Fold-change detection of NF-κB at target genes with different transcript outputs

Abstract

The transcription factor NF-{kappa}B promotes inflammatory and stress-responsive gene transcription across a range of cell types in response to the cytokine tumor necrosis factor- (TNF). Although NF-{kappa}B signaling exhibits significant variability across single cells, some target genes exhibit fold-change detection of NF-{kappa}B, which may buffer against stochastic variation in signaling molecules. However, this observation was made at target genes supporting high levels of TNF-inducible transcription. It is unknown if fold-change detection is maintained at NF-{kappa}B target genes with low levels of TNF-inducible transcription, for which stochastic promoter events may be more pronounced. Here we used a microfluidic cell-trapping device to measure how TNF-induced activation of NF-{kappa}B controls transcription in single Jurkat T cells at the promoters of integrated HIV and the endogenous cytokine gene IL6, which produce only a few transcripts per cell. We tracked TNF-stimulated NF-{kappa}B RelA nuclear translocation by live-cell imaging and then quantified transcript number by RNA FISH in the same cell. We found that TNF-induced transcription correlates with fold change in nuclear NF-{kappa}B with similar strength at low versus high abundance target genes. A computational model of TNF-NF-{kappa}B signaling, which implements fold-change detection from competition for binding to {kappa}B motifs, was sufficient to reproduce fold-change detection across the experimentally measured range of transcript outputs. Nevertheless, we found that gene-specific trends in transcriptional noise and levels of promoter-bound NF-{kappa}B predicted by the model were inconsistent with our experimental observations at low abundance gene targets. Our results reveal a gap in our understanding of RelA-mediated transcription for low abundance transcripts and suggest that cells use additional biological mechanisms to maintain robustness of NF-{kappa}B fold-change detection while tuning transcriptional output.