A multi‐scale approach reveals that NF‐κB cR el enforces a B‐cell decision to divide

Abstract

Understanding the functions of multi-cellular organs in terms of the molecular networks within each cell is an important step in the quest to predict phenotype from genotype. B-lymphocyte population dynamics, which are predictive of immune response and vaccine effectiveness, are determined by individual cells undergoing division or death seemingly stochastically. Based on tracking single-cell time-lapse trajectories of hundreds of B cells, single-cell transcriptome, and immunofluorescence analyses, we constructed an agent-based multi-modular computational model to simulate lymphocyte population dynamics in terms of the molecular networks that control NF-κB signaling, the cell cycle, and apoptosis. Combining modeling and experimentation, we found that NF-κB cRel enforces the execution of a cellular decision between mutually exclusive fates by promoting survival in growing cells. But as cRel deficiency causes growing B cells to die at similar rates to non-growing cells, our analysis reveals that the phenomenological decision model of wild-type cells is rooted in a biased race of cell fates. We show that a multi-scale modeling approach allows for the prediction of dynamic organ-level physiology in terms of intra-cellular molecular networks.

A Regulatory Circuit Controlling the Dynamics of NFκB cRel Transitions B Cells from Proliferation to Plasma Cell Differentiation

“…During the proliferative burst, some cells differentiated into ASCs, resulting in ASC population dynamics that are distinct but necessarily coordinated with the ABC population dynamics. In the Rel À/À simulation ( Figure 7A, right), the total population expansion was dramatically diminished (in agreement with experimental literature; Kö ntgen et al, 1995; Shokhirev et al, The multi-scale model is composed of the NFkB regulatory network (green box), the apoptosis gene regulatory network (gray box), the cell-cycle gene regulatory network (blue box) as published previously (Mitchell et al, 2018;Shokhirev et al, 2015), and the ASC differentiation circuit (violet and red box) added here. (B) Line plots indicate dynamics of NFkB, cPARP, Cdh1, and Blimp1 in 3 representative cells.…”

“…In ABCs, the NFkB dimers RelA:p50 and cRel:p50 are induced (Kaileh and Sen, 2012). Whereas cRel activity is required for cell survival, growth, and division during B cell activation (Pohl et al, 2002;Shokhirev et al, 2015), RelA is required for the generation of GC-derived PCs by contributing to Blimp1 activation (Heise et al, 2014). Thus, both cRel and RelA are indispensable for humoral immunity but for different functional reasons.…”

“…Mathematical modeling approaches have proven valuable to understanding complex dynamic molecular regulatory networks. ABC population expansion dynamics are well accounted for by a multi-scale model of the intracellular molecular network of NFkB regulating apoptosis and the cell cycle (Mitchell et al, 2018;Shokhirev et al, 2015), and this model proved useful in understanding the function of cRel in cell survival, growth, and division (Shokhirev et al, 2015). In the case of the ASC differentiation circuit, the scarcity of quantitative biochemical data first prompted logical models that can qualitatively recapitulate the state of regulatory networks in the terminal fates of B cells (Mé ndez and Mendoza, 2016) or a dynamical system of only three regulators (Martínez et al, 2012).…”

Polypyrimidine Tract Binding Protein blocks microRNA-­124 biogenesis to enforce its neuronal specific expression

“…Parameter sampling has been widely used to model cellular heterogeneity (Shokhirev et al 2015;; Yao et al 2016;; Mitchell and Hoffmann 2018). Simulations of 1000 individual cells were run for each model with each parameter sampled from a 4-fold range distribution centered on the optimal parameter identified by the original fitting to experimental data ( Figure 5D).…”

Image data in need of a home

“…As a consequence, a patchwork of heterogeneous solutions has been adopted. In some cases, authors have developed web resources hosted by their own institution (e.g., http://www.cellmorph.org, for Fuchs et al , , or http://www.signalingsystems.ucla.edu/code/max/ for Shokhirev et al , ). In other cases, datasets were deposited as “flat files” in a general repository for unstructured data, such as Dryad (see e.g., http://dx.doi.org/10.5061/dryad.r4n35 for Schmidt‐Glenewinkel & Barkai, ) or in journal‐specific resources such as the Journal of Cell Biology 's DataViewer (http://jcb-dataviewer.rupress.org/?view=hcs).…”