Interplay between gene expression noise and regulatory network architecture

Abstract

Complex regulatory networks orchestrate most cellular processes in biological systems. Genes in such networks are subject to expression noise, resulting in isogenic cell populations exhibiting cell-to-cell variation in protein levels. Increasing evidence suggests that cells have evolved regulatory strategies to limit, tolerate or amplify expression noise. In this context, fundamental questions arise: how can the architecture of gene regulatory networks generate, make use of or be constrained by expression noise? Here, we discuss the interplay between expression noise and gene regulatory network at different levels of organization, ranging from a single regulatory interaction to entire regulatory networks. We then consider how this interplay impacts a variety of phenomena, such as pathogenicity, disease, adaptation to changing environments, differential cell-fate outcome and incomplete or partial penetrance effects. Finally, we highlight recent technological developments that permit measurements at the single-cell level, and discuss directions for future research.

Figure 1. Interplay between expression noise and regulatory network architecture at distinct levels of network organization

(a) Gene Regulatory Networks (GRNs) consist of distinct levels of organisation, ranging from a single auto-regulatory gene to entire networks. Filled circles represent genes and plain arrows denote regulatory interactions. (b) Self-activating loops (cyan) and self-inhibitory loops (yellow) show opposite noise properties (adapted from [14]). In the first panel, the (normalized) steady state concentration of TF is reached faster in the case of self-inhibition than self-activation. In the second panel, distribution of expression levels is broader in the case of self-activation (cyan, plain curve), and may also be bimodal (cyan, dashed curve). Both behaviors described may be influenced by other factors such as promoter strength, initial conditions and degradation kinetics. (c) Noise-driven state-switching and noise propagation in regulatory interactions. The abundance of a target gene (TG) may be altered by stochastic fluctuations in the abundance of a regulating transcription factor (TF). Noise can also propagate in linear cascades of regulatory interactions. (d) The topology of gene circuits encodes distinct noise susceptibility. For instance, feed-forward loops (FFLs) cluster in two groups of architecture with opposite noise filtering capacity (adapted from [22]). The noise profile for three different logical integration of inputs in the gene C is shown; they include AND (both A and B), OR (A or B) and XOR (either A or B but never both). In B. subtilis, the ComK-ComS circuit controls competence events (upper panel). The use of a synthetic alternative circuit (bottom panel) can achieve higher accuracy in the duration of cycles (red histogram), at the cost of fragility in fluctuating environments (adapted from [25]) (e) The expression dynamics of a gene circuit can be influenced by the presence of a downstream circuit to which it is connected (retroactivity, dashed arrow). The sequestration of the interfacing TF (iTF) on promoters of genes exterior to the upstream circuit increases the autocorrelation time of its expression level, i.e. reduces variation in short time windows, thereby affecting expression noise (adapted from [31]). (f) Combinatorial regulation and hierarchical organisation of GRNs can contribute to adaptability. Distinct noise properties of TFs in different hierarchical layers facilitate sampling of multiple sub-networks, and hence different phenotypes, by the individuals of a clonal cell population (adapted from [42]; see Figure 2a). The volume bell symbol denotes gene expression noise.

Figure 2. Functional outcomes of the interplay between noise and network topology

(a) Tuning of expression noise during evolution can occur in a gene-centric manner by positive/negative selection of molecular determinants of intrinsic noise. From low noise to high noise, the phenotypic diversity among individuals of a population increases, which allows for persistence in deleterious environmental fluctuations, stress conditions and antibiotic treatments. (b) Adaptation to fluctuating environments is facilitated by expression noise of key regulatory genes in a clonal cell population. For instance, upon nutrient starvation (red star), individual yeast cells in a population undergo sporulation in an unsynchronized fashion (horizontal profiles). This heterogeneity in sporulation timing is linked to expression noise in the master regulator Ime1p. This favors the maintenance of non-sporulated cells that are pre-adapted in case of reversion to nutrient rich condition (adapted from [59]). The small red circle denotes the point of commitment to the sporulation pathway. (c) Expression noise may determine cellular differentiation, and frequently involves bistable circuit architectures, within which small fluctuations in TF abundance can trigger distinct stable states. This permits the differentiation into distinct lineages from a progenitor cell type.

Box 1 Figure I

Quantification of expression noise.

Box 2 Figure I

Major factors contributing to expression noise.