Collective Dynamics of Gene Expression in Cell Populations

Ludwig-Maximilians-Universität München, Germany
PLoS ONE (Impact Factor: 3.23). 06/2011; 6(6):e20530. DOI: 10.1371/journal.pone.0020530
Source: PubMed


The phenotypic state of the cell is commonly thought to be determined by the set of expressed genes. However, given the apparent complexity of genetic networks, it remains open what processes stabilize a particular phenotypic state. Moreover, it is not clear how unique is the mapping between the vector of expressed genes and the cell's phenotypic state. To gain insight on these issues, we study here the expression dynamics of metabolically essential genes in twin cell populations. We show that two yeast cell populations derived from a single steady-state mother population and exhibiting a similar growth phenotype in response to an environmental challenge, displayed diverse expression patterns of essential genes. The observed diversity in the mean expression between populations could not result from stochastic cell-to-cell variability, which would be averaged out in our large cell populations. Remarkably, within a population, sets of expressed genes exhibited coherent dynamics over many generations. Thus, the emerging gene expression patterns resulted from collective population dynamics. It suggests that in a wide range of biological contexts, gene expression reflects a self-organization process coupled to population-environment dynamics.

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    • "More generally, cooperative interactions and their stability provide a fascinating context in which to investigate the relations between these two levels of organization — the individual and the population. Indeed, a biological population is more than a collection of individuals: it is characterized by its interactions — direct and indirect, by its memory through inheritance, and by its relation with the environment (Moore et al., 2013; Stolovicki and Braun, 2011). Therefore, phenomena at the population level, including evolutionary dynamics and long-term stability of individual traits, are necessarily affected by all these ingredients. "
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    ABSTRACT: Cooperative interactions, their stability and evolution, provide an interesting context in which to study the interface between cellular and population levels of organization. Here we study a public goods model relevant to microorganism populations actively extracting a growth resource from their environment. Cells can display one of two phenotypes—a productive phenotype that extracts the resources at a cost, and a non-productive phenotype that only consumes the same resource. Both proliferate and are free to move by diffusion; growth rate and diffusion coefficient depend only weakly phenotype. We analyze the continuous differential equation model as well as simulate stochastically the full dynamics. We find that the two sub-populations, which cannot coexist in a well-mixed environment, develop spatio-temporal patterns that enable long-term coexistence in the shared environment. These patterns are purely fluctuation-driven, as the corresponding continuous spatial system does not display Turing instability. The average stability of coexistence patterns derives from a dynamic mechanism in which the producing sub-population equilibrates with the environmental resource and holds it close to an extinction transition of the other sub-population, causing it to constantly hover around this transition. Thus the ecological interactions support a mechanism reminiscent of self-organized criticality; power-law distributions and long-range correlations are found. The results are discussed in the context of general pattern formation and critical behavior in ecology as well as in an experimental context.
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    • "Moreover, the fraction of cells that can grow into adapted colonies on agar plates was shown to actually initially decrease with time from their first encounter with the glucose medium [25]. Gene expression measurements have shown that adaptation was accompanied by a large-scale genomic response [28], forming temporal patterns involving hundreds of genes and reflecting dynamics of highly-correlated cells within the population [29]. Our previous work identified multiple trajectories leading to adaptation. "
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    • "3). We also know from temporal analysis of gene expression (Stolovicki et al. 2006; Stern et al. 2007; Stolovicki and Braun 2011) that before becoming adapted the cells have a transient response that is later stabilized in the adapted state. We therefore suggest that in naive cells, the challenge induces a transient de-repression of HIS3 soon after the cells were transferred to Glu–his. "
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