Gene Networks with Transcriptional Bursting Recapitulate Rare Transient Coordinated High Expression States in Cancer

Schuh, Lea; Saint-Antoine, Michael; Sanford, Eric M.; Emert, Benjamin; Singh, Abhyudai; Marr, Carsten; Raj, Arjun; Goyal, Yogesh

doi:10.1016/j.cels.2020.03.004

Cited by 67 publications

(76 citation statements)

References 34 publications

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“…If so, the key to more durable therapy lies not in deeper inhibition of the core MAPK cascade, which might be toxic to non-transformed cells, but rather the identification of vulnerabilities specific to the persister state. This will benefit from better understanding of this state (Shaffer et al, 2017) and whether persister cells have recurrent features or vulnerabilities that can be targeted therapeutically (Schuh et al, 2020). These might include proteins downstream of multiple growth factor receptors (e.g., SHP2) (Chen et al, 2016), accumulation of DNA damage (Margue et al, 2019), or dependence on specific survival pathways (Coppé et al, 2019;Fallahi-Sichani et al, 2015;Hangauer et al, 2017).…”

Section: Physiological Configurationmentioning

confidence: 99%

Receptor-Driven ERK Pulses Reconfigure MAPK Signaling and Enable Persistence of Drug-Adapted BRAF-Mutant Melanoma Cells

et al. 2020

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Section: Physiological Configurationmentioning

confidence: 99%

Receptor-Driven ERK Pulses Reconfigure MAPK Signaling and Enable Persistence of Drug-Adapted BRAF-Mutant Melanoma Cells

et al. 2020

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“…The interpretation of equation (14) is clear: in the deterministic limit, the rate of change in protein level is the burst frequency times burst size minus decay rate. Notably, both infinitesimally delayed and undelayed formulations lead to the same deterministic flow (14).…”

Section: Lyapunov Function Of the Deterministic Flowmentioning

confidence: 99%

“…with equality in (17) holding if x(t) = x is a fixed point of the deterministic flow (14). Thus, the distribution potential (16) is a Lyapunov function of (14), and has local minima (maxima) where the deterministic flow (14) has stable (unstable) fixed points. The dominant contribution towards the total probability mass (15) will thereby come from the neighbourhoods of points where the potential is minimal.…”

Section: Lyapunov Function Of the Deterministic Flowmentioning

confidence: 99%

“…The iteration of the algorithm requires to draw two random variates, θ andθ, from the uniform distribution in the unit interval. The first is used to generate, by the inversion sampling (14) are shown as dashed/dotted horizontal lines. Upper Right: Transitions between the basins of attractions of the stable steady states occur on an extremely slow timescale.…”

Section: Bistable Switchmentioning

confidence: 99%

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Exact and WKB-approximate distributions in a gene expression model with feedback in burst frequency, burst size, and protein stability

Bokes

2020

Preprint

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The expression of individual genes into functional protein molecules is a noisy dynamical process. Here we model the protein concentration as a jump--drift process which combines discrete stochastic production bursts (jumps) with continuous deterministic decay (drift). We allow the drift rate, the jump rate, and the jump size to depend on the current protein level in an arbitrary fashion to implement feedback in protein stability, burst frequency, and burst size. Two versions of feedback in burst size are considered: in the ``infinitesimally delayed'' version, only those molecules of protein that have been present before a burst started can regulate the size of the burst; in the ``undelayed'' version, newly produced molecules also partake in the regulation of the further growth of a burst. Excluding the infinitesimal delay in burst size, the model is explicitly solvable. With the inclusion of the infinitesimal delay, an exact distribution to the model is no longer available, but we are able to construct a WKB approximation that applies in the asymptotic regime of small but frequent bursts. Comparing the asymptotic behaviour of the two model versions, we report that they yield the same WKB quasi-potential but a different exponential prefactor. We illustrate the difference on the case of a bimodal protein distribution sustained by a sigmoid feedback in burst size: we show that the omission of the infinitesimal delay overestimates the weight of the upper mode of the protein distribution. The analytic results are supported by kinetic Monte-Carlo simulations.

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“…Indeed, in vitro studies have suggested that breast cancer cells are capable of freely transitioning between several phenotypic states including a putative cancer stem-cell phenotype 16 , suggesting that cell states are susceptible to nongenetic fluctuations. In vitro studies of melanoma have revealed that transcription bursting can give rise to a transient small population (~1 in 3,000) of pre-resistant cells based on concurrent high expression of several genes involved in resistance pathways 11,17 . Nongenetic heterogeneity has directly been shown to contribute to acquired drug resistance via inheritance of acquired characteristics in tracked single cells 18 .…”

Section: Introductionmentioning

confidence: 99%

4sUDRB-sequencing for genome-wide transcription bursting quantification in breast cancer cells

Beckman

Jiménez

Moerland

et al. 2020

Preprint

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Epigenetics maintains cell-identity specific gene-expression patterns. However, within a population of isogenic cells of the same identity, a substantial variability in gene expression and responsiveness is still observed. Transcription bursting is a substantial source of this gene-expression variability or ‘noise’, contributing to phenotypic heterogeneity and potentially driving both physiological and pathological processes such as differentiation or tumorigenesis and drug resistance. Identification of transcription-bursting dynamics at a genome-wide scale has been restricted to inferring bursts in mRNA production computationally from the heterogeneity of mRNA levels in single cell transcriptomic data. Systematic characterisation of the genomic and epigenetic chromatin context of genes with defined transcription bursting behaviour has been incomplete. Here, we measured the bursting of transcription itself by genome-wide nascent RNA sequencing of breast cancer MCF-7 cells upon synchronisation of transcription with a transcription elongation inhibitor and by calibration using live cell imaging of nascent PP7-tagged GREB1 transcription. Comparing across the entire genome, we find transcription bursting to be ubiquitous, with burst sizes of up to 160 transcripts. Transcription bursting attributes ~85% to a trend in the variation in steady state gene expression between genes, whereas both burst frequency and nascent transcript degradation attribute minimally. Individual genes deviate strongly from this trend and engage both in anomalous burst size and frequency. We find that the presence of the TATA box or Inr sequence within gene promoters significantly predicts a larger burst size, as do promoter-associated YY1 and E2F1 transcription-factor binding motifs. Enrichment of the transcription start site with epigenetic marks such as H3K79me2 and H3Kl4ac is also strongly associated with the transcription burst size. Finally, we show that in these MCF-7 breast-cancer cells, genes with a larger transcription burst size exhibit a larger immediate transcriptional response following endocrine drug treatment. Our genome-wide transcription-bursting analysis method paves the way to elucidate the dynamic role of epigenetic regulation on dynamic transcription in pathophysiology.

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Gene Networks with Transcriptional Bursting Recapitulate Rare Transient Coordinated High Expression States in Cancer

Cited by 67 publications

References 34 publications

Receptor-Driven ERK Pulses Reconfigure MAPK Signaling and Enable Persistence of Drug-Adapted BRAF-Mutant Melanoma Cells

Receptor-Driven ERK Pulses Reconfigure MAPK Signaling and Enable Persistence of Drug-Adapted BRAF-Mutant Melanoma Cells

Exact and WKB-approximate distributions in a gene expression model with feedback in burst frequency, burst size, and protein stability

4sUDRB-sequencing for genome-wide transcription bursting quantification in breast cancer cells

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