Rausenberger, Julia
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Photoconversion and nuclear trafficking cycles determine phytochrome A's response profile to far-red light
Phytochrome A (phyA) is the only photoreceptor in plants, initiating responses in far-red light and, as such, essential for survival in canopy shade. Although the absorption and the ratio of active versus total phyA are maximal in red light, far-red light is the most efficient trigger of phyA-dependent responses. Using a joint experimental-theoretical approach, we unravel the mechanism underlying this shift of the phyA action peak from red to far-red light and show that it relies on specific molecular interactions rather than on intrinsic changes to phyA's spectral properties. According to our model, the dissociation rate of the phyA-FHY1/FHL nuclear import complex is a principle determinant of the phyA action peak. The findings suggest how higher plants acquired the ability to sense far-red light from an ancestral photoreceptor tuned to respond to red light.
An integrative model for phytochrome B mediated photomorphogenesis. From protein dynamics to physiology
Plants have evolved various sophisticated mechanisms to respond and adapt to changes of abiotic factors in their natural environment. Light is one of the most important abiotic environmental factors and it regulates plant growth and development throughout their entire life cycle. To monitor the intensity and spectral composition of the ambient light environment, plants have evolved multiple photoreceptors, including the red/far-red light-sensing phytochromes.
Signatures of gene expression noise in cellular systems
Noise in gene expression, either due to inherent stochasticity or to varying inter- and intracellular environment, can generate significant cell-to-cell variability of protein levels in clonal populations. To quantify the different sources of gene expression noise, several theoretical studies have been performed using either a quasi-stationary approximation for the emerging master equation or employing a time-dependent description, when cell division is taken explicitly into account. Here, we give an overview of the different origins of gene expression noise which were found experimentally and introduce the basic stochastic modeling approaches. We extend, and apply a time-dependent description of gene expression noise to experimental data. The analysis shows that the induction level of the transcription factor can be employed to discriminate the noise profiles and their characteristic signatures. On the basis of experimentally measured cell distributions, our simulations suggest that transcription factor binding and promoter activation can be modeled independently of each other with sufficient accuracy.