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. 2019 Feb 19;116(4):709-724.
doi: 10.1016/j.bpj.2019.01.011. Epub 2019 Jan 12.

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

Victor C Wong  1 , Shibin Mathew  2 , Ramesh Ramji  3 , Suzanne Gaudet  4 , Kathryn Miller-Jensen  5
Affiliations

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

Victor C Wong et al. Biophys J. .

Abstract

The transcription factor nuclear factor (NF)-κB promotes inflammatory and stress-responsive gene transcription across a range of cell types in response to the cytokine tumor necrosis factor (TNF). Although NF-κB signaling exhibits significant variability across single cells, some target genes supporting high levels of TNF-inducible transcription exhibit fold-change detection of NF-κB, which may buffer against stochastic variation in signaling molecules. It is unknown whether fold-change detection is maintained at NF-κB 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-κB 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-κB 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-κB. A computational model of TNF-NF-κB signaling, which implements fold-change detection from competition for binding to κB 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-κB fold-change detection.

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Figures

Figure 1
Figure 1
Optimization of a microfluidic-device-enabled protocol to connect signaling dynamics to smFISH measurements in single suspension cells. (A) A schematic of the live-cell-to-fixed-cell protocol. A cell suspension is pipetted into the inlet of a microfluidic device and flows into the channel containing traps that catch cells via gravity. Upon stimulation with a medium exchange, live cells are imaged in the traps for a desired amount of time. Then, cells are fixed and assayed by single-molecule RNA fluorescence in situ hybridization (smFISH). Data are then processed, and the live-cell and fixed-cell images are analyzed together for the same single cells. (B) Representative images of Jurkat J65c cells treated with 20 ng/mL TNF for 1 h, fixed in traps, and labeled for single NFKBIA mRNAs with carboxyfluorescein-conjugated probes (green). Nuclei stained with Hoechst (blue); scale bars, 5 μm. (C) A bar graph of mean NFKBIA transcript counts, as measured by smFISH for unstimulated cells and cells stimulated with 20 ng/mL TNF for 1 h measured in the passive-flow device (purple) and in a tissue culture dish (gray). Data are presented as the mean ± 95% CIs obtained by bootstrapping. (D) Violin plots of TNF-stimulated NFKBIA transcript distributions measured by smFISH at 1 h in the passive-flow device (purple) or tissue culture dish (gray). The median is indicated by the red line. There are no significant differences between the distributions (p > 0.05 by Kolmogorov-Smirnov (K-S) test). (E and F) A bar graph of mean HIV-GFP transcript count (E) and violin plots of HIV-GFP transcript distributions (F) measured by smFISH after 2 h of TNF stimulation. All other details are as in (C) and (D).
Figure 2
Figure 2
Fold-change detection of nuclear RelA is observed in Jurkat T cells at promoters exhibiting high levels of TNF-inducible transcription. (A) Time-lapse images of Ch-RelA paired with smFISH images of NFKBIA transcripts at 2 h after 20 ng/mL TNF treatment from the same cells. Scale bars, 5 μm. (B) Violin plots of transcript number distributions for NFKBIA (gray) and TNFAIP3 (purple) measured by smFISH before and 2 h after TNF stimulation in the device. The median is indicated by the red line. (C) Time-course traces of nuclear Ch-RelA from J65c cells collected in the passive-flow device after 20 ng/mL TNF treatment for two independent experiments. Data are presented as the mean ± SD of individual cell traces (n = 41, traces linked to NFKBIA, gray; n = 26, traces linked to TNFAIP3, purple). Lack of statistical significance of differences in dynamics was determined by comparing distributions of tmax and tduration of nuclear RelA peak intensity between the data sets (p > 0.05 by K-S test). AU, arbitrary units. (D) A schematic indicating features calculated from individual nuclear Ch-RelA time courses including initial, maximal, and final fluorescence (Finit, Fmax, and Ffinal); time at which Fmax is reached (tmax); and area under the curve (AUC). (E) A scatter plot of smFISH-measured transcript number (2 h post-TNF addition) versus the maximal fold change in nuclear Ch-RelA (Fmax/Finit) for NFKBIA (gray) and TNFAIP3 (purple). (F) A heat map of Pearson correlation coefficients (r) with smFISH-measured transcript number for all extracted metrics describing same-cell nuclear Ch-RelA time courses. Nonsignificant correlations (p > 0.05) are indicated by a dashed X.
Figure 3
Figure 3
Maximal fold change in nuclear RelA correlates with transcript output for gene promoters exhibiting low levels of TNF-inducible transcription. (A) A bar graph of mean smFISH-measured transcript counts of HIV 6.6 (blue), HIV 4.4 (yellow), and IL6 (green) before and 2 h after 20 ng/mL TNF stimulation. Data are presented as the mean ± 95% CIs obtained by bootstrapping. (B) Violin plots of transcript number distributions for HIV 6.6, HIV 4.4, and IL6 measured by smFISH, before and 2 h after TNF stimulation. The median is indicated by the red line. Statistical significance for differences in the medians of two distributions is indicated (∗P < 0.05; ∗∗∗∗P < 0.0001; Mann-Whitney U test). (C) Scatter plots of smFISH-measured transcript number (2 h post-TNF) for HIV 6.6 (blue), HIV 4.4 (yellow), and IL6 (green) versus maximal nuclear Ch-RelA abundance (Fmax; left), AUC (middle), and maximal fold change in nuclear Ch-RelA (Fmax/Finit; right). (D) A heat map of Pearson correlation coefficients (r) with smFISH-measured transcript numbers for all extracted metrics describing same-cell nuclear Ch-RelA time courses. Nonsignificant correlations (p > 0.05) indicated by a dashed X.
Figure 4
Figure 4
Decreasing transcriptional output by decreasing relative RelA binding maintains fold-change detection but is inconsistent with experimental RelA binding data. (A) A schematic of how the I1-FFL motif was implemented in a computational model of NF-κB RelA signaling. (B) A bar graph of mean transcriptional output simulated by the model (left) and measured experimentally by smFISH (right) before and 2 h after TNF stimulation. Simulations were run for k4 = 1 × k and the indicated k3 values, where k4 is the affinity of competitor for the competitor promoter and k and k3 are the respective affinities of nNFκB and competitor for the induced target promoter; cell-to-cell variability was modeled by sampling for total RelA-containing NF-κB (NF) and TNF-induced IKK activity (ka). Values that reproduced the experimentally observed transcript range are shaded in yellow. Error bars represent the ±95% CIs obtained by bootstrapping. Experimental data are similarly presented as the mean ± 95% CIs obtained by bootstrapping. (C) A bar graph of Pearson correlations between Fmax/Finit and transcript abundance 2 h after TNF stimulation for simulated results (left) and experimental data (right). Simulations were run as described in (B). Error bars again represent 95% CIs. (D) A bar graph of relative variance in transcript distributions post- versus pre-TNF treatment (t = 2 h, 20 ng/mL) for simulation results (left) and experimental data (right). Relative variance is defined as variance at t = 2 h over the variance at t = 0; error bars represent 95% CIs. (E) Time courses of simulated RelA binding (left) and measured enrichment of RelA (% input measured by ChIP; right) after TNF addition. Simulations were run as described in (B) (error bars for 95% CIs are smaller than the markers). ChIP data are presented as mean ± standard error of independent biological duplicate (HIV 4.4 and HIV 6.6) or triplicate (endogenous promoters). There are no significant differences in measured time courses (p > 0.05, calculated by two-way ANOVA).
Figure 5
Figure 5
Tuning multiple model parameters for each target promoter can reproduce experimentally observed behaviors. (A) A heat map of transcript output 2 h after TNF stimulation for a range of RelA target binding affinities (k3) and relative competitor abundance (k4) values (varied across small rectangles, subsampling our simulation results to multipliers 0.1000, 0.1848, 0.3414, 0.6307, 1.1652, 2.1528, and 5.4063) and target transcript synthesis (c1t) and degradation (c3t) rates (varied within small rectangles using the full set of simulations described in Materials and Methods). The heat map color scale is set to the experimentally observed range of transcript output for all transcripts combined. Areas in gray color scale are outside of the experimentally observed range. Simulations were done for a Hill coefficient of ht = 3.2. Small blue squares highlight areas further explored in Fig. S6. (B) Possible parameter sets for each target transcript are highlighted in color over the same k3-k4-c1t-c3t space as in (A) and for the indicated Hill coefficient (ht), if they reproduce experimentally observed baseline and TNF-induced transcript levels at 2 h and have a Pearson correlation coefficient of r > 0.6 for fold-change detection.
Figure 6
Figure 6
Parametrizations of an I1-FFL model of NF-κB-driven transcription can capture features of TNF-induced increases in HIV transcription as well as that of low-abundance endogenous genes. (A–D) Heat maps are shown of baseline (A) and post-TNF (t = 2 h; B) mean transcript number, relative variance (expressed as log10 (Vart = 2 h/Vart = 0 h) (C), and Pearson correlation of transcript number with fold change in nuclear RelA (r; D) for a range of target transcript transcription rates (c1t versus its default value c1td) and Hill coefficients (ht). Parametrizations with good fit for NFKBIA (gray), TNFAIP3 (lilac), HIV 6.6 (blue), HIV 4.4 (yellow), and IL6 (green) are outlined. All simulations were run using fixed values of k3 (0.3414 × k), k4 (1.1652 × k), and c3t (0.6307 × c3) chosen from a region that showed good fit to mean transcript numbers for HIV 6.6 and HIV 4.4 (Fig. 5B). Cell-to-cell variability was generated by sampling over ka and total RelA-containing NF-κB (parameter NF; Table S1) as described in Materials and Methods. (E) Two-dimensional scatter plots showing the relationship between target mRNA abundance and maximal fold change in nuclear NFκB (for t = 2 h) for model simulations with parametrizations chosen in (A)–(D) to recapitulate HIV 4.4 and HIV 6.6 data (top, yellow and blue, respectively) or simulations that show quantitatively and qualitatively different mRNAs versus maximal fold change distributions (bottom, gray). Selected parameter values are indicated (right); cell-to-cell variability was obtained by sampling total NFκB and ka values, and all other parameters were held at their default value in the model. (F) A schematic diagram showing some of the informative features of same-cell single-cell data measuring both an input and an output of a signaling pathway.
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