Curated Optogenetic Publication Database

Abstract: Microbial cell factories promise sustainable biomanufacturing, yet balancing cellular growth and product synthesis remains a fundamental productivity bottleneck. While traditional static metabolic engineering fails to precisely orchestrate metabolic flux across temporal and spatial dimensions, optogenetic approaches offer unprecedented spatiotemporal control and programmability. This study developed an integrated framework combining dual-mode optogenetic switches with a machine learning-guided optimization strategy to achieve precise temporal orchestration of metabolic pathways. Using Saccharomyces cerevisiae (strain BY4742, which exhibits minimal endogenous 2,3-butanediol (2,3-BDO) production) as the model host, the 2,3-BDO biosynthetic pathway was constructed. The deployment of dual light-responsive modules for protein degradation and stabilization, together with systematic optimization of illumination conditions, enabled dynamic coordination of the growth and production phases, increasing the 2,3-BDO titre to 3.52 times that of the static control in shake flasks. To extend beyond empirical optimization, a convolutional neural network (CNN) was integrated with a differential evolution (DE) algorithm to identify the optimal dynamic illumination schedule, resulting in a further 15.60% increase in the predicted and experimentally validated 2,3-BDO titre. The optimized strategy was subsequently validated in a 5-L photobioreactor, where dynamic optogenetic regulation combined with optimized fermentation conditions yielded 23.95 g L-1 2,3-BDO under fed-batch conditions. This study establishes a machine learning-assisted optogenetic framework for dynamic metabolic regulation and provides an effective strategy for improving microbial biomanufacturing.

Abstract: RNA molecules perform various crucial roles in diverse cellular processes, from translating genetic information to decoding the genome, regulating gene expression and catalyzing chemical reactions. RNA-binding proteins (RBPs) play an essential role in regulating the diverse behaviors and functions of RNA in live cells, but techniques for the spatiotemporal control of RBP activities and RNA functions are rarely reported yet highly desirable. We recently reported the development of LicV, a synthetic photoswitchable RBP that can bind to a specific RNA sequence in response to blue light irradiation. LicV has been used successfully for the optogenetic control of RNA localization, splicing, translation and stability, as well as for the photoswitchable regulation of transcription and genomic locus labeling. Compared to classical genetic or pharmacologic perturbations, LicV-based light-switchable effectors have the advantages of large dynamic range between dark and light conditions and submicron and millisecond spatiotemporal resolutions. In this protocol, we provide an easy, efficient and generalizable strategy for engineering photoswitchable RBPs for the spatiotemporal control of RNA metabolism. We also provide a detailed protocol for the conversion of a CRISPR-Cas system to optogenetic control. The protocols typically take 2-3 d, including transfection and results analysis. Most of this protocol is applicable to the development of novel LicV-based photoswitchable effectors for the optogenetic control of other RNA metabolisms and CRISPR-Cas functions.