Dynamic epistasis analysis reveals how chromatin remodeling regulates transcriptional bursting

Transcriptional bursting has been linked to the stochastic positioning of nucleosomes. However, how bursting is regulated by remodeling of promoter nucleosomes is unknown. Here, we use single-molecule live-cell imaging of GAL10 transcription in budding yeast to measure how transcriptional bursting changes upon single and double perturbations of chromatin remodeling factors, the transcription factor Gal4 and preinitiation complex (PIC) components. Using dynamic epistasis analysis, we reveal how remodeling of different nucleosomes regulates individual transcriptional bursting parameters. At the nucleosome covering the Gal4 binding sites, RSC acts synergistically with Gal4 binding to facilitate each burst. Conversely, nucleosome remodeling at the TATA box controls only the first burst upon galactose induction. In the absence of remodelers, nucleosomes at canonical TATA boxes are displaced by TBP binding to allow for transcription activation. Overall, our results reveal how promoter nucleosome remodeling, together with transcription factor and PIC binding regulates the kinetics of transcriptional bursting.

Kinetic characteristics of transcriptional bursting in a complex gene model with cyclic promoter structure

While transcription occurs often in a bursty manner, various possible regulations can lead to complex promoter patterns such as promoter cycles, giving rise to an important issue: How do promoter kinetics shape transcriptional bursting kinetics? Here we introduce and analyze a general model of the promoter cycle consisting of multi-OFF states and multi-ON states, focusing on the effects of multi-ON mechanisms on transcriptional bursting kinetics. The derived analytical results indicate that bust size follows a mixed geometric distribution rather than a single geometric distribution assumed in previous studies, and ON and OFF times obey their own mixed exponential distributions. In addition, we find that the multi-ON mechanism can lead to bimodal burst-size distribution, antagonistic timing of ON and OFF, and diverse burst frequencies, each further contributing to cell-to-cell variability in the mRNA expression level. These results not only reveal essential features of transcriptional bursting kinetics patterns shaped by multi-state mechanisms but also can be used to the inferences of transcriptional bursting kinetics and promoter structure based on experimental data.

Reverse engineering of a mechanistic model of gene expression using metastability and temporal dynamics

Differentiation can be modeled at the single cell level as a stochastic process resulting from the dynamical functioning of an underlying Gene Regulatory Network (GRN), driving stem or progenitor cells to one or many differentiated cell types. Metastability seems inherent to differentiation process as a consequence of the limited number of cell types. Moreover, mRNA is known to be generally produced by bursts, which can give rise to highly variable non-Gaussian behavior, making the estimation of a GRN from transcriptional profiles challenging. In this article, we present CARDAMOM (Cell type Analysis from scRna-seq Data achieved from a Mixture MOdel), a new algorithm for inferring a GRN from timestamped scRNA-seq data, which crucially exploits these notions of metastability and transcriptional bursting. We show that such inference can be seen as the successive resolution of as many regression problem as timepoints, after a preliminary clustering of the whole set of cells with regards to their associated bursts frequency. We demonstrate the ability of CARDAMOM to infer a reliable GRN from in silico expression datasets, with good computational speed. To the best of our knowledge, this is the first description of a method which uses the concept of metastability for performing GRN inference.

Transcription bursting and epigenetic plasticity: an updated view

The vast majority of eukaryotic transcription occurs in bursts during discrete periods of promoter activity, separated by periods of deep repression and inactivity. Elucidating the factors responsible for triggering transitions between these two states has been extremely challenging, partly due to the difficulties in measuring transcriptional bursting genome-wide, but also due to the vast array of candidate transcriptional and epigenetic factors and their complex and dynamic interactions. Additionally, this long-held view of transcriptional bursting as a two-state process has become increasingly challenged, and a resulting lack in consensus on terminology of the involved events has further complicated our understanding of the molecular mechanisms involved. Here, we review the impact of epigenetics on dynamic gene expression, with a focus on transcription bursting. We summarise current understanding of the epigenetic regulation of transcription bursting and propose new terminology for the interpretation of future results measuring transcription dynamics.

On-microscope staging of live cells reveals changes in the dynamics of transcriptional bursting during differentiation

Determining the mechanisms by which genes are switched on and off during development and differentiation is a key aim of current biomedical research. Gene transcription has been widely observed to occur in a discontinuous fashion, with short bursts of activity interspersed with longer periods of inactivity. It is currently not known if or how this dynamic behaviour changes as mammalian cells differentiate. To investigate this, using a newly developed on-microscope analysis, we monitored mouse α-globin transcription in live cells throughout sequential stages of erythropoiesis. We find that changes in the overall levels of α-globin transcription are most closely associated with changes in the fraction of time a gene spends in the active transcriptional state. We identify differences in the patterns of transcriptional bursting throughout differentiation, with maximal transcriptional activity occurring in the mid-phase of differentiation. Early in differentiation, we observe increased fluctuation in the patterns of transcriptional activity whereas at the peak of gene expression, in early and intermediate erythroblasts, transcription appears to be relatively stable and efficient. Later during differentiation as α-globin expression declines, we again observed more variability in transcription within individual cells. We propose that the observed changes in transcriptional behaviour may reflect changes in the stability of enhancer-promoter interactions and the formation of active transcriptional compartments as gene expression is turned on and subsequently declines at sequential stages of differentiation.

Transcriptional burst kinetics are linked to short term transcriptional memory

Transcriptional bursting is thought to be a stochastic process that allows the dynamic regulation of most genes. The random telegraph model assumes the existence of two states, ON and OFF. However recent studies indicate the presence of additional ON states, suggesting that bursting kinetics and their regulation can be quite complex. We have developed a system to study transcriptional bursting in the context of p53 biology using the endogenous p21 gene tagged with MS2 in human cells. Remarkably, we find that transcriptional bursts from the p21 gene contain multiple ON and OFF states that can be regulated by elevation of p53 levels. Distinct ON states are characterized by differences in burst duration, classified as Short and Long, with long bursts associated with higher Pol II initiation rates. Importantly, the different ON states display memory effects that allow us to predict the likelihood of properties of future bursting events. Long bursting events result in faster re-activation, longer subsequent bursts and higher transcriptional output in the future compared to short bursts. Bursting memory persists up to 2 hours suggesting a stable inheritable promoter architecture. Bursting memory at the p21 gene is the strongest under basal conditions and is suppressed by UV and inhibition of H3K9me1/2, which also increase transcriptional noise. Stabilization of p53 by Nutlin-3a partially reverses suppression of bursting memory suggesting that higher p53 levels may be a key in enforcing memory under conditions of cellular stress. Overall our data uncover a new found bursting property termed Short-Term Transcriptional Memory (STTM) that has the potential to fine-tune transcriptional output at the p21 gene.

Stochastic pausing at latent HIV-1 promoters generates transcriptional bursting

Promoter-proximal pausing of RNA polymerase II is a key process regulating gene expression. In latent HIV-1 cells, it prevents viral transcription and is essential for latency maintenance, while in acutely infected cells the viral factor Tat releases paused polymerase to induce viral expression. Pausing is fundamental for HIV-1, but how it contributes to bursting and stochastic viral reactivation is unclear. Here, we performed single molecule imaging of HIV-1 transcription. We developed a quantitative analysis method that manages multiple time scales from seconds to days and that rapidly fits many models of promoter dynamics. We found that RNA polymerases enter a long-lived pause at latent HIV-1 promoters (>20 minutes), thereby effectively limiting viral transcription. Surprisingly and in contrast to current models, pausing appears stochastic and not obligatory, with only a small fraction of the polymerases undergoing long-lived pausing in absence of Tat. One consequence of stochastic pausing is that HIV-1 transcription occurs in bursts in latent cells, thereby facilitating latency exit and providing a rationale for the stochasticity of viral rebounds.

Reverse engineering of a mechanistic model of gene expression using metastability and temporal dynamics

Differentiation can be modeled at the single cell level as a stochastic process resulting from the dynamical functioning of an underlying Gene Regulatory Network (GRN), driving stem or progenitor cells to one or many differentiated cell types. Metastability seems inherent to differentiation process as a consequence of the limited number of cell types. Moreover, mRNA is known to be generally produced by bursts, which can give rise to highly variable non-Gaussian behavior, making the estimation of a GRN from transcriptional profiles challenging. In this article, we present CARDAMOM (Cell type Analysis from scRna-seq Data achieved from a Mixture MOdel), a new algorithm for inferring a GRN from timestamped scRNA-seq data, which crucially exploits these notions of metastability and transcriptional bursting. We show that such inference can be seen as the successive resolution of as many regression problem as timepoints, after a preliminary clustering of the whole set of cells with regards to their associated bursts frequency. We demonstrate the ability of CARDAMOM to infer a reliable GRN from in silico expression datasets, with good computational speed. To the best of our knowledge, this is the first description of a method which uses the concept of metastability for performing GRN inference.