Asymmetry between Activation and Deactivation during a Transcriptional Pulse

Dunham, Lee; Momiji, Hiroshi; Harper, Claire V.; Downton, Polly; Hey, Kirsty; McNamara, A.; Featherstone, Karen; Spiller, David G.; Rand, D.A.J.; Finkenstädt, Bärbel; White, Michael R. H.; Davis, Julian

doi:10.1016/j.cels.2017.10.013

Cited by 14 publications

(13 citation statements)

References 63 publications

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“…Unfortunately, mechanistically detailed NEQ models entail an explosion in the complexity of the corresponding reaction schemes and the number of associated parameters: On the one hand, such models are intractable to infer from data, while on the other, it is difficult to understand which details are essential for the emergence of regulatory function. As a result, existing models that have been confronted with data typically assume or detect nonequilibrium “state transitions” of a promoter without any reference to TF binding ( 29 – 32 ) or only include a phenomenological description of how TFs modulate state transition rates ( 33 ). To our knowledge, a class of nonequilibrium gene-expression models that accounts for the chemical kinetics of multiple TF–DNA interactions without losing control over complexity is still lacking.…”

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confidence: 99%

Nonequilibrium models of optimal enhancer function

Grah

Zoller

Tkačik

2020

Proc. Natl. Acad. Sci. U.S.A.

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In prokaryotes, thermodynamic models of gene regulation provide a highly quantitative mapping from promoter sequences to gene-expression levels that is compatible with in vivo and in vitro biophysical measurements. Such concordance has not been achieved for models of enhancer function in eukaryotes. In equilibrium models, it is difficult to reconcile the reported short transcription factor (TF) residence times on the DNA with the high specificity of regulation. In nonequilibrium models, progress is difficult due to an explosion in the number of parameters. Here, we navigate this complexity by looking for minimal nonequilibrium enhancer models that yield desired regulatory phenotypes: low TF residence time, high specificity, and tunable cooperativity. We find that a single extra parameter, interpretable as the “linking rate,” by which bound TFs interact with Mediator components, enables our models to escape equilibrium bounds and access optimal regulatory phenotypes, while remaining consistent with the reported phenomenology and simple enough to be inferred from upcoming experiments. We further find that high specificity in nonequilibrium models is in a trade-off with gene-expression noise, predicting bursty dynamics—an experimentally observed hallmark of eukaryotic transcription. By drastically reducing the vast parameter space of nonequilibrium enhancer models to a much smaller subspace that optimally realizes biological function, we deliver a rich class of models that could be tractably inferred from data in the near future.

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confidence: 99%

Nonequilibrium models of optimal enhancer function

Grah

Zoller

Tkačik

2020

Proc. Natl. Acad. Sci. U.S.A.

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“…In our earlier work, single-cell imaging of the hPRL WT BAC in pituitary cell lines and tissues showed that the hPRL gene displays dramatic pulses in transcriptional activity ( 19 , 20 ). This activity has been observed for many other genes, including the human growth hormone gene ( 21 ), and appears to be a general phenomenon that is intrinsic to gene regulation ( 21-27 ). The characteristics of these transcriptional pulses may be susceptible to modulation, as part of normal physiological control, and this might be expected to impact on overall levels of gene expression.…”

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confidence: 78%

Transcription Factor Pit-1 Affects Transcriptional Timing in the Dual-Promoter Human Prolactin Gene

et al. 2021

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Gene transcription occurs in short bursts interspersed with silent periods, and these kinetics can be altered by promoter structure. The effect of alternate promoter architecture on transcription bursting is not known. We studied the human prolactin (hPRL) gene that contains two promoters, a pituitary-specific promoter that requires the transcription factor Pit-1, and displays dramatic transcriptional bursting activity, and an alternate upstream promoter that is active in non-pituitary tissues. We studied large hPRL genomic fragments with luciferase reporters, and used bacterial artificial chromosome (BAC) recombineering to manipulate critical promoter regions. Stochastic switch mathematical modelling of single-cell time-lapse luminescence image data revealed that the Pit-1-dependent promoter showed longer, higher-amplitude transcriptional bursts. Knockdown studies confirmed that the presence of Pit-1 stabilised and prolonged periods of active transcription. Pit-1 therefore plays an active role in establishing the timing of transcription cycles, in addition to its cell-specific functions.

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“…The asymmetry in timing along with the large difference in the associated transcription rates is responsible for the dynamic appearance of short and intense bursts, and the findings here are consistent with results obtained from fitting switch-type stochastic models to single cell reporter imaging time series data for other genes ( Harper et al ., 2011 ; Hey et al , 2015 ). In particular, Dunham et al (2017 ) further characterize this asymmetry by showing that switches from the OFF to the ON states are typically abrupt and result in short and intense bursts which are followed by a gradual deactivation of the gene. The estimation results for the inverted switch rates ( Fig.…”

Section: Experimental Data Analysismentioning

confidence: 95%

Bayesian inference on stochastic gene transcription from flow cytometry data

et al. 2018

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MotivationTranscription in single cells is an inherently stochastic process as mRNA levels vary greatly between cells, even for genetically identical cells under the same experimental and environmental conditions. We present a stochastic two-state switch model for the population of mRNA molecules in single cells where genes stochastically alternate between a more active ON state and a less active OFF state. We prove that the stationary solution of such a model can be written as a mixture of a Poisson and a Poisson-beta probability distribution. This finding facilitates inference for single cell expression data, observed at a single time point, from flow cytometry experiments such as FACS or fluorescence in situ hybridization (FISH) as it allows one to sample directly from the equilibrium distribution of the mRNA population. We hence propose a Bayesian inferential methodology using a pseudo-marginal approach and a recent approximation to integrate over unobserved states associated with measurement error.ResultsWe provide a general inferential framework which can be widely used to study transcription in single cells from the kind of data arising in flow cytometry experiments. The approach allows us to separate between the intrinsic stochasticity of the molecular dynamics and the measurement noise. The methodology is tested in simulation studies and results are obtained for experimental multiple single cell expression data from FISH flow cytometry experiments.Availability and implementationAll analyses were implemented in R. Source code and the experimental data are available at https://github.com/SimoneTiberi/Bayesian-inference-on-stochastic-gene-transcription-from-flow-cytometry-data.Supplementary information Supplementary data are available at Bioinformatics online.

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Asymmetry between Activation and Deactivation during a Transcriptional Pulse

Cited by 14 publications

References 63 publications

Nonequilibrium models of optimal enhancer function

Nonequilibrium models of optimal enhancer function

Transcription Factor Pit-1 Affects Transcriptional Timing in the Dual-Promoter Human Prolactin Gene

Bayesian inference on stochastic gene transcription from flow cytometry data

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