Abstract
Transcription occurs in stochastic bursts. Early models based upon RNA hybridisation studies suggest bursting dynamics arise from alternating inactive and permissive states. Here we investigate bursting mechanism in live cells by quantitative imaging of actin gene transcription, combined with molecular genetics, stochastic simulation and probabilistic modelling. In contrast to early models, our data indicate a continuum of transcriptional states, with a slowly fluctuating initiation rate converting the gene between different levels of activity, interspersed with extended periods of inactivity. We place an upper limit of 40 s on the lifetime of fluctuations in elongation rate, with initiation rate variations persisting an order of magnitude longer. TATA mutations reduce the accessibility of high activity states, leaving the lifetime of on- and off-states unchanged. A continuum or spectrum of gene states potentially enables a wide dynamic range for cell responses to stimuli.
Highlights
Transcription of genes is discontinuous, occurring in irregular bursts or pulses of activity, interspersed by irregular intervals of inactivity (Golding et al, 2005; Chubb et al, 2006; Raj et al, 2006)
Notably budding yeast (Zenklusen et al, 2008), the variance in RNA abundance measured by single molecule RNA fluorescence in situ hybridisation (Femino et al, 1998; Mueller et al, 2013) can fit this one state scenario, where the distribution of RNA per cell is well characterised by a Poisson distribution
We have carried out in-depth measurements of the transcription dynamics of an endogenous, highly-expressed gene in Dictyostelium. This analysis has provided an insight into the dynamics of transcriptional regulation at short timescales, allowing models of the underlying mechanics to be discriminated
Summary
Transcription of genes is discontinuous, occurring in irregular bursts or pulses of activity, interspersed by irregular intervals of inactivity (Golding et al, 2005; Chubb et al, 2006; Raj et al, 2006). The one state model does not fit the smFISH data, with measured RNA abundance showing too much variability between cells than can be produced by a constantly active gene. To explain this increased variance, the more complex random telegraph (or two state) model is often invoked (Paulsson, 2005). In this model, the gene switches stochastically between an active state, where mRNA production occurs with constant probability per unit time, and an inactive state, with no mRNA production.
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