Orthogonal control of expression mean and variance by epigenetic features at different genomic loci

Abstract

While gene expression noise has been shown to drive dramatic phenotypic variations, the molecular basis for this variability in mammalian systems is not well understood. Gene expression has been shown to be regulated by promoter architecture and the associated chromatin environment. However, the exact contribution of these two factors in regulating expression noise has not been explored. Using a dual-reporter lentiviral model system, we deconvolved the influence of the promoter sequence to systematically study the contribution of the chromatin environment at different genomic locations in regulating expression noise. By integrating a large-scale analysis to quantify mRNA levels by smFISH and protein levels by flow cytometry in single cells, we found that mean expression and noise are uncorrelated across genomic locations. Furthermore, we showed that this independence could be explained by the orthogonal control of mean expression by the transcript burst size and noise by the burst frequency. Finally, we showed that genomic locations displaying higher expression noise are associated with more repressed chromatin, thereby indicating the contribution of the chromatin environment in regulating expression noise.

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

“…In the two-state model of promoter activity (Figure 3A, black), activation of transcription occurs by increasing either burst frequency (rate of transition to active state) or burst size (mean number of transcripts produced per burst in active state). We fit transcript number distributions for LA and HA clones pre- and post-TNF addition to the predicted transcript number probability density function of the two-state model (Dey et al, 2015; Raj et al, 2006). We found that the two-state model closely fit all distributions (Figure S3).…”

“…We compared our basal (no TNF) smFISH data for latent but inducible HIV integrations to smFISH transcript data collected for HIV LTR integrations with basal transcription in the absence of Tat (Dey et al, 2015) and analyzed the RNA mean versus noise relationship (noise defined as relative variance or CV 2 ; Figure 7A). Our latent LTRs had much lower transcription than these LTRs, yet they still fell along the same inverse power-law noise-versus-mean trend line.…”

“…Information about how perturbations affect burst size and frequency can be inferred from the noise-versus-mean plots (Dar et al, 2012; Dey et al, 2015; Box 1). Increasing transcription solely by increasing burst frequency results in a shift along the negatively sloped line of constant burst size and decreasing noise.…”

“…Previous studies have used HIV LTRs integrated throughout the genome to assess how genomic location alters transcriptional bursting (Dar et al, 2012, 2016; Dey et al, 2015; Singh et al, 2010; Skupsky et al, 2010). These studies examined HIV integrations supporting moderate-to-high basal activity (5 to ≥40 average mRNAs) in the absence of stimulation or Tat feedback.…”

“…Evidence supports roles for nucleosome positioning and occupancy (Dey et al, 2015; Raser and O’Shea, 2004), chromatin modifications (Suter et al, 2011; Viñuelas et al, 2013; Weinberger et al, 2012), and transcription factor concentration (Octavio et al, 2009; Senecal et al, 2014), but how these factors interact to regulate bursting and alter phenotypes remains unclear.…”