Epigenetic gene expression noise and phenotypic diversification of clonal cell populations

Neildez-Nguyen, Thi My Anh; Parisot, Aurélie; Vignal, Céline; Rameau, Philippe; Stockholm, Daniel; Picot, Julien; Allo, Valérie; Bec, Christine Le; Laplace, Corinne; Páldi, Andràs

doi:10.1111/j.1432-0436.2007.00219.x

Cited by 58 publications

(49 citation statements)

References 23 publications

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“…Two identical tumor cells may become phenotypically diverge due to stochastic variation in gene expression levels or differences in their local micro-environment, including tumor cell density [15][16][17][18]. As an example, cell density modulates focal adhesion kinase (Fak) activation, a regulator of glioma invasion [19,20].…”

Section: Resultsmentioning

confidence: 99%

Density-Dependent Regulation of Glioma Cell Proliferation and Invasion Mediated by miR-9

Katakowski

Charteris

Chopp

et al. 2016

Cancer Microenvironment

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The phenotypic axis of invasion and proliferation in malignant glioma cells is a well-documented phenomenon. Invasive glioma cells exhibit a decreased proliferation rate and a resistance to apoptosis, and invasive tumor cells dispersed in brain subsequently revert to proliferation and contribute to secondary tumor formation. One miRNA can affect dozens of mRNAs, and some miRNAs are potent oncogenes. Multiple miRNAs are implicated in glioma malignancy, and several of which have been identified to regulate tumor cell motility and division. Using rat 9 L gliosarcoma and human U87 glioblastoma cell lines, we investigated miRNAs associated with the switch between glioma cell invasion and proliferation. Using micro-dissection of 9 L glioma tumor xenografts in rat brain, we identified disparate expression of miR-9 between cells within the periphery of the primary tumor, and those comprising tumor islets within the invasive zone. Modifying miR-9 expression in in vitro assays, we report that miR-9 controls the axis of glioma cell invasion/proliferation, and that its contribution to invasion or proliferation is biphasic and dependent upon local tumor cell density. In addition, immunohistochemistry revealed elevated hypoxia inducible factor 1 alpha (HIF-1α) in the invasive zone as compared to the primary tumor periphery. We also found that hypoxia promotes miR-9 expression in glioma cells. Based upon these findings, we propose a hypothesis for the contribution of miR-9 to the dynamics glioma invasion and satellite tumor formation in brain adjacent to tumor.

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Section: Resultsmentioning

confidence: 99%

Density-Dependent Regulation of Glioma Cell Proliferation and Invasion Mediated by miR-9

Katakowski

Charteris

Chopp

et al. 2016

Cancer Microenvironment

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“…Such non-genetic heterogeneity has been indicated in immune cell functionality (Feinerman et al, 2010; Shalek et al, 2013) and has also been suggested as a driving force of stem cell development and cell fate decisions, such as lineage choice in hematopoietic stem cells (Chambers et al, 2007; Chang et al, 2008; Dietrich & Hiiragi, 2007; Kalmar et al, 2009; Kobayashi et al, 2009; Singh et al, 2007; Stockholm et al, 2007). Cellular heterogeneity is also an underlying source of the development of phenotypically different subpopulations due to individual cell responses to changes in microenvironment within genetically identical populations (Neildez-Nguyen et al, 2008). Such functional subgroups can also have substantial pharmacological consequences, notably with regards to cancer treatment, where partial drug resistance in tumor cell populations poses a significant problem (Cohen et al, 2008; Gascoigne & Taylor, 2008; Niepel, Spencer & Sorger, 2009; Orth et al, 2008; Sharma et al, 2010; Shi, Orth & Mitchison, 2008).…”

Section: Introductionmentioning

confidence: 99%

Single cell transcriptional analysis reveals novel innate immune cell types

Kippner

Kim

Gibson

et al. 2014

PeerJ

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Single-cell analysis has the potential to provide us with a host of new knowledge about biological systems, but it comes with the challenge of correctly interpreting the biological information. While emerging techniques have made it possible to measure inter-cellular variability at the transcriptome level, no consensus yet exists on the most appropriate method of data analysis of such single cell data. Methods for analysis of transcriptional data at the population level are well established but are not well suited to single cell analysis due to their dependence on population averages. In order to address this question, we have systematically tested combinations of methods for primary data analysis on single cell transcription data generated from two types of primary immune cells, neutrophils and T lymphocytes. Cells were obtained from healthy individuals, and single cell transcript expression data was obtained by a combination of single cell sorting and nanoscale quantitative real time PCR (qRT-PCR) for markers of cell type, intracellular signaling, and immune functionality. Gene expression analysis was focused on hierarchical clustering to determine the existence of cellular subgroups within the populations. Nine combinations of criteria for data exclusion and normalization were tested and evaluated. Bimodality in gene expression indicated the presence of cellular subgroups which were also revealed by data clustering. We observed evidence for two clearly defined cellular subtypes in the neutrophil populations and at least two in the T lymphocyte populations. When normalizing the data by different methods, we observed varying outcomes with corresponding interpretations of the biological characteristics of the cell populations. Normalization of the data by linear standardization taking into account technical effects such as plate effects, resulted in interpretations that most closely matched biological expectations. Single cell transcription profiling provides evidence of cellular subclasses in neutrophils and leukocytes that may be independent of traditional classifications based on cell surface markers. The choice of primary data analysis method had a substantial effect on the interpretation of the data. Adjustment for technical effects is critical to prevent misinterpretation of single cell transcript data.

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“…Indeed, scatter plots from two-color reporter assays in at least two recent studies suggest that the promoters driving the reporter genes may be competing for extrinsic regulators. 33,34 Downstream feedback loops may further amplify such competition effects. Taken together, the above results and these recent studies indicate that gene expression may occasionally be reliant on limited intracellular resources.…”

Section: B Non-competitive Repressor Utilizationmentioning

confidence: 99%

“…Since these original studies in bacteria, the double-reporter system has been successfully adapted to other organisms. [33][34][35] Besides the doublereporter system, other methods proposed for estimating the intrinsic and extrinsic components of gene expression noise have relied on total noise measurements for various copy numbers of a reporter gene 36,37 or mutations altering biological parameters relevant for intrinsic noise only (such as the promoter sequence, and the rates of transcription and translation). 32,38,39 Below we summarize the essence of the theory 17 underlying the use of the double-reporter methodology 18 to separate the intrinsic and extrinsic components of gene expression noise.…”

Section: Intrinsic and Extrinsic Noise: An Overviewmentioning

confidence: 99%

A common repressor pool results in indeterminacy of extrinsic noise

Stamatakis

Adams

Balázsi

2011

Chaos: An Interdisciplinary Journal of Nonlinear Science

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For just over a decade, stochastic gene expression has been the focus of many experimental and theoretical studies. It is now widely accepted that noise in gene expression can be decomposed into extrinsic and intrinsic components, which have orthogonal contributions to the total noise. Intrinsic noise stems from the random occurrence of biochemical reactions and is inherent to gene expression. Extrinsic noise originates from fluctuations in the concentrations of regulatory components or random transitions in the cell’s state and is imposed to the gene of interest by the intra- and extra-cellular environment. The basic assumption has been that extrinsic noise acts as a pure input on the gene of interest, which exerts no feedback on the extrinsic noise source. Thus, multiple copies of a gene would be uniformly influenced by an extrinsic noise source. Here, we report that this assumption falls short when multiple genes share a common pool of a regulatory molecule. Due to the competitive utilization of the molecules existing in this pool, genes are no longer uniformly influenced by the extrinsic noise source. Rather, they exert negative regulation on each other and thus extrinsic noise cannot be determined by the currently established method.

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Epigenetic gene expression noise and phenotypic diversification of clonal cell populations

Cited by 58 publications

References 23 publications

Density-Dependent Regulation of Glioma Cell Proliferation and Invasion Mediated by miR-9

Density-Dependent Regulation of Glioma Cell Proliferation and Invasion Mediated by miR-9

Single cell transcriptional analysis reveals novel innate immune cell types

A common repressor pool results in indeterminacy of extrinsic noise

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