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Towards an understanding of lineage specification in hematopoietic stem cells: A mathematical model for the interaction of transcription factors GATA-1 and PU.1
Abstract
In addition to their self-renewal capabilities, hematopoietic stem cells guarantee the continuous supply of fully differentiated, functional cells of various types in the peripheral blood. The process which controls differentiation into the different lineages of the hematopoietic system (erythroid, myeloid, lymphoid) is referred to as lineage specification. It requires a potentially multi-step decision sequence which determines the fate of the cells and their successors. It is generally accepted that lineage specification is regulated by a complex system of interacting transcription factors. However, the underlying principles controlling this regulation are currently unknown. Here, we propose a simple quantitative model describing the interaction of two transcription factors. This model is motivated by experimental observations on the transcription factors GATA-1 and PU.1, both known to act as key regulators and potential antagonists in the erythroid vs. myeloid differentiation processes of hematopoietic progenitor cells. We demonstrate the ability of the model to account for the observed switching behavior of a transition from a state of low expression of both factors (undifferentiated state) to the dominance of one factor (differentiated state). Depending on the parameter choice, the model predicts two different possibilities to explain the experimentally suggested, stem cell characterizing priming state of low level co-expression. Whereas increasing transcription rates are sufficient to induce differentiation in one scenario, an additional system perturbation (by stochastic fluctuations or directed impulses) of transcription factor levels is required in the other case.
... GATA1 and PU.1 (SPI1) mutually inhibit one another; GATA1 is expressed in the ME lineage and PU.1 is expressed in the GM lineage. This mutually repressive GRN has been extensively studied and characterized in models, mostly consisting of ordinary differential equations that permit bistability, thus enabling investigation into the dynamics of this myeloid lineage decision (Chickarmane et al., 2009;Duff et al., 2012;Huang et al., 2007;Chang et al., 2008;Roeder and Glauche, 2006). ...
... Despite many theoretical and experimental advances in our understanding of gene regulatory network (GRN) dynamics, our ability to use GRN models to explain cell-fate decision-making during differentiation of multipotent progenitor cells remains limited. Here, with application to a well-studied cell fate GRN, the GATA1-PU.1 mutual inhibition loop (Chickarmane et al., 2009;Chang et al., 2008;Roeder and Glauche, 2006), we introduced a new model that can simultaneously describe GRN dynamics and single cell-resolved cell-cell communication. Notably, although cell-cell communication is often assumed to be a crucial component of cell differentiation, it is rarely incorporated into models. ...
... This myeloid progenitor cell-lineage decision has been rigorously studied, and robust models exist of the intracellular GRN dynamics (Chickarmane et al., 2009;Duff et al., 2012;Chang et al., 2008;Huang et al., 2007;Roeder and Glauche, 2006;Strasser et al., 2012). Here, we implement the ODE model defined by Chickarmane et al., which follows the Shea-Ackers formalism for transcription factor dynamics (Shea and Ackers, 1985;Chickarmane et al., 2009). ...
Cells do not make fate decisions independently. Arguably, every cell-fate decision occurs in response to environmental signals. In many cases, cell-cell communication alters the dynamics of the internal gene regulatory network of a cell to initiate cell-fate transitions, yet models rarely take this into account. Here, we have developed a multiscale perspective to study the granulocyte-monocyte versus megakaryocyte-erythrocyte fate decisions. This transition is dictated by the GATA1-PU.1 network: a classical example of a bistable cell-fate system. We show that, for a wide range of cell communication topologies, even subtle changes in signaling can have pronounced effects on cell-fate decisions. We go on to show how cell-cell coupling through signaling can spontaneously break the symmetry of a homogenous cell population. Noise, both intrinsic and extrinsic, shapes the decision landscape profoundly, and affects the transcriptional dynamics underlying this important hematopoietic cell-fate decision-making system.
This article has an associated ‘The people behind the papers’ interview.
... In order to generate the model we have incorporated known interactions in the differentiation process of Th1-Th2 cells into a regulatory network diagram, similar to other published models of this system [2,3]. Similar models were used to study also other systems of binary cell fate decisions, and have shown that the steady states of the system exhibit bi-or tri-modality [2][3][4][5][6][7]. However, in contrast to previous studies, we identify a novel regime where a mono-stable solution exists. ...
... 3), where no TF is expressed, the two fully polarized solution (Eqs. [4][5] where only one TF is expressed while the other is inhibited, and the novel mixed state (Eq. 6) where both TF are expressed simultaneously. ...
... Next, we examine the case where the positive autoregulatory feedback is greater than one. In this case, as shown previously by a number of studies [2][3][4][5][6][7], the dynamic behavior is mostly bi-stable by nature, as can be seen from analyzing the Jacobian of the system: ...
A parsimonious model of the GRN module governing cell differentiation shows a wide range of continuously tunable mono-stable solutions when positive feedback is gradual and dominates cross-inhibition.
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... We illustrate the practical effectiveness of our method by applying it to simulation data from a typical example of cell developmental circuit consisting of a pair of self-activating and mutual inhibiting genes A and B [14]. This simplified gene regulatory circuit has been shown to be critical to cell fate decision and commitment in multiple instances of multipotent stem or progenitor cells [14,30,31]. For this system, our approach of estimating transition rate from data showed superior performance than the previous nudged elastic band (NEB) method [20] by comparisons with simulations. ...
... A canonical gene regulatory focused on this work, as shown in Figure 1A, consisting of mutual inhibition between two opposing fates controlled by two TFs A and B, has been shown to govern cell fate decision and commitment in several instances of multipotent stem or progenitor cells [30,31]. We consider both the internal and external noise form of this stochastic dynamical system. ...
The dynamical properties of many complex physical and biological systems can be quantified from the energy landscape theory. Previous approaches focused on estimating the transition rate from landscape reconstruction based on data. However, for general non-equilibrium systems (such as gene regulatory systems), both the energy landscape and the probability flux are important to determine the transition rate between attractors. In this work, we proposed a data-driven approach to estimate non-equilibrium transition rate, which combines the kernel density estimation and non-equilibrium transition rate theory. Our approach shows superior performance in estimating transition rate from data, compared with previous methods, due to the introduction of a nonparametric density estimation method and the new saddle point by considering the effects of flux. We demonstrate the practical validity of our approach by applying it to a simplified cell fate decision model and a high-dimensional stem cell differentiation model. Our approach can be applied to other biological and physical systems.
... The first mathematical model to study the regulatory mechanism of genes GATA1 and PU.1 is proposed in the form of the Shea-Ackers formalism [114]. The assumption of the model is based on the experimental observations as outlined below: ...
... Detailed descriptions of this model can be found in the referenced paper [114]. This model sheds new light on the mechanisms underlying HSCs differentiation. ...
Mathematical modelling and inference methods are powerful tools to study genetic regulatory networks. This thesis focuses on the development of mathematical methods to explore and analyze the dynamical mechanisms of genetic regulation related to cell fate determination in hematopoiesis.
... GATA1 and PU.1 (SPI1) mutually inhibit one another; GATA1 is expressed in the ME lineage and PU.1 is expressed in the GM lineage. This mutually repressive GRN has been extensively studied and characterized in models, mostly consisting of ordinary differential equations that permit bistability, thus enabling investigation into the dynamics of this myeloid lineage decision [7][8][9][10][11] Given the GATA1-PU.1 mutual inhibitory loop that leads to bistability, changing the initial conditions (gene expression levels) within a bistable region is sufficient to change the cell fate. It has thus been proposed that random fluctuations of GATA1 and PU.1 levels are primarily responsible for determining cell fate in the bipotent progenitor population that has dual ME and GM lineage potential [10]. ...
... For this genetic switch, high expression of G corresponds to commitment to the erythroid/megakaryocyte lineage and high expression of P corresponds to commitment to the granulocyte/monocyte lineage. This myeloid progenitor cell lineage decision has been rigorously studied, and there exist robust models of the intracellular GRN dynamics [7,8,10,11,23,24]. Here we implement the ODE model defined by Chickarmane et al., given by Eqs. ...
The role of cell-cell communication in cell fate decision-making has not been well-characterized through a dynamical systems perspective. To do so, here we develop multiscale models that couple cell-cell communication with cell-internal gene regulatory network dynamics. This allows us to study the influence of external signaling on cell fate decision-making at the resolution of single cells. We study the granulocyte-monocyte vs. megakaryocyte-erythrocyte fate decision, dictated by the GATA1-PU.1 network, as an exemplary bistable cell fate system, modeling the cell-internal dynamic with ordinary differential equations and the cell-cell communication via a Poisson process. We show that, for a wide range of cell communication topologies, subtle changes in signaling can lead to dramatic changes in cell fate. We find that cell-cell coupling can explain how populations of heterogeneous cell types can arise. Analysis of intrinsic and extrinsic cell-cell communication noise demonstrates that noise alone can alter the cell fate decision-making boundaries. These results illustrate how external signals alter transcriptional dynamics, and provide insight into hematopoietic cell fate decision-making.
... In particular the self-activation and mutual antagonism interactions in the GATA1/PU.1 pair have been considered a paradigmatic system for lineage specification [5,6]. The properties of the system that lead to bistability and support the existence of a primed progenitor state have been explored, both through pure theoretical efforts [7,8] as well as approaches based on experimental data [9]. A computational analysis was also ventured in [4], expanding the GATA1/PU.1 switch to GATA2 with the aim of establishing the nature of unknown interactions between these three regulators, excitatory and repressive, provided by ChIPSeq analysis. ...
... The relevance of bistable switches has been modeled and demonstrated for a number of differentiation systems ( [15], and references therein). In the erythroid lineage, previous studies have theoretically deduced that bistability can emerge along the GATA1/PU.1 axis [7,9]. A subsequent modeling effort has suggested that, considering a detailed description of the interactions between these two genes, the GATA1/PU.1 pair is not sufficient to generate bistability by itself, and a third element must be involved [8]. ...
It is widely conjectured that the mutually antagonizing pair of transcription factors GATA1 and PU.1, deter-mines the choice between the erythroid and myeloid lineages in hematopoiesis. In theoretical approaches, this appears natural with a bistable switch driving the decision. Recent extensive binding and gene expression experiments with some focus on the triad GATA1, GATA2 and PU.1 indicate that GATA2 may be more involved in this lineage decision than previously anticipated. Here, we analyze these experimental data by modeling regulatory sub-networks with deterministic rate equations. Using network dynamical parameters determined by the data, we deduce from increasing the self-interaction bindings in silico among the triad genes that GATA2 and PU.1 exhibit non-linear behavior with one unstable and one stable state. This is in contrast to GATA1, which shows smoother behavior. We extend the network to include the downstream regulators FOG1 and CEBPA, and extract the nature of the corresponding regulatory interactions, excitatory or suppressing, between this pair and the triad by fitting to experimental gene expression time series. Based on this extended network, we simulate and explore different knockout scenarios, providing insight into the role of these regulators in the process of lineage specification, as well as predictions for future experimental validation. We address the mechanism of GATA switching as a mechanism of lineage differentiation by investigating the dynamics of FOG1 regulation by GATA2 and GATA1. Overall, this analysis strongly suggests that within this network, GATA2 is the key driver of erythroid lineage specification through its repression of PU.1, whereas GATA1 appears to be more relevant for the downstream differentiation events.
... One hematopoietic lineage decision is the choice of hematopoietic stem and progenitor cells (HSPCs) between the megakaryocyticerythroid (MegE) and the granulocyte-macrophage (GM) lineage 7 . The mutually exclusive expression of the transcription factors PU.1 and GATA1 in mature GM and MegE cells, respectively (see e.g., 8 for an overview), and their mutual binding and crossantagonism inspired toggle switch models that predict transcription factor dynamics before and during this decision [9][10][11][12][13][14] . These models assume the switch to one of the cross-antagonistic transcription factors to precede and induce GM vs. MegE lineage choice, and serve as the de facto paradigm of binary cell fate choice on a molecular level 15 . ...
... Now we compare these findings to a model where a toggle switch involving PU.1 drives cell differentiation. We implemented a popular toggle switch model that is thought to drive binary lineage decision composed of two mutually repressing transcription factors (Fig. 4d inset; see Supplementary Note 3 for model details) [9][10][11][12][13][14]19 . This model exhibits three stable states (Fig. 4d): The state where both proteins are expressed at similar levels is associated with a progenitor cell. ...
Molecular regulation of cell fate decisions underlies health and disease. To identify molecules that are active or regulated during a decision, and not before or after, the decision time point is crucial. However, cell fate markers are usually delayed and the time of decision therefore unknown. Fortunately, dividing cells induce temporal correlations in their progeny, which allow for retrospective inference of the decision time point. We present a computational method to infer decision time points from correlated marker signals in genealogies and apply it to differentiating hematopoietic stem cells. We find that myeloid lineage decisions happen generations before lineage marker onsets. Inferred decision time points are in agreement with data from colony assay experiments. The levels of the myeloid transcription factor PU.1 do not change during, but long after the predicted lineage decision event, indicating that the PU.1/GATA1 toggle switch paradigm cannot explain the initiation of early myeloid lineage choice.
... are the equilibrium dissociation constants for the respective reactions. Here we have assumed that the LSMGs bind co-operatively to each others promoters with binding site affinity 2. This assumption has been taken in other computational models of transcription factor binding [26], and ensures that the resulting feedback loops are nonlinear, yet also keeps the resulting mathematics transparent. In order to determine the effect of binding site affinity on model solutions, we conducted extensive numerical simulations using a range of other binding affinities. ...
... Thus, J r has only one eigenvalue: −b (with multiplicity r). Since b > 0 the stability of the generic fixed point given in Eq.(26) is determined solely by the eigenvalues of J pq . Since X is flow invariant, this means that we need only consider solutions of the form x = (α, α, . . . ...
Transcriptional control of the mesenchymal lineages; Derivation of model equations; Mathematical details
(0.16 MB PDF)
... Several examples of these binary cell fate choice mechanisms have emerged in the last decade. A more general view postulates that stem/progenitor states, corresponding to metastable states, are maintained by the balanced expression of two rival lineage specifiers, which compete for differentiation into mutually exclusive cell fates (Huang et al., 2006;Roeder and Glauche, 2006). Moreover, disturbance of this equilibrium leads to cell fate differentiation. ...
... Previous theoretical and experimental studies indicated that two rival cell fate determinants show a balanced expression pattern in the stem/progenitor cell and breaking this balance would lead to differentiation (Huang et al., 2006;Roeder and Glauche, 2006). Assuming that this notion also holds true for neurogenesis, we next aimed to identify pairs of TFs that exhibited this expression pattern between the SGZ and GCL, and between the SVZ and OB. ...
Neurogenesis-the generation of new neurons-is an ongoing process that persists in the adult mammalian brain of several species, including humans. In this work we analyze two discrete brain regions: the subventricular zone (SVZ) lining the walls of the lateral ventricles; and the subgranular zone (SGZ) of the dentate gyrus (DG) of the hippocampus in mice and shed light on the SVZ and SGZ specific neurogenesis. We propose a computational model that relies on the construction and analysis of region specific gene regulatory networks (GRNs) from the publicly available data on these two regions. Using this model a number of putative factors involved in neuronal stem cell (NSC) identity and maintenance were identified. We also demonstrate potential gender and niche-derived differences based on cell surface and nuclear receptors via Ar, Hif1a, and Nr3c1. We have also conducted cell fate determinant analysis for SVZ NSC populations to Olfactory Bulb interneurons and SGZ NSC populations to the granule cells of the Granular Cell Layer. We report 31 candidate cell fate determinant gene pairs, ready to be validated. We focus on Ar-Pax6 in SVZ and Sox2-Ncor1 in SGZ. Both pairs are expressed and localized in the suggested anatomical structures as shown by in situ hybridization and found to physically interact. Finally, we conclude that there are fundamental differences between SGZ and SVZ neurogenesis. We argue that these regulatory mechanisms are linked to the observed differential neurogenic potential of these regions. The presence of nuclear and cell surface receptors in the region specific regulatory circuits indicate the significance of niche derived extracellular factors, hormones and region specific factors such as the oxygen sensitivity, dictating SGZ and SVZ specific neurogenesis.
... [16][17][18][19] En términos generales los diferentes nichos pueden modificar sus propiedades reguladoras en respuesta a las necesidades particulares del tejido; sin embargo, independientemente del nicho en cuestión, hay mecanismos comunes de señalización que es muy importante conocer y que están mejor caracterizados en el sistema hematopoyético; pero no ha sido sencillo el estudio de los mecanismos moleculares que controlan la hematopoyesis, porque no se puede mantener a las CMH in vitro por largos períodos; además, se presenta la dificultad de controlar la autorrenovación frente a la diferenciación. 20,21 Específicamente en el proceso de autorrenovación intervienen inhibidores del ciclo celular, genes implicados en rearreglos cromosómicos, proteínas esenciales para el desarrollo y factores específicos como el Notch 1, Shh (Sonic hedgehog), el factor de transcripción Hox B4, el inhibidor de la quinasa dependiente de la ciclina P21/waf 1, las proteínas Wnt, entre otros. Para mantener con éxito el estado indiferenciado se requiere la integración de diferentes vías de señalización intrínsecas con las señales extrínsecas emitidas desde el microambiente. ...
Las autorrenovación y la diferenciación son características de las células madre que varían entre los diferentes tipos celulares según el tejido en el que se encuentren y el microambiente que las rodee. En ambos procesos intervienen inhibidores del ciclo celular, genes implicados en rearreglos cromosómicos, proteínas del desarrollo esencial y vías de señalización específicas. La autorrenovación está regulada por diversos mecanismos, entre los cuales se destacan las vías Wnt, Notch y Hedgehog, y los factores BMI-1, p16Ink4a, ARF, NANOG, OCT3/4, SOX2, HOXB4 y sus páralogos. Los adelantos en el conocimiento de la biología de las células madre y de los mecanismos moleculares que regulan la autorrenovación y la diferenciación han convertido a estas células en una importante promesa para la investigación básica y aplicada.
... To see how the DNN works for inferring the network structure, we first study a simplified two-variable model called mutual inhibited self-activation (MISA) model ( Figure 1C). The MISA model consists of mutual inhibition between two opposing fates controlled by two transcription factors X 1 and X 2 , which has been shown to govern cell fate decision and commitment in multiple instances of multipotent stem or progenitor cells (20,(46)(47)(48). In biological modeling, Hill functions are often used to describe the activation and inhibition regulations: ...
The reconstruction of gene regulatory networks (GRNs) from data is vital in systems biology. Although different approaches have been proposed to infer causality from data, some challenges remain, such as how to accurately infer the direction and type of interactions, how to deal with complex network involving multiple feedbacks, as well as how to infer causality between variables from real-world data, especially single cell data. Here, we tackle these problems by deep neural networks (DNNs). The underlying regulatory network for different systems (gene regulations, ecology, diseases, development) can be successfully reconstructed from trained DNN models. We show that DNN is superior to existing approaches including Boolean network, Random Forest and partial cross mapping for network inference. Further, by interrogating the ensemble DNN model trained from single cell data from dynamical system perspective, we are able to unravel complex cell fate dynamics during preimplantation development. We also propose a data-driven approach to quantify the energy landscape for gene regulatory systems, by combining DNN with the partial self-consistent mean field approximation (PSCA) approach. We anticipate the proposed method can be applied to other fields to decipher the underlying dynamical mechanisms of systems from data.
... These models have been widely used in order to describe different cellular processes such as the epithelial-mesenchymal transition (EMT) (Bracken et al. 2008;Johnston et al. 2005;Siemens et al. 2011), hematopoietic stem cells (Huang et al. 2007;Laslo et al. 2006;Roeder and Glauche 2006), embryonic stem cells (Chickarmane et al. 2006) or other cell-fate differentiation phenomena involved in Xenopus (Ferrell and Machleder 1998;Novak and Tyson 1993), Drosophila (Papatsenko and Levine 2011) or Escherichia coli (Gardner et al. 2000;Li and Zhang 2020;Ozbudak et al. 2004). ...
Cell-fate transition can be modeled by ordinary differential equations (ODEs) which describe the behavior of several molecules in interaction, and for which each stable equilibrium corresponds to a possible phenotype (or ‘biological trait’). In this paper, we focus on simple ODE systems modeling two molecules which each negatively (or positively) regulate the other. It is well-known that such models may lead to monostability or multistability, depending on the selected parameters. However, extensive numerical simulations have led systems biologists to conjecture that in the vast majority of cases, there cannot be more than two stable points. Our main result is a proof of this conjecture. More specifically, we provide a criterion ensuring at most bistability, which is indeed satisfied by most commonly used functions. This includes Hill functions, but also a wide family of convex and sigmoid functions. We also determine which parameters lead to monostability, and which lead to bistability, by developing a more general framework encompassing all our results.
... These models have been widely used in order to describe different cellular processes such as the epithelial-mesenchymal transition (EMT) [1,11,20], hematopoietic stem cells [9,12,19], embryonic stem cells [4] or other cell-fate differentiation phenomena involved in Xenopus [5,16], Drosophila [18] or Escherichia coli [6,13,17]. ...
Cell-fate transition can be modeled by ordinary differential equations (ODEs) which describe the behavior of several molecules in interaction, and for which each stable equilibrium corresponds to a possible phenotype (or 'biological trait'). In this paper, we focus on simple ODE systems modeling two molecules which each negatively (or positively) regulate the other. It is well-known that such models may lead to monostability or multistability, depending on the selected parameters. However, extensive numerical simulations have led systems biologists to conjecture that in the vast majority of cases, there cannot be more than two stable points. Our main result is a proof of this conjecture. More specifically, we provide a criterion ensuring at most bistability, which is indeed satisfied by most commonly used functions. This includes Hill functions, but also a wide family of convex and sigmoid functions. We also determine which parameters lead to monostability, and which lead to bistability, by developing a more general framework encompassing all our results.
... In these models, the extracellular signals may vary according to the position of a cell within a spatial domain. It is possible that these complex mechanisms could also participate in defining the fate of SCs' progeny 5,[17][18][19][20][21][22][23][24][25] , where some of the spatial signals could be emanating from the organizer or other niche cells 1,2,[26][27][28][29][30][31][32][33][34][35] . Dynamical models of gene regulatory networks define an attractor landscape: a multidimensional and non-linear potential that restricts the possible transitions among the network attractors (cell types) 16,[36][37][38] . ...
Asymmetric divisions maintain long-term stem cell populations while producing new cells that proliferate and then differentiate. Recent reports in animal systems show that divisions of stem cells can be uncoupled from their progeny differentiation, and the outcome of a division could be influenced by microenvironmental signals. But the underlying system-level mechanisms, and whether this dynamics also occur in plant stem cell niches (SCN), remain elusive. This article presents a cell fate regulatory network model that contributes to understanding such mechanism and identify critical cues for cell fate transitions in the root SCN. Novel computational and experimental results show that the transcriptional regulator SHR is critical for the most frequent asymmetric division previously described for quiescent centre stem cells. A multi-scale model of the root tip that simulated each cell’s intracellular regulatory network, and the dynamics of SHR intercellular transport as a cell-cell coupling mechanism, was developed. It revealed that quiescent centre cell divisions produce two identical cells, that may acquire different fates depending on the feedback between SHR’s availability and the state of the regulatory network. Novel experimental data presented here validates our model, which in turn, constitutes the first proposed systemic mechanism for uncoupled SCN cell division and differentiation.
... The situation a ≠ 0, including self-promotion in the regulatory motif, describes more architectures such as B cells promoting an antibody class switch 74 , gene regulatory networks with slow promoter kinetics 75 and cell-fate development and differentiation in eukaryotic cells 55,57,[76][77][78][79] . A famous example is the GATA-1-PU.1 system controlling hematopoietic stem cell differentiation, studied theoretically 47,75,[80][81][82][83] (with some consideration of the influence of energy variability 44 ). The relationship between pluripotent stem cell behaviour and energy metabolism has also been extensively studied 16 . ...
Cells generate phenotypic diversity both during development and in response to stressful and changing environments, aiding survival. Functionally vital cell fate decisions from a range of phenotypic choices are made by regulatory networks, the dynamics of which rely on gene expression and hence depend on the cellular energy budget (and particularly ATP levels). However, despite pronounced cell-to-cell ATP differences observed across biological systems, the influence of energy availability on regulatory network dynamics is often overlooked as a cellular decision-making modulator, limiting our knowledge of how energy budgets affect cell behaviour. Here, we consider a mathematical model of a highly generalisable, ATP-dependent, decision-making regulatory network, and show that cell-to-cell ATP variability changes the sets of decisions a cell can make. Our model shows that increasing intracellular energy levels can increase the number of supported stable phenotypes, corresponding to increased decision-making capacity. Model cells with sub-threshold intracellular energy are limited to a singular phenotype, forcing the adoption of a specific cell fate. We suggest that energetic differences between cells may be an important consideration to help explain observed variability in cellular decision-making across biological systems.
... The situation a = 0, including self-promotion in the regulatory motif, describes more architectures such as B cells promoting an antibody class switch [71], gene regulatory networks with slow promoter kinetics [72] and cell-fate development and differentiation in eukaryotic cells [73,74,50,75,52,76]. A famous example is the GATA-1-PU.1 system controlling hematopoietic stem cell differentiation [72,77,78,46,43,79,80]. We predict that ATP variability will influence fate decisions across this broad variety of systems, organisms and branches of life. ...
Cells are able to generate phenotypic diversity both during development and in response to stressful and changing environments, aiding survival. The biologically and medically vital process of a cell assuming a functionally important fate from a range of phenotypic possibilities can be thought of as a cell decision. To make these decisions, a cell relies on energy dependent pathways of signalling and expression. However, energy availability is often overlooked as a modulator of cellular decision-making. As cells can vary dramatically in energy availability, this limits our knowledge of how this key biological axis affects cell behaviour. Here, we consider the energy dependence of a highly generalisable decision-making regulatory network, and show that energy variability changes the sets of decisions a cell can make and the ease with which they can be made. Increasing intracellular energy levels can increase the number of stable phenotypes it can generate, corresponding to increased decision-making capacity. For this decision-making architecture, a cell with intracellular energy below a threshold is limited to a singular phenotype, potentially forcing the adoption of a specific cell fate. We suggest that common energetic differences between cells may explain some of the observed variability in cellular decision-making, and demonstrate the importance of considering energy levels in several diverse biological decision-making phenomena.
... Next, we test if our simplifying assumptions of independent differentiation and delay due to a stochastic gene expression process are compatible with existing paradigms of lineage choice. To address this, we implement the popular toggle switch model that is thought to drive binary lineage decision [9,10,11,12,13,14,15] composed of two mutually repressing transcription factors X and Y (see Supplementary Figure 11A). The model is summarized by four chemical reactions (synthesis and degradation of the respective proteins): ...
... Instead of comparing one TF across cell types, Okawa et al (26) and Cahan et al (23) compared pairs of TFs (Table II and Fig. 3). The comparison of pairs is based on the concept that balanced expression between two TFs is associated with cell identity (36)(37)(38). Okawa et al (26) proposed the normalized ratio difference (NRD) to score all pairs of TFs that are similarly expressed in a progenitor cell type, and highly different in and between daughter cell types. Cahan et al (23) additionally compared pairs of TFs, using the metric of the context likelihood of relatedness (CLR). ...
There is a need for specific cell types in regenerative medicine and biological research. Frequently, specific cell types may not be easily obtained or the quantity obtained is insufficient for study. Therefore, reprogramming by the direct conversion (transdifferentiation) or re‑induction of induced pluripotent stem cells has been used to obtain cells expressing similar profiles to those of the desired types. Therefore, a specific cocktail of transcription factors (TFs) is required for induction. Nevertheless, identifying the correct combination of TFs is difficult. Although certain computational approaches have been proposed for this task, their methods are complex, and corresponding implementations are difficult to use and generalize for specific source or target cell types. In the present review four computational approaches that have been proposed to obtain likely TFs were compared and discussed. A simplified view of the computational complexity of these methods is provided that consists of three basic ideas: i) The definition of target and non‑target cell types; ii) the estimation of candidate TFs; and iii) filtering candidates. This simplified view was validated by analyzing a well‑documented cardiomyocyte differentiation. Subsequently, these reviewed methods were compared when applied to an unknown differentiation of corneal endothelial cells. The generated results may provide important insights for laboratory assays. Data and computer scripts that may assist with direct conversions in other cell types are also provided.
... Early works to use continuous-valued models for characterizing and quantifying stem cell differentiation processes in terms of switch behavior include Roeder & Glauche (2006) for hematopoiesis and Chickarmane, Troein, Nuber, Sauro, & Peterson (2006) for embryonic stem cells. The former was motivated by experimental observations on the TFs GATA-1 and PU.1, both known to act as key regulators and potential antagonists in the erythroid versus myeloid differentiation processes of hematopoietic progenitor cells as in Figure 1. ...
As cell and molecular biology is becoming increasingly quantitative, there is an upsurge of interest in mechanistic modeling at different levels of resolution. Such models mostly concern kinetics and include gene and protein interactions as well as cell population dynamics. The final goal of these models is to provide experimental predictions, which is now taking on. However, even without matured predictions, kinetic models serve the purpose of compressing a plurality of experimental results into something that can empower the data interpretation, and importantly, suggesting new experiments by turning “knobs” in silico. Once formulated, kinetic models can be executed in terms of molecular rate equations for concentrations or by stochastic simulations when only a limited number of copies are involved. Developmental processes, in particular those of stem and progenitor cell commitments, are not only topical but also particularly suitable for kinetic modeling due to the finite number of key genes involved in cellular decisions. Stem and progenitor cell commitment processes have been subject to intense experimental studies over the last decade with some emphasis on embryonic and hematopoietic stem cells. Gene and protein interactions governing these processes can be modeled by binary Boolean rules or by continuous‐valued models with interactions set by binding strengths. Conceptual insights along with tested predictions have emerged from such kinetic models. Here we review kinetic modeling efforts applied to stem cell developmental systems with focus on hematopoiesis. We highlight the future challenges including multi‐scale models integrating cell dynamical and transcriptional models.
This article is categorized under:
• Models of Systems Properties and Processes > Mechanistic Models
• Developmental Biology > Stem Cell Biology and Regeneration
... A classical GRN motif, the toggle switch, constitutes a molecular mechanism that determines cell-fate decisions, and provides stability to transcriptional programs of binary cell-fate choices. Overexpression of each transcription factor (TF) corresponds to one of the two mutually exclusive cell fates, whereas a ''balanced'' expression of both TFs maintains the stem/progenitor state (Huang et al., 2007;Jacob and Monod, 1961;Roeder and Glauche, 2006). The toggle switch has been experimentally shown to play an important role in binary cell-fate control of stem/progenitor cells (Graf, 2002;Lin et al., 2008;Ralston and Rossant, 2005). ...
... The branching point that divides erythroid and myeloid lineages is particularly well-studied within the haematopoietic hierarchy, and has been shown to be controlled by two master genes: GATA1 and PU.1 [72][73][74]. These models are able to describe the process of choosing between the two cell fates and propose that i) cells are "primed" before differentiation and that ii) the primed state can arise following a loss of cooperativity between the two genes. ...
Stem cells are fundamental to human life and offer great therapeutic potential, yet their biology remains incompletely - or in cases even poorly - understood. The field of stem cell biology has grown substantially in recent years due to a combination of experimental and theoretical contributions: the experimental branch of this work provides data in an ever-increasing number of dimensions, while the theoretical branch seeks to determine suitable models of the fundamental stem cell processes that these data describe. The application of population dynamics to biology is amongst the oldest applications of mathematics to biology, and the population dynamics perspective continues to offer much today. Here we describe the impact that such a perspective has made in the field of stem cell biology. Using haematopoietic stem cells as our model system, we discuss the approaches that have been used to study their key properties, such as capacity for self-renewal, differentiation, and cell fate lineage choice. We will also discuss the relevance of population dynamics in models of stem cells and cancer, where competition naturally emerges as an influential factor on the temporal evolution of cell populations. This article is protected by copyright. All rights reserved.
... A classical GRN motif, the toggle switch, constitutes a molecular mechanism that determines cell-fate decisions, and provides stability to transcriptional programs of binary cell-fate choices. Overexpression of each transcription factor (TF) corresponds to one of the two mutually exclusive cell fates, whereas a ''balanced'' expression of both TFs maintains the stem/progenitor state (Huang et al., 2007;Jacob and Monod, 1961;Roeder and Glauche, 2006). The toggle switch has been experimentally shown to play an important role in binary cell-fate control of stem/progenitor cells (Graf, 2002;Lin et al., 2008;Ralston and Rossant, 2005). ...
Identification of cell-fate determinants for directing stem cell differentiation remains a challenge. Moreover, little is known about how cell-fate determinants are regulated in functionally important subnetworks in large gene-regulatory networks (i.e., GRN motifs). Here we propose a model of stem cell differentiation in which cell-fate determinants work synergistically to determine different cellular identities, and reside in a class of GRN motifs known as feedback loops. Based on this model, we develop a computational method that can systematically predict cell-fate determinants and their GRN motifs. The method was able to recapitulate experimentally validated cell-fate determinants, and validation of two predicted cell-fate determinants confirmed that overexpression of ESR1 and RUNX2 in mouse neural stem cells induces neuronal and astrocyte differentiation, respectively. Thus, the presented GRN-based model of stem cell differentiation and computational method can guide differentiation experiments in stem cell research and regenerative medicine.
... If this question of multistability equivalence could be answered, methods and results from systems analysis of low-dimensional systems and modeling via high-dimensional systems could be combined and thus greatly enhance the understanding of a particular GRN system at hand. For example, models of cell differentiation illustrating key dynamic properties like multistability are typically constructed as low-dimensional models [6,3,8]. Yet, common differentiation networks are known to involve dozens of genes, and at least structural models are sometimes known or can be inferred from high-throughput measurements or literature meta-analyses [16]. ...
This paper formulates and addresses the problem of equivalence in terms of multistability properties between nonlinear models of gene regulatory systems of different dimensionality. Given a nonlinear dynamical model of a gene regulatory network and the structure of another higher-dimensional gene regulatory network, the aim is to find a dynamical model for the latter that has the same equilibria and stability properties as the former. We propose construction rules for the dynamics of a high-dimensional system, given the low-dimensional system and the high-dimensional network structure. These construction rules yield a multistability-equivalent system, as we prove in this work. We demonstrate the value of our method by applying it to an example of a multistable gene regulatory network involved in mesenchymal stem cell differentiation. Here, differentiation is described by a core motif of three genetic regulators, but the detailed network contains at least nine genes. The proposed construction method allows to transfer the multistability based differentation mechanism of the core motif to the more detailed gene regulatory network. Copyright © 2016 John Wiley & Sons, Ltd.
... The examples of such potent regulatory factors with simple circuits of interactions between them have prompted creation of models in which developmental choice is based on a proposed duel between the contending 'master regulators' (32)(33)(34). This kind of model is very easy to convert into systems of differential equations, which makes such a model appealing across disciplines. ...
The pathway to generate T cells from hematopoietic stem cells guides progenitors through a succession of fate choices while balancing differentiation progression against proliferation, stage to stage. Many elements of the regulatory system that controls this process are known, but the requirement for multiple, functionally distinct transcription factors needs clarification in terms of gene network architecture. Here, we compare the features of the T-cell specification system with the rule sets underlying two other influential types of gene network models: first, the combinatorial, hierarchical regulatory systems that generate the orderly, synchronized increases in complexity in most invertebrate embryos; second, the dueling 'master regulator' systems that are commonly used to explain bistability in microbial systems and in many fate choices in terminal differentiation. The T-cell specification process shares certain features with each of these prevalent models but differs from both of them in central respects. The T-cell system is highly combinatorial but also highly dose-sensitive in its use of crucial regulatory factors. The roles of these factors are not always T-lineage-specific, but they balance and modulate each other's activities long before any mutually exclusive silencing occurs. T-cell specification may provide a new hybrid model for gene networks in vertebrate developmental systems.
... The parameters ρ i (i = x, y) are the auto-activation coefficients and v i (i = x, y) quantify the cross-activation or crossinhibition strengths, depending on their values, i.e., mutual inhibition when 0 < v i < 1 and mutual activation when v i > 1. The model and its simplified form are often used to describe the coexistence of multiple stable states in cell fate decisions [31][32][33]. As shown in [7], strong auto-activation (ρ x 1, ρ y 1) in the model (12)-(13) is necessary for the existence of multiple stable states. ...
Cells accomplish the process of fate decisions and form terminal lineages through a series of binary choices in which cells switch stable states from one branch to another as the interacting strengths of regulatory factors continuously vary. Various combinatorial effects may occur because almost all regulatory processes are managed in a combinatorial fashion. Combinatorial regulation is crucial for cell fate decisions because it may effectively integrate many different signaling pathways to meet the higher regulation demand during cell development. However, whether the contribution of combinatorial regulation to the state transition is better than that of a single one and if so, what the optimal combination strategy is, seem to be significant issue from the point of view of both biology and mathematics. Using the approaches of combinatorial perturbations and bifurcation analysis, we provide a general framework for the quantitative analysis of synergism in molecular networks. Different from the known methods, the bifurcation-based approach depends only on stable state responses to stimuli because the state transition induced by combinatorial perturbations occurs between stable states. More importantly, an optimal combinatorial perturbation strategy can be determined by investigating the relationship between the bifurcation curve of a synergistic perturbation pair and the level set of a specific objective function. The approach is applied to two models, i.e., a theoretical multistable decision model and a biologically realistic CREB model, to show its validity, although the approach holds for a general class of biological systems.
... We do not talk about these ones, way too complex to get either an analytic work out of it, but also to investigate the influence of specific parameters. Stochasticity and deterministic approaches were amongst the most appropriate answers to the modeling challenges and started to be popular in the scientific community dealing with lineage choice or cell fate [137], [242], [246], [160]. ...
The objective of this paper is to give a review of the main works dealing with mathematical modeling of blood cell formation, disorders and treatments within the past fifty years. From the first models to the most recent ones, this research field has inspired many leading experts in mathematics, biology, physics, physiology and computer sciences. Each contribution was a step further to the understanding of these complex processes. This work summarizes the key ones and tries to show not only the evolution of the interest for this problem but also the different research trends throughout the decades up to the latest models of the past years.
... A computational model of the transcriptional circuitry that regulates the fate of embryonic stem cells showed that a network of just three transcription factors, OCT4, SOX2, and NANOG, connected by multiple positive feedback loops can generate a bistable switch, alternating between two phenotypic states of the system: self-renewal and differentiation (Chickarmane et al., 2006). Similar theoretical models (Huang et al., 2007;Roeder and Glauche, 2006) have been used to show that mutual inhibition of the two transcription factors GATA-1 and PU.1 coupled with positive autocatalytic regulation can help explain lineage choice (myeloid vs erythroid differentiation) in hematopoietic stem cells, where the system switches from a 'priming' state representing the stem cell fate with low-level coexpression of the two transcription factors to a 'differentiated' state where one factor dominates at the expense of the other. Gene expression profiling showed that distinct trajectories of neutrophil differentiation induced by two different reagents converge to a common state of expression of 2773 relevant genes, lending strong support to the idea of cell fates as high-dimensional attractor states (Huang, 2005;Huang et al., 2005). ...
From a public health perspective the main contributions of toxicity testing and toxicology research are to assess, with minimum uncertainty, the risks posed to human populations by exposure to specified concentrations of potentially toxic compounds. Toxicology studies also evaluate the risks posed by exposure of environmental species or ecosystems. In what way do these estimates of expected risks serve the public? First, they provide measures to determine if the compounds are themselves likely to cause harm in a specific population. Second, they provide a means to compare one chemical with another, that is, to assess relative risks, thereby aiding in selecting particular chemicals for a specific societal use. Third, these health risk estimates can be useful in a larger context to compare risks from different activities, that is, from chemical exposure to disinfection by-products versus risks of discontinuation of water disinfection, a point of controversy with disinfection by-products in drinking water. These various risk assessment activities are only as rigorous as the data that are available for the decision-making process and require contributions from a variety of biological disciplines, including toxicology and molecular biology.
... When derived form adults, one of the applications is related to transplantation, namely either to promote regeneration of diseased or damaged tissue or to rescue defective genes [26]. Foster et al. developed a mathematical model for cell differentiation that predicts presence of multiple stable states for differentiated cells, bifurcations and switch-like transitions [27,28]. Later, Schittler et al. expanded the model to include the progenitor state and studied the system of binary differentiation with respect to various stimuli [29]. ...
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Background: The chemical master equation is the fundamental equation of stochastic chemical kinetics. This differential-difference equation describes temporal evolution of the probability density function for states of a chemical system. A state of the system, usually encoded as a vector, represents the number of entities or copy numbers of interacting species, which are changing according to a list of possible reactions. It is often the case, especially when the state vector is high-dimensional, that the number of possible states the system may occupy is too large to be handled computationally. One way to get around this problem is to consider only those states that are associated with probabilities that are greater than a certain threshold level.
... In hematopoiesis there exist several lineage branch points with identified key transcription factors and external signals [4][5][6]. For instance, a particularly well studied subnetwork is the one involving the genes PU.1 and GATA-1, which underlies the erythroid-myeloid lineage-determination step and has been proved to exhibit both a commitment switch and priming features [7][8][9][10][11][12]. In many cases, switch-like gene circuits have evolved to realize a sort of cellular built-in memory, which can lead to phenotypic diversity [13]. ...
Despite progresses in identifying the cellular mechanisms at the basis of the differentiation of hematopoietic stem/progenitor cells, little is known about the regulatory circuitry at the basis of lineage commitment of hematopoietic multipotent progenitors. To address this issue, we propose a computational approach to give further insights in the comprehension of this genetic mechanism. Differently from T lymphopoiesis, however, there is at present no mathematical model describing lineage restriction of multipotent progenitors to early B-cell precursors. Here, we provide a first model—constructed on the basis of current experimental evidence from literature and of publicly available microarray datasets—of the genetic regulatory network driving the cellular fate determination at the stage of lymphoid lineage commitment, with particular regard to the multipotent-B-cell progenitor transition. By applying multistability analysis methods, we are able to assess the capability of the model to capture t
... Curiously, these positive feedback loops, like many others in developmental processes, appear to comprise double-negative feedback processes to provide the required positive feedback. (5) Similar theoretical models (Huang et al., 2007;Roeder and Glauche, 2006) have been used to show that mutual inhibition of the two transcription factors GATA-1 and PU.1 coupled with positive auto-catalytic regulation can help explain lineage choice (myeloid vs erythroid differentiation) in hematopoietic stem cells. This system switches from a "primed" state representing the stem cell fate with low-level co-expression of the two transcription factors, to a differentiated state where one factor dominates at the expense of the other. ...
Biological Switches and Signaling MotifsDiscreteness in Cellular Fates and the Underlying Genetic ProgramSwitching and BistabilityThe Gene Autoregulation MotifConclusions
References
... As a result of a competition between two proteins (PU.1 and GATA-1), the MEP differentiates either into an erythroid progenitor or into a megakaryocytic progenitor. This choice has been modelled by Roeder and Glauche [92] and Huang et al. [55]. In both studies, models proposed by the authors demonstrated a bistable behaviour. ...
This PhD thesis is devoted to mathematical modelling of haematopoiesis and blood diseases. We investigate several models, which deal with different and complementary aspects of haematopoiesis.The first part of the thesis concerns a multi-scale model of erythropoiesis where intracellular regulatory networks, which determine cell choice between self-renewal, differentiation and apoptosis, are coupled with dynamics of cell populations. Using experimental data on anemia in mice, we evaluate the roles of different feedback mechanisms in response to stress situations. At the next stage of modelling, spatial cell distribution in the bone marrow is taken into account, the question which has not been studied before. We describe normal haematopoiesis with a system of reaction-diffusion-convection equations and prove existence of a stationary cell distribution. We then introduce malignant cells into the model. For some parameter values the disease free solution becomes unstable and another one, which corresponds to leukaemia, appears. This leads to the formation of tumour which spreads in the bone marrow as a travelling wave. The speed of its propagation is studied analytically and numerically. Bone marrow cells exchange different signals that regulate cell behaviour. We study, next, an integro-differential equation which describes cell communication and prove the existence of travelling wave solutions using topological degree and the Leray-Schauder method. Individual based approach is used to study distribution of different cell types in the bone marrow. Finally, we investigate a Physiologically Based Pharmacokinetics-Pharmacodynamics model of leukaemia treatment with AraC drug. AraC acts as chemotherapy, inducing apoptosis of all proliferating cells, normal and malignant. Pharmacokinetics provides the evolution of intracellular AraC. This, in turn, determines cell population dynamics and, consequently, efficacy of treatment with different protocols.
... The model predictions of transduction efficiency will likely deviate greatly from the experimental observations if the current model is applied to transducing HSCs. Mathematical models have been developed to represent the expansion and differentiation of HSCs in culture especially to investigate the occurrence of leukemia(Colijn and Mackey 2005;Roeder and Glauche 2006). By integrating these proliferation and differentiation kinetics into the target cell population balances, the model should be able to predict the transduction of different hematopoietic stem cell populations. ...
Patient-derived xenografts represent the gold standard in pre-clinical research models. The chicken embryo chorioallantoic membrane (CAM) is used in functional studies for studying biological processes such as blood vessel development and embryogenesis, biocompatible material testing, and more recently three-dimensional patient-derived xenograft (PDX) tumor modeling. We describe here a detailed method used to readily engraft established mouse PDX and primary patient tumor specimens on the CAM with as little as 25 mg of tissue per embryonated egg.
The large-scale development of high-throughput sequencing technologies has not only allowed the generation of reliable omics data related to various regulatory layers but also the development of novel computational models in the field of stem cell research. These computational approaches have enabled the disentangling of a complex interplay between these interrelated layers of regulation by interpreting large quantities of biomedical data in a systematic way. In the context of stem cell research, network modeling of complex gene-gene interactions has been successfully used for understanding the mechanisms underlying stem cell differentiation and cellular conversion. Notably, it has proven helpful for predicting cell-fate determinants and signaling molecules controlling such processes. This chapter will provide an overview of various computational approaches that rely on single-cell and/or bulk RNA sequencing data for elucidating the molecular underpinnings of cell subpopulation identities, lineage specification, and the process of cell-fate decisions. Furthermore, we discuss how these computational methods provide the right framework for computational modeling of biological systems in order to address long-standing challenges in the stem cell field by guiding experimental efforts in stem cell research and regenerative medicine.
Billions of functionally distinct blood cells emerge from a pool of hematopoietic stem cells in our bodies every day. This progressive differentiation process is hierarchically structured and remarkably robust. We provide an introductory review to mathematical approaches addressing the functional aspects of how lineage choice is potentially implemented on a molecular level. Emerging from studies on the mutual repression of key transcription factors we illustrate how those simple concepts have been challenged in recent years and subsequently extended. Especially the analysis of omics data on the single cell level with computational tools provide descriptive insights on a yet unknown level, while their embedding into a consistent mechanistic and mathematical framework is still incomplete.
In the past years, the mechanisms of cell reprogramming and transdifferentiation via the way of gene regulation, stochastic fluctuations, or chemical induction to realize cell type transitions from the perspectives of single cells were explored. In multicellular organisms, intercellular communication plays crucial roles in cell fate decisions. However, the importance of intercellular communication to the processes of cell reprogramming and transdifferentiation is often neglected. In this paper, the mechanisms of cell reprogramming and transdifferentiation by intercellular communication are investigated. A two-gene circuit with mutual inhibition and self-activation as a basic model is selected. Then, a coupling mechanism via intercellular communication by introducing a specific signaling molecule into the gene circuit is considered. Finally, the influence of coupling intensity on the dynamics of the coupled system of two cells is analyzed. Moreover, when the coupling intensity changes with respect to the cell number in a discrete way, the effects of coupling intensity on cell reprogramming and transdifferentiation are discussed. Some theoretical analysis of stability and bifurcation of the systems are also given. Our research shows that cells can realize cell reprogramming and transdifferentiation via intercellular interaction at opportune coupling intensity. These results not only further enrich previous studies but also are beneficial to understand the mechanisms of cell reprogramming and transdifferentiation via intercellular communication in the growth and development of multicellular organisms.
Multistability is a key property of dynamical systems modeling cellular regulatory networks implicated in cell fate decisions, where, different stable steady states usually represent distinct cell phenotypes. Monotone network motifs are highly represented in these regulatory networks. In this paper, we leverage the properties of monotone dynamical systems to provide theoretical results that guide the selection of inputs that trigger a transition, i.e., reprogram the network, to a desired stable steady state. We first show that monotone dynamical systems with bounded trajectories admit a minimum and a maximum stable steady state. Then, we provide input choices that are guaranteed to reprogram the system to these extreme steady states. For intermediate states, we provide an input space that is guaranteed to contain an input that reprograms the system to the desired state. We then provide implementation guidelines for finite-time procedures that search this space for such an input, along with rules to prune parts of the space during search. We demonstrate these results on simulations of two recurrent regulatory network motifs: self-activation within mutual antagonism and self-activation within mutual cooperation. Our results depend uniquely on the structure of the network and are independent of specific parameter values.
This paper explains a substantial feature of symmetry breaking of dynamical systems that include bistability from the mathematical point of view to highlight important consequences of this phenomenon to biochemical and system biology studies since symmetry breaking as a bifurcation itself can serve as a source of branching. We take hematopoietic stem cells modeling as a particular case.
A central issue in the analysis of multi-stable systems is that of controlling the relative size of the basins of attraction of alternative states through suitable choices of system parameters. We are interested here mainly in the stochastic version of this problem, that of shaping the stationary probability distribution of a Markov chain so that various alternative modes become more likely than others. Although many of our results are more general, we were motivated by an important biological question, that of cell differentiation. In the mathematical modeling of cell differentiation, it is common to think of internal states of cells (quanfitied by activation levels of certain genes) as determining the different cell types. Specifically, we study here the "PU.1/GATA-1 circuit" which is involved in the control of the development of mature blood cells from hematopoietic stem cells (HSCs). All mature, specialized blood cells have been shown to be derived from multipotent HSCs. Our first contribution is to introduce a rigorous chemical reaction network model of the PU.1/GATA-1 circuit, which incorporates current biological knowledge. We then find that the resulting ODE model of these biomolecular reactions is incapable of exhibiting multistability, contradicting the fact that differentiation networks have, by definition, alternative stable steady states. When considering instead the stochastic version of this chemical network, we analytically construct the stationary distribution, and are able to show that this distribution is indeed capable of admitting a multiplicity of modes. Finally, we study how a judicious choice of system parameters serves to bias the probabilities towards different stationary states. We remark that certain changes in system parameters can be physically implemented by a biological feedback mechanism; tuning this feedback gives extra degrees of freedom that allow one to assign higher likelihood to some cell types over others.
Lineage switches are genetic regulatory motifs that govern and maintain the commitment of a developing cell to a particular cell fate. A canonical example of a lineage switch is the pair of transcription factors PU.1 and GATA-1, of which the former is affiliated with the myeloid and the latter with the erythroid lineage within the haematopoietic system. On a molecular level, PU.1 and GATA-1 positively regulate themselves and antagonize each other via direct protein–protein interactions. Here we use mathematical modelling to identify a novel type of dynamic behaviour that can be supported by such a regulatory architecture. Guided by the specifics of the PU.1–GATA-1 interaction, we formulate, using the law of mass action, a system of differential equations for the key molecular concentrations. After a series of systematic approximations, the system is reduced to a simpler one, which is tractable to phase-plane and linearization methods. The reduced system formally resembles, and generalizes, a well-known model for competitive species from mathematical ecology. However, in addition to the qualitative regimes exhibited by a pair of competitive species (exclusivity, bistable exclusivity, stable-node coexpression) it also allows for oscillatory limit-cycle coexpression. A key outcome of the model is that, in the context of cell-fate choice, such oscillations could be harnessed by a differentiating cell to prime alternately for opposite outcomes; a bifurcation-theory approach is adopted to characterize this possibility.
As our catalog of cell states expands, appropriate characterization of these states and the transitions between them is crucial. Here we discuss the roles of intermediate cell states (ICSs) in this growing collection. We begin with definitions and discuss evidence for the existence of ICSs and their relevance in various tissues. We then provide a list of possible functions for ICSs with examples. Finally, we describe means by which ICSs and their functional roles can be identified from single-cell data or predicted from models.
In this chapter we will deal with the aspects which are specific to blood cancer, avoiding to broaden our analysis to the general area of modeling tumor growth and therapy, which is huge, though it includes many subjects involving blood too (like angiogenesis and tumors perfusion, drugs delivery to tumors, etc.) which occupy a large space in the literature of mathematical modeling of tumors. Even the restricted field of modeling leukemic disorders is extremely large for the great variety of the subjects and of the approaches that have been adopted in the literature. The reader will realize the impressive complexity of the present topic already from the sketchy classification of leukemic disorders in Sect. 8.2.
We review recent mathematical models in stem cell differentiation with a focus on the role of epigenetics in cell fate determination. Gene regulatory networks can be described as a dynamical system. Within this high-dimensional system cell states correspond to attractors. This study spotlights the quasi-potential landscape to represent the transition between cell states as functions of stochastic and deterministic influences. Furthermore, we will investigate how these models apply to the area of neural differentiation, with a focus on the role of the Sonic Hedgehog (Shh), Notch, and Wnt pathways. Finally, we will discuss the epigenetic landscape model in relation to cancer and cancer stem cells.
Engineering complex biological systems is fundamentally different from engineering nonliving systems. Multicellular living systems exhibit multistability, the coexistence of multiple stable attractors which arise from gene regulatory networks, encode the discrete cell types and collectively establish a 'rugged' potential-like landscape. While robustness of one attractor is the chief concern in engineering, the relevant dynamics in multicellular systems operates in the regime of frequent transitions among the attractors, corresponding to cell-type switching in development and in artificial cell reprogramming. This entails a more general formalism. Here we present a mathematical framework for constructing the quasi-potential landscape which relates the attractor to each other, derived from the decomposition of the vector field given by the ODEs which describe the dynamics of the gene networks. The rate for a transition between attractors and its 'least action path' are computed based on the Freidlin-Wentzell large deviation theory. These theoretical concepts provide the tools for rational design of gene network manipulations to steer cell fates for regenerative medicine instead of using trial-and-errors approaches.
The thesis is devoted to mathematical modeling of hematopoiesis and blood diseases. It is based on the development of hybrid discrete continuous models and to their applications to investigate production of blood cell (hematopoiesis) and blood diseases such as lymphoma and myeloma. The first part of the thesis concerns production of blood cells in the bone marrow. We will mainly study production of red blood cells, erythropoiesis. In mammals erythropoiesis occurs in special structures, erythroblastic islands. Their functioning is determined by complex intracellular and extracellular regulations which include various cell types, hormones and growth factors. The results of modeling are compared with biological and medical data for humans and mice. The purpose of the second part of the thesis is to model some blood diseases, T cell Lymphoblastic lymphoma (T-LBL) and multiple myeloma (MM) and their treatment. TLBL develops in the thymus and it affects the immune system. In MM malignant cells invade the bone marrow and destroy erythroblastic islands preventing normal functioning of erythropoiesis. We developed multi-scale models of these diseases in order to take into account intracellular molecular regulation, cellular level and extracellular regulation. The response to treatment depends on the individual characteristics of the patients. Various scenarios are considered including successful treatment, relapse and development of the resistance to treatment. The last part of the thesis is devoted to a reaction-diffusion model which can be used to describe Darwinian evolution of cancer cells. Existence of pulse solutions, which can describe localized cell populations and their evolution, is proved
Glossary
Definition of the Subject
Introduction
Overview: Studies of Networks in Systems Biology
Network Architecture
Network Dynamics
Cell Fates, Cell types: Terminology and Concepts
History of Explaning Cell Types
Boolean Networks as Model for Complex GRNs
Three Regimes of Behaviors for Boolean Networks
Experimental Evidence from Systems Biology
Future Directions and Questions
Bibliography
A key goal of regenerative medicine and bioengineering is the quantitative and robust control over the fate and behavior of individual cells and their populations, both in vitro and in vivo. Central to this endeavor are stem cells (SCs), which can be functionally defined as undifferentiated cells of a multicellular organism that balance the capacity for sustained self-renewal with the potential to differentiate into specialized cell types. The biology of multicellular organisms necessitates the existence and precise control of SCs to facilitate development from a single cell during embryogenesis, and tissue homeostasis in the face of continual loss of terminally differentiated cells. It is therefore not surprising that SCs have been identified and isolated from numerous adult human tissues, as well as more recently, the inner cell mass of the preimplantation human blastocyst. SCs promise a renewable source of human tissue for research, pharmaceutical testing, and cell-based therapies. Fulfilling this promise will require not only the precise control of SC self-renewal and differentiation, but also imposing this control on the formation of more functionally complex tissue-like structures.
This paper deals with the fractional order model for GATA-switching for regulating the differentiation of a hematopoietic stem cell. We give a detailed analysis for the asymptotic stability of the model. The Adams-Bashforth-Moulton algorithm has been used to solve and simulate the system of differential equations.
Mathematical models of stem cell differentiation are commonly based upon the concept of subsequent cell fate decisions, each controlled by a gene regulatory network. These networks exhibit a multistable behavior and cause the system to switch between qualitatively distinct stable steady states. However, the network structure of such a switching module is often uncertain, and there is lack of knowledge about the exact reaction kinetics. In this paper, we therefore perform an elementary study of small networks consisting of three interacting transcriptional regulators responsible for cell differentiation: We investigate which network structures can reproduce a certain multistable behavior, and how robustly this behavior is realized by each network. In order to approach these questions, we use a modeling framework which only uses qualitative information about the network, yet allows model discrimination as well as to evaluate the robustness of the desired multistability properties. We reveal structural network properties which are necessary and sufficient to realize distinct steady state patterns required for cell differentiation. Our results also show that structural and robustness properties of the networks are related to each other.
Hematologists have traditionally studied blood and its components by simplifying it into its components and functions. A variety of new techniques have generated large and complex datasets. Coupled to an appreciation of blood as a dynamic system, a new approach in systems hematology is needed. Systems hematology embraces the multi-scale complexity with a combination of mathematical, engineering, and computational tools for constructing and validating models of biological phenomena. The validity of mathematical modeling in hematopoiesis was established early by the pioneering work of Till and McCulloch. This volume seeks to introduce to the various scientists and physicians to the multi-faceted field of hematology by highlighting recent works in systems biology. Deterministic, stochastic, statistical, and network-based models have been used to better understand a range of topics in hematopoiesis, including blood cell production, the periodicity of cyclical neutropenia, stem cell production in response to cytokine administration, and the emergence of drug resistance. Future advances require technological improvements in computing power, imaging, and proteomics as well as greater collaboration between experimentalists and modelers. Altogether, systems hematology will improve our understanding of normal and abnormal hematopoiesis, better define stem cells and their daughter cells, and potentially lead to more effective therapies.
A region located at kbp −3.9 to −2.6 5′ to the first hematopoietic exon of the GATA-1 gene is necessary to recapitulate gene expression in both the primitive and definitive erythroid lineages. In transfection
analyses, this region activated reporter gene expression from an artificial promoter in a position- and orientation-independent
manner, indicating that the region functions as the GATA-1 gene hematopoietic enhancer (G1HE). However, when analyzed in transgenic embryos in vivo, G1HE activity was orientation dependent
and also required the presence of the endogenousGATA-1 gene hematopoietic promoter. To define the boundaries of G1HE, a series of deletion constructs were prepared and tested in
transfection and transgenic mice analyses. We show that G1HE contains a 149-bp core region which is critical for GATA-1gene expression in both primitive and definitive erythroid cells but that expression in megakaryocytes requires the core plus
additional sequences from G1HE. This core region contains one GATA, one GAT, and two E boxes. Mutational analyses revealed
that only the GATA box is critical for gene-regulatory activity. Importantly, G1HE was active in SCL−/− embryos. These results thus demonstrate the presence of a critical network of GATA factors and GATA binding sites that controls
the expression of this gene.
The process through which multipotential hematopoietic cells commit to distinct lineages involves the induction of specific transcription factors. PU.1 (also known as Spi-1) and GATA-1 are transcription factors essential for the development of myeloid and erythroid lineages, respectively. Overexpression of PU.1 and GATA-1 can block differentiation in lineages in which they normally are down-regulated, indicating that not only positive but negative regulation of these factors plays a role in normal hematopoietic lineage development. Here we demonstrate that a region of the PU.1 Ets domain (the winged helix–turn–helix wing) interacts with the conserved carboxyl-terminal zinc finger of GATA-1 and GATA-2 and that GATA proteins inhibit PU.1 transactivation of critical myeloid target genes. We demonstrate further that GATA inhibits binding of PU.1 to c-Jun, a critical coactivator of PU.1 transactivation of myeloid promoters. Finally, PU.1 protein can inhibit both GATA-1 and GATA-2 transactivation function. Our results suggest that interactions between PU.1 and GATA proteins play a critical role in the decision of stem cells to commit to erythroid vs. myeloid lineages.
Transcription factors have been shown to play a role as "master switch" factors in the programming of hematopoietic cell commitment and differentiation. PU.1 is a hematopoietic-specific member of the Ets family of transcription factors. In human bone marrow CD34-enriched progenitor cells, PU.1 expression was upregulated during the early phases of granulocytic/monocytic differentiation, preceding expression of its target genes encoding CD11b and the macrophage-colony-stimulating factor receptor, whereas PU.1 was expressed at stable levels throughout erythroid differentiation. To study PU.1 function, we synthesized double-stranded phosphorothioate oligonucleotides containing a characterized PU.1 site and demonstrated their ability to specifically compete for PU.1 DNA binding. When added to CD34+ cells in vitro, wild-type PU.1-binding oligonucleotides significantly blocked hematopoietic colony formation, whereas mutated PU.1 oligonucleotides which no longer bind PU.1 had no specific inhibitory effect. These results demonstrate that PU.1 is developmentally upregulated during normal human myelopoiesis and that the function of PU.1 is critical for the development of in vitro hematopoiesis.
We have tested the hypothesis that multipotential hemopoietic stem and progenitor cells prime several different lineage-affiliated programs of gene activity prior to unilineage commitment and differentiation. Using single cell RT-PCR we show that erythroid (beta-globin) and myeloid (myeloperoxidase) gene expression programs can be initiated by the same cell prior to exclusive commitment to the erythroid or granulocytic lineages. Furthermore, the multipotential state is characterized by the coexpression of several lineage-affiliated cytokine receptors. These data support a model of hemopoietic lineage specification in which unilineage commitment is prefaced by a "promiscuous" phase of multilineage locus activation.
Biochemical and genetic approaches have identified the molecular mechanisms of many genetic reactions, particularly in bacteria. Now a comparably detailed understanding is needed of how groupings of genes and related protein reactions interact to orchestrate cellular functions over the cell cycle, to implement preprogrammed cellular development, or to dynamically change a cell's processes and structures in response to environmental signals. Simulations using realistic, molecular-level models of genetic mechanisms and of signal transduction networks are needed to analyze dynamic behavior of multigene systems, to predict behavior of mutant circuits, and to identify the design principles applicable to design of genetic regulatory circuits. When the underlying design rules for regulatory circuits are understood, it will be far easier to recognize common circuit motifs, to identify functions of individual proteins in regulation, and to redesign circuits for altered functions.
Malignant transformation usually inhibits terminal cell differentiation but the precise mechanisms involved are not understood. PU.1 is a hematopoietic-specific Ets family transcription factor that is required for development of some lymphoid and myeloid lineages. PU.1 can also act as an oncoprotein as activation of its expression in erythroid precursors by proviral insertion or transgenesis causes erythroleukemias in mice. Restoration of terminal differentiation in the mouse erythroleukemia (MEL) cells requires a decline in the level of PU.1, indicating that PU.1 can block erythroid differentiation. Here we investigate the mechanism by which PU.1 interferes with erythroid differentiation. We find that PU.1 interacts directly with GATA-1, a zinc finger transcription factor required for erythroid differentiation. Interaction between PU.1 and GATA-1 requires intact DNA-binding domains in both proteins. PU.1 represses GATA-1-mediated transcriptional activation. Both the DNA binding and transactivation domains of PU.1 are required for repression and both domains are also needed to block terminal differentiation in MEL cells. We also show that ectopic expression of PU.1 in Xenopus embryos is sufficient to block erythropoiesis during normal development. Furthermore, introduction of exogenous GATA-1 in both MEL cells and Xenopus embryos and explants relieves the block to erythroid differentiation imposed by PU.1. Our results indicate that the stoichiometry of directly interacting but opposing transcription factors may be a crucial determinant governing processes of normal differentiation and malignant transformation.
The PU.1 gene encodes an Ets family transcription factor which controls expression of many B cell- and macrophage-specific genes. Expression of the gene is critical for development of lymphoid and myeloid cell lineages, since PU.1-deficient mice exhibit defects in the development of these cell lineages. The PU.1 gene is identical to the Spi-1 gene isolated from common proviral integration sites in Friend virus-induced murine erythroleukemia (MEL), and deregulated expression of the gene is believed to be an essential step of the disease. We recently demonstrated that overexpression of PU.1 inhibits erythroid differentiation of MEL cells induced with the differentiating agent DMSO. We also noticed unexpectedly that overexpression of PU.1 together with DMSO induces marked growth arrest and apoptosis in MEL cells, supporting the notion that some oncogenes induce growth inhibition and apoptosis rather than cell proliferation and transformation under specific circumstances as shown with the c-myc gene. In this review, the role of PU.1 in hematopoietic cell differentiation, proliferation and apoptosis is described and the possible molecular mechanisms of PU.1-induced effects in MEL cells are discussed.
It has been proposed' that gene-regulatory circuits with virtually any desired property can be constructed from networks of simple regulatory elements. These properties, which include multistability and oscillations, have been found in specialized gene circuits such as the bacteriophage lambda switch and the Cyanobacteria circadian oscillator. However, these behaviours have not been demonstrated in networks of non-specialized regulatory components. Here we present the construction of a genetic toggle switch-a synthetic, bistable gene-regulatory network-in Escherichia coli and provide a simple theory that predicts the conditions necessary for bistability. The toggle is constructed from any two repressible promoters arranged in a mutually inhibitory network. It is flipped between stable states using transient chemical or thermal induction and exhibits a nearly ideal switching threshold. As a practical device, the toggle switch forms a synthetic, addressable cellular memory unit and has implications for biotechnology, biocomputing and gene therapy.
The GATA-1 transcription factor is capable of suppressing the myeloid gene expression program when ectopically expressed in myeloid cells. We examined the ability of GATA-1 to repress the expression and function of the PU.1 transcription factor, a central regulator of myeloid differentiation. We found that GATA-1 is capable of suppressing the myeloid phenotype without interfering with PU.1 gene expression, but instead was capable of inhibiting the activity of the PU.1 protein in a dose-dependent manner. This inhibition was independent of the ability of GATA-1 to bind DNA, suggesting that it is mediated by protein-protein interaction. We examined the ability of PU.1 to interact with GATA-1 and found a direct interaction between the PU.1 ETS domain and the C-terminal finger region of GATA-1. Replacing the PU.1 ETS domain with the GAL4 DNA-binding domain removed the ability of GATA-1 to inhibit PU.1 activity, indicating that the PU.1 DNA-binding domain, rather than the transactivation domain, is the target for GATA-1-mediated repression. We therefore propose that GATA-1 represses myeloid gene expression, at least in part, through its ability to directly interact with the PU.1 ETS domain and thereby interfere with PU.1 function. (Blood. 2000;95:2543-2551)
The lineage-specific transcription factors GATA-1 and PU.1 can physically interact to inhibit each other's function, but the mechanism of repression of GATA-1 function by PU.1 has not been elucidated. Both the N terminus and the C terminus of PU.1 can physically interact with the C-terminal zinc finger of GATA-1. It is demonstrated that the PU.1 N terminus, but not the C terminus, is required for inhibiting GATA-1 function. Induced overexpression of PU.1 in K562 erythroleukemia cells blocks hemin-induced erythroid differentiation. In this system, PU.1 does not affect the expression of GATA-1 messenger RNA, protein, or nuclear localization. However, GATA-1 DNA binding decreases dramatically. By means of electrophoretic mobility shift assays with purified proteins, it is demonstrated that the N-terminal 70 amino acids of PU.1 can specifically block GATA-1 DNA binding. In addition, PU.1 had a similar effect in the G1ER cell line, in which the GATA-1 null erythroid cell line G1E has been transduced with a GATA-1-estrogen receptor fusion gene, which is directly dependent on induction of the GATA-1 fusion protein to effect erythroid maturation. Consistent with in vitro binding assays, overexpression of PU.1 blocked DNA binding of the GATA-1 fusion protein as well as GATA-1-mediated erythroid differentiation of these G1ER cells. These results demonstrate a novel mechanism by which function of a lineage-specific transcription factor is inhibited by another lineage-restricted factor through direct protein-protein interactions. These findings contribute to understanding how protein-protein interactions participate in hematopoietic differentiation and leukemogenesis. (Blood. 2000;96:2641-2648)
A mathematical model for regulation of the tryptophan operon is presented. This model takes into account repression, feedback enzyme inhibition, and transcriptional attenuation. Special attention is given to model parameter estimation based on experimental data. The model's system of delay differential equations is numerically solved, and the results are compared with experimental data on the temporal evolution of enzyme activity in cultures of Escherichia coli after a nutritional shift (minimal + tryptophan medium to minimal medium). Good agreement is obtained between the numeric simulations and the experimental results for wild-type E. coli, as well as for two different mutant strains.
Feedback is a ubiquitous control mechanism of gene networks. Here, we have used positive feedback to construct a synthetic eukaryotic gene switch in Saccharomyces cerevisiae. Within this system, a continuous gradient of constitutively expressed transcriptional activator is translated into a cell phenotype switch when the activator is expressed autocatalytically. This finding is consistent with a mathematical model whose analysis shows that continuous input parameters are converted into a bimodal probability distribution by positive feedback, and that this resembles analog-digital conversion. The autocatalytic switch is a robust property in eukaryotic gene expression. Although the behavior of individual cells within a population is random, the proportion of the cell population displaying either low or high expression states can be regulated. These results have implications for understanding the graded and probabilistic mechanisms of enhancer action and cell differentiation.
GATA-1 and the ets factor PU.1 have been reported to functionally antagonize one another in the regulation of erythroid versus myeloid gene transcription and development. The CCAAT enhancer binding protein epsilon (C/EBPepsilon) is expressed as multiple isoforms and has been shown to be essential to myeloid (granulocyte) terminal differentiation. We have defined a novel synergistic, as opposed to antagonistic, combinatorial interaction between GATA-1 and PU.1, and a unique repressor role for certain C/EBPepsilon isoforms in the transcriptional regulation of a model eosinophil granulocyte gene, the major basic protein (MBP). The eosinophil-specific P2 promoter of the MBP gene contains GATA-1, C/EBP, and PU.1 consensus sites that bind these factors in nuclear extracts of the eosinophil myelocyte cell line, AML14.3D10. The promoter is transactivated by GATA-1 alone but is synergistically transactivated by low levels of PU.1 in the context of optimal levels of GATA-1. The C/EBPepsilon(27) isoform strongly represses GATA-1 activity and completely blocks GATA-1/PU.1 synergy. In vitro mutational analyses of the MBP-P2 promoter showed that both the GATA-1/PU.1 synergy, and repressor activity of C/EBPepsilon(27) are mediated via protein-protein interactions through the C/EBP and/or GATA-binding sites but not the PU.1 sites. Co-immunoprecipitations using lysates of AML14.3D10 eosinophils show that both C/EBPepsilon(32/30) and epsilon(27) physically interact in vivo with PU.1 and GATA-1, demonstrating functional interactions among these factors in eosinophil progenitors. Our findings identify novel combinatorial protein-protein interactions for GATA-1, PU.1, and C/EBPepsilon isoforms in eosinophil gene transcription that include GATA-1/PU.1 synergy and repressor activity for C/EBPepsilon(27).
This paper surveys the general theory of operon regulation as first formulated by Goodwin and Griffith, and then goes on to consider in detail models of regulation of tryptophan production by Bliss, Sinha, and Santillan and Mackey, and the interrelationships between them. We further give a linear stability analysis of the Santillan and Mackey model for wild type E. coli as well as three different mutant strains that have been previously studied in the literature. This stability analysis indicates that the tryptophan production systems should be stable, which is in accord with our numerical results. (c) 2001 American Institute of Physics.
PU.1 and GATA-1 are two hematopoietic specific transcription factors that play key roles in development of the myeloid and erythroid lineages, respectively. The two proteins bind to one another and inhibit each other's function in transcriptional activation and promotion of their respective differentiation programs. This mutual antagonism may be an important aspect of lineage commitment decisions. PU.1 can also act as an oncoprotein since deregulated expression of PU.1 in erythroid precursors causes erythroleukemias in mice. Studies of cultured mouse erythroleukemia cell lines indicate that one aspect of PU.1 function in erythroleukemogenesis is its ability to block erythroid differentiation by repressing GATA-1 (N. Rekhtman, F. Radparvar, T. Evans, and A. I. Skoultchi, Genes Dev. 13:1398-1411, 1999). We have investigated the mechanism of PU.1-mediated repression of GATA-1. We report here that PU.1 binds to GATA-1 on DNA. We localized the repression activity of PU.1 to a small acidic N-terminal domain that interacts with the C pocket of pRB, a well-known transcriptional corepressor. Repression of GATA-1 by PU.1 requires pRB, and pRB colocalizes with PU.1 and GATA-1 at repressed GATA-1 target genes. PU.1 and pRB also cooperate to block erythroid differentiation. Our results suggest that one of the mechanisms by which PU.1 antagonizes GATA-1 is by binding to it at GATA-1 target genes and tethering to these sites a corepressor that blocks transcriptional activity and thereby erythroid differentiation.
Recent studies have provided insights into the modular structure of genetic regulatory networks and emphasized the interest of quantitative functional descriptions. Here, to provide a priori knowledge of the structure of functional modules, we describe an evolutionary procedure in silico that creates small gene networks performing basic tasks. We used it to create networks functioning as bistable switches or oscillators. The obtained circuits provide a variety of functional designs, demonstrate the crucial role of posttranscriptional interactions, and highlight design principles also found in known biological networks. The procedure should prove helpful as a way to understand and create small functional modules with diverse functions as well as to analyze large networks.
Regulation of the hematopoietic transcription factor PU.1 (Spi-1) plays a critical role in the development of white cells,
and abnormal expression of PU.1 can lead to leukemia. We previously reported that the PU.1 promoter cannot induce expression
of a reporter gene in vivo, and cell-type-specific expression of PU.1 in stable lines was conferred by a 3.4-kb DNA fragment
including a DNase I hypersensitive site located 14 kb upstream of the transcription start site. Here we demonstrate that this
kb −14 site confers lineage-specific reporter gene expression in vivo. This kb −14 upstream regulatory element contains two
300-bp regions which are highly conserved in five mammalian species. In Friend virus-induced erythroleukemia, the spleen focus-forming
virus integrates into the PU.1 locus between these two conserved regions. DNA binding experiments demonstrated that PU.1 itself
and Elf-1 bind to a highly conserved site within the proximal homologous region in vivo. A mutation of this site abolishing
binding of PU.1 and Elf-1 led to a marked decrease in the ability of this upstream element to direct activity of reporter
gene in myelomonocytic cell lines. These data suggest that a potential positive autoregulatory loop mediated through an upstream
regulatory element is essential for proper PU.1 gene expression.
Hematopoietic stem cells (HSCs) maintain hematopoiesis by giving rise to all types of blood cells. Recent reports suggest that HSCs also possess the potential to generate nonhematopoietic tissues. To evaluate the underlying mechanisms in the commitment of HSCs into multitissue and multihematopoietic lineages, we performed oligonucleotide array analyses targeting for prospectively purified HSCs, multipotent progenitors (MPPs), common lymphoid progenitors (CLPs), and common myeloid progenitors (CMPs). Here we show that HSCs coexpress multiple nonhematopoietic genes as well as hematopoietic genes; MPPs coexpress myeloid and lymphoid genes; CMPs coexpress myeloerythroid, but not lymphoid genes, whereas CLPs coexpress T-, B-, and natural killer-lymphoid, but not myeloid, genes. Thus, the stepwise decrease in transcriptional accessibility for multilineage-affiliated genes may represent progressive restriction of developmental potentials in early hematopoiesis. These data support the hypothesis that stem cells possess a wide-open chromatin structure to maintain their multipotentiality, which is progressively quenched as they go down a particular pathway of differentiation.
We consider some of the problems involved in current discussions on stem cells in adult mammalian tissues. The present concepts involve a number of pitfalls, weaknesses and logical, semantic and classification problems. This indicates the necessity for new and well-defined concepts that are amenable to experimental analysis. One of the major difficulties in considering stem cells is that they are defined in terms of their functional capabilities which can only be assessed by testing the abilities of the cells, which itself may alter their characteristics during the assay procedure: a situation similar to the uncertainty principle in physics. The terms that describe stem cell functions are often not well defined and are used loosely, which can lead to confusion. If such context-dependent interactions exist between the manipulation and measurement process and the challenged stem cells, the question of, for example, the number of stem cells, in a tissue has to be posed in a new way. Rather than obtaining a single number one might end up with various different numbers under different circumstances, all being complementary. This might suggest that stemness is not a property but a spectrum of capabilities from which to choose. This concept might facilitate a reconciliation between the different and sometimes opposing experimental results. Given certain experimental evidence, we have attempted to provide a novel concept to describe structured cell populations in tissues involving stem cells, transit cells and mature cells. It is based on the primary assumption that the proliferation and differentiation/maturation processes are in principle independent entities in the sense that each may proceed without necessarily affecting the other. Stem cells may divide without maturation while cells approaching functional competence may mature but do not divide. In contrast, transit cells divide and mature showing intermediate properties between stem cells and mature functional cells. The need to describe this transition process and the variable coupling between proliferation and maturation leads us to formulate a spiral model of cell and tissue organisation. This concept is illustrated for the intestinal epithelium. It is concluded that the small intestinal crypts contain 4–16 actual stem cells in steady state but up to 30–40 potential stem cells (clonogenic cells) which may take over stem cell properties following perturbations. This implies that transit cells can under certain circumstances behave like actual stem cells while they undergo maturation under other conditions. There is also evidence that the proliferation and differentiation/maturation processes are subject to controls that ultimately lead to a change in the spiral trajectories.(ABSTRACT TRUNCATED AT 400 WORDS)
The lineage-specific transcription factors GATA-1 and PU.1 can physically interact to inhibit each other's function, but the mechanism of repression of GATA-1 function by PU.1 has not been elucidated. Both the N terminus and the C terminus of PU.1 can physically interact with the C-terminal zinc finger of GATA-1. It is demonstrated that the PU.1 N terminus, but not the C terminus, is required for inhibiting GATA-1 function. Induced overexpression of PU.1 in K562 erythroleukemia cells blocks hemin-induced erythroid differentiation. In this system, PU.1 does not affect the expression of GATA-1 messenger RNA, protein, or nuclear localization. However, GATA-1 DNA binding decreases dramatically. By means of electrophoretic mobility shift assays with purified proteins, it is demonstrated that the N-terminal 70 amino acids of PU.1 can specifically block GATA-1 DNA binding. In addition, PU.1 had a similar effect in the G1ER cell line, in which the GATA-1 null erythroid cell line G1E has been transduced with a GATA-1–estrogen receptor fusion gene, which is directly dependent on induction of the GATA-1 fusion protein to effect erythroid maturation. Consistent with in vitro binding assays, overexpression of PU.1 blocked DNA binding of the GATA-1 fusion protein as well as GATA-1–mediated erythroid differentiation of these G1ER cells. These results demonstrate a novel mechanism by which function of a lineage-specific transcription factor is inhibited by another lineage-restricted factor through direct protein–protein interactions. These findings contribute to understanding how protein–protein interactions participate in hematopoietic differentiation and leukemogenesis.
The GATA-1 transcription factor is capable of suppressing the myeloid gene expression program when ectopically expressed in myeloid cells. We examined the ability of GATA-1 to repress the expression and function of the PU.1 transcription factor, a central regulator of myeloid differentiation. We found that GATA-1 is capable of suppressing the myeloid phenotype without interfering with PU.1 gene expression, but instead was capable of inhibiting the activity of the PU.1 protein in a dose-dependent manner. This inhibition was independent of the ability of GATA-1 to bind DNA, suggesting that it is mediated by protein-protein interaction. We examined the ability of PU.1 to interact with GATA-1 and found a direct interaction between the PU.1 ETS domain and the C-terminal finger region of GATA-1. Replacing the PU.1 ETS domain with the GAL4 DNA-binding domain removed the ability of GATA-1 to inhibit PU.1 activity, indicating that the PU.1 DNA-binding domain, rather than the transactivation domain, is the target for GATA-1–mediated repression. We therefore propose that GATA-1 represses myeloid gene expression, at least in part, through its ability to directly interact with the PU.1 ETS domain and thereby interfere with PU.1 function.
The phenotype of individual hematopoietic cells, like all other differentiated mammalian cells, is determined by selective transcription of a subset of the genes encoded within the genome. This overview summarizes the recent evidence that transcriptional regulation at the level of individual cells is best described in terms of the regulation of the probability of transcription rather than the rate. In this model, heterogeneous gene expression among populations of cells arises by chance, and the degree of heterogeneity is a function of the stability of the mRNA and protein products of individual genes. The probabilistic nature of transcriptional regulation provides one explanation for stochastic phenomena, such as stem cell lineage commitment, and monoallelic expression of inducible genes, such as lymphokines and cytokines.
This chapter focuses on the biology of the hemopoietic stem cells. The term hemopoietic stem cell is often used very loosely, embracing a wide range of cells and including many that are directly descended from the true stem cells. At least 99% of hemopoietic cells in bone marrow can be ruled out as stem cells. These are the morphologically recognizable cell lineages, classically assessed by the clinician for diagnostic and prognostic purposes and the lineage-restricted progenitor cells which form colonies in soft agar gels when presented with specific hemopoietic growth factors. Functional assays are essential for the multi or pluripotent cell populations which have no specific recognizable morphological characteristics. This chapter discusses about the spleen colony-forming unit assay, CFU-S. It explains concepts related to marrow repopulating ability. It also describes long-term culture initiating cells. The chapter elaborates in detail about properties of hemopoietic stem cells. The chapter concludes with a discussion on differentiation of the stem cells.
Recent studies have shown that hematopoietic transcription factors can engage in multiple protein-protein interactions. Accumulating evidence indicates that specific complexes define differentiation lineages and differentiation stages. It is proposed that these complexes acquire new functions during blood cell differentiation through successive changes in composition — much as discussion topics of groups at a cocktail party take new directions as new people join and others leave.
Transcription of erythroid-expressed genes and normal erythroid development in vivo are dependent on a regulatory protein (GATA-1) that recognizes a consensus GATA motif. GATA-1 expression is itself restricted to erythroid progenitors and to two related hematopoietic lineages, megakaryocytes and mast cells. During cellular maturation the levels of GATA-1 RNA and protein increase progressively. In an effort to delineate mechanisms by which this pivotal transcription factor is itself regulated we have characterized the mouse GATA-1 gene and cis-elements within its promoter. We find that the isolated promoter retains cell specificity exhibited by the intact gene. Full promoter activity requires the presence of proximal CACCC box sequences and an upstream, double GATA motif that binds a single GATA-1 molecule in an asymmetric fashion. Using in vivo footprinting of mouse erythroleukemic cells we detect protein binding in vivo to both cis-elements. On the basis of these findings we propose that a positive feedback loop mediated through GATA-1 serves two complementary functions: maintenance of the differentiated state by locking the promoter into an "on" state, and programming the progressive increase in protein content throughout cellular maturation.
We consider some of the problems involved in current discussions on stem cells in adult mammalian tissues. The present concepts involve a number of pitfalls, weaknesses and logical, semantic and classification problems. This indicates the necessity for new and well-defined concepts that are amenable to experimental analysis. One of the major difficulties in considering stem cells is that they are defined in terms of their functional capabilities which can only be assessed by testing the abilities of the cells, which itself may alter their characteristics during the assay procedure: a situation similar to the uncertainty principle in physics. The terms that describe stem cell functions are often not well defined and are used loosely, which can lead to confusion. If such context-dependent interactions exist between the manipulation and measurement process and the challenged stem cells, the question of, for example, the number of stem cells, in a tissue has to be posed in a new way. Rather than obtaining a single number one might end up with various different numbers under different circumstances, all being complementary. This might suggest that stemness is not a property but a spectrum of capabilities from which to choose. This concept might facilitate a reconciliation between the different and sometimes opposing experimental results. Given certain experimental evidence, we have attempted to provide a novel concept to describe structured cell populations in tissues involving stem cells, transit cells and mature cells. It is based on the primary assumption that the proliferation and differentiation/maturation processes are in principle independent entities in the sense that each may proceed without necessarily affecting the other. Stem cells may divide without maturation while cells approaching functional competence may mature but do not divide. In contrast, transit cells divide and mature showing intermediate properties between stem cells and mature functional cells. The need to describe this transition process and the variable coupling between proliferation and maturation leads us to formulate a spiral model of cell and tissue organisation. This concept is illustrated for the intestinal epithelium. It is concluded that the small intestinal crypts contain 4-16 actual stem cells in steady state but up to 30-40 potential stem cells (clonogenic cells) which may take over stem cell properties following perturbations. This implies that transit cells can under certain circumstances behave like actual stem cells while they undergo maturation under other conditions. There is also evidence that the proliferation and differentiation/maturation processes are subject to controls that ultimately lead to a change in the spiral trajectories.(ABSTRACT TRUNCATED AT 400 WORDS)
We propose a mapping to study the qualitative properties of continuous biochemical control networks which are invariant to the parameters used to describe the networks but depend only on the logical structure of the networks. For the networks, we are able to place a lower limit on the number of steady states and strong restrictions on the phase relations between components on cycles and transients. The logical structure and the dynamical behavior for a number of simple systems of biological interest, the feedback (predator-prey) oscillator, the bistable switch, the phase dependent switch, are discussed. We discuss the possibility that these techniques may be extended to study the dynamics of large many component systems.
PU.1 (Spi-1), a member of the Ets transcription factor family, is predominantly expressed in myeloid (granulocytes, monocytes and macrophages) and B cells. PU.1 is upregulated early during commitment of multipotential progenitors to the myeloid lineages and inhibition of PU.1 function in human CD34+ progenitors prior to this upregulation blocks myeloid colony formation. Since PU.1 expression appears to play a role in hematopoietic development, we characterized the PU.1 promoter. Here we report that the murine PU.1 promoter, as well as the human promoter, demonstrate tissue-specific reporter gene expression in myeloid cell lines but not in T cells and HeLa (non-hematopoietic cells) cells. Deletion analysis of the PU.1 promoter indicates that tissue-specific functional elements are encoded in the -61 to -39 bp and -7 to +34 bp regions. The first region contains a functional octamer (Oct) site at -54 bp and an Sp1 site at -39 bp. The second contains a binding site at +20 bp for both PU.1 itself and the related ets family member Spi-B. In vivo footprinting assays demonstrate that a hypersensitive band was detected at the PU.1 site in myeloid cells but not in HeLa. A mutation of the PU.1 site which abolished PU.1 binding caused a significant decrease in promoter activity. Mutation of the Oct and/or Sp1 site results in a lesser decrease of promoter activity in myeloid cells. Co-transfection of PU.1 or Spi-B in cells lacking PU.1 and Spi-B specifically transactivated a minimal promoter containing the PU.1 binding site, indicating that PU.1 can activate its own promoter elements in an autoregulatory loop. Positive autoregulation of the PU.1 promoter may play an important role in the function of PU.1 in myeloid cells.
Lineage commitment and differentiation are likely to be coordinated by the combined effects of multiple transcription factors acting on numerous different target genes. The mechanisms by which lineage-restricted patterns of transcription factor expression are established are therefore of particular relevance to our understanding of the role of transcription factors both in normal development and in oncogenesis. Here, we report that the genes for the lineage-restricted transcription factors SCL, GATA-1 and GATA-2 are expressed in all multipotent, IL-3-dependent, haemopoietic progenitor cell lines tested. Moreover, a liquid differentiation assay has been used to demonstrate down regulation of SCL, GATA-1, GATA-2 and PU-1 during differentiation into non-expressing lineages. These data support the concept that multiple lineage-restricted transcription factors are expressed prior to lineage commitment.
Hematopoiesis entails the generation of stem cells, the proliferation and maintenance of multipotential progenitors, and lineage commitment and maturation. During the past year, critical components of these steps have been defined. Notable are gene-targeting experiments in mice in which one or more hematopoietic lineages have been shown to be ablated upon inactivation of several nuclear regulatory proteins (GATA-2, Tal-1/SCL, Rbtn2/LMO2, PU.1, Ikaros, E2A, and Pax-5), and experiments that establish GATA-1 as a factor capable of programming at least three lineages (erythroid, thrombocytic, and eosinophilic) from a transformed avian progenitor cell.
Multipotent haemopoietic progenitor cells appear to be 'primed' for commitment by co-expression of a multiplicity of genes characteristic of different lineages. Lineage commitment proceeds as the consolidation of a distinct pattern of gene expression out of this milieu.
Previously we have shown that overexpression of PU.1, an Ets family transcription factor, in murine erythroleukemia (MEL) cells results in apoptotic cell death in the presence of the differentiation-inducing reagent dimethyl sulfoxide (DMSO). In this study, we examined the dynamics of GATA-1 and NF-E2 hematopoietic transcription factors during the induction of apoptosis, because GATA-1 has been shown to be implicated in survival of erythroid cells. Formation of the GATA-1-DNA complex as judged by EMSA was markedly reduced when apoptosis was induced, although subcellular localization of the GATA-1 protein and expression levels of the GATA-1 mRNA and protein were not changed during the apoptotic process. Complex formation was not reduced when apoptosis was avoided by adding 30% serum in culture medium and when mutant PU.1 proteins with the deletion of the DNA-binding (Ets) or transactivation domain were expressed. Complex formation in nuclear extracts of parental MEL cells was reduced when they were mixed with those of apoptotic cells, suggesting that apoptotic cells may contain a factor(s) preventing GATA-1 from binding to DNA. In contrast to GATA-1, formation of the NF-E2-DNA complex was not changed during the process of apoptosis, although the expression level of the NF-E2 p45 gene was reduced in the process. These results suggest that reduction of the DNA-binding activity of GATA-1 may partly account for PU.1-mediated apoptosis in MEL cells.
The phenotype of individual hematopoietic cells, like all other differentiated mammalian cells, is determined by selective transcription of a subset of the genes encoded within the genome. This overview summarizes the recent evidence that transcriptional regulation at the level of individual cells is best described in terms of the regulation of the probability of transcription rather than the rate. In this model, heterogeneous gene expression among populations of cells arises by chance, and the degree of heterogeneity is a function of the stability of the mRNA and protein products of individual genes. The probabilistic nature of transcriptional regulation provides one explanation for stochastic phenomena, such as stem cell lineage commitment, and monoallelic expression of inducible genes, such as lymphokines and cytokines.
Diverse types of blood cell (lineages) are produced from rare haematopoietic stem cells that reside in the bone marrow. This process, known as haematopoiesis, provides a valuable model for examining how genetic programs are established and executed in vertebrates, and also how homeostasis of blood formation is altered in leukaemias. So, how does an apparently small group of critical lineage-restricted nuclear regulatory factors specify the diversity of haematopoietic cells? Recent findings not only indicate how this may be achieved but also show the extraordinary plasticity of tissue stem cells in vivo.
The latest findings on the structure of chromatin, its organization in the nucleus, and its involvement in regulating gene expression were presented at a recent meeting at the Juan March Foundation in Madrid, Spain.
A wide range of organisms use circadian clocks to keep internal sense of daily time and regulate their behavior accordingly. Most of these clocks use intracellular genetic networks based on positive and negative regulatory elements. The integration of these "circuits" at the cellular level imposes strong constraints on their functioning and design. Here, we study a recently proposed model [Barkai, N. & Leibler, S. (2000) Nature (London), 403, 267-268] that incorporates just the essential elements found experimentally. We show that this type of oscillator is driven mainly by two elements: the concentration of a repressor protein and the dynamics of an activator protein forming an inactive complex with the repressor. Thus, the clock does not need to rely on mRNA dynamics to oscillate, which makes it especially resistant to fluctuations. Oscillations can be present even when the time average of the number of mRNA molecules goes below one. Under some conditions, this oscillator is not only resistant to but, paradoxically, also enhanced by the intrinsic biochemical noise.
The classical definition of adult tissue stem cells (TSC) is fundamentally based on a functional perspective. A TSC is an undifferentiated cell, capable of proliferation, self-renewal, production of a large number of differentiated functional progeny, regenerating tissue after injury and a flexibility in the use of these options. Here, we discuss the necessity for amending this definition in the light of recent insight into stem cell biology regarding stem cell heterogeneity, lineage plasticity, clonal fluctuation and cell-environment interactions. We conclude that the definition needs amendments. A decade ago the flexibility criterion has attracted little attention but recent findings have indicated its importance. Flexibility and reversibility of tissue and lineage specification (tissue plasticity) and of properties within a tissue (within-tissue plasticity) have major implications with regard to concepts of stem cell function. We advocate to give up the view of TSC as being entities with a preprogrammed development and to replace it by a concept that makes the capabilities for flexible and regulated tissue self-organization based on cell-cell and cell-environment interactions the new paradigm. This concept would permit to incorporate the context-dependent lineage plasticity, within-lineage plasticity and generation of stem cell heterogeneity as a result of a dynamically regulated process. Such concepts need a rigorous examination by formal modeling including simulation studies. We provide some general ideas on how to proceed with such theories and illustrate this with worked models for tissue stem cells of the hematopoietic system and the intestinal epithelium.
Previous work has demonstrated that lineage-specific transcription factors play essential roles in red blood cell development. More recent studies have shown that these factors participate in critical protein-protein interactions in addition to binding DNA. The zinc finger transcription factor GATA-1, a central mediator of erythroid gene expression, interacts with multiple proteins including FOG-1, EKLF, SP1, CBP/p300 and PU.1. The mechanisms by which these interactions influence GATA-1 function, as well as any possible relationships between these seemingly disparate complexes, remain incompletely understood. However, several new findings have provided further insight into the functional significance of some of these interactions. Studies involving point mutants of GATA-1 have shown that a direct physical interaction between GATA-1 and FOG-1 is essential for normal human erythroid and megakaryocyte maturation in vivo. In addition, evidence has emerged that physical interaction between GATA-1 and the myeloid/lymphoid specific factor PU.1, an oncogene implicated in murine erythroleukemia, acts to functionally cross-antagonize one another. This provides a possible mechanism by which dysregulated expression of hematopoietic transcription factors leads to lineage maturation arrest in leukemias.
Most biological regulation systems comprise feedback circuits as crucial components. Negative feedback circuits have been well understood for a very long time; indeed, their understanding has been the basis for the engineering of cybernetic machines exhibiting stable behaviour. The importance of positive feedback circuits, considered as "vicious circles", has however been underestimated. In this article, we give a demonstration based on degree theory for vector fields of the conjecture, made by René Thomas, that the presence of positive feedback circuits is a necessary condition for autonomous differential systems, covering a wide class of biologically relevant systems, to possess multiple steady states. We also show ways to derive constraints on the weights of positive and negative feedback circuits. These qualitative and quantitative results provide, respectively, structural constraints (i.e. related to the interaction graph) and numerical constraints (i.e. related to the magnitudes of the interactions) on systems exhibiting complex behaviours, and should make it easier to reverse-engineer the interaction networks animating those systems on the basis of partial, sometimes unreliable, experimental data. We illustrate these concepts on a model multistable switch, in the context of cellular differentiation, showing a requirement for sufficient cooperativity. Further developments are expected in the discovery and modelling of regulatory networks in general, and in the interpretation of bio-array hybridization and proteomics experiments in particular.
Although much is understood about the ways in which transcription factors regulate various differentiation systems, and one of the hallmarks of many human cancers is a lack of cellular differentiation, relatively few reports have linked these two processes. Recent studies of acute myeloid leukaemia (AML), however, have indicated how disruption of transcription-factor function can disrupt normal cellular differentiation and lead to cancer. This model involves lineage-specific transcription factors, which are involved in normal haematopoietic differentiation. These factors are often targeted in AML--either by direct mutation or by interference from translocation proteins. Uncovering these underlying pathways will improve the diagnosis and treatment of AML, and provide a working model for other types of human cancer, including solid tumours.
Inducible genes are expressed in the presence of an external stimulus. Individual cells may exhibit either a binary or graded response to such signals. It has been hypothesized that the chemical kinetics of transcription factor/DNA interactions can account for both these scenarios (EMBO J. 9(9) (1990) 2835; BioEssays 14(5) (1992) 341). To explore this question, we have conducted work based on the experimental results of Fiering et al. (Genes Dev. 4 (10) (1990) 1823). In these experiments, three upstream NF-AT binding sites control transcription of the lacZ gene, which codes for the enzyme beta-Galactosidase. The experimental data show a binary response for this system. We consider the effects of fluctuations in NF-AT binding on the response of the system. Our modeling results are in good qualitative agreement with the experimental data, and illustrate how the binary and graded responses can stem from the same underlying mechanism.
We develop a mathematical model of the phage lambda lysis/lysogeny switch, taking into account recent experimental evidence demonstrating enhanced cooperativity between the left and right operator regions. Model parameters are estimated from available experimental data. The model is shown to have a single stable steady state for these estimated parameter values, and this steady state corresponds to the lysogenic state. When the CI degradation rate (gammacI) is slightly increased from its normal value (gammacI approximately 0.0 min(-1)), two additional steady states appear (through a saddle-node bifurcation) in addition to the lysogenic state. One of these new steady states is stable and corresponds to the lytic state. The other steady state is an (unstable) saddle node. The coexistence these two globally stable steady states (the lytic and lysogenic states) is maintained with further increases of gammacI until gammacI approximately 0.35 min(-1), when the lysogenic steady state and the saddle node collide and vanish (through a reverse saddle node bifurcation) leaving only the lytic state surviving. These results allow us to understand the high degree of stability of the lysogenic state because, normally, it is the only steady state. Further implications of these results for the stability of the phage lambda switch are discussed, as well as possible experimental tests of the model.
Many genes have been identified as driving cellular differentiation, but because of their complex interactions, the understanding of their collective behaviour requires mathematical modelling. Intriguingly, it has been observed in numerous developmental contexts, and particularly haematopoiesis, that genes regulating differentiation are initially co-expressed in progenitors despite their antagonism, before one is upregulated and others downregulated. We characterise conditions under which three classes of generic "master regulatory networks", modelled at the molecular level after experimentally observed interactions (including bHLH protein dimerisation), and including an arbitrary number of antagonistic components, can behave as a "multi-switch", directing differentiation in an all-or-none fashion to a specific cell-type chosen among more than two possible outcomes. bHLH dimerisation networks can readily display coexistence of many antagonistic factors when competition is low (a simple characterisation is derived). Decision-making can be forced by a transient increase in competition, which could correspond to some unexplained experimental observations related to Id proteins; the speed of response varies with the initial conditions the network is subjected to, which could explain some aspects of cell behaviour upon reprogramming. The coexistence of antagonistic factors at low levels, early in the differentiation process or in pluripotent stem cells, could be an intrinsic property of the interaction between those factors, not requiring a specific regulatory system.
Myeloid cells (granulocytes and monocytes) are derived from multipotent hematopoietic stem cells. Gene transcription plays a critical role in hematopoietic differentiation. However, there is no single transcription factor that is expressed exclusively by myeloid cells and that, alone, acts as a "master" regulator of myeloid fate choice. Rather, myeloid gene expression is controlled by the combinatorial effects of several key transcription factors. Hematopoiesis has traditionally been viewed as linear and hierarchical, but there is increasing evidence of plasticity during blood cell development. Transcription factors strongly influence cellular lineage during hematopoiesis and expression of some transcription factors can alter the fate of developing hematopoietic progenitor cells. PU.1 and CCAAT/enhancer-binding protein alpha (C/EBPalpha) regulate expression of numerous myeloid genes, and gene disruption studies have shown that they play essential, nonredundant roles in myeloid cell development. They function in cooperation with other transcription factors, co-activators, and co-repressors to regulate genes in the context of chromatin. Because of their essential roles in regulating myeloid genes and in myeloid cell development, it has been hypothesized that abnormal expression of PU.1 and C/EBPalpha would contribute to aberrant myeloid differentiation, i.e. acute leukemia. Such a direct link has been elusive until recently. However, there is now persuasive evidence that mutations in both PU.1 and C/EBPalpha contribute directly to development of acute myelogenous leukemia. Thus, normal myeloid development and acute leukemia are now understood to represent opposite sides of the same hematopoietic coin.
Genetically identical cells exposed to the same environmental conditions can show significant variation in molecular content and marked differences in phenotypic characteristics. This variability is linked to stochasticity in gene expression, which is generally viewed as having detrimental effects on cellular function with potential implications for disease. However, stochasticity in gene expression can also be advantageous. It can provide the flexibility needed by cells to adapt to fluctuating environments or respond to sudden stresses, and a mechanism by which population heterogeneity can be established during cellular differentiation and development.
Hematopoiesis has provided a valuable model for understanding how genetic programs are established to decide cell fates in multipotent stem or progenitor cells. The identification of common myeloid and lymphoid progenitors has allowed us to directly assess the regulatory mechanisms of lineage commitment. Multiple genes of hematopoietic lineages, including transcription factors, are coexpressed in hematopoietic stem cells and progenitors, a phenomenon referred to as "lineage priming." The accessibility for multiple transcription factors promiscuously allows flexibility in cell fate commitments at the multipotent stages. The changes in the expression levels and timing of transcription factors can induce lineage conversion of committed cells, indicating that the regulation of transcription factors might be primarily critical for maintaining hierarchical hematopoietic development.
Fluctuations are an intrinsic property of genetic networks due to the small number of interacting molecules. We study the role of dimerization reactions in controlling these fluctuations in a simple genetic circuit with negative feedback. We compare two different pathways. In the dimeric pathway the proteins to be regulated form dimers in solution that afterward bind to an operator site and inhibit transcription. In the monomeric pathway monomers bind to the operator site and then recruit another monomer to form a dimer directly on the DNA. We find that while both pathways implement the same negative feedback mechanism, the protein number fluctuations in the dimeric pathway are drastically reduced compared to the monomeric pathway. This difference in the ability to reduce fluctuations may be of importance in the design of genetic networks.
A transcription factor party during blood cell differentiation
- Sieweke
Sieweke, M. H., Graf, T., 1998. A transcription factor party during blood cell
differentiation. Curr Opin Genet Dev 8 (5), 545–51.