Multilineage Transcriptional Priming and Determination of Alternate Hematopoietic Cell Fates

Laslo, Peter; Spooner, Chauncey J.; Warmflash, Aryeh; Lancki, David W.; Lee, Hyun Jun; Sciammas, Roger; Gantner, Benjamin N.; Dinner, Aaron R.; Singh, Harinder

doi:10.1016/j.cell.2006.06.052

Cited by 578 publications

(666 citation statements)

References 47 publications

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“…The Egr genes and their coactivators/corepressors NAB1 and NAB2 are emerging as determents of genetic programs critical for cell fate and differentiation [7]. Egr-1, Egr-2, Egr-3, and NAB2 are all induced upon TCR engagement, however, they play different roles in regulating T cell function.…”

Section: Resultsmentioning

confidence: 99%

“…There are four known family members, each of which contains a highly conserved zinc finger DNA binding domain. Egr-mediated transcription has been shown to have important roles in multiple pathways including central nervous system function and development, prostate cancer development, thymic T cell development, and hematopoietic cell fate determination [2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

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Opposing regulation of T cell function by Egr‐1/NAB2 and Egr‐2/Egr‐3

et al. 2008

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TCR-induced NF-AT activation leads to the up-regulation of multiple genes involved in T cell anergy. Since NF-AT is also involved in T cell activation, we have endeavored to dissect TCR-induced activating and inhibitory genetic programs. This approach revealed roles for the early growth response (Egr) family of transcription factors and the Egr coactivator/corepressor NGFI-A-binding protein (NAB)2 in regulating T cell function. TCR-induced Egr-1 and NAB2 enhance T cell function, while Egr-2 and Egr-3 inhibit T cell function. In this report, we demonstrate that Egr-2 and Egr-3 are induced by NF-AT in the absence of AP-1, while Egr-1 and NAB2 both require AP-1-mediated transcription. Our data suggest that Egr-3 is upstream of Egr-2, and that mechanistically Egr-2 and Egr-3 suppress Egr-1 and NAB2 expression. Functionally, T cells from Egr-2 and Egr-3 null mice are hyperresponsive while T cells from Egr-3 transgenic, overexpressing mice are hyporesponsive. Furthermore, an in vivo model of autoimmune pneumonitis reveals that T cells from Egr-3 null mice hasten death while Egr-3-overexpressing T cells cause less disease. Overall, our data suggest that just as the Egr/NAB network of genes control cell fate in other systems, TCR-induced Egr-1, 2, 3 and NAB2 control the fate of antigen recognition in T cells.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Opposing regulation of T cell function by Egr‐1/NAB2 and Egr‐2/Egr‐3

et al. 2008

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“…in ecology, 68 developmental biology, 69 gene regulation studies on the Lac operon, 8 systems biology, 70 epigenetic regulation 13 and haematology. [10][11][12] All models used in these fields share the key feature of cooperative (i.e., nonlinear) positive feedback that fosters the formation of multiple attractors. Irrespective of their mechanistic underpinning, the mathematical backbone of the models resembles:…”

Section: Box 1 Positive Feedback Creates Attractorsmentioning

confidence: 99%

“…Examples are the Lac operon 8 and cells of the haematopoietic lineage. [9][10][11][12] Recently, epigenetic regulation mediated by histone modification has been studied using similar mathematical models. [13][14][15] At the molecular level these mathematical models describe quite different genetic and epigenetic systems.…”

mentioning

confidence: 99%

Cell division curtails helper phenotype plasticity and expedites helper T‐cell differentiation

Ham

Boer

2012

Immunol Cell Biol

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Following activation by antigen, helper T cells differentiate into one of many effector phenotypes. Formulating mechanistic mathematical models combining regulatory networks at the transcriptional, translational and epigenetic level, we study how individual helper T cells may adopt their different phenotypes. For each cytokine phenotype, for example, T helper type 1 (Th1) and type 2 (Th2) cells, we find that the intracellular molecular network allows a cell to adopt one of the three states, which we interpret as naive, active and memory states. Cell division markedly speeds up the differentiation into a particular memory state because of DNA demythelation. In a memory state, cells readily resume production of the same cytokine they produced before. Using stochastic models we show that helper T-cell plasticity (that is, the ability to switch phenotype) is low during clonal expansion. Although most memory cells rapidly secrete the original cytokine upon restimulation, some adopt another phenotype and produce different cytokines, allowing for considerable diversity in the phenotypes that are adopted during a memory response. In summary, we show that helper T-cell division expedites cell differentiation by increasing DNA demethylation. We also show that plasticity is low during the clonal expansion phase, but that helper T cells may adopt alternative phenotypes during a memory response. Keywords: epigenetic regulation; helper T-cell phenotypes; mathematical modelling; transcriptional regulation For more than a century, biologists have studied the mechanisms that enable cells to encode genetic information, and how this information is passed on to the cell's progeny. 1 Besides the genetic DNA code of a cell that is replicated faithfully upon every cell division and therefore virtually identical in every cell within an individual, 2 additional 'epigenetic' information fixes differential cell development by prompting daughter cells to express a similar set of genes as their mother cell. 3 This facilitates and thus preserves the differentiation process that the mother cell and its ancestors have undergone as part of cellular diversification within an organism.A variety of biological systems have now been studied using highthroughput technologies, which provide data that allow a better understanding of information processing within a cell. [4][5][6][7] The availability of quantitative data and the apparent complexity of cellular regulatory networks have led to a data-driven mathematical modelling approach that is usually referred to as computational or systems biology. Mathematical models have revealed nonlinearity in genetic networks that allow cells to switch between states. Examples are the Lac operon 8 and cells of the haematopoietic lineage. [9][10][11][12] Recently, epigenetic regulation mediated by histone modification has been studied using similar mathematical models. [13][14][15] At the molecular level these mathematical models describe quite different genetic and epigenetic systems. Mathematically they share im...

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“…toggle switch – (Fig. 1, blue box) that has been shown to determine cellular commitment and provides stability to transcriptional programs mediating binary cell fate choices 83, 84, 85, 86, 87. This has been observed in other cellular systems, such as the common myeloid progenitor (mutual inhibition of Gata1 and PU.1 ) 88 and embryonic stem cells (mutual inhibition of Oct4 and Cdx2 ) 89.…”

Section: Specific Gene Regulatory Network Circuits Regulate the Balanmentioning

confidence: 90%

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Angarica

Sol

2016

BioEssays

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Pluripotency can be considered a functional characteristic of pluripotent stem cells (PSCs) populations and their niches, rather than a property of individual cells. In this view, individual cells within the population independently adopt a variety of different expression states, maintained by different signaling, transcriptional, and epigenetics regulatory networks. In this review, we propose that generation of integrative network models from single cell data will be essential for getting a better understanding of the regulation of self‐renewal and differentiation. In particular, we suggest that the identification of network stability determinants in these integrative models will provide important insights into the mechanisms mediating the transduction of signals from the niche, and how these signals can trigger differentiation. In this regard, the differential use of these stability determinants in subpopulation‐specific regulatory networks would mediate differentiation into different cell fates. We suggest that this approach could offer a promising avenue for the development of novel strategies for increasing the efficiency and fidelity of differentiation, which could have a strong impact on regenerative medicine.

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Multilineage Transcriptional Priming and Determination of Alternate Hematopoietic Cell Fates

Cited by 578 publications

References 47 publications

Opposing regulation of T cell function by Egr‐1/NAB2 and Egr‐2/Egr‐3

Opposing regulation of T cell function by Egr‐1/NAB2 and Egr‐2/Egr‐3

Cell division curtails helper phenotype plasticity and expedites helper T‐cell differentiation

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

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