Light is a powerful tool for manipulating living cells because it can be applied with high resolution across space and over time. We previously constructed a red-light sensitive E. coli transcription system based on a chimera between the red/far red switchable cyanobacterial phytochrome Cph1 and the E. coli EnvZ/OmpR two-component signaling pathways. Here we report the development of a green light inducible transcription system in E. coli based on a recently discovered green/red photoswitchable two-component system from cyanobacteria. We demonstrate that transcriptional output is proportional to the intensity of green light applied and that the green sensor is orthogonal to the red sensor at intensities of 532nm light less than 0.01W/m2. Expression of both sensors in a single cell allows two-color optical control of transcription in both batch culture and in patterns across a lawn of engineered cells. Because each sensor functions as a photoreversible switch, this system should allow the spatial and temporal control of the expression of multiple genes though different combinations of light wavelengths. This feature should aid precision single cell and population-level studies in systems and synthetic biology.

This mini-symposium aims to provide an integrated perspective on recent developments in optogenetics. Research in this emerging field combines optical methods with targeted expression of genetically encoded, protein-based probes to achieve experimental manipulation and measurement of neural systems with superior temporal and spatial resolution. The essential components of the optogenetic toolbox consist of two kinds of molecular devices: actuators and reporters, which respectively enable light-mediated control or monitoring of molecular processes. The first generation of genetically encoded calcium reporters, fluorescent proteins, and neural activators has already had a great impact on neuroscience. Now, a second generation of voltage reporters, neural silencers, and functionally extended fluorescent proteins hold great promise for continuing this revolution. In this review, we will evaluate and highlight the limitations of presently available optogenic tools and discuss where these technologies and their applications are headed in the future.

Genetically-encodable optical reporters, such as Green Fluorescent Protein, have revolutionized the observation and measurement of cellular states. However, the inverse challenge of using light to precisely control cellular behavior has only recently begun to be addressed; semi-synthetic chromophore-tethered receptors1 and naturally-occurring channel rhodopsins have been used to directly perturb neuronal networks2,3. The difficulty of engineering light sensitive proteins remains a significant impediment to the optical control to most cell-biological processes. Here we demonstrate the use of a new genetically-encoded light-control system based on an optimized reversible protein-protein interaction from the phytochrome signaling network of Arabidopsis thaliana. Because protein-protein interactions are one of the most general currencies of cellular information, this system can in principal be generically used to control diverse functions. Here we show that this system can be used to precisely and reversibly translocate target proteins to the membrane with micrometer spatial resolution and second time resolution. We show that light-gated translocation of the upstream activators of rho-family GTPases, which control the actin cytoskeleton, can be used to precisely reshape and direct the cell morphology of mammalian cells. The light-gated protein-protein interaction that has been optimized in this work should be useful for the design of diverse light-programmable reagents, potentially enabling a new generation of perturbative, quantitative experiments in cell biology.

Summary

Edge detection is a signal processing algorithm common in artificial intelligence and image recognition programs. We have constructed a genetically encoded edge detection algorithm that programs an isogenic community of E.coli to sense an image of light, communicate to identify the light-dark edges, and visually present the result of the computation. The algorithm is implemented using multiple genetic circuits. An engineered light sensor enables cells to distinguish between light and dark regions. In the dark, cells produce a diffusible chemical signal that diffuses into light regions. Genetic logic gates are used so that only cells that sense light and the diffusible signal produce a positive output. A mathematical model constructed from first principles and parameterized with experimental measurements of the component circuits predicts the performance of the complete program. Quantitatively accurate models will facilitate the engineering of more complex biological behaviors and inform bottom-up studies of natural genetic regulatory networks.

Summary

Bacterial pathogenesis requires the precise spatial and temporal control of gene expression, the dynamics of which are controlled by regulatory networks. A network encoded within Salmonella Pathogenicity Island 1 controls the expression of a type III protein secretion system involved in the invasion of host cells. The dynamics of this network are measured in single cells using promoter-green fluorescent protein (gfp) reporters and flow cytometry. During induction, there is a temporal order of gene expression, with transcriptional inputs turning on first, followed by structure, and effector genes. The promoters show varying stochastic properties, where graded inputs are converted into all-or-none and hybrid responses. The relaxation dynamics are measured by shifting cells from inducing into non-inducing conditions and measuring the fluorescence decay. The gfp expressed from promoters controlling the transcriptional inputs (hilC and hilD) and structural genes (prgH) decay exponentially with a characteristic time of 50–55 minutes. In contrast, the gfp expressed from a promoter controlling the expression of effectors (sicA) persists for 110 ± 9 minutes. This promoter is controlled by a genetic circuit formed by a transcription factor (InvF), chaperone (SicA) and secreted protein (SipC) that regulates effector expression in response to the secretion capacity of the cell. A mathematical model of this circuit demonstrates that the delay is due to a split positive feedback loop. This model is tested in a ΔsicA knockout where sicA is complemented with and without the feedback loop. The delay is eliminated when the feedback loop is deleted. Further, a robustness analysis of the model predicts that the delay time can be tuned by changing the affinity of SicA:InvF multimers to an operator in the sicA promoter. This prediction is used to construct a targeted library, which contains mutants with both longer and shorter delays. This combination of theory and experiments provides a platform to predict how genetic perturbations lead to changes in the global dynamics of a regulatory network.