Biology subjects

Hettich, J.

Publications and source records attributed to Hettich, J..

5 recordsLinked to original sources

bioRxiv2020

Single molecule tracking and analysis framework including theory-predicted parameter settings

Imaging, tracking and analyzing individual biomolecules in living systems is a powerful technology to obtain quantitative kinetic and spatial information such as reaction rates, diffusion coefficients and localization maps. Common tracking tools often operate on single movies and require additional manual steps to analyze whole data sets or to compare different experimental conditions. We report a fast and comprehensive single molecule tracking and analysis framework (TrackIt) to simultaneously process several multi-movie data sets. A user-friendly GUI offers convenient tracking visualization, multiple state-of-the-art analysis procedures, display of results, and data im- and export at different levels to utilize external software tools. We applied our framework to quantify dissociation rates of a transcription factor in the nucleus and found that tracking errors, similar to fluorophore photobleaching, have to be considered for reliable analysis. Accordingly, we developed an algorithm, which accounts for both tracking losses and suggests optimized tracking parameters when evaluating reaction rates. Our versatile and extensible framework facilitates quantitative analysis of single molecule experiments at different experimental conditions.

biophysics↗

bioRxiv2020

Transcription factor residence time dominates over concentration in transcription activation

Transcription is a vital process activated by transcription factor (TF) binding. The active gene releases a burst of transcripts before turning inactive again. While the basic course of transcription is well understood, it is unclear how binding of a TF affects the frequency, duration and size of a transcriptional burst. We systematically varied the residence time and concentration of a synthetic TF and characterized the transcription of a reporter gene by combining single molecule imaging, single molecule RNA-FISH, live transcript visualisation and analysis with a novel algorithm, Burst Inference from mRNA Distributions (BIRD). For this well-defined system, we found that TF binding solely affected burst frequency and variations in TF residence time had a stronger influence than variations in concentration. This enabled us to device a model of gene transcription, in which TF binding triggers multiple successive steps before the gene transits to the active state and actual mRNA synthesis is decoupled from TF presence. We quantified all transition times of the TF and the gene, including the TF search time and the delay between TF binding and the onset of transcription. Our quantitative measurements and analysis revealed detailed kinetic insight, which may serve as basis for a bottom-up understanding of gene regulation.

biophysics↗

bioRxiv2020

Myosin VI moves on nuclear actin filaments and supports long-range chromatin rearrangements

Nuclear myosin VI (MVI) enhances RNA polymerase II - dependent transcription, but the molecular mechanism is unclear. We used live cell single molecule tracking to follow individual MVI molecules inside the nucleus and observed micrometer-long motion of the motor. Besides static chromatin interactions lasting for tens of seconds, ATPase-dependent directed motion occurred with a velocity of 2 {micro}m/s. The movement was frequently interrupted by short periods of slow restricted diffusion and increased in frequency upon stimulation of transcription. Mutagenesis and perturbation experiments demonstrated that nuclear MVI motion is independent of dimerization and occurs on nuclear actin filaments, which we also observed by two-color imaging. Using chromosome paint to quantify distances between chromosomes, we found that MVI is required for transcription-dependent long-range chromatin rearrangements. Our measurements reveal a transcription-coupled function of MVI in the nucleus, where it actively undergoes directed movement along nuclear actin filaments. Motion is potentially mediated by cooperating monomeric motors and might assist in enhancing transcription by supporting long-range chromatin rearrangements.

cell biology↗

bioRxiv2019

Computer aided interactive gene network simulations including stochastic molecular kinetics and noise

BackgroundThe 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. ResultsWe report CaiNet, a fast computer-aided interactive network simulation environment optimized to set up, simulate and infer GRNs 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 between 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 the modular approach of CaiNet 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. 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. ConclusionCaiNet constitutes a user-friendly framework to simulate GRNs at molecular detail and to infer the topology and steady-state parameters of GRNs. Thus, it should prove helpful to analyze or predict the temporal progression of reaction networks or GRNs in cellular and organismic biology. CaiNet is freely available at https://gitlab.com/GebhardtLab/CaiNet.

systems biology↗

bioRxiv2019

Inferring quantity and qualities of superimposed reaction rates in single molecule survival time distributions

Actions of molecular species, for example binding of transcription factors to chromatin, are intrinsically stochastic and may comprise several mutually exclusive pathways. Inverse Laplace transformation in principle resolves the rate constants and frequencies of superimposed reaction processes, however current approaches are challenged by single molecule fluorescence time series prone to photobleaching. Here, we present a genuine rate identification method (GRID) that infers the quantity, rates and frequencies of dissociation processes from single molecule fluorescence survival time distributions using a dense grid of possible decay rates. In particular, GRID is able to resolve broad clusters of rate constants not accessible to common models of one to three exponential decay rates. We validate GRID by simulations and apply it to the problem of in-vivo TF-DNA dissociation, which recently gained interest due to novel single molecule imaging technologies. We consider dissociation of the transcription factor CDX2 from chromatin. GRID resolves distinct, decay rates and identifies residence time classes overlooked by other methods. We confirm that such sparsely distributed decay rates are compatible with common models of TF sliding on DNA.

biophysics↗