Efficient splicing-based RNA regulators for tetracycline-inducible gene expression in human cell culture and C. elegans

Synthetic riboswitches gain increasing interest for controlling transgene expression in diverse applications ranging from synthetic biology, functional genomics, and pharmaceutical target validation to potential therapeutic approaches. However, existing systems often lack the pharmaceutically suited ligands and dynamic responses needed for advanced applications. Here we present a series of synthetic riboswitches for controlling gene expression through the regulation of alternative splicing. Placing the 5′-splice site into a stem structure of a tetracycline-sensing aptamer allows us to regulate the accessibility of the splice site. In the presence of tetracycline, an exon with a premature termination codon is skipped and gene expression can occur, whereas in its absence the exon is included into the coding sequence, repressing functional protein expression. We were able to identify RNA switches controlling protein expression in human cells with high dynamic ranges and different levels of protein expression. We present minimalistic versions of this system that circumvent the need to insert an additional exon. Further, we demonstrate the robustness of our approach by transferring the devices into the important research model organism Caenorhabditis elegans, where high levels of functional protein with very low background expression could be achieved.

A Web Server for Designing Molecular Switches Composed of Two Interacting RNAs

The programmability of RNA–RNA interactions through intermolecular base-pairing has been successfully exploited to design a variety of RNA devices that artificially regulate gene expression. An in silico design for interacting structured RNA sequences that satisfies multiple design criteria becomes a complex multi-objective problem. Although multi-objective optimization is a powerful technique that explores a vast solution space without empirical weights between design objectives, to date, no web service for multi-objective design of RNA switches that utilizes RNA–RNA interaction has been proposed. We developed a web server, which is based on a multi-objective design algorithm called MODENA, to design two interacting RNAs that form a complex in silico. By predicting the secondary structures with RactIP during the design process, we can design RNAs that form a joint secondary structure with an external pseudoknot. The energy barrier upon the complex formation is modeled by an interaction seed that is optimized in the design algorithm. We benchmarked the RNA switch design approaches (MODENA+RactIP and MODENA+RNAcofold) for the target structures based on natural RNA-RNA interactions. As a result, MODENA+RactIP showed high design performance for the benchmark datasets.

A deep learning approach to programmable RNA switches

Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced pattern recognition from deep learning. Here, we investigate Deep Neural Networks (DNN) to predict toehold switch function as a canonical riboswitch model in synthetic biology. To facilitate DNN training, we synthesize and characterize in vivo a dataset of 91,534 toehold switches spanning 23 viral genomes and 906 human transcription factors. DNNs trained on nucleotide sequences outperform (R2 = 0.43–0.70) previous state-of-the-art thermodynamic and kinetic models (R2 = 0.04–0.15) and allow for human-understandable attention-visualizations (VIS4Map) to identify success and failure modes. This work shows that deep learning approaches can be used for functionality predictions and insight generation in RNA synthetic biology.

Let’s switch on AAV!

RNA switches expand the reach of AAV vectors.

Crowdsourced RNA design discovers diverse, reversible, efficient, self-contained molecular sensors

Internet-based scientific communities promise a means to apply distributed, diverse human intelligence towards previously intractable scientific problems. However, current implementations have not allowed communities to propose experiments to test all emerging hypotheses at scale or to modify hypotheses in response to experiments. We report high-throughput methods for molecular characterization of nucleic acids that enable the large-scale videogame-based crowdsourcing of functional RNA sensor design, followed by high-throughput functional characterization. Iterative design testing of thousands of crowdsourced RNA sensor designs produced near-thermodynamically optimal and reversible RNA switches that act as self-contained molecular sensors and couple five distinct small molecule inputs to three distinct protein binding and fluorogenic outputs—results that surpass computational and expert-based design. This work represents a new paradigm for widely distributed experimental bioscience.One Sentence SummaryOnline community discovers standalone RNA sensors.