Curated Optogenetic Publication Database

Search precisely and efficiently by using the advantage of the hand-assigned publication tags that allow you to search for papers involving a specific trait, e.g. a particular optogenetic switch or a host organism.

Showing 1 - 5 of 5 results

Light inducible protein degradation in E. coli with LOVtag.

blue AsLOV2 EL222 E. coli
bioRxiv, 26 Feb 2023 DOI: 10.1101/2023.02.25.530042 Link to full text
Abstract: Molecular tools for optogenetic control allow for spatial and temporal regulation of cell behavior. In particular, light controlled protein degradation is a valuable mechanism of regulation because it can be highly modular, used in tandem with other control mechanisms, and maintain functionality throughout growth phases. Here, we engineered LOVtag, a protein tag that can be appended to a protein of interest for inducible degradation in Escherichia coli using blue light. We demonstrate the modularity of LOVtag by using it to tag a range of proteins, including the LacI repressor, CRISPRa activator, and the AcrB efflux pump. Additionally, we demonstrate the utility of pairing the LOVtag with existing optogenetic tools to enhance performance by developing a combined EL222 and LOVtag system. Finally, we use the LOVtag in a metabolic engineering application to demonstrate post-translational control of metabolism. Together, our results highlight the modularity and functionality of the LOVtag system, and introduce a powerful new tool for bacterial optogenetics.

An optogenetic toolkit for light-inducible antibiotic resistance.

blue VVD E. coli Transgene expression Nucleic acid editing
Nat Commun, 23 Feb 2023 DOI: 10.1038/s41467-023-36670-2 Link to full text
Abstract: Antibiotics are a key control mechanism for synthetic biology and microbiology. Resistance genes are used to select desired cells and regulate bacterial populations, however their use to-date has been largely static. Precise spatiotemporal control of antibiotic resistance could enable a wide variety of applications that require dynamic control of susceptibility and survival. Here, we use light-inducible Cre recombinase to activate expression of drug resistance genes in Escherichia coli. We demonstrate light-activated resistance to four antibiotics: carbenicillin, kanamycin, chloramphenicol, and tetracycline. Cells exposed to blue light survive in the presence of lethal antibiotic concentrations, while those kept in the dark do not. To optimize resistance induction, we vary promoter, ribosome binding site, and enzyme variant strength using chromosome and plasmid-based constructs. We then link inducible resistance to expression of a heterologous fatty acid enzyme to increase production of octanoic acid. These optogenetic resistance tools pave the way for spatiotemporal control of cell survival.

Deep model predictive control of gene expression in thousands of single cells.

green CcaS/CcaR E. coli Transgene expression
bioRxiv, 31 Oct 2022 DOI: 10.1101/2022.10.28.514305 Link to full text
Abstract: Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observations of natural dynamics, which can preclude studies of elusive and noisy cellular events where large amounts of data are required to reveal statistically significant effects. Here, using recent advances in the fields of machine learning and control theory, we train a deep neural network to accurately predict the response of an optogenetic system in Escherichia coli cells. We then use the network in a deep model predictive control framework to impose arbitrary and cell-specific gene expression dynamics on thousands of single cells in real time, applying the framework to generate complex time-varying patterns. We also showcase the framework’s ability to link expression patterns to dynamic functional outcomes by controlling expression of the tetA antibiotic resistance gene. This study highlights how deep learning-enabled feedback control can be used to tailor distributions of gene expression dynamics with high accuracy and throughput.

Light-Inducible Recombinases for Bacterial Optogenetics.

blue Magnets VVD E. coli Nucleic acid editing
ACS Synth Biol, 21 Jan 2020 DOI: 10.1021/acssynbio.9b00395 Link to full text
Abstract: Optogenetic tools can provide direct and programmable control of gene expression. Light-inducible recombinases, in particular, offer a powerful method for achieving precise spatiotemporal control of DNA modification. However, to-date this technology has been largely limited to eukaryotic systems. Here, we develop optogenetic recombinases for Escherichia coli that activate in response to blue light. Our approach uses a split recombinase coupled with photodimers, where blue light brings the split protein together to form a functional recombinase. We tested both Cre and Flp recombinases, Vivid and Magnet photodimers, and alternative protein split sites in our analysis. The optimal configuration, Opto-Cre-Vvd, exhibits strong blue light-responsive excision and low ambient light sensitivity. For this system we characterize the effect of light intensity and the temporal dynamics of light-induced recombination. These tools expand the microbial optogenetic toolbox, offering the potential for precise control of DNA excision with light-inducible recombinases in bacteria.

Cell-machine interfaces for characterizing gene regulatory network dynamics.

green red Cyanobacteriochromes Phytochromes Review
Curr Opin Syst Biol, 1 Feb 2019 DOI: 10.1016/j.coisb.2019.01.001 Link to full text
Abstract: Gene regulatory networks and the dynamic responses they produce offer a wealth of information about how biological systems process information about their environment. Recently, researchers interested in dissecting these networks have been outsourcing various parts of their experimental workflow to computers. Here we review how, using microfluidic or optogenetic tools coupled with fluorescence imaging, it is now possible to interface cells and computers. These platforms enable scientists to perform informative dynamic stimulations of genetic pathways and monitor their reaction. It is also possible to close the loop and regulate genes in real time, providing an unprecedented view of how signals propagate through the network. Finally, we outline new tools that can be used within the framework of cell-machine interfaces.
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