Leveraging Cell-To-Cell Variability to Understand Signal Transduction Networks

Abstract

Secreted ligands such as tumor necrosis factor (TNF) regulate cell behavior by triggering series of intracellular signaling events. One striking aspect of the response to many ligands is its quantitative, or sometimes qualitative, cell-to-cell variability. We are leveraging the cell-to-cell variability in the response of cancer cells to TNF to better understand the system properties of the regulation of transcription by NF-κB.In response to TNF, intracellular signals promote relocalisation of NF-κB transcription factors from the cytoplasm to the nucleus where they promote transcription of inflammatory and stress-responsive genes. Because dysregulation of NF-κB is associated with chronic inflammatory diseases, autoimmunity and cancer, one might expect the nuclear abundance of NF-κB to be tightly regulated. Instead, the amount of nuclear NF-κB varies considerably from cell to cell, even in the absence of stimulus. To resolve this paradox and determine how transcription-inducing signals are encoded, we quantified single-cell NF-κB translocation dynamics and transcriptional responses in the same cells. We found that TNF-induced transcription correlates best with fold-change in nuclear NF-κB, not absolute nuclear NF-κB abundance. This fold-change detection property suggests that the system encodes memory of its pre-ligand state. To complement our experimental approaches we use computational modeling and have found that an incoherent feed-forward loop, from competition for binding to κB motifs, can provide the required memory. A model with competition recapitulates the distinct patterns of transcription we observed experimentally for different NF-κB-dependent genes. Fold-change detection buffers against stochastic variation in signaling molecules and explains how cells tolerate variability in NF-κB abundance and localization. Overall, our approaches provide a framework for understanding how transcriptional networks interpret and act on dynamical signals in ligand-induced cellular responses.