Fold-Change Detection of NF-κB at Target Genes with Different Transcript Outputs

Abstract

The transcription factor nuclear factor (NF)-kB promotes inflammatory and stress-responsive gene transcription across a range of cell types in response to the cytokine tumor necrosis factor (TNF). Although NF-kB signaling exhibits significant variability across single cells, some target genes supporting high levels of TNF-inducible transcription exhibit fold-change detection of NF-kB, which may buffer against stochastic variation in signaling molecules. It is unknown whether fold-change detection is maintained at NF-kB 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-kB 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-kB RelA nuclear translocation by live-cell imaging and then quantified transcript number by RNA FISH in the same cell. We found that TNF-induced transcript abundance at 2 h for low-and high-abundance target genes correlates with similar strength with the fold change in nuclear NF-kB. A computational model of TNF-NF-kB signaling, which implements fold-change detection from competition for binding to kB motifs, could reproduce fold-change detection across the experimentally measured range of transcript outputs. However, multiple model parameters affecting transcription had to be simultaneously varied across promoters to maintain fold-change detection while also matching other trends in the single-cell data for low-abundance transcripts. Our results suggest that cells use multiple biological mechanisms to tune transcriptional output while maintaining robustness of NF-kB fold-change detection.

Gene-Specific Linear Trends Constrain Transcriptional Variability of the Toll-like Receptor Signaling

“…A formal noise decomposition of the TNF-a and IL-1b dose-response count data (Rhee et al, 2014) showed a dominant contribution from the intrinsic noise with an extrinsic noise component (Figure S11B). The latter is consistent with the extrinsic variability due to shared TLR signaling machinery, for example, signaling dynamics (Muldoon et al, 2020;Wong et al, 2018Wong et al, , 2019.…”

“…(ii) The percentage of variance explained by regressing TNFa against IL1b counts is <20%, but~41% for IL1a against IL1b (Figure S21). The former is consistent with extrinsic variability due to shared TLR signalling machinery, for example signalling dynamics (Wong et al, 2018(Wong et al, , 2019, while the latter highlight the shared chromatin regulatory step. (3) Currently, our smFISH datasets include between 10 2 and 10 3 individual cells per conditions (up to 18 conditions per probe) and up to 96 cells in scRNA-seq.…”

“…In the TLR system, the latter is likely related to the levels of upstream TFs or their activation patterns. For example, although heterogeneous, the NF-kB system activation exhibits dose-dependent and temporal regulation (Adamson et al, 2016;Bagnall et al, 2018;Muldoon et al, 2020;Selimkhanov et al, 2014;Sung et al, 2014;Wong et al, 2019), which might enable the fine-tuning of the underlying transcription and gene activation rates. It would be intriguing to understand whether gene-specific trends are sensitive to therapeutic compounds, or, in fact, pathogen stimulation.…”

Techniques for Studying Decoding of Single Cell Dynamics

“…Subsequent mathematical modeling revealed a potential mechanism based on an incoherent feed-forward loop generated by transcription factor competition (61). Recent work, also using a smFISH-based strategy, further showed that fold change of NF-κB was an accurate predictor of transcript levels at promoters with both high and low levels of TNF-induced transcription (62).…”

Systems approaches to investigate the role of NF-κB signaling in aging

“…To determine whether fold-change detection in localization of NF-κB is universally implemented in NF-κB target genes, Wong et al [ 176 ] used an I1-FFL model and single-cell live imaging to show that even genes with low levels of TNF-induced transcription induced by NF-κB are responsive to the fold-change in nuclear NF-κB [ 178 ]. Using live cell imaging, the authors tracked nuclear NF-κB after TNF-α stimulation and quantified the number of transcripts by RNA fluorescence in situ hybridization in the same cells.…”