eGFRD in all dimensions.

Biochemical reactions often occur at low copy numbers but at once in crowded and diverse environments. Space and stochasticity therefore play an essential role in biochemical networks. Spatial-stochastic simulations have become a prominent tool for understanding how stochasticity at the microscopic level influences the macroscopic behavior of such systems. While particle-based models guarantee the level of detail necessary to accurately describe the microscopic dynamics at very low copy numbers, the algorithms used to simulate them typically imply trade-offs between computational efficiency and biochemical accuracy. eGFRD (enhanced Green's Function Reaction Dynamics) is an exact algorithm that evades such trade-offs by partitioning the N-particle system into M ≤ N analytically tractable one- and two-particle systems; the analytical solutions (Green's functions) then are used to implement an event-driven particle-based scheme that allows particles to make large jumps in time and space while retaining access to their state variables at arbitrary simulation times. Here we present "eGFRD2," a new eGFRD version that implements the principle of eGFRD in all dimensions, thus enabling efficient particle-based simulation of biochemical reaction-diffusion processes in the 3D cytoplasm, on 2D planes representing membranes, and on 1D elongated cylinders representative of, e.g., cytoskeletal tracks or DNA; in 1D, it also incorporates convective motion used to model active transport. We find that, for low particle densities, eGFRD2 is up to 6 orders of magnitude faster than conventional Brownian dynamics. We exemplify the capabilities of eGFRD2 by simulating an idealized model of Pom1 gradient formation, which involves 3D diffusion, active transport on microtubules, and autophosphorylation on the membrane, confirming recent experimental and theoretical results on this system to hold under genuinely stochastic conditions.

Positive feedback can lead to dynamic nanometer-scale clustering on cell membranes.

Clustering of molecules on biological membranes is a widely observed phenomenon. A key example is the clustering of the oncoprotein Ras, which is known to be important for signal transduction in mammalian cells. Yet, the mechanism by which Ras clusters form and are maintained remains unclear. Recently, it has been discovered that activated Ras promotes further Ras activation. Here we show using particle-based simulation that this positive feedback is sufficient to produce persistent clusters of active Ras molecules at the nanometer scale via a dynamic nucleation mechanism. Furthermore, we find that our cluster statistics are consistent with experimental observations of the Ras system. Interestingly, we show that our model does not support a Turing regime of macroscopic reaction-diffusion patterning, and therefore that the clustering we observe is a purely stochastic effect, arising from the coupling of positive feedback with the discrete nature of individual molecules. These results underscore the importance of stochastic and dynamic properties of reaction diffusion systems for biological behavior.

The interplay between metabolic stochasticity and cAMP-CRP regulation in single E. coli cells.

The inherent stochasticity of metabolism raises a critical question for understanding homeostasis: are cellular processes regulated in response to internal fluctuations? Here, we show that, in E. coli cells under constant external conditions, catabolic enzyme expression continuously responds to metabolic fluctuations. The underlying regulatory feedback is enabled by the cyclic AMP (cAMP) and cAMP receptor protein (CRP) system, which controls catabolic enzyme expression based on metabolite concentrations. Using single-cell microscopy, genetic constructs in which this feedback is disabled, and mathematical modeling, we show how fluctuations circulate through the metabolic and genetic network at sub-cell-cycle timescales. Modeling identifies four noise propagation modes, including one specific to CRP regulation. Together, these modes correctly predict noise circulation at perturbed cAMP levels. The cAMP-CRP system may thus have evolved to control internal metabolic fluctuations in addition to external growth conditions. We conjecture that second messengers may more broadly function to achieve cellular homeostasis.

Single-cell transcriptomics provides insights into hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is a genetic heart disease that is characterized by unexplained segmental hypertrophy that is usually most pronounced in the septum. While sarcomeric gene mutations are often the genetic basis for HCM, the mechanistic origin for the heterogeneous remodeling remains largely unknown. A better understanding of the gene networks driving the cardiomyocyte (CM) hypertrophy is required to improve therapeutic strategies. Patients suffering from HCM often receive a septal myectomy surgery to relieve outflow tract obstruction due to hypertrophy. Using single-cell RNA sequencing (scRNA-seq) on septal myectomy samples from patients with HCM, we identify functional links between genes, transcription factors, and cell size relevant for HCM. The data show the utility of using scRNA-seq on the human hypertrophic heart, highlight CM heterogeneity, and provide a wealth of insights into molecular events involved in HCM that can eventually contribute to the development of enhanced therapies.

Epicardial differentiation drives fibro-fatty remodeling in arrhythmogenic cardiomyopathy.

Arrhythmogenic cardiomyopathy (ACM) is an inherited disorder often caused by pathogenic variants in desmosomal genes and characterized by progressive fibrotic and fat tissue accumulation in the heart. The cellular origin and responsible molecular mechanisms of fibro-fatty deposits have been a matter of debate, due to limitations in animal models recapitulating this phenotype. Here, we used human-induced pluripotent stem cell (hiPSC)­derived cardiac cultures, single-cell RNA sequencing (scRNA-seq), and explanted human ACM hearts to study the epicardial contribution to fibro-fatty remodeling in ACM. hiPSC-epicardial cells generated from patients with ACM showed spontaneous fibro-fatty cellular differentiation that was absent in isogenic controls. This was further corroborated upon siRNA-mediated targeting of desmosomal genes in hiPSC-epicardial cells generated from healthy donors. scRNA-seq analysis identified the transcription factor TFAP2A (activating enhancer-binding protein 2 alpha) as a key trigger promoting this process. Gain- and loss-of-function studies on hiPSC-epicardial cells and primary adult epicardial-derived cells demonstrated that TFAP2A mediated epicardial differentiation through enhancing epithelial-to-mesenchymal transition (EMT). Furthermore, examination of explanted hearts from patients with ACM revealed epicardial activation and expression of TFAP2A in the subepicardial mesenchyme. These data suggest that TFAP2A-mediated epicardial EMT underlies fibro-fatty remodeling in ACM, a process amenable to therapeutic intervention.

Size Laws and Division Ring Dynamics in Filamentous Escherichia coli cells.

Our understanding of bacterial cell size control is based mainly on stress-free growth conditions in the laboratory [1-10]. In the real world, however, bacteria are routinely faced with stresses that produce long filamentous cell morphologies [11-28]. Escherichia coli is observed to filament in response to DNA damage [22-25], antibiotic treatment [11-14, 28], host immune systems [15, 16], temperature [17], starvation [20], and more [18, 19, 21], conditions which are relevant to clinical settings and food preservation [26]. This shape plasticity is considered a survival strategy [27]. Size control in this regime remains largely unexplored. Here we report that E. coli cells use a dynamic size ruler to determine division locations combined with an adder-like mechanism to trigger divisions. As filamentous cells increase in size due to growth, or decrease in size due to divisions, its multiple Fts division rings abruptly reorganize to remain one characteristic cell length away from the cell pole and two such length units away from each other. These rules can be explained by spatiotemporal oscillations of Min proteins. Upon removal of filamentation stress, the cells undergo a sequence of division events, randomly at one of the possible division sites, on average after the time required to grow one characteristic cell size. These results indicate that E. coli cells continuously keep track of absolute length to control size, suggest a wider relevance for the adder principle beyond the control of normally sized cells, and provide a new perspective on the function of the Fts and Min systems.