. 2018 Jan 16;22(3):585-599.
NF-κB-Chromatin Interactions Drive Diverse Phenotypes by Modulating Transcriptional Noise
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NF-κB-Chromatin Interactions Drive Diverse Phenotypes by Modulating Transcriptional Noise
Free PMC article
Noisy gene expression generates diverse phenotypes, but little is known about mechanisms that modulate noise. Combining experiments and modeling, we studied how tumor necrosis factor (TNF) initiates noisy expression of latent HIV via the transcription factor nuclear factor κB (NF-κB) and how the HIV genomic integration site modulates noise to generate divergent (low-versus-high) phenotypes of viral activation. We show that TNF-induced transcriptional noise varies more than mean transcript number and that amplification of this noise explains low-versus-high viral activation. For a given integration site, live-cell imaging shows that NF-κB activation correlates with viral activation, but across integration sites, NF-κB activation cannot account for differences in transcriptional noise and phenotypes. Instead, differences in transcriptional noise are associated with differences in chromatin state and RNA polymerase II regulation. We conclude that, whereas NF-κB regulates transcript abundance in each cell, the chromatin environment modulates noise in the population to support diverse HIV activation in response to TNF.
HIV latency; NF-κB signaling; chromatin; gene expression noise; transcriptional bursting.
Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.
Conflict of interest statement
DECLARATION OF INTERESTS
The authors declare that they have no conflicts of interest.
Figure 1. TNF Induces Diverse Viral Activation Phenotypes Associated with Different Chromatin Environments
(A) Diagrams of HIV-LTR integration types (left) and potential sources of regulation of HIV-LTR activation investigated in this study (right). Integrations can be latent (silent with or without TNF), active (expressing even with no TNF), or latent but TNF inducible (focus of this study, with a spectrum of low to high activating phenotypes with TNF). Latent-but-TNF-inducible HIV-LTR activation may be modulated by chromatin state, NF-kB signaling, RNPII regulation, and transcription noise, which itself may be amplified by Tat positive feedback. We investigate both early signaling and transcription response (<4 hr after TNF addition) and resulting phenotypic outcomes (12–24 hr) as measured by onset time, maximal expression, and fraction of activating cells. (B) Histograms of HIV transcript number per cell in the basal state (no TNF, no CHX), as measured by smFISH for indicated clonal single-HIV-integrant Jurkat populations. J-Lat 8.4 basal transcript distribution is significantly different from the others (p < 0.05 by Kolmogorov-Smirnov [K-S] test); all others are similar [p > 0.05]; n = 63–210 cells per clone). (C) Bar graph of mean number of HIV transcripts from distributions in (B). Error bars represent 95% confidence intervals (CIs) of the basal mean (bootstrapping for combined smFISH experiments). Independent replicates quantifying basal J65c transcription distributions were not significantly different (Figure S1E). (D) Single-cell time courses of HIV-GFP signal after TNF addition (no CHX), measured by fluorescence time-lapse imaging. Cells with measurable activation are shown from ~40 to 80 cells imaged per clone (J65c 6.6, n = 54 cells; J65c 4.4, n = 25 cells; J-Lat 8.4, n = 16 cells; and J-Lat 10.6, n = 34 cells). (E and F) Bar graphs of mean HIV-GFP onset time (E) and maximum GFP fluorescence intensity (F) by 12 hr after TNF addition (no CHX) for LA (J-Lat 8.4 and J65c 4.4, orange) and HA (J65c 6.6 and J-Lat 10.6, blue) clones. Error bars represent bootstrapped 95% CIs. (G) Bar graphs of the percentage of HIV-GFP+ cells 24 hr after TNF addition (no CHX) for LA (orange) and HA (blue) clones. We show mean ± SD of independent biological triplicates. (H and I) Bar graphs of basal enrichment (% input by ChIP) of histone H3 (H) and AcH3 (I) at the LTR and GAPDH for LA (orange) and HA (blue) clones. Data from cells pretreated with 160 ng/mL CHX for 1 hr (mean ± SD of independent biological duplicate). *p < 0.05, **p < 0.005 by ANOVA. No significant differences in H3 and AcH3 were measured for GAPDH across clones. (J) Time courses of enrichment (percent input by ChIP) of RelA at the LTR for LA J65c 4.4 (orange) and HAJ65c 6.6 (blue) after TNF addition. We show mean ± SD of independent biological duplicates; we found no significant differences in time course as calculated by two-way ANOVA. See also Figure S1.
Figure 2. HIV Transcription Increases to a Similar Mean Level but with Distinct Noise in LA versus HA Clones after TNF Treatment without Tat Positive Feedback
(A) Representative maximum intensity projection images of an HIV-GFP smFISH fluorescence z stack in LA J65c 4.4 (left) or HA J65c 6.6 (right) cells before (top) and 2 hr after (bottom) 20 ng/mL TNF treatment in the presence of CHX. Blue dashed lines outline cells. Initially GFP+ cells were excluded from additional analyses. Scale bar, 5 μm. (B) Histogram of HIV-GFP transcript number per cell before (gray) and after (red) a 2-hr (J65c 4.4, J65c 6.6 and J-Lat 10.6) or 4-hr (J-Lat 8.4) TNF treatment with 160 ng/mL CHX. Each distribution has 30 bins; n = 196 - 650 cells per condition. (C–F) Bar graphs of fraction of cells with at least 3 transcripts (C), mean transcript numbers per cell (D), CVs (E), and Fano factors (F) before and after a 2-hr or 4-hr 20 ng/mL TNF treatment with 160 ng/mL CHX for LA clones (J-Lat 8.4 and J65c 4.4; orange) and HA clones (J-Lat 10.6 and J65c 6.6; blue). Error bars represent bootstrapped 95% CIs, combining the data from independent smFISH experiments (n > 196 cells for each condition) (Supplemental Experimental Procedures). The difference between two moments was inferred to be significant (p < 0.05) if the 95% CIs did not overlap. See also Figure S2.
Figure 3. Two-State Model Fits Are Consistent with TNF Treatment Increasing Transcription Burst Frequency in LA Clones and Burst Size in HA Clones, Accounting for Differences in Viral Activation
(A) Schema of the two-state promoter model (black) and transcription amplification by Tat positive feedback (gray). Burst frequency (
k) and burst size ( a b = k were fit based on measured transcript number distributions; Tat positive feedback was simulated computationally. (B and C) Bar graphs of burst frequency (B) and burst size (C) parameter fits for transcript number distributions measured before and after 20 ng/mL TNF treatment (with 160 ng/mL CHX; no Tat feedback) for LA (J-Lat 8.4 and J65c 4.4; orange) and HA clones (J-Lat 10.6 and J65c 6.6; blue). Error bars show bootstrapped 95% CIs. Differences between parameters were inferred to be significant (p < 0.05) if 95% CIs did not overlap. (D) Simulations of Tat protein abundance (model equivalent of measured HIV-GFP) after TNF treatment of HA and LA clones when amplified by Tat positive feedback. For each clone, basal steady state was simulated with burst sizes and burst frequency values similar to fitted values derived from experimental data. To simulate TNF treatment, at time m/k i) t = 0, burst frequency (LA clones, left) or burst size (HA clones, right) was increased to the fitted value, whereas all other parameters remained the same. Tat protein levels were simulated for n = 500 individual cells for 12 hr (Supplemental Experimental Procedures). See also Figures S3 and S4.
Figure 4. RelA Signaling Accounts for Cell-to-Cell Variability in Viral Activation for Both LA and HA Clones
(A) Time-lapse images Ch-RelA (top) and HIV-GFP (bottom) in HA J65c 6.6 cells after treatment with 20 ng/mL TNF (no CHX). Cells with strong (arrows) and weak (asterisks) nuclear translocation of Ch-RelA are indicated. Scale bar, 10 μm. (B) Single-cell time courses of nuclear Ch-RelA intensity for LA J65c 4.4 (top) and HA J65c 6.6 (bottom) cells treated with 20 ng/mL TNF (no CHX). Time courses for HIV-GFP signal from the same cells are in Figure 1D (n = 68 cells [HA J65c 6.6] and n = 77 cells [LA J65c 4.4]). (C) Time courses for pairs of LA J65c 4.4 (orange) and HA J65c 6.6 (blue) cells selected for similar Ch-RelA nuclear translocation (left) and their respective viral activation (right). (D) Scatterplots of maximum nuclear RelA (
F; top) and maximal nuclear RelA fold change max (F; bottom) versus HIV-GFP area under the curve (AUC) for LA J65c 4.4 (left) and HA J65c 6.6 (right). Spearman correlation coefficients (r max/F i s) and their p values are reported. (E) Bar graph of Spearman correlation of each nuclear Ch-RelA dynamic feature (defined in Figure S5C) with HIV-GFPAUC (defined in Figure S5F) for HA J65c 6.6 (blue) and LA J65c 4.4 (orange). Error bars show bootstrapped 95% CIs; *p < 0.001. See also Figure S5.
Figure 5. RelA and Chromatin Linked to Differential Regulation of RNPII Activity at the HIV LTR in LA versus HA Clones
(A–C) Plots showing enrichment (% input by ChIP) of histone H3 (A) and AcH3 (B) at the LTR for HA J65c 6.6 (blue) and LA J65c 4.4 (orange) in the basal state (0 hr) and after treatment (2 and 4 hr) with 20 ng/mL TNF with 160 ng/mL CHX. Ratio of AcH3:H3 (C) was calculated as ratio of % inputs. All show mean ± SD of independent biological duplicates. (D–G) Enrichment of RNPII (D), ser5-p RNPII (E), ser2-p RNPII (F), and (G) NELF-E for HA J65c 6.6 (blue) and LA J65c 4.4 (orange) as quantified by ChIP. The same conditions as in (A)-(C) were used, and data represent mean ± SD of independent biological duplicates (or triplicate for RNPII). For (A)–(G), statistical significance between the HA J65c 6.6 and LA J65c 4.4 time courses are indicated (two-way ANOVA; *p < 0.05; **p < 0.005; ***p < 0.0001; n.s., not significant). The change between 0 hr and 4 hr was statistically significant for all HA J65c 6.6 time courses and for H3, AcH3, and the AcH3:H3 ratio LA J65c 4.4 time courses. (H) Diagram of our working model of molecular events regulating transcriptional and phenotypic outcomes at latent-but-TNF-inducible HIV LTRs (gray, literature inferred; black, supported by our data). At HA LTRs (bottom), high AcH3 levels are linked to efficient RelA-mediated recruitment of initiating RNPII (ser5-p) and NELF in addition to elongating RNPII (ser2-p), yielding increased transcription burst size and high activation. At LA LTRs (top) with lower AcH3 levels, RelA- mediated histone acetyl transferase (HAT) binding and chromatin remodeling must occur before efficient recruitment of initiating RNPII (ser5-p) and only burst frequency is increased, yielding low activation. HDAC is recruited to HIV-LTRs by NF-κB p50:p50, and TSA and SAHA are two HDAC inhibitors used to increase AcH3 at HIV-LTRs. See also Figure S6.
Figure 6. TSA Pretreatment Increases Acetylation at the LA J65c 4.4 LTR and Changes Its Transcriptional and Activation Response to One Similar to that of the HA J65 6.6 LTR
(A) Bar graph showing basal state enrichment (% input by ChIP) of histone H3 (left), AcH3 (center), and their ratio (right) at the LTR for LA J65c 4.4 treated with 400 nM TSA (with 160 ng/mL CHX) for 4 hr (green bars), compared to basal state results for untreated LA J65c 4.4 (orange) and HA J65c 6.6 (blue) cells replotted from Figures 1H and 1I. Data represent means ± SD of independent biological duplicates. (B) Bar graphs of mean mRNA per cell (left), burst size (middle), and burst frequency (right) for TSA-pretreated LA J65c 4.4 (green bars) before and after 2- or 4-hr treatment with 20 ng/mL TNF (with CHX), compared to no-TSA data for LA J65c 4.4 (orange dots) and HA J65c 6.6 (blue dots) replotted from Figures 2D, 3B, and 3C; error bars represent bootstrapped 95% CIs. (C–E) Plots of ChIP-quantified enrichment of RNPII (C), ser5-p RNPII (D), and ser2-p RNPII (E) for LAJ65c 4.4 + TSA (green) before and after 2- or 4-hr treatment with 20 ng/mL TNF (with CHX). For comparison, no-TSA data for LA J65c 4.4 (orange) and HA J65c 6.6 (blue) cells are replotted from Figures 5D–5F; all shown as mean ± SD of biological duplicates. HA J65c 6.6 and LA J65c 4.4 + TSA are not statistically significantly different (two-way ANOVA, p > 0.05). (F and I) Schema of the timeline of pretreatment for 400 nM TSA (F) and 20 ng/mL TNF (I). (G and J) Plots of percentage of GFP+ cells for LA J65c 4.4 and HA J65c 6.6 measured by flow cytometry after 0, 12, and 24 hr of 20 ng/mL TNF treatment (no CHX), after indicated TSA (G) or TNF pretreatment (J). Means ± SD of independent biological duplicates are shown. Statistical significance between each pair of pretreated versus non-pretreated condition time courses is indicated (two-way ANOVA; *p < 0.05; **p < 0.01; ***p < 0.001; n.s., not significant). (H and K) Plot of activation synergy from indicated TSA (H) or TNF pretreatment (K) quantified by the Bliss independence model for LA J65c 4.4 (orange markers) and HA J65c 6.6 (blue markers). Data represent mean ± SD of independent biological duplicates. See also Figure S7.
Figure 7. Latent-but-Inducible HIV LTRs Occupy Genomic Locations with Noisy Basal Transcription and Divergent TNF Responses
(A) Log-log graph of mean versus noise (CV
2) of basal mRNA distributions for latent LTRs from this study (blue and orange) and LTRs measured in Dey et al. (2015) (gray). Linear regression (R 2) is reported (p < 0.001). (B) Schema showing theoretical lines of Poisson and bursty transcription across the genome for log-log mean versus noise (CV 2) plots. Increasing transcription can occur by increasing burst size (moves to a new trend line) or frequency (moves along the same trend line). (C) Log-log graph of mean versus noise (CV 2) showing shifts in transcript distribution from before (filled circles) to after (open circles) a 2-hr 20 ng/mL TNF treatment (with CHX) for LA clones (orange; along the same trend line) and HA clones or TSA-pretreated LA J65c 4.4 (blue; shift to a new burst size trend line).
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Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Promoter Regions, Genetic / genetics*
Transcriptional Activation / genetics*