Publikationen 2022

Maximilian Fellermann, Mia Stemmer, Reiner Noschka, Fanny Wondany, Stephan Fischer, Jens Michaelis, Steffen Stenger, Holger Barth

The protein toxin C3bot from Clostridium botulinum is a mono-ADP-ribosyltransferase that selectively intoxicates monocyte-derived cells such as macrophages, osteoclasts, and dendritic cells (DCs) by cytosolic modification of Rho-A, -B, and -C. Here, we investigated the application of C3bot as well as its non-toxic variant C3bot_E174Q as transporters for selective delivery of cargo molecules into macrophages and DCs. C3bot and C3bot_E174Q facilitated the uptake of eGFP into early endosomes of human-monocyte-derived macrophages, as revealed by stimulated emission depletion (STED) super-resolution microscopy. The fusion of the cargo model peptide eGFP neither affected the cell-type selectivity (enhanced uptake into human macrophages ex vivo compared to lymphocytes) nor the cytosolic release of C3bot. Moreover, by cell fractionation, we demonstrated that C3bot and C3bot_E174Q strongly enhanced the cytosolic release of functional eGFP. Subsequently, a modular system was created on the basis of C3bot_E174Q for covalent linkage of cargos via thiol–maleimide click chemistry. The functionality of this system was proven by loading small molecule fluorophores or an established reporter enzyme and investigating the cellular uptake and cytosolic release of cargo. Taken together, non-toxic C3bot_E174Q is a promising candidate for the cell-type-selective delivery of small molecules, peptides, and proteins into the cytosol of macrophages and DCs.
https://www.mdpi.com/2072-6651/14/10/711

Raphael Nold, Charles Babin, Joel Schmidt, Tobias Linkewitz, María T. Pérez Zaballos, Rainer Stöhr, Roman Kolesov, Vadim Vorobyov, Daniil M. Lukin, Rüdiger Boppert, Stefanie Barz, Jelena Vučković, J. Christof M. Gebhardt, Florian Kaiser, and Jörg Wrachtrup

The ability to perform high-precision optical measurements is paramount in science and engineering. Laser interferometry enables interaction-free sensing with a precision ultimately limited by shot noise. Quantum optical sensors can surpass this limit but single- or multiphoton schemes are challenged by low experimental sampling rates, while squeezed-light approaches require complex optical setups and sophisticated time gating. Here, we introduce a simple method that infers optical phase shifts through standard intensity measurements while still maintaining the quantum advantage in the measurement precision. Capitalizing on the robustness and high sampling rates of our device, we implement a quantum optical microphone in the audio band. Its performance is benchmarked against a classical laser microphone in a standardized medically approved speech-recognition test on 45 subjects. We find that quantum-recorded words improve the speech-recognition threshold by $-0.57{\mathrm{dB}}_{\mathrm{SPL}}$, thus making the quantum advantage audible. Not only do these results open the door toward applications in quantum nonlinear interferometry but they also show that quantum phenomena can be experienced by humans. https://doi.org/10.1103/PRXQuantum.3.020358

Timo Kuhn, Amit N. Landge, David Mörsdorf, Jonas Coßmann, Johanna Gerstenecker, Daniel Čapek, Patrick Müller and J. Christof M. Gebhardt

The hindered diffusion model postulates that the movement of a signaling molecule through an embryo is affected by tissue geometry and binding-mediated hindrance, but these effects have not been directly demonstrated in vivo. Here, we visualize extracellular movement and binding of individual molecules of the activator-inhibitor signaling pair Nodal and Lefty in live developing zebrafish embryos using reflected light-sheet microscopy. We observe that diffusion coefficients of molecules are high in extracellular cavities, whereas mobility is reduced and bound fractions are high within cell-cell interfaces. Counterintuitively, molecules nevertheless accumulate in cavities, which we attribute to the geometry of the extracellular space by agent-based simulations. We further find that Nodal has a larger bound fraction than Lefty and shows a binding time of tens of seconds. Together, our measurements and simulations provide direct support for the hindered diffusion model and yield insights into the nanometer-to-micrometer-scale mechanisms that lead to macroscopic signal dispersal.
https://doi.org/10.1038/s41467-022-33704-z

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.
https://doi.org/10.1186/s12859-021-04541-6