Dynamic Crowding Regulates Transcription

Abstract

The nuclear environment is highly crowded by biological macromolecules, including chromatin and mobile proteins, which alter the kinetics and efficiency of transcriptional machinery. These alterations have been described, both theoretically and experimentally, for steady-state crowding densities; however, temporal changes in crowding density ("dynamic crowding") have yet to be integrated with gene expression. Dynamic crowding is pertinent to nuclear biology because processes such as chromatin translocation and protein diffusion lend to highly mobile biological crowders. Therefore, to capture such dynamic crowding and investigate its influence on transcription, we employ a three-pronged, systems-molecular approach. A system of chemical reactions represents the transcription pathway, the rates of which are determined by molecular-scale simulations; Brownian dynamics and Monte Carlo simulations quantify protein diffusion and DNA-protein binding affinity, dependent on macromolecular density. Altogether, this approach shows that transcription depends critically on dynamic crowding as the gene expression resultant from dynamic crowding can be profoundly different than that of steady-state crowding. In fact, expression levels can display both amplification and suppression and are notably different for genes or gene populations with different chemical and structural properties. These properties can be exploited to impose circadian expression, which is asymmetric and varies in strength, or to explain expression in cells under biomechanical stress. Therefore, this work demonstrates that dynamic crowding nontrivially alters transcription kinetics and presents dynamic crowding within the bulk nuclear nanoenvironment as a novel regulatory framework for gene expression.

Figure 1

Transitioning from a steady-state crowding model to a dynamic crowding model. The workflow developed for the steady-state model is largely continued for the dynamic crowding model (blue, (A)). However, now the primary input for both the simulations, and therefore the kinetic model, is dynamic crowding (red, (A)). Additionally, the kinetic model was modified so that all nuclear reaction rates are $\varphi$ dependent (purple, (A)). The kinetic model describes the physical steps of transcription, including the search and binding of transcriptional proteins to gene promotors (blue, (B)), transcription of processing of pre-mRNA (green, (B)), and transportation of mRNA to the cytoplasm (red, (B)). When solved at steady state (without dynamic crowding), gene expression is nonmonotonic, with less sensitivity to crowding as gene concentration ([C]) increases (C). Steady-state gene expression (crosses, C) can be recovered by solving the dynamical model to steady state (D). To see this figure in color, go online.

Figure 2

We approximate homeostasis by nonequilibrium, oscillatory dynamic crowding (A). After a transition state, oscillatory dynamic crowding causes suppression of genes that have a high expression at steady state, amplification of genes that have a low expression at steady state, and relatively unchanged expression of genes with midrange expression (here, [C] = 1 nM) (B). Both the steady-state and dynamic expression of each of these genes depends on the initial condition of the dynamic crowding profile. When normalized by expression at this initial condition, these genes display different sensitivities to dynamic crowding at oscillating stability (C). To see this figure in color, go online.

Figure 3

Once dynamic crowding begins ([C] = 1 nM, ${\varphi }_0$ = 0.3, A = 0.09, b = 300 s), the species involved in the reversible reactions (TF, RNAp, C1, and C2) reach equilibrium almost immediately. This dynamic crowding shifts the reversible reactions toward the reactants; therefore, once all species reach oscillating stability, expression will be downregulated on average. Until that point, pre-mRNA monotonically decreases. This initially decreases the free snRNP as more snRNP is driven to bind to C3. C3 and snRNP are anticorrelated throughout the entire time course. With an increase in C3, mRNA is created more quickly, which in turn increases mRNA in the cytoplasm. All of these species relax slowly; therefore, the irreversible reactions are responsible for the long transition time between steady-state crowding and oscillating stability. The slow relaxation is due to the slow reaction rate coefficients of irreversible reactions, k_M, k_M′, and γ. All reaction rate coefficients have different average values $\overline{\left(k\left(\varphi \right)\right)}$ than would be expected for $k\left(\overline{\varphi }\right).$ Running average is defined as the average over every 100 s.

Figure 4

Once expression reaches an oscillating stability, both the average expression (A, C, and E) and the variance of oscillation (B, D, and F) vary with respect to the concentration of genes ([C]), the average crowding level $\left({\varphi }_0\right)$, the period of oscillation (b), and the amplitude of oscillation (A). The combination of these parameters determine the gene expression level. The majority of conditions have a variance less than 5%; however, variance is increased by increasing A or b (B, D, and F). Increasing A also universally amplifies the impact of dynamic crowding on the average expression (A, B, and E). As predicted by steady state, expression behaves nonmonotonically with $\varphi$ for the same [C] and b ([C] = 1 nM and b = 300 s in C and D). The majority of dynamic crowding parameters cause at least slight downregulation (A). This is not the case for 1) low ${\varphi }_0$ and low A, which cause dynamic expression to remain approximately neutral; 2) highly crowded environments with rapid dynamic crowding, which amplify expression (A, top right corners); or 3) highly crowded environments that contain low [C], which is universally amplified by dynamic crowding (E, [C] = 1 nM, A = 0.09). To see this figure in color, go online.

Figure 5

Dynamic crowding can be used to model processes of gene regulation and disease. The addition of a 24-h secondary frequency to the fluctuations studied at homeostasis displays circadian regulation ($\varphi \left(t\right)=0.3+0.05\ast \text{sin}\left(\left(\left(2\pi /\left(15\ast 60\right)\right)t\right)\right)+0.1\ast \text{sin}\left(\left(2\pi /\left(24\ast 3600\right)\right)t\right)$) (A). The 24-h secondary frequency causes the expression to switch from amplification to suppression every 12 h, sensitive to gene concentration. Alternatively, transient forces, such as those experienced during extravasation, have a qualitatively different gene expression profile than that of continuous, sinusoidal dynamical crowding (B). The force of extravasation (A = 0.2, ${\varphi }_0=0.3)$ causes universal downregulation of gene expression for all gene concentrations and lengths of extravasation. To see this figure in color, go online.