Modulation of the promoter activation rate dictates the transcriptional response to graded BMP signaling levels in the<i>Drosophila</i>embryo

Hoppe, Caroline; Bowles, Jonathan R.; Minchington, Thomas G; Sutcliffe, Catherine; Upadhyai, Priyanka; Rattray, Magnus; Ashe, Hilary L.

doi:10.1101/837179

Cited by 9 publications

(26 citation statements)

References 64 publications

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“…A demonstration of applying the model to infer transcriptional parameters in Drosophila was outlined in (Hoppe et al, 2020). In that study, burstInfer was used to investigate regulation of BMP target genes in the early embryo through dividing embryos into regions corresponding to different BMP signalling levels then training the model on the MS2 datasets for each of these regions.…”

Section: Discussionmentioning

confidence: 99%

“…In order to quantify the dependency of expression levels on each of these three parameters (along with other derived parameters, such as burst duration and frequency), correlation analysis was carried out on the single-cell expression data and inferred parameters. This analysis revealed a very strong correlation between expression levels and occupancy, with effectively no correlation between expression and k off (Hoppe et al, 2020). Pol II loading rate (the HMM emission parameter) was flat across the expression domain Figure (5B).…”

Section: Application Of the Algorithm To Real Datamentioning

confidence: 94%

“…The fluorescence data presented here are shown in terms of arbitrary units of fluorescence. Quantification of the transcriptional output of cells using smFISH may be used to calibrate the signal in terms of Pol II number instead (Garcia et al, 2013;Lammers et al, 2020;Hoppe et al, 2020). The maximum fluorescence emission per time step t for each effective state is defined as v(k) = F r(k), where F is a calibration factor used to convert the units of arbitrary fluorescence to units of Pol II (Lammers et al, 2020).…”

Section: Model Formulationmentioning

confidence: 99%

“…Example inferred single-cell parameter using Drosophila ush data fromHoppe et al (2020). A: The expression domain of the ush gene shown in the cartoon was divided into three separate regions, corresponding to high, medium and low levels of expression, with the model trained separately on each of these three regions.…”

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confidence: 99%

“…C: Example fluorescent traces and corresponding inferred promoter paths for each of the three regions. SeeHoppe et al (2020) for further details.…”

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confidence: 99%

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Scalable inference of transcriptional kinetic parameters from MS2 time series data

Bowles

Hoppe

Ashe

et al. 2020

Preprint

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MotivationThe MS2-McP (MS2 coat protein) live imaging system allows for visualisation of transcription dynamics through the introduction of hairpin stem-loop sequences into a gene. A fluorescent signal at the site of nascent transcription in the nucleus quantifies mRNA production. computational modelling can be used to infer the promoter states along with the kinetic parameters governing transcription, such as promoter switching frequency and polymerase loading rate. However, modelling of the fluorescent trace presents a challenge due its persistence; the observed fluorescence at a given time point depends on both current and previous promoter states. A memory-adjusted Hidden Markov Model (mHMM) was recently introduced to allow inference of promoter activity from MS2-McP data. However, the computational time for inference scales exponentially with gene length and the mHMM is therefore not currently practical for application to many eukaryotic genes.ResultsWe present a scalable implementation of the mHMM for fast inference of promoter activity and transcriptional kinetic parameters. This new method can model genes of arbitrary length through the use of a time-adaptive truncated compound state space. The truncated state space provides a good approximation to the full state space by retaining the most likely set of states at each time during the forward pass of the algorithm. Testing on MS2-MCP fluorescent data collected from early Drosophila melanogaster embryos indicates that the method provides accurate inference of kinetic parameters within a computationally feasible timeframe. The inferred promoter traces generated by the model can also be used to infer single-cell transcriptional parameters.AvailabilityPython implementation available at https://github.com/ManchesterBioinference/burstInfer, along with code to reproduce the examples presented here.

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Section: Discussionmentioning

confidence: 99%

Section: Application Of the Algorithm To Real Datamentioning

confidence: 94%

Section: Model Formulationmentioning

confidence: 99%

mentioning

confidence: 99%

“…C: Example fluorescent traces and corresponding inferred promoter paths for each of the three regions. SeeHoppe et al (2020) for further details.…”

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confidence: 99%

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Scalable inference of transcriptional kinetic parameters from MS2 time series data

Bowles

Hoppe

Ashe

et al. 2020

Preprint

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Transcriptional Decoding of Morphogen Gradients during Development

Garcia

Larson

2020

Developmental Cell

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Quantitative imaging of transcription in livingDrosophilaembryos reveals the impact of core promoter motifs on promoter state dynamics

Pimmett

Dejean

Fernandez

et al. 2021

Preprint

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Genes are expressed in stochastic transcriptional bursts linked to alternating active and inactive promoter states. A major challenge in transcription is understanding how promoter composition dictates bursting, particularly in multicellular organisms. We investigate two key Drosophila developmental promoter motifs, the TATA box (TATA) and the Initiator (INR). Using live imaging in Drosophila embryos and new computational methods, we demonstrate that bursting occurs on multiple timescales ranging from seconds to minutes. TATA-containing promoters and INR-containing promoters exhibit distinct dynamics, with one or two separate rate-limiting steps respectively. A TATA box is associated with long active states, high rates of polymerase initiation, and short-lived, infrequent inactive states. In contrast, the INR motif leads to two inactive states, one of which relates to promoter-proximal polymerase pausing. Surprisingly, the model suggests pausing is not obligatory, but occurs stochastically for a subset of polymerases. Overall, our results provide a rationale for promoter switching during zygotic genome activation.

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Modulation of the promoter activation rate dictates the transcriptional response to graded BMP signaling levels in theDrosophilaembryo

Cited by 9 publications

References 64 publications

Scalable inference of transcriptional kinetic parameters from MS2 time series data

Scalable inference of transcriptional kinetic parameters from MS2 time series data

Transcriptional Decoding of Morphogen Gradients during Development

Quantitative imaging of transcription in livingDrosophilaembryos reveals the impact of core promoter motifs on promoter state dynamics

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