Single-molecule tracking of Nodal and Lefty in live zebrafish embryos supports hindered diffusion model
Timo Kuhn, Amit N. Landge, David Mörsdorf, Jonas Coßmann, Johanna Gerstenecker, Patrick Müller and J. Christof M. Gebhardt
The influential hindered diffusion model postulates that the global movement of a signaling molecule through an embryo is affected by local tissue geometry and binding-mediated hindrance, but these effects have not been directly demonstrated in vivo for any signaling molecule. Nodal and Lefty are a prime example of an activator- inhibitor signaling pair whose different global diffusivities are thought to arise from differential hindrance. Here, we used single-molecule tracking of Nodal and Lefty to directly probe the tenets of the hindered diffusion model on the nanoscale. We visualized individual fluorescently-tagged Nodal and Lefty molecules in developing zebrafish embryos using reflected light-sheet microscopy. Single-particle tracking revealed molecules in three states: molecules diffusing in extracellular cavities, molecules diffusing within cell-cell interfaces, and molecules bound to cell membranes. While the diffusion coefficients of molecules were high in extracellular cavities, mobility was reduced and bound fractions were higher within cell-cell interfaces; counterintuitively, molecules nevertheless accumulated in cavities. Using agent-based simulations, we identified the geometry of the extracellular space as a key factor influencing the accumulation of molecules in cavities. For Nodal, the fraction of molecules in the bound state was larger than for Lefty, and individual Nodal molecules had binding times of tens of seconds. Together, our single-molecule measurements and simulations provide direct support for the hindered diffusion model in a developing embryo and yield unprecedented insights into the nanometer to micrometer scale transport mechanisms that together lead to macroscopic signal dispersal and gradient formation.
Myosin VI regulates the spatial organisation of mammalian transcription initiation
Yukti Hari-Gupta , Natalia Fili, Ália dos Santos, Alexander W. Cook, Rosemarie E. Gough,
Hannah C. W. Reed, Lin Wang, Jesse Aaron, Tomas Venit, Eric Wait,
Andreas Grosse-Berkenbusch, J. Christof M. Gebhardt, Piergiorgio Percipalle, Teng-Leong Chew,
Marisa Martin-Fernandez & Christopher P. Toseland
During transcription, RNA Polymerase II (RNAPII) is spatially organised within the nucleus into clusters that correlate with transcription activity. While this is a hallmark of genome regulation in mammalian cells, the mechanisms concerning the assembly, organisation and stability remain unknown. Here, we have used combination of single molecule imaging and genomic approaches to explore the role of nuclear myosin VI (MVI) in the nanoscale organisation of RNAPII. We reveal that MVI in the nucleus acts as the molecular anchor that holds RNAPII in high density clusters. Perturbation of MVI leads to the disruption of RNAPII localisation, chromatin organisation and subsequently a decrease in gene expression. Overall, we uncover the fundamental role of MVI in the spatial regulation of gene expression.
Periodic synchronization of isolated network elements facilitates simulating and inferring gene regulatory networks including stochastic molecular kinetics
Johannes Hettich and Christof M. Gebhardt
The temporal progression of many fundamental processes in cells and organisms, including homeostasis, differentiation and development, are governed by gene regulatory networks (GRNs). GRNs balance fluctuations in the output of their genes, which trace back to the stochasticity of molecular interactions. Although highly desirable to understand life processes, predicting the temporal progression of gene products within a GRN is challenging when considering stochastic events such as transcription factor–DNA interactions or protein production and degradation.
We report a method to simulate and infer GRNs including genes and biochemical reactions at molecular detail. In our approach, we consider each network element to be isolated from other elements during small time intervals, after which we synchronize molecule numbers across all network elements. Thereby, the temporal behaviour of network elements is decoupled and can be treated by local stochastic or deterministic solutions. We demonstrate the working principle of this modular approach with a repressive gene cascade comprising four genes. By considering a deterministic time evolution within each time interval for all elements, our method approaches the solution of the system of deterministic differential equations associated with the GRN. By allowing genes to stochastically switch between on and off states or by considering stochastic production of gene outputs, we are able to include increasing levels of stochastic detail and approximate the solution of a Gillespie simulation. Thereby, CaiNet is able to reproduce noise-induced bi-stability and oscillations in dynamically complex GRNs. Notably, our modular approach further allows for a simple consideration of deterministic delays. We further infer relevant regulatory connections and steady-state parameters of a GRN of up to ten genes from steady-state measurements by identifying each gene of the network with a single perceptron in an artificial neuronal network and using a gradient decent method originally designed to train recurrent neural networks. To facilitate setting up GRNs and using our simulation and inference method, we provide a fast computer-aided interactive network simulation environment, CaiNet.
We developed a method to simulate GRNs at molecular detail and to infer the topology and steady-state parameters of GRNs. Our method and associated user-friendly framework CaiNet should prove helpful to analyze or predict the temporal progression of reaction networks or GRNs in cellular and organismic biology.