Quantitative modeling of the terminal differentiation of B cells and mechanisms of lymphomagenesis

Abstract

Mature B-cell exit from germinal centers is controlled by a transcriptional regulatory module that integrates antigen and T-cell signals and, ultimately, leads to terminal differentiation into memory B cells or plasma cells. Despite a compact structure, the module dynamics are highly complex because of the presence of several feedback loops and self-regulatory interactions, and understanding its dysregulation, frequently associated with lymphomagenesis, requires robust dynamical modeling techniques. We present a quantitative kinetic model of three key gene regulators, BCL6, IRF4, and BLIMP, and use gene expression profile data from mature human B cells to determine appropriate model parameters. The model predicts the existence of two different hysteresis cycles that direct B cells through an irreversible transition toward a differentiated cellular state. By synthetically perturbing the interactions in this network, we can elucidate known mechanisms of lymphomagenesis and suggest candidate tumorigenic alterations, indicating that the model is a valuable quantitative tool to simulate B-cell exit from the germinal center under a variety of physiological and pathological conditions.

Fig. 1.

Time-dependent regulatory network of GC B cells. (A) Regulatory network at the centroblast stage. Upstream signals promote the expression of BCL6, a potent transcriptional repressor that controls the regulatory program of the GC. BCL6 directly represses BLIMP1, a key regulator necessary for plasma cell establishment. (B) At the centrocyte stage, the B cells compete for survival signals delivered by the BCRs and T cells, which lead to degradation of BCL6 protein and up-regulation of IRF4. (C) In the plasma cell stage, BLIMP1 and IRF4 are expressed and contribute to the transcriptional silencing of BCL6. The cell is locked in this terminally differentiated stage by a self-positive regulatory loop on IRF4.

Fig. 2.

Hysteretic behavior of IRF4 for different values of β. The blue line shows the evolutionary steady states reached by a cell stimulated through the CD40 pathway. (A and B) After the signaling process is over, the cell reverts back to the initial GC steady state. (C) Above a critical ratio of production and degradation of IRF4, the GC state is no longer accessible and the GC B cells differentiate into a plasma cell. Therefore, the differentiation process has become a terminal, irreversible event.

Fig. 3.

Irreversibility due to cosignaling of BCR and CD40. A and B show the stationary points of a B-cell at different levels of BCR stimulation. Blue and red dotted lines indicate stable and unstable stationary points respectively. C and D show the signaling intensity through the BCR (C) and the BCR and CD40 (D) pathways as a function of time. (A) After BCR signaling, BCL6 protein is degraded (arrows a–d) but after the cessation of the signal, returns to levels prior to stimulation. (B) Coordinated BCR and CD40 stimulation promotes a jump to a different branch of stationary points leading to an irreversible plasma cell phenotype.

Fig. 4.

(A) Schematic representation of the most common genetic alterations in DLBCL. (B–F) Simulations of the different cancer models mimicking the genetic alterations in DLBCL. M1, loss of BCL6 auto-regulation; M2, constitutive high expression of BCL6; M3, synergistic loss of IRF4 and BLIMP1-mediated BCL6 silencing; M7, reduced BLIMP1 protein stability; M8, NF-κB constitutive signaling. Thick lines show protein levels in the cancer models, whereas thin lines show protein levels in the normal GC exit pathway model for comparison.