Conceived and designed the experiments: VC CP. Performed the experiments: VC CP. Analyzed the data: VC TE CP. Contributed reagents/materials/analysis tools: VC. Wrote the paper: VC TE CP. Contributed with stem cell biology knowledge: TE.
The authors have declared that no competing interests exist.
Hematopoietic stem cell lineage choices are decided by genetic networks that are turned ON/OFF in a switch-like manner. However, prior to lineage commitment, genes are primed at low expression levels. Understanding the underlying molecular circuitry in terms of how it governs both a primed state and, at the other extreme, a committed state is of relevance not only to hematopoiesis but also to developmental systems in general. We develop a computational model for the hematopoietic erythroid-myeloid lineage decision, which is determined by a genetic switch involving the genes PU.1 and GATA-1. Dynamical models based upon known interactions between these master genes, such as mutual antagonism and autoregulation, fail to make the system bistable, a desired feature for robust lineage determination. We therefore suggest a new mechanism involving a cofactor that is regulated as well as recruited by one of the master genes to bind to the antagonistic partner that is necessary for bistability and hence switch-like behavior. An interesting fallout from this architecture is that suppression of the cofactor through external means can lead to a loss of cooperativity, and hence to a primed state for PU.1 and GATA-1. The PU.1–GATA-1 switch also interacts with another mutually antagonistic pair,
An important question in developmental biology is how different lineage choices are regulated at the genetic level. Robust lineage decisions are implemented by genetic switches, whereby one set of master genes are ON and another set are OFF, leading to a specific expression pattern of genes for a particular lineage. We develop a computational model to illustrate these principles as applied to the hematopoietic erythroid-myeloid lineage choice, where two master regulator genes, PU.1 and GATA-1, function as a genetic switch. The model, which is based upon known interactions, suggests missing interactions between the master genes, which we hypothesize, so as to reproduce the desired dynamics. Furthermore, there exist feedback interactions between the master genes and their downstream targets. When these are included in the model, the dynamics imply that the feedback is responsible for irreversible commitment. Our results suggest the search for missing interactions between the master genes in terms of a coregulated cofactor. The second important result of the model is that reprogramming irreversible cell fate may be possible by perturbing feedback regulation between the master genes and their downstream targets. Hence, dynamical modeling provides prediction of novel mechanisms and also strategies for reprogramming the fates of cells.
Stem cell fates are decided upon the basis of which genes are turned ON/OFF. However, prior to commitment, it has been observed that many genes are expressed at intermediate or basal levels for the hematopoietic stem cell system
From forced expression studies in both cell lines and primary cells, it is evident that GATA-1 and PU.1 are able to specify erythroid and myeloid cell fates (see
From a dynamical point of view, a biological network, such as the PU.1–GATA-1 genetic switch, which is responsible in determining two different lineages, would be expected to exhibit bistability. In general switch-like behavior can give rise to phenotypic diversity
The PU.1–GATA-1 system, which displays switch behavior
Although both models
(A) The PU.1–GATA-1 circuit, showing their auto-regulatory and mutually antagonistic interactions, as well as further interaction through the ‘master regulator gene’ X. The environmental signals into PU.1, GATA-1 and X that integrate the nuclear circuitry with the external environment are denoted A, B and C respectively. (B) The PU.1–GATA-1 switch shown together with the reinforcement from the downstream
Hematopoiesis is a hierarchically structured process with a series of progenitors or intermediates which serve as semi-stable and restricted states for future lineage decisions. This organization implies that network information must be handed over from one cell type to another in a way that maintains prior settings and precludes reversibility. Here we have examined the principles of how hand-over and irreversibility might be achieved in the context of the pair of transcription factors
The model for the PU.1–GATA-1 system is based upon assumptions that follow experimental results
The equations for PU.1 and GATA-1 protein concentrations, denoted by [P] and [G] respectively, have the form,
With [X] denoting the concentration of X, we obtain a modified set of equations for the network (see
In
SN denotes saddle nodes and the unstable points are drawn as dotted lines. (A) The system exhibits bistable behavior as a function of A (
A crucial point is that a generic availability of co-activators is not sufficient to provide the cooperativity that is required for the bistability. The co-activators must be
Recent experiments
The following scheme for lineage choice for the switch emerges:
Initially both PU.1 and GATA-1 are expressed at low levels via the external factors A and B, and X is kept at a low level,
A lineage choice is then made once the inhibition of X is released by the removal of C.
PU.1 and GATA-1 connect to the downstream genes
As can be seen, the feedback induces irreversibility; the switch becomes fully committed and the final concentration levels do not change much, even when the input signal B is removed. Same notations as in
An important consideration is whether the feedback from
The curves exhibit bistable behavior but not irreversibility. Same notations as in
We have investigated the effects of various aspects of the architecture on the dynamics of the network; the regulation of the X gene and autoregulation in the
It is intriguing to consider FOG-1 to be in fact the X gene, since FOG-1 has been found to bind together with GATA-1, at several target genes. We have explored the possibility of FOG-1 playing the role of the X gene (details are discussed in
We have devised a simple model for the PU.1-GATA-1 genetic switch which, in addition to known interactions, involves a feedforward mechanism through a connector gene X. This mechanism provides the required cooperativity resulting in a bistable switch. In addition, if X is suppressed the cooperativity of the system is lost, and it becomes possible to have both PU.1 and GATA-1 at reasonably expressed levels—the primed state. The network components therefore regulate cooperativity, which can be affected by external signals.
It is interesting to note that, a similar regulatory scheme, in which a connector gene (X) bridges the master regulators, through a feedforward structure
The second issue is how irreversibility of the erythroid-myeloid lineage switch can be achieved through feedback from other lineage components, namely FOG-1–
The system can be reprogrammed by reducing the feedback from GATA-1 downstream to FOG-1, or by the upstream activation of
Identification of the X gene should be possible through loss-of-function studies of the PU.1–GATA-1 system. Combining ChIP-chip with gene expression experiments
Mutual antagonism among pairs of genes has been suggested as a general mechanism for lineage commitment
One issue not addressed here are the effects of noise. Stochasticity in gene expression has now been both theoretically as well as experimentally explored and been shown to be due to both intrinsic as well as extrinsic factors
The network dynamics is modeled using the Shea-Ackers formalism
The dynamical equations corresponding to Equation (1) are given by
1 | 0.25 | 1.0 | 0.25 | 1 | 0.01 | 1 | 0.25 | 1.0 | 0.25 | 1 | 0.01 | ||
0.01 | 0.01 | 10 | 0.01 | 0.13 | |||||||||
1 | 0.25 | 1.0 | 0.25 | 1 | 1 | 0.25 | 0.27 | 1.0 | 0.25 | 1 | 2 | 0.27 | |
0.01 | 0.01 | 10 | 1 | 0.075 | 1.0 | 0.075 | 1 | 1 | 0.025 | 1.0 | 0.025 | 1 |
(A) Values used for the PU.1–GATA-1 network in Equation 3. (B) Values of the additional parameters used for the PU.1–GATA-1 network including the gene X in Equation 4. For
The dynamical equations corresponding to Equation (3) are given by
Here we have assumed that an external signal C regulates X independently of PU.1 and GATA-1, and in particular can be used to repress it. Hence when C is not present, X is fully expressed,
The dynamical equations corresponding to the network in
In Equation (5), the external signals to [F] and [E] are A1 and A2 respectively and the parameters values are displayed in
Simulations of the differential equations were implemented using MATLAB software (The Mathworks) and the Systems Biology Workbench (SBW/BioSPICE) tools
Effects of the gene X. The nullclines, d[P]/dt = 0 and d[G]/dt = 0, from Eqs. (3,4), with parameters in
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Concentrations of GATA-1, PU.1 and X as functions of the environmental signal A, when the binding strength of the repressive heterodimer GATA-1-X is made to bind strongly to the PU.1 regulatory region (ε4 = 0.25). The curves exhibit irreversibility.
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Concentrations of GATA-1, PU.1 and X as functions of the environmental signal B, when the binding strength of the repressive heterodimer GATA-1–X is made to bind strongly to the PU.1 regulatory region (ε4 = 0.25). The bistable curves are not irreversible.
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Concentrations of GATA-1, PU.1, FOG-1 and
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Concentrations of GATA-1, PU.1, FOG-1 and
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Concentrations of GATA-1, PU.1, FOG-1 and
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Concentrations of GATA-1, PU.1, FOG-1 and
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Concentrations of GATA-1, PU.1, FOG-1 and
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Concentrations of GATA-1, PU.1 as functions of the environmental signal A for the case when GATA-1 dimers associate with PU.1 to repress each other's expression, as well as auto-regulate GATA-1. For low values of A, the system is unable to be primed, and in fact as shown by the arrows, the bistable switch ultimately becomes irreversible, if GATA-1 dimers self associate even stronger.
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VC would like to thank Elliot Meyerowitz and the Meyerowitz lab members for discussions. We also thank Roger Patient and Matthew Loose for valuable comments on an earlier version of the manuscript.