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Register a new userThe emergence of the two cell fates and their associated switching for a negative auto-regulating gene
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Decisions in the cell that lead to its ultimate fate are important for cellular functions such as proliferation, growth, differentiation, development and death. Understanding this decision process is imperative for advancements in the treatment of diseases such as cancer. It is clear that underlying gene regulatory networks and surrounding environments of the cells are crucial for function. The self-repressor is a very abundant gene regulatory motif, and is often believed to have only one cell fate. In this study, we elucidate the effects of microenvironments mimicking the epigenetic effects on cell fates through the introduction of inducers capable of binding to a self-repressing gene product (protein), thus regulating the associated gene. This alters the effective regulatory binding speed of the self-repressor regulatory protein to its destination DNA without changing the gene itself. The steady state observations and real time monitoring of the self-repressor expression dynamics reveal the emergence of the two cell fates, The simulations are consistent with the experimental findings. We provide physical and quantitative explanations for the origin of the two phenotypic cell fates. We find that two cell fates, rather than a single fate, and their associated switching dynamics emerge from a change in effective gene regulation strengths. The switching time scale is quantified. Our results reveal a new mechanism for the emergence of multiple cell fates. This provides an origin for the heterogeneity often observed among cell states, while illustrating the influence of microenvironments on cell fates and their decision-making processes without genetic changes
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Expression of cellular genes is regulated by binding of transcription factors to their promoter, either activating or inhibiting transcription of a gene. Particularly interesting is the case when the expressed protein regulates its own transcription. In this paper the features of this self-regulating process are investigated. In the here presented model the gene can be in two states. Either a protein is bound to its promoter or not. The steady state distributions of protein during and at the end of both states are analyzed. Moreover a powerful numerical method based on the corresponding master equation to compute the protein distribution in the steady state is presented and compared to an already existing method. Additionally the special case of self-regulation, in which protein can only be produced, if one of these proteins is bound to the promoter region, is analyzed. Furthermore a self-regulating gene is compared to a similar gene, which also has two states and produces the same amount of proteins but is not regulated by its protein-product.
The arabinose utilization system of E. coli displays a stochastic all or nothing response at intermediate levels of arabinose, where the population divides into a fraction catabolizing the sugar at a high rate (ON state) and a fraction not utilizing arabinose (OFF state). Here we study this decision process in individual cells, focusing on the dynamics of the transition from the OFF to the ON state. Using quantitative time-lapse microscopy, we determine the time delay between inducer addition and fluorescence onset of a GFP reporter. Through independent characterization of the GFP maturation process, we can separate the lag time caused by the reporter from the intrinsic activation time of the arabinose system. The resulting distribution of intrinsic time delays scales inversely with the external arabinose concentration, and is compatible with a simple stochastic model for arabinose uptake. Our findings support the idea that the heterogeneous timing of gene induction is causally related to a broad distribution of uptake proteins at the time of sugar addition.
We assess the impact of cell cycle noise on gene circuit dynamics. For bistable genetic switches and excitable circuits, we find that transitions between metastable states most likely occur just after cell division and that this concentration effect intensifies in the presence of transcriptional delay. We explain this concentration effect with a 3-states stochastic model. For genetic oscillators, we quantify the temporal correlations between daughter cells induced by cell division. Temporal correlations must be captured properly in order to accurately quantify noise sources within gene networks.
Genes and proteins regulate cellular functions through complex circuits of biochemical reactions. Fluctuations in the components of these regulatory networks result in noise that invariably corrupts the signal, possibly compromising function. Here, we create a practical formalism based on ideas introduced by Wiener and Kolmogorov (WK) for filtering noise in engineered communications systems to quantitatively assess the extent to which noise can be controlled in biological processes involving negative feedback. Application of the theory, which reproduces the previously proven scaling of the lower bound for noise suppression in terms of the number of signaling events, shows that a tetracycline repressor-based negative-regulatory gene circuit behaves as a WK filter. For the class of Hill-like nonlinear regulatory functions, this type of filter provides the optimal reduction in noise. Our theoretical approach can be readily combined with experimental measurements of response functions in a wide variety of genetic circuits, to elucidate the general principles by which biological networks minimize noise.
Phenotype transition takes place in many biological processes such as differentiation, and understanding how a cell reprograms its global gene expression profile is a problem of rate theories. A cell phenotype transition accompanies with switching of expression rates of clusters of genes, analogous to domain flipping in an Ising system. Here through analyzing single cell RNA sequencing data in the framework of transition path theory, we set to study how such a genome-wide expression program switching proceeds in three different cell transition processes. For each process after reconstructing a Markov transition model in the cell state space, we formed an ensemble of shortest paths connecting the initial and final cell states, reconstructed a reaction coordinate describing the transition progression, and inferred the gene regulation network (GRN) along the reaction coordinate. In all three processes we observed common pattern that the frustration of gene regulatory network (GRN), defined as overall confliction between the regulation received by genes and their expression states, first increases then decreases when approaching a new phenotype. The results support a mechanism of concerted silencing of genes that are active in the initial phenotype and activation of genes that are active in the final phenotype.
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