NIH Director's Pioneer Award: Antigenic Cartography

流感病毒是经典的 抗原性可变的病原体的例子，似乎有无穷无尽的能力 逃避免疫反应。例如，由于输入了甲型H3N1流感亚型 1968年左右的人口中，针对它的疫苗已经更新了24次，以 跟踪病毒准物种的进化并保持有效。大多数 分析病毒进化的生物信息学方法是基于基因分析； 然而，正是病毒的表型(抗原)特性决定了它的成功 逃避先前的免疫并引起感染。事实上，抗原性数据是 选择流感疫苗中使用的病毒株的主要标准是 对流感的许多基础和应用研究都很重要。然而，没有 定量测定抗原差异的可靠方法。 抗原差异通常使用某种形式的结合分析来确定(对于 流感病毒，血凝抑制试验，对其他病原体 中和试验、酶联免疫吸附试验等。在这种分析中，通常要滴定一组抗血清。 针对一系列抗原，数据以表格形式组织，并通过 眼睛。这已经是50多年来的程序了。这些数据很难解释。 在数量上，有时甚至对于专家来说，给出的图景是不一致的。初级阶段 之所以会出现这种困难，是因为数据中包含了不规范之处，或者说悖论。在这样的情况下 不规则性是一个抗血清可能检测到两个抗原之间的差异，而 另一个人则不会。另一种不规则性是异源滴度有时更高 而不是同源滴度。此外，通常很难比较不同来源的数据 实验室。这些，再加上其他不规则性，导致结合化验数据仅 被认为足够可靠来判断大的抗原性差异？就流感而言 病毒差异足够大，因此有必要更新疫苗 紧张。通过只能判断数千种流感病毒之间的明显差异 每一年都具有特征的菌株，人们错失了优化疫苗菌株的机会 选择，以检测病毒准种进化中的信号，这可能会使 警告有必要更新疫苗，了解流感的流行病学，判断疫苗接种如何影响病毒的演变，并制定新的 有可能从根本上改变我们的选择的干预策略 控制疫情和大流行性流感。 流感病毒是抗原性可变病原体的一个例子；其他包括 人类免疫缺陷病毒和丙型肝炎病毒。抗原多样性的程度 随着干预措施增加产生逃逸突变体的选择压力， 因此，对这些表型差异的表征只会变得更加重要。

Influenza viruses are classic examples of antigenically variable pathogens, and have a seemingly endless capacity to evade the immune response. For example, since the influenza A(H3N2) subtype entered the human population circa 1968 the vaccine against it has had to be updated 24 times to track the evolution of the viral quasispecies and to remain effective. Most of the bioinformatics methods for analyzing viral evolution are based on genetic analyses; however, it is the phenotypic (antigenic) properties of the virus that determine its success at escaping prior immunity and causing infection. Indeed, the antigenic data are the primary criteria for selecting the virus strain used in the influenza vaccine, and which are important for much basic and applied research on influenza. However, there is no reliable method to determine quantitatively antigenic differences. Antigenic differences are typically determined using some form of binding assay (for influenza virus, the hemagglutination inhibition assay, for other pathogens a neutralization assay, ELISA, etc.). In such assays, typically a panel of antisera is titrated against a series of antigens and the data are organized in tabular form and analyzed by eye. This has been the procedure for over 50 years. These data are difficult to interpret quantitatively, and sometimes even for experts give an inconsistent picture. The primary reason for this difficulty is that the data contain irregularities, or paradoxes. On such irregularity is that one antiserum might detect a difference between two antigens, while another will not. Another irregularity is that heterologous titers are sometimes higher than homologous titers. Furthermore, it is often difficult to compare data from different laboratories. These, along with other irregularities result in binding assay data only being considered reliable enough to judge large antigenic differences?in the case of influenza virus differences of sufficient magnitude that they necessitate an update of the vaccine strain. By only being able to judge gross differences among the thousands of influenza strains characterized each year, one misses opportunities to optimize the vaccine strain choice, to detect signals in the evolution of the viral quasispecies that could give advance warning of the necessity to update the vaccine, to understand the epidemiology of influenza, to judge how vaccination affects the viral evolution, and to devise novel intervention strategies which have the potential to fundamentally change our options to control epidemic and pandemic influenza. Influenza virus is one example of an antigencially variable pathogen; others include human immunodifficiency virus and hepatitis C virus. The degree of antigenic diversity will increase as interventions increase selection pressure to generate escape mutants, and the characterization of these phenotype differences will thus only increase in importance.