaccination. For example, influenza A(H3N2) entered the human population in 1968 causing a pandemic and has since been monitored, along with other seasonal influenza viruses, for the emergence of antigenic drift variants through intensive global surveillance and laboratory characterisation. Statistical models of the relationship between genetic differences among viruses and their antigenic similarity provide useful information to inform vaccine development, though accurate identification of causative mutations is complicated by highly correlated genetic signals that arise due to the evolutionary process. Here, using a sparse hierarchical Bayesian analogue of an experimentally validated model for integrating genetic and antigenic data, we identify the genetic changes in influenza A(H3N2) virus that underpin antigenic drift. We show that incorporating protein structural data into variable selection helps resolve ambiguities arising due to correlated signals, with the proportion of variables representing haemagglutinin positions decisively included, or excluded, increased from 59.8% to 72.4%. The accuracy of variable selection judged by proximity to experimentally determined antigenic sites was improved simultaneously. Structure-guided variable selection thus improves confidence in the identification of genetic explanations of antigenic variation and we also show that prioritising the identification of causative mutations is not detrimental to the predictive capability of the analysis. Indeed, incorporating structural information into variable selection resulted in a model that could more accurately predict antigenic assay titres for phenotypically-uncharacterised virus from genetic sequence. Combined, these analyses have the potential to inform choices of reference viruses, the targeting of laboratory assays, and predictions of the evolutionary success of different genotypes, and can therefore be used to inform vaccine selection processes.
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1977-2009 年人类前季节性甲型 H1N1 流感血凝抑制数据，来自英国伦敦世界卫生组织流感参考和研究合作中心
DOI：
--
发表时间：
2016
期刊：
影响因子：
--
作者：
V. Gregory;William T. Harvey;R. Daniels;R. Reeve;L. Whittaker;C. Halai;A. Douglas;Roselle M. Gonsalves;J. Skehel;A. Hay;J. McCauley;D. Haydon
通讯作者：
D. Haydon
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
影响因子：
64.8
作者：
Luksza, Marta;Laessig, Michael
通讯作者：
Laessig, Michael
影响因子：
5.3
作者：
Suchard MA;Lemey P;Baele G;Ayres DL;Drummond AJ;Rambaut A
通讯作者：
Rambaut A