Transcription factors show rapid and reversible binding to chromatin in living cells, and transcription occurs in sporadic bursts, but how these phenomena are related is unknown. Using a combination of in vitro and in vivo single‐molecule imaging approaches, we directly correlated binding of the Gal4 transcription factor with the transcriptional bursting kinetics of the Gal4 target genes GAL3 and GAL10 in living yeast cells. We find that Gal4 dwell time sets the transcriptional burst size. Gal4 dwell time depends on the affinity of the binding site and is reduced by orders of magnitude by nucleosomes. Using a novel imaging platform called orbital tracking, we simultaneously tracked transcription factor binding and transcription at one locus, revealing the timing and correlation between Gal4 binding and transcription. Collectively, our data support a model in which multiple RNA polymerases initiate transcription during one burst as long as the transcription factor is bound to DNA, and bursts terminate upon transcription factor dissociation.
Summary Despite the widespread use of glucocorticoids (GCs), their anti-inflammatory effects are not understood mechanistically. Numerous investigations have examined the effects of glucocorticoid receptor (GR) activation prior to inflammatory challenges. However, clinical situations are emulated by a GC intervention initiated in the midst of rampant inflammatory responses. To characterize the effects of a late GC treatment, we profiled macrophage transcriptional and chromatinscapes with Dexamethasone (Dex) treatment before or after stimulation by lipopolysaccharide (LPS). The late activation of GR had a similar gene expression profile as from GR pre-activation, while ameliorating the disruption of metabolic genes. Chromatin occupancy of GR was not predictive of Dex-regulated gene expression, contradicting the ‘trans-repression by tethering’ model. Rather, GR activation resulted in genome-wide blockade of NF-κB interaction with chromatin and directly induced inhibitors of NF-κB and AP-1. Our investigation using GC treatments with clinically relevant timing highlights mechanisms underlying GR actions for modulating the ‘inflamed epigenome’.
Transcriptional rates are often estimated by fitting the distribution of mature mRNA numbers measured using smFISH (single molecule fluorescence in situ hybridization) with the distribution predicted by the telegraph model of gene expression, which defines two promoter states of activity and inactivity. However, fluctuations in mature mRNA numbers are strongly affected by processes downstream of transcription. In addition, the telegraph model assumes one gene copy, but in experiments cells may have two gene copies as cells replicate their genome during the cell cycle. Whilst it is often presumed that post-transcriptional noise and gene copy number variation affect transcriptional parameter estimation, the size of the error introduced remains unclear. To address this issue, here we measure both mature and nascent mRNA distributions of GAL10 in yeast cells using smFISH and classify each cell according to its cell cycle phase. We infer transcriptional parameters from mature and nascent mRNA distributions, with and without accounting for cell cycle phase and compare the results to live-cell transcription measurements of the same gene. We find that: (i) correcting for cell cycle dynamics decreases the promoter switching rates and the initiation rate, and increases the fraction of time spent in the active state, as well as the burst size; (ii) additional correction for post-transcriptional noise leads to further increases in the burst size and to a large reduction in the errors in parameter estimation. Furthermore, we outline how to correctly adjust for measurement noise in smFISH due to uncertainty in transcription site localisation when introns cannot be labelled. Simulations with parameters estimated from nascent smFISH data, which is corrected for cell cycle phases and measurement noise, leads to autocorrelation functions that agree with those obtained from live-cell imaging.
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