Reduction in gene expression noise by targeted increase in accessibility at gene loci

Fraser, La Tasha C.R.; Dikdan, Ryan; Dey, Supravat; Singh, Abhyudai; Tyagi, Sanjay

doi:10.1073/pnas.2018640118

Cited by 36 publications

(32 citation statements)

References 46 publications

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“…We showed that by increasing histone acetylation via treatment with the HDAC inhibitor trichostatin A (TSA), we could lower gene activation noise. This result is consistent with a more recent and experimentally elegant study demonstrating that increasing histone acetylation via targeted recruitment of the histone acetyltransferase p300 similarly decreased noise in gene expression following TF stimulation [71]. Our model experimentally reproduces this result (Fig.…”

Section: Discussionsupporting

confidence: 92%

A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription

Bullock

Moreno-Martinez

Miller‐Jensen

2022

Preprint

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Activation of gene expression in response to environmental cues results in substantial phenotypic heterogeneity between cells that can impact a wide range of outcomes including differentiation, viral activation, and drug resistance. An important source of gene expression noise is transcriptional bursting, or the observation that transcripts are produced during infrequent bursts of promoter activity. Chromatin accessibility, which regulates assembly of polymerase complexes on promoters, impacts transcriptional bursting, suggesting that how an activating signal affects transcriptional noise will depend on the initial chromatin state at the promoter. To explore this possibility, we simulated transcriptional activation using a transcriptional cycling model with three promoter states that represent chromatin remodeling, polymerase binding and pause release. We initiated this model over a large parameter range representing target genes with different chromatin environments, and found that, upon increasing the polymerase pause release rate to activate transcription, changes in gene expression noise varied significantly across initial promoter states. This model captured phenotypic differences in activation of latent HIV viruses integrated at different chromatin locations and mediated by the transcription factor NF-kB. Activating transcription in the model via increasing one or more of the transcript production rates, as occurs following NF-kB activation, reproduced experimentally measured transcript distributions for four different latent HIV viruses, as well as the bimodal pattern of HIV protein expression that leads to a subset of reactivated virus. Importantly, the parameter ‘activation path’ differentially affected gene expression noise, and ultimately viral activation, in line with experimental observations. This work demonstrates how upstream signaling pathways can be connected to biological processes that underlie transcriptional bursting, resulting in target gene-specific noise profiles following stimulation of a single upstream pathway.

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Section: Discussionsupporting

confidence: 92%

A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription

Bullock

Moreno-Martinez

Miller‐Jensen

2022

Preprint

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“…e Distance in base pairs from the isoform start to the nearest CAGE peak summit from Ref. [43]. f HOTAIR exon E7 diagram showing 3’ end polyA tails.…”

Section: Resultsmentioning

confidence: 99%

“…Full-length gels are presented in Additional file 3 Fig.S6. c IGV tracks over HOTAIR locus showing predicted HOTAIR regulatory elements (REMs) from the EpiRegio database [44, 60] (red: activating, blue: repressing), average ATAC-Seq tracks from ASCs [40, 41], Myoblasts [42] and HeLa cells [43], and ChIP primers location. d ChIP-qPCR analysis of histone modifications during adipogenesis of ASCs (mean ± SEM of n ≥ 3 independent differentiation experiments; **p < 0.01 Mixed effect analysis).…”

Section: Resultsmentioning

confidence: 99%

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De novoannotation of lncRNAHOTAIRtranscripts by long-read RNA capture-seq reveals a differentiation-driven isoform switch

Potolitsyna

Pickering

Tooming‐Klunderud

et al. 2022

Preprint

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LncRNAs are tissue-specific and emerge as important regulators of various biological processes and as disease biomarkers. HOTAIR is a well-established pro-oncogenic lncRNA which has been attributed a variety of functions in cancer and native contexts. However, a lack of an exhaustive, cell type-specific annotation questions whether HOTAIR functions are supported by the expression of multiple isoforms. Using a capture long-read sequencing approach, we characterize HOTAIR isoforms expressed in human primary adipose stem cells. We identify a highly cell type-specific HOTAIR isoform and uncover a shift in the HOTAIR isoform balance at differentiation onset. Composition of the HOTAIR isoform pool is regulated by distinct promoter usage and is under control of hormonal and nutrient-sensing pathways. Conclusion: Our results highlight the complexity and cell type-specificity of HOTAIR isoforms and open perspectives on functional implications of these variants and their balance to key cellular processes.

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“…Advances in single-cell technologies have exposed remarkable differences in phenotype and expression patterns between individual cells within the same isogenic cell population [1]- [9]. While some of this variation can be linked to extrinsic factors (i.e., cell-cycle stage, cell size, local extracellular environment), increasing evidence points to the role of stochastic processes inherent to gene expression in driving random fluctuations (noise) in gene product levels [10]- [24]. This intercellular phenotypic heterogeneity can play important functional roles in diverse biological processes, from driving genetically-identical cells to different cell fates [25]- [35] to allowing microbes and cancer cells to hedge their bets against uncertain environmental changes [36]- [47].…”

Section: Introductionmentioning

confidence: 99%

A fluctuation-based approach to infer kinetics and topology of cell-state switching

Saint-Antoine

Grima

Singh

2022

Preprint

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In the noisy cellular environment, RNAs and proteins are subject to considerable stochastic fluctuations in copy numbers over time. As a consequence, single cells within the same isoclonal population can differ in their expression profile and reside in different phenotypic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time makes it a particularly hard phenomenon to characterize. Here we propose a novel fluctuation-test approach to infer the kinetics of transitions between cell states. More specifically, single cells are randomly drawn from the population and grown into cell colonies. After growth for a fixed number of generations, the number of cells residing in different states is assayed for each colony. In a simple system with reversible switching between two cell states, our analysis shows that the extent of colony-to-colony fluctuations in the fraction of cells in a given state is monotonically related to the switching kinetics. Several closed-form formulas for inferring the switching rates from experimentally quantified fluctuations are presented. We further extend this approach to multiple cell states where harnessing fluctuation signatures can reveal both the topology and the rates of cell-state switching. In summary, our analysis provides a powerful approach for dissecting cell-state transitions based on a single time point measurement. This is especially important for scenarios where a measurement involves killing the cell (for example, performing single-cell RNA-seq or assaying whether a microbial/cancer cell is in a drug-sensitive or drug-tolerant state), and hence the state of the same cell cannot be measured at different time points.

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Reduction in gene expression noise by targeted increase in accessibility at gene loci

Cited by 36 publications

References 46 publications

A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription

A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription

De novoannotation of lncRNAHOTAIRtranscripts by long-read RNA capture-seq reveals a differentiation-driven isoform switch

A fluctuation-based approach to infer kinetics and topology of cell-state switching

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