NF-κB-Chromatin Interactions Drive Diverse Phenotypes by Modulating Transcriptional Noise

Wong, Vernon G.; Bass, Victor L.; Bullock, M. Elise; Chavali, Arvind K.; Lee, Robin; Mothes, Walther; Gaudet, Suzanne; Miller‐Jensen, Kathryn

doi:10.1016/j.celrep.2017.12.080

Cited by 45 publications

(126 citation statements)

References 63 publications

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“…NF-B is also a positive transcriptional regulator of human immunodeficiency virus-1 (HIV), which infects CD4+ T cells (11). We recently observed that TNF-activated NF-B deterministically controls activation of an HIV reporter, even for latent viruses with very low TNF-inducible transcription (12). Interestingly, we observed significant differences in TNF-induced recruitment of RNA polymerase II at the promoters of these low abundance HIV target genes in Jurkat cells as compared to the high abundance target NFKBIA (12).…”

Section: Introductionmentioning

confidence: 99%

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

Wong¹,

Ramji²,

Gaudet³

et al. 2018

Preprint

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The transcription factor NF-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-B signaling exhibits significant variability across single cells, some target genes exhibit fold-change detection of NF-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-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-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 transcription correlates with fold change in nuclear NF-κB with similar strength at low versus high abundance target genes. A computational model of TNF-NF-κB signaling, which implements fold-change detection from competition for binding to κB 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-κB 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-κB foldchange detection while tuning transcriptional output.

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Section: Introductionmentioning

confidence: 99%

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

Wong¹,

Ramji²,

Gaudet³

et al. 2018

Preprint

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“…MB-3 is an inhibitor of GCN5, a histone acetyl transferase (HAT) that activates global gene expression [41]. Even in such a seemingly well-defined case, it should nevertheless be remembered that a very complex relationship may lie between the biochemical action of a drug (HAT inhibition) and its biological effect on SGE [40].…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Numerous arguments suggest that SGE plays an important role in a wide range of biological processes ranging from bet hedging [36] to the fractional killing of cancer cells [4]. SGE is also involved in decision-making in viruses [39,38,40] and in prokaryotes [21,8], but its role in the differentiation ability of metazoan cells remains an open question. We aim at assessing whether SGE is involved in differentiation or not [20,18].…”

Section: Introductionmentioning

confidence: 99%

Drugs modulating stochastic gene expression affect the erythroid differentiation process

Guillemin

Duchesne

Crauste

et al. 2018

Preprint

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Background: To understand how a metazoan cell makes the decision to differentiate, we assessed the role of stochastic gene expression (SGE) during the erythroid differentiation process. Our hypothesis is that stochastic gene expression has a role in single-cell decision-making. In agreement with this hypothesis, we and others recently showed that SGE significantly increased during differentiation. However, evidence for the causative role of SGE is still lacking. Such demonstration would require being able to experimentally manipulate SGE levels and analyze the resulting impact of these variations on cell differentiation. Result: We identified three drugs that modulate SGE in primary erythroid progenitor cells. Artemisinin and Indomethacin simultaneously decreased SGE and reduced the amount of differentiated cells. Inversely, α-methylene-γ-butyrolactone-3 (MB-3) simultaneously increased the level of SGE and the amount of differentiated cells. We then used a dynamical modelling approach which confirmed that differentiation rates were indeed affected by the drug treatment. Conclusion: Using single-cell analysis and modeling tools, we provide experimental evidence that in a physiologically relevant cellular system, control of SGE can directly modify differentiation, supporting a causal link between the two.

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“…To compare against our application, we selected the software package FISH-quant (27) primarily because it implements a 3D Gaussian fitting method for detection of transcripts and it's actively used in the research community. For example, we had previously used FISH-quant to detect single mRNA molecules in the context of stochastic transcription events (28).…”

Section: Spot Detection In Dnemo Is Rapid and Accuratementioning

confidence: 99%

dNEMO: a tool for quantification of mRNA and punctate structures in time-lapse images of single cells

Kowalczyk

Cruz

Guo

et al. 2019

Preprint

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Many biological processes are regulated by single molecules and molecular assemblies within cells that are visible by microscopy as punctate features, often diffraction limited.Here we present detecting-NEMO (dNEMO), a computational tool optimized for accurate and rapid measurement of fluorescent puncta in fixed-cell and time-lapse images. The spot detection algorithm uses the à trous wavelet transform, a computationally inexpensive method that is robust to imaging noise. By combining automated with manual spot curation in the user interface, fluorescent puncta can be carefully selected and measured against their local background to extract high quality single-cell data. Integrated into the workflow are segmentation and spot-inspection tools that enable almost real-time interaction with images without time consuming pre-processing steps. Although the software is agnostic to the type of puncta imaged, we demonstrate dNEMO using smFISH to measure transcript numbers in single cells in addition to the transient formation of IKK/NEMO puncta from time-lapse images of cells exposed to inflammatory stimuli. INTRODUCTION:Quantitative imaging of single cells enables measurement with subcellular resolution of dynamic biological processes that regulate critical cellular behaviors. Processes of the central dogma such as active transcription, single mRNA transcripts, sites of active protein translation, and other regulatory multi-protein assemblies can be observed by fluorescence microscopy as punctate structures within the cell (1-6). Spatiotemporal dynamics of signaling proteins, quantified in single cells by live-cell imaging, have revealed mechanistic insights into signal-response relationships in signal transduction networks in addition to sources of cell-to-cell variability (7-9). However, most live-cell imaging approaches use fluorescent biosensors and fusion proteins that report within large subcellular compartments, such as the cytoplasm or nucleus, and quantification of punctate structures is often limited to fixed-cell and low-throughput applications. Accurate detection and quantification of biological puncta is necessary to examine their roles in regulating cellular behaviors, and computational analysis is often the rate-limiting step of experimental pipelines.The nuclear factor (NF)-κB signal transduction pathway is a master regulator of inflammatory responses to injury and infection (10). Following activation of the pathway by inflammatory cytokines, such as interleukin-1 (IL-1) or tumor necrosis factor (TNF) among others, a series of intracellular signaling events transduce the NF-κB signal.Upstream kinase activation by the NF-κB essential modulator protein (NEMO, also known as IKKɣ) is a necessary step in regulation of the classical NF-κB signaling cascade (11).Following cytokine stimulation, NEMO is recruited to polyubiquitin scaffolds associated with cytokine-ligated receptor complexes where NEMO-interacting IκB kinases (IKKs) are

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NF-κB-Chromatin Interactions Drive Diverse Phenotypes by Modulating Transcriptional Noise

Cited by 45 publications

References 63 publications

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

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

Drugs modulating stochastic gene expression affect the erythroid differentiation process

dNEMO: a tool for quantification of mRNA and punctate structures in time-lapse images of single cells

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