When does humoral memory enhance infection?

• Type of Material: Article
• Authors: Ariel Nikas, Emory University; Hasan Ahmed, Emory University; Mia R. Moore, Fred Hutchinson Cancer Center; Veronika I. Zarnitsyna, Emory University; Rustom Antia, Emory University
• Language: English
• Date: 2023-08-21
• Publisher: PLoS
• Publication Version: Final Published Version
• Copyright Statement: © 2023 Nikas et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): https://doi.org/10.1371%2Fjournal.pcbi.1011377
• Title of Journal or Parent Work: PLoS Computational Biology
• Volume: 19
• Issue: 8
• Start Page: e1011377
• Grant/Funding Information: This work was supported by the National Heart, Lung, and Blood Institute grant U01 HL139483 to A.N., V.Z., and R.A. This work was also supported by the National Institute of Allergy and Infectious Diseases grants U01 AI150747 to A.N., H.A., V.Z., and R.A. and U01 AI144616 to H.A. and R.A. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
• Keywords: pathogen; antibodies; adaptive immune system; EI-HM (enhancement of infection by humoral memory); humoral memory; antibody dependent enhancement (ADE); infection load
• Research Categories: Health Sciences, Immunology; Health Sciences, Pathology

Abstract

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.