Stochasticity in gene expression: from theories to phenotypes

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Abstract
Stochasticity in gene expression is manifested as fluctuations in the abundance of expressed molecules at the single-cell level, and variability and heterogeneity within populations of genetically identical cells. Analyses of simple models indicate that stochasticity in gene expression is dominated by translational bursting, arising from a low number of expressed mRNAs, and transcriptional bursting, arising from slow transitions between promoter states. Transcriptional bursting, which arises from random transitions between chromatin states, might cause stochastic all-or-nothing responses in eukaryotic cells and lead to the emergence of populations that contain a mixture of expressing and non-expressing cells. Experimental evidence indicates that translational bursting is a dominant source of stochasticity in prokaryote gene expression, and that both translational and transcriptional bursting contribute to stochasticity in eukaryote gene expression. Evidence also indicates that translational bursting in eukaryotes is an evolvable trait that is subject to natural selection. Transcriptional bursting has been implicated in syndromes that are associated with haploinsufficiency. Sources that are extrinsic to the process of gene expression, such as fluctuations in regulatory signals, also contribute significantly to stochasticity in gene expression. Gene-intrinsic and gene-extrinsic noise can be distinguished experimentally using a two-reporter assay. Fluctuations in regulatory signals are important for the function of transcriptional regulatory networks. In genetic cascades, such fluctuations lead to increased population variability at intermediate expression levels and an initial population asynchrony that increases with cascade length. Increased variability in a regulatory signal might also cause the emergence of mixed populations, containing cells that show either high or low expression levels of the target gene. Negative and positive feedback typically leads to reduction and amplification, respectively, of fluctuations and population heterogeneity. Positive feedback can yield unique or multiple cellular-expression states, depending on the strength of the feedback. Stochasticity in gene expression might provide microorganisms with the flexibility required to respond and adapt to environmental changes and stresses, and can prevent cells from being trapped in suboptimal epigenetic states and phenotypes. Stochastic mechanisms have also been implicated in cellular differentiation and development. They provide a means of generating the initial population heterogeneity on which regulatory mechanisms can function to establish and propagate the expression of cell-type-specific genes.

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