Cell cycle-coupled transcriptional network regulates human B cell fate bifurcation 2257411
Antibody response quality and durability depend on activated B cells bifurcating into plasmablast (PB) or germinal center B cell (GCBC) fates. PBs are short-lived and secrete low-affinity antibodies, whereas GCBCs undergo somatic hypermutation and selection before potentially generating long-lived plasma cells that produce high-affinity antibodies. The gene regulatory networks (GRNs) governing these trajectories in human B cells remain poorly defined. We profiled in vitro—activated human B cells using time-series single-cell multi-omics (RNA/ATAC-seq) and applied machine learning to predict linkages between transcription factors (TFs), cis-regulatory elements, and target genes, building B cell state-specific GRNs. Using these GRNs, we simulated TF perturbations and tested the predicted effects using CRISPR screening in primary B cells. Simulations and experiments converged with predictions of TF activity at single-nucleotide resolution, revealing dominant and reciprocal actions of IRF4 and partners at IRF-related motifs. Single-cell perturbation analysis uncovered a reciprocal negative feedback loop that modulated B cell fate choice, involving BATF, IRF4 and BLIMP1. Additionally, we found that IRF4 and BLIMP1 co-repressed the cell cycle regulator MYC before PB differentiation. G0 lengthening accelerated the switch to a IRF4hi/BLIMP1hi state and enhanced the probability of PB specification, thereby generating a self-reinforcing regulatory module that couples cell cycle dynamics to B cell fate choice. This work establishes a generalizable framework for assembling and testing GRNs in the context of immune cell fate decisions. Applying this approach to human B cells revealed previously unreported feedback loops that link cell division to fate specification. These findings have implications for strategies to modulate antibody responses in humans. Cancer Research Institute (#4185) ; NIAID (#5T32AI089443) Immune Response Regulation: Molecular Mechanisms (IRM)