Department of Microbiology and Immunology, Emory University School of Medicine, Atlanta, Georgia, United States of America.
Department of Biology, Emory University, Atlanta, Georgia, United States of America.
PLoS Comput Biol. 2023 Aug 21;19(8):e1011377. doi: 10.1371/journal.pcbi.1011377. eCollection 2023 Aug.
Antibodies and humoral memory are key components of the adaptive immune system. We consider and computationally model mechanisms by which humoral memory present at baseline might increase rather than decrease infection load; we refer to this effect as EI-HM (enhancement of infection by humoral memory). We first consider antibody dependent enhancement (ADE) in which antibody enhances the growth of the pathogen, typically a virus, and typically at intermediate 'Goldilocks' levels of antibody. Our ADE model reproduces ADE in vitro and enhancement of infection in vivo from passive antibody transfer. But notably the simplest implementation of our ADE model never results in EI-HM. Adding complexity, by making the cross-reactive antibody much less neutralizing than the de novo generated antibody or by including a sufficiently strong non-antibody immune response, allows for ADE-mediated EI-HM. We next consider the possibility that cross-reactive memory causes EI-HM by crowding out a possibly superior de novo immune response. We show that, even without ADE, EI-HM can occur when the cross-reactive response is both less potent and 'directly' (i.e. independently of infection load) suppressive with regard to the de novo response. In this case adding a non-antibody immune response to our computational model greatly reduces or completely eliminates EI-HM, which suggests that 'crowding out' is unlikely to cause substantial EI-HM. Hence, our results provide examples in which simple models give qualitatively opposite results compared to models with plausible complexity. Our results may be helpful in interpreting and reconciling disparate experimental findings, especially from dengue, and for vaccination.
抗体和体液记忆是适应性免疫系统的关键组成部分。我们考虑并通过计算模型来研究基线时存在的体液记忆如何增加而不是降低感染负荷的机制；我们将这种效应称为 EI-HM（体液记忆增强感染）。我们首先考虑抗体依赖性增强（ADE），其中抗体增强病原体（通常是病毒）的生长，通常在抗体的中间“金发姑娘”水平。我们的 ADE 模型在体外重现了 ADE，并通过被动抗体转移增强了体内感染。但值得注意的是，我们的 ADE 模型的最简单实现从未导致 EI-HM。通过使交叉反应性抗体比新生成的抗体的中和能力低得多，或者通过包括足够强的非抗体免疫反应，增加复杂性，允许 ADE 介导的 EI-HM。接下来，我们考虑交叉反应性记忆通过排挤可能更优越的新生成免疫反应而导致 EI-HM 的可能性。我们表明，即使没有 ADE，当交叉反应性反应在针对新生成反应的强度和“直接”（即独立于感染负荷）抑制方面都较弱时，也会发生 EI-HM。在这种情况下，向我们的计算模型中添加非抗体免疫反应会大大降低或完全消除 EI-HM，这表明“排挤”不太可能导致实质性的 EI-HM。因此，我们的结果提供了与具有合理复杂性的模型相比，简单模型给出定性相反结果的示例。我们的结果可能有助于解释和协调来自登革热和疫苗接种等方面的不同实验结果。
