Computational Model Reveals a Stochastic Mechanism behind Germinal Center Clonal Bursts

Abstract

Germinal centers (GCs) are specialized compartments within the secondary lymphoid organs where B cells proliferate, differentiate, and mutate their antibody genes in response to the presence of foreign antigens. Through the GC lifespan, interclonal competition between B cells leads to increased affinity of the B cell receptors for antigens accompanied by a loss of clonal diversity, although the mechanisms underlying clonal dynamics are not completely understood. We present here a multi-scale quantitative model of the GC reaction that integrates an intracellular component, accounting for the genetic events that shape B cell differentiation, and an extracellular stochastic component, which accounts for the random cellular interactions within the GC. In addition, B cell receptors are represented as sequences of nucleotides that mature and diversify through somatic hypermutations. We exploit extensive experimental characterizations of the GC dynamics to parameterize our model, and visualize affinity maturation by means of evolutionary phylogenetic trees. Our explicit modeling of B cell maturation enables us to characterise the evolutionary processes and competition at the heart of the GC dynamics, and explains the emergence of clonal dominance as a result of initially small stochastic advantages in the affinity to antigen. Interestingly, a subset of the GC undergoes massive expansion of higher-affinity B cell variants (clonal bursts), leading to a loss of clonal diversity at a significantly faster rate than in GCs that do not exhibit clonal dominance. Our work contributes towards an in silico vaccine design, and has implications for the better understanding of the mechanisms underlying autoimmune disease and GC-derived lymphomas.

Single cell BCR and transcriptome analysis after respiratory virus infection reveals spatiotemporal dynamics of antigen-specific B cell responses

“…Nevertheless, BCR from GC-derived Bmem and PB and even PreMem-C9 were indistinguishable by all the measures presented here. Our data suggests that stochasticity (Blink et al, 2005;Good-Jacobson and Shlomchik, 2010;Pelissier et al, 2020;Smith et al, 2000), might be one of the major determinants of Bmem differentiation from GC. Indeed, we identify clonal family as the main determinant of B cell affinity, a finding that would have been obviously impossible when using transgenic monoclonal mice.…”

Multiscale Modeling of Germinal Center Recapitulates the Temporal Transition From Memory B Cells to Plasma Cells Differentiation as Regulated by Antigen Affinity-Based Tfh Cell Help

“…An important insight from our model (simulations 2 and 4) is the observation that regulation of the BLIMP1 level through affinity-dependent but not constant CD40 signaling, results in the occurrence of a temporal transition from MBC to PC output during the GC reaction (19,45). In addition, simulation 2 showed that the LEDA theory (i.e., a mechanism in which the decision for output cell differentiation is solely based on asymmetric division but not on BLIMP1 level) does not exclude a temporal transition.…”

“…However, the work of Krautler and co-workers seems to support the conclusion that Tfh-cell acts via signals other than CD40. Moreover, the stochastic selection of low-affinity B cells has been proposed as yet another mechanism to produced MBCs (18,45,56) or PCs (56)(57)(58)(59).…”

What Will B Will B: Identifying Molecular Determinants of Diverse B-Cell Fate Decisions Through Systems Biology

“…These structures spatially organize and traffic B-cells, enabling interactions with antigen-presenting cells and T-cells ( De Silva and Klein, 2015 ; Mesin et al, 2016 ). Recently, these extra- and intercellular processes have been modeled through stochastic approaches ( Thomas et al, 2019 ; Pélissier et al, 2020 ). Integrating the molecular determinants of B-cell fate decision into models of B-cell fates within the germinal center will be informative for therapeutic targeting of B-cells ( Kepler and Perelson, 1993 ; Figge, 2005 ; Meyer-Hermann et al, 2012 ; Robert et al, 2017 ; Thomas et al, 2019 ; Pélissier et al, 2020 ; Verheijen et al, 2020 ).…”