Abstract:While RNA structures have been extensively characterized in vitro, very few techniques exist to probe RNA structures inside cells. Here, we have exploited mechanisms of post-transcriptional regulation to synthesize fluorescence-based probes that assay RNA structures in vivo. Our probing system involves the co-expression of two constructs: (i) a target RNA and (ii) a reporter containing a probe complementary to a region in the target RNA attached to an RBS-sequestering hairpin and fused to a sequence encoding t… Show more
“…Toehold translational switches -rethinking riboregulator design principles to enable protein-like dynamic ranges Synthetic RNA translational activators called 'riboregulators' were one of the first synthetic regulatory RNAs described [2] and have been used in applications ranging from biocontainment [12] to probing cellular RNA folds [13]. Inspired by natural bacterial small RNA (sRNA) regulators, riboregulators use designed RNA hairpin structures to block translation in cis by sequestering the RBS of a gene.…”
Section: New Rna Regulatory Mechanisms Solve Key Challenges and Creatmentioning
Since our ability to engineer biological systems is directly related to our ability to control gene expression, a central focus of synthetic biology has been to develop programmable genetic regulatory systems. Researchers are increasingly turning to RNA regulators for this task because of their versatility, and the emergence of new powerful RNA design principles. Here we review advances that are transforming the way we use RNAs to engineer biological systems. First, we examine new designable RNA mechanisms that are enabling large libraries of regulators with protein-like dynamic ranges. Next, we review emerging applications, from RNA genetic circuits to molecular diagnostics. Finally, we describe new experimental and computational tools that promise to accelerate our understanding of RNA folding, function and design.
“…Toehold translational switches -rethinking riboregulator design principles to enable protein-like dynamic ranges Synthetic RNA translational activators called 'riboregulators' were one of the first synthetic regulatory RNAs described [2] and have been used in applications ranging from biocontainment [12] to probing cellular RNA folds [13]. Inspired by natural bacterial small RNA (sRNA) regulators, riboregulators use designed RNA hairpin structures to block translation in cis by sequestering the RBS of a gene.…”
Section: New Rna Regulatory Mechanisms Solve Key Challenges and Creatmentioning
Since our ability to engineer biological systems is directly related to our ability to control gene expression, a central focus of synthetic biology has been to develop programmable genetic regulatory systems. Researchers are increasingly turning to RNA regulators for this task because of their versatility, and the emergence of new powerful RNA design principles. Here we review advances that are transforming the way we use RNAs to engineer biological systems. First, we examine new designable RNA mechanisms that are enabling large libraries of regulators with protein-like dynamic ranges. Next, we review emerging applications, from RNA genetic circuits to molecular diagnostics. Finally, we describe new experimental and computational tools that promise to accelerate our understanding of RNA folding, function and design.
“…Likewise, information regarding binding energy calculations has been valuable for further enhance knockdown efficiency by mutagenesis approaches that fine-tune target binding regions on the ncRNA [56]. We suspect that recent in vivo structural analysis methods will be of high benefit to this problem [69,72,73].…”
Section: Resultsmentioning
confidence: 99%
“…A key design parameter of STARs is the selection of the hairpin sequence and length coupled to the STAR length. This system parallels translational control schemes that have been reported in the context of SD-antiSD hairpins that act as riboregulators at the 5' of a target gene [68,69]. STARs have been designed to successfully target natural terminators in E. coli, but given that these are at the end of genes, they have undetectable effects on expression of the surrounding genes.…”
Section: Design Of Synthetic Ncrnas: Modular Blueprints Inspired By Nmentioning
Increasing demands for efficient, sustainable chemical production continue to motiv ate engineering of microbial strains. Noncoding RNAs (ncRNAs) represent a class of powerful regulators of cellular processes and are emerging as significant tools for strain engineering, particularly for complex phenotypes. Current strategies include adjusting expression levels of natural ncRNAs and developing synthetic ncRNAs to target specific genes. In this review, we summarize and analyze the effectiveness of the design blueprints for a variety of natural and synthetic ncRNA systems recently re ported in the literature to offer a concise and easy to read engineering guide to exploiting trans-regulatory ncRNAs for strain engineering.
“…the iRS3 by using various complementary probes designed a priori to target a region within the intron [Sowa et al, 2015]. However, the number of variations of the probe increases quadratically in the number of nucleotides; therefore, the number of candidate probes is usually extremely large.…”
We present a sparse knowledge gradient (SpKG) algorithm for adaptively selecting the targeted regions within a large RNA molecule to identify which regions are most amenable to interactions with other molecules. Experimentally, such regions can be inferred from fluorescence measurements obtained by binding a complementary probe with fluorescence markers to the targeted regions. We use a biophysical model which shows that the fluorescence ratio under the log scale has a sparse linear relationship with the coefficients describing the accessibility of each nucleotide, since not all sites are accessible (due to the folding of the molecule). The SpKG algorithm uniquely combines the Bayesian ranking and selection problem with the frequentist ℓ 1 regularized regression approach Lasso. We use this algorithm to identify the sparsity pattern of the linear model as well as sequentially decide the best regions to test before experimental budget is exhausted. Besides, we also develop two other new algorithms: batch SpKG algorithm, which generates more suggestions sequentially to run parallel experiments; and batch SpKG with a procedure which we call length mutagenesis. It dynamically adds in new alternatives, in the form of types of probes, are created by inserting, deleting or mutating nucleotides within existing probes. In simulation, we demonstrate these algorithms on the Group I intron (a mid-size RNA molecule), showing that they efficiently learn the *
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