From affinity selection to kinetic selection in Germinal Centre modelling

Abstract

Affinity maturation is an evolutionary process by which the affinity of antibodies (Abs) against specific antigens (Ags) increases through rounds of B-cell proliferation, somatic hypermutation, and positive selection in germinal centres (GC). The positive selection of B cells depends on affinity, but the underlying mechanisms of affinity discrimination and affinity-based selection are not well understood. It has been suggested that selection in GC depends on both rapid binding of B-cell receptors (BcRs) to Ags which is kinetically favourable and tight binding of BcRs to Ags, which is thermodynamically favourable; however, it has not been shown whether a selection bias for kinetic properties is present in the GC. To investigate the GC selection bias towards rapid and tight binding, we developed an agent-based model of GC and compared the evolution of founder B cells with initially identical low affinities but with different association/dissociation rates for Ag presented by follicular dendritic cells in three Ag collection mechanisms. We compared an Ag collection mechanism based on association/dissociation rates of B-cell interaction with presented Ag, which includes a probabilistic rupture of bonds between the B-cell and Ag (Scenario-1) with a reference scenario based on an affinity-based Ag collection mechanism (Scenario-0). Simulations showed that the mechanism of Ag collection affects the GC dynamics and the GC outputs concerning fast/slow (un)binding of B cells to FDC-presented Ags. In particular, clones with lower dissociation rates outcompete clones with higher association rates in Scenario-1, while remaining B cells from clones with higher association rates reach higher affinities. Accordingly, plasma cell and memory B cell populations were biased towards B-cell clones with lower dissociation rates. Without such probabilistic ruptures during the Ag extraction process (Scenario-2), the selective advantage for clones with very low dissociation rates diminished, and the affinity maturation level of all clones decreased to the reference level.

A Multiscale Spatial Modeling Framework for the Germinal Center Response

“…GC Issues explored with earlier versions of these models include: signaling vs. chemotaxis modes of action (Beyer et al 2002, Meyer-Hermann 2002, Meyer-Hermann and Beyer 2002), requirement of DZ re-entry for GC development and affinity maturation (Meyer-Hermann and Maini 2005a), whether affinity maturation is driven by competition for antigen or Tfh cells, GC termination resulting from antigen depletion (Meyer-Hermann et al 2006, Meyer-Hermann 2007), persistent random walk of B cells observed with two-photon imaging and the requirement of active chemotaxis for maintenance of GC zonation (Meyer-Hermann and Maini 2005b, Figge et al 2008, Binder and Meyer-Hermann 2016), and affinity-dependent proliferative burst size enabling large, high-affinity GC B cell populations (Meyer-Hermann 2014), Issues explored with later iterations of these models include: model variants informed by FOXO1, MYC, and mTOR signaling dynamics on clonal dominance and independent control of B cell selection and division fate decisions (Meyer-Hermann 2021), the effects of periodic cycling of antigen immune complex in FDCs on GC development (Arulraj et al 2021b), differences in the lifetime of individual GCs resulting from variations in antigen availability and founder cell composition (Arulraj et al 2021a), contribution of GC-GC interactions to variability in the timing of individual GC maturation (Arulraj et al 2022a), different mechanisms of GC shutdown (Arulraj et al 2022b), the effects of kinetic rates of BCR-antigen binding on antigen uptake by B cells and GC dynamics and outputs (Lashgari et al 2022), and the evolution of clonal diversity and dominance and the modulating effects of antigen amount, Tfh cell availability, and seeding B cell affinity (Meyer-Hermann et al 2018, Garg et al 2023),…”

ENCORE. A practical implementation to improve reproducibility and transparency of computational research.

“…Mathematical modelling and simulations can be part of the computational phase. Alternatively, one may solely or partially rely on existing data from (public) repositories or conduct studies that are based on computer simulations (Lashgari et al, 2022;Merino Tejero et al, 2020). Stage 4 (publication and archiving) is increasingly driven by initiatives aiming for open-access publications and data (Pulverer, 2018;van der Heyden & van Veen, 2018) and open-source software (Garijo et al, 2022;Open Source Initiative, 2023).…”