Our ability to efficiently and predictably program cells is central to the fields of bioengineering and synthetic biology. Once thought to be a passive carrier of genetic information, RNA is now more appreciated as the main organizer of cellular networks. To harness the unique abilities of RNA molecules for programming cells, we show here how to rationally design novel synthetic RNA elements to recapitulate the functions of natural noncoding RNAs (ncRNAs), and how to assemble these synthetic elements into higher-order biological systems.
To create synthetic RNA elements, we start with two primary types of ncRNA-mediated natural systems. Both modulate RNA-level regulatory signals encoded in the 5' untranslated region, and are mediated by ncRNAs. In the first system the ncRNA represses transcription elongation, whereas in the second system the ncRNA inhibits translation initiation.
To create orthogonal RNA elements that work independently in the same cell, we systematically modify the RNA-RNA interaction in the natural systems. Our characterization results in families of orthogonally acting RNA elements for both transcription and translation controls. Furthermore, we develop mathematical thermodynamic models to predict new RNA elements in silico for translation controls. To engineer synthetic RNAs to sense and integrate cellular signals, we design allosteric RNA chimera molecules by fusing ncRNAs to RNA aptamers. We demonstrate the design principles for creating such chimeric RNA molecules that can sense proteins or small molecules and control transcription or translation. We show that the design strategy is modular, which allows us to reconfigure different ncRNA mutants and RNA aptamers to engineer orthogonal RNA chimeras that respond to different ligands and regulate different gene targets. We further show that multiple RNA chimeras allow logical integration of molecular signals in the same cell for cellular information processing.
We assemble multiple synthetic RNA elements to create basic regulatory network motifs. These include independent control, logic control, and cascading control. We characterize the performance and properties of these engineered RNA circuits such as their time response, signal sensitivity, and noises across cell populations. We further explore a strategy that can effectively convert orthogonal translational regulators into orthogonal transcriptional regulators, which can be used to perform multi-input logic computation. In an effort to engineer feedback circuits, we demonstrate the use of translational repressor and activator based on RNA-binding proteins. The designed positive or negative feedback circuits form a basis for programming complex functions.
To improve the predictability of engineered biological systems, we develop a synthetic RNA processing platform from the bacterial CRISPR genetic immune pathway. The synthetic RNA processing system can efficiently and specifically cleave desired precursor mRNAs at designed loci. Using this system, we show that transcript cleavage enables quantitative programming of gene expression by modular assembly of promoters, ribosome binding sites, cis regulatory elements, and riboregulators. These basic components can be grouped into multi-gene synthetic operons that behave predictably only after RNA processing. Physical separation of otherwise linked elements within biological assemblies allows design of sophisticated RNA-level regulatory systems that are not possible without it. Thus, our results exemplify a crucial design principle based on controllable RNA processing for improving the modularity and reliability of genetic systems.
To sum, our work established bacterial ncRNAs as an intriguing engineering substrate for scalable genetic circuit design and for programming cells. We provide a set of engineering principles for designing synthetic RNA elements as well as using them to sense signals and form genetic circuits. Our RNA-based engineering platform provides a versatile and powerful strategy for designing higher-order cellular information processing and computation systems, which can be readily applied to practical applications including chemical production, environment remediation, and therapeutics.