MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London, United Kingdom.
WHO Collaborating Centre for Reference and Research on Influenza, Peter Doherty Institute for Infection and Immunity, Melbourne, Australia.
PLoS Comput Biol. 2019 Aug 19;15(8):e1007294. doi: 10.1371/journal.pcbi.1007294. eCollection 2019 Aug.
The strength and breadth of an individual's antibody repertoire is an important predictor of their response to influenza infection or vaccination. Although progress has been made in understanding qualitatively how repeated exposures shape the antibody mediated immune response, quantitative understanding remains limited. We developed a set of mathematical models describing short-term antibody kinetics following influenza infection or vaccination and fit them to haemagglutination inhibition (HI) titres from 5 groups of ferrets which were exposed to different combinations of trivalent inactivated influenza vaccine (TIV with or without adjuvant), A/H3N2 priming inoculation and post-vaccination A/H1N1 inoculation. We fit models with various immunological mechanisms that have been empirically observed but have not previously been included in mathematical models of antibody landscapes, including: titre ceiling effects, antigenic seniority and exposure-type specific cross reactivity. Based on the parameter estimates of the best supported models, we describe a number of key immunological features. We found quantifiable differences in the degree of homologous and cross-reactive antibody boosting elicited by different exposure types. Infection and adjuvanted vaccination generally resulted in strong, broadly reactive responses whereas unadjuvanted vaccination resulted in a weak, narrow response. We found that the order of exposure mattered: priming with A/H3N2 improved subsequent vaccine response, and the second dose of adjuvanted vaccination resulted in substantially greater antibody boosting than the first. Either antigenic seniority or a titre ceiling effect were included in the two best fitting models, suggesting a role for a mechanism describing diminishing antibody boosting with repeated exposures. Although there was considerable uncertainty in our estimates of antibody waning parameters, our results suggest that both short and long term waning were present and would be identifiable with a larger set of experiments. These results highlight the potential use of repeat exposure animal models in revealing short-term, strain-specific immune dynamics of influenza.
个体抗体库的强度和广度是其对流感感染或疫苗接种反应的重要预测指标。尽管在定性上了解重复暴露如何塑造抗体介导的免疫反应方面已经取得了进展，但定量理解仍然有限。我们开发了一组描述流感感染或接种疫苗后短期抗体动力学的数学模型，并将其拟合到 5 组雪貂的血凝抑制（HI）效价中，这些雪貂暴露于不同组合的三价灭活流感疫苗（含或不含佐剂）、A/H3N2 初免接种和接种疫苗后 A/H1N1 接种。我们拟合了具有各种免疫机制的模型，这些机制已经在经验上得到观察，但以前没有包含在抗体景观的数学模型中，包括：效价上限效应、抗原资历和暴露类型特异性交叉反应性。基于最佳支持模型的参数估计，我们描述了一些关键的免疫学特征。我们发现不同暴露类型引起的同源和交叉反应性抗体增强的程度存在可量化差异。感染和含佐剂的疫苗接种通常会产生强大、广泛反应的反应，而无佐剂的疫苗接种则会产生弱、窄的反应。我们发现暴露顺序很重要：用 A/H3N2 进行初免可以改善随后的疫苗反应，而第二次接种含佐剂的疫苗会导致比第一次接种更大的抗体增强。最佳拟合的两个模型中都包含了抗原资历或效价上限效应，这表明有一种机制描述了随着重复暴露而导致的抗体增强逐渐减弱。尽管我们对抗体衰减参数的估计存在相当大的不确定性，但我们的结果表明，无论是短期还是长期衰减都存在，并且可以通过更大的实验集来识别。这些结果突出了重复暴露动物模型在揭示流感短期、株特异性免疫动态方面的潜在用途。
