tetano
Editor, Senior Moderator
PLoS Pathog
. 2022 Jan 31;18(1):e1010243.
doi: 10.1371/journal.ppat.1010243. Online ahead of print.
Model-based inference of neutralizing antibody avidities against influenza virus
Janina Linnik[SUP] 1 2 [/SUP], Mohammedyaseen Syedbasha[SUP] 3 [/SUP], Yvonne Hollenstein[SUP] 3 [/SUP], Jörg Halter[SUP] 4 [/SUP], Adrian Egli[SUP] 3 5 [/SUP], Jörg Stelling[SUP] 1 2 [/SUP]
Affiliations
Abstract
To assess the response to vaccination, quantity (concentration) and quality (avidity) of neutralizing antibodies are the most important parameters. Specifically, an increase in avidity indicates germinal center formation, which is required for establishing long-term protection. For influenza, the classical hemagglutination inhibition (HI) assay, however, quantifies a combination of both, and to separately determine avidity requires high experimental effort. We developed from first principles a biophysical model of hemagglutination inhibition to infer IgG antibody avidities from measured HI titers and IgG concentrations. The model accurately describes the relationship between neutralizing antibody concentration/avidity and HI titer, and explains quantitative aspects of the HI assay, such as robustness to pipetting errors and detection limit. We applied our model to infer avidities against the pandemic 2009 H1N1 influenza virus in vaccinated patients (n = 45) after hematopoietic stem cell transplantation (HSCT) and validated our results with independent avidity measurements using an enzyme-linked immunosorbent assay with urea elution. Avidities inferred by the model correlated with experimentally determined avidities (ρ = 0.54, 95% CI = [0.31, 0.70], P < 10-4). The model predicted that increases in IgG concentration mainly contribute to the observed HI titer increases in HSCT patients and that immunosuppressive treatment is associated with lower baseline avidities. Since our approach requires only easy-to-establish measurements as input, we anticipate that it will help to disentangle causes for poor vaccination outcomes also in larger patient populations. This study demonstrates that biophysical modelling can provide quantitative insights into agglutination assays and complement experimental measurements to refine antibody response analyses.
. 2022 Jan 31;18(1):e1010243.
doi: 10.1371/journal.ppat.1010243. Online ahead of print.
Model-based inference of neutralizing antibody avidities against influenza virus
Janina Linnik[SUP] 1 2 [/SUP], Mohammedyaseen Syedbasha[SUP] 3 [/SUP], Yvonne Hollenstein[SUP] 3 [/SUP], Jörg Halter[SUP] 4 [/SUP], Adrian Egli[SUP] 3 5 [/SUP], Jörg Stelling[SUP] 1 2 [/SUP]
Affiliations
- PMID: 35100312
- DOI: 10.1371/journal.ppat.1010243
Abstract
To assess the response to vaccination, quantity (concentration) and quality (avidity) of neutralizing antibodies are the most important parameters. Specifically, an increase in avidity indicates germinal center formation, which is required for establishing long-term protection. For influenza, the classical hemagglutination inhibition (HI) assay, however, quantifies a combination of both, and to separately determine avidity requires high experimental effort. We developed from first principles a biophysical model of hemagglutination inhibition to infer IgG antibody avidities from measured HI titers and IgG concentrations. The model accurately describes the relationship between neutralizing antibody concentration/avidity and HI titer, and explains quantitative aspects of the HI assay, such as robustness to pipetting errors and detection limit. We applied our model to infer avidities against the pandemic 2009 H1N1 influenza virus in vaccinated patients (n = 45) after hematopoietic stem cell transplantation (HSCT) and validated our results with independent avidity measurements using an enzyme-linked immunosorbent assay with urea elution. Avidities inferred by the model correlated with experimentally determined avidities (ρ = 0.54, 95% CI = [0.31, 0.70], P < 10-4). The model predicted that increases in IgG concentration mainly contribute to the observed HI titer increases in HSCT patients and that immunosuppressive treatment is associated with lower baseline avidities. Since our approach requires only easy-to-establish measurements as input, we anticipate that it will help to disentangle causes for poor vaccination outcomes also in larger patient populations. This study demonstrates that biophysical modelling can provide quantitative insights into agglutination assays and complement experimental measurements to refine antibody response analyses.