High-throughput cellular RNA device engineering

Abstract

Methods for rapidly assessing sequence-structure-function landscapes and developing conditional gene-regulatory devices are critical to our ability to manipulate and interface with biology. We describe a framework for engineering RNA devices from preexisting aptamers that exhibit ligand-responsive ribozyme tertiary interactions. Our methodology utilizes cell sorting, high-throughput sequencing and statistical data analyses to enable parallel measurements of the activities of hundreds of thousands of sequences from RNA device libraries in the absence and presence of ligands. Our tertiary-interaction RNA devices performed better in terms of gene silencing, activation ratio and ligand sensitivity than optimized RNA devices that rely on secondary-structure changes. We applied our method to build biosensors for diverse ligands and determine consensus sequences that enable ligand-responsive tertiary interactions. These methods advance our ability to develop broadly applicable genetic tools and to elucidate the underlying sequence-structure-function relationships that empower rational design of complex biomolecules.

Fig. 1

High-throughput RNA device engineering method. (a) RNA device gene-regulatory mechanism. The RNA device is encoded into the 3’ UTR of a gene, such that device cleavage results in transcript destabilization and reduced expression levels. Binding of ligand (blue circle) to the RNA device disrupts tertiary interactions required for self-cleavage, thereby stabilizing the transcript and upregulating gene expression. (b) HHRz (sTRSV) interactions. Interactions are indicated following Leontis-Westhof notation with the addition of green “I-beams” showing non-adjacent base-stacking interactions (c) Library design for theophylline-responsive tertiary interaction switches. Loop libraries (N3-N8; green) are grafted onto stem II of the sTRSV HHRz (blue), with the theophylline aptamer (gold) on the opposing stem. The aptamer and loop library sequences replace tertiary-interacting regions of the ribozyme, constituted naturally by loops I and II. Red arrow indicates the ribozyme cleavage site. Nucleotides in contact with the theophylline ligand are indicated in green. The single nucleotide difference between the CAG and AAG aptamer variants is shown with a joint cytosine/adenine nucleotide. The library also contains the corresponding structure with the aptamer grafted onto stem I (not shown). (d) Overview of the FACS-Seq method for high-throughput RNA device engineering. Device libraries are gap-repaired into a two-color reporter construct in yeast. Cells harboring the libraries are grown under selected conditions and separately sorted 8-ways using gates uniformly log-spaced over the GFP/mCherry ratio (upper-right inset). For each sorted bin, plasmid DNA is extracted and uniquely barcoded before the entire set is mixed and sequenced. The activities (µ) of each sequence for each particular condition are computed from the NGS bin counts.

Fig. 2

GFP/mCherry activity ratios (µ) for all members of the theophylline aptamer libraries based on FACS-Seq assays. Each point on the plots represents a unique library sequence that has the indicated GFP/mCherry values under the specified conditions. (a) GFP/mCherry values (µ) for theophylline aptamer library members from the two replicate runs without target present (N=5,389). (b) GFP/mCherry values (µ) for theophylline aptamer library members in the presence (5 mM) and absence of theophylline (N=16,024; combined replicate data). Only data for which at least 50 cells were counted is used in each analysis. The striations at particular values result from sequences for which all NGS reads for that sequence were from the same FACS bin, resulting in an estimate at the midpoint of the bin.

Fig. 3

Validation of theophylline-responsive tertiary interaction switches identified through the FACS-Seq method. (a) Comparison of NGS- and flow cytometry-based GFP/mCherry activity measurements (µ) for individual library members. Each point (N=30) is a single library sequence identified from the NGS analysis. Values are reported at 0 mM (red) and 5 mM (blue) theophylline. Error bars for the flow cytometry validation represent the standard error of the mean over at least three biological replicates from independent transformants. Error bars for measurements from the NGS analysis represent the range covered by two biological replicates. (b) Flow cytometry-based activity measurements for selected theophylline-responsive switches identified from the NGS analysis. Median GFP/mCherry ratios are reported for cells harboring the indicated switches and controls grown in 0 mM (red) and 5 mM (blue) theophylline. Activation ratios are reported above each construct. Error bars represent standard error of each mean over at least three biological replicates from independent transformants. (c) EC₅₀ values versus activity ratios for theophylline-responsive switches (cross, secondary structure device; circle, tertiary interaction device). Flow cytometry data (Supplementary fig. 3) were fit to a 4-parameter logistic model with the Hill slope fixed at 1.0, from which the EC₅₀ and 80% confidence interval were determined. Activity ratio is reported as the ratio of µ for switches grown in 0 and 5 mM theophylline; error bars indicate standard deviation over four biological replicate experiments for each condition. (d) Ribozyme dissociation rate constants as measured through a SPR cleavage assay for devices measured at 0 mM (red) and 1 mM (blue) theophylline. Error bars represent the standard error of the mean from at least triplicate assays. (e) IC₅₀ values versus dissociation rate ratios for theophylline-responsive switches (cross, secondary structure device; circle, tertiary interaction device). For each condition, the measurements from at least three separate sets of assays were fit to a 4-parameter logistic model with the Hill slope fixed at 1.0, to compute an IC₅₀ value for each set (Supplementary fig. 4). Dissociation rate ratio is reported as the ratio of k_d at 0 and 1 mM theophylline; error bars represent the 80% confidence interval for the fitted values.

Fig. 4

Extension of the FACS-Seq method to identifying tertiary interaction switches for other aptamer-target pairs. (a, b) GFP/mCherry ratios (µ) for tetracycline (a) and neomycin (b) aptamer library members in the presence and absence of target based on FACS-Seq assays. Each point on the plots represents a unique library sequence (tetracycline: N=3,873; neomycin: N=5,286; combined replicate data) that has the indicated (by color) µ under the specified conditions. Only sequences for which at least 50 cells were counted over the two combined replicates are used in each analysis. (c, d) Flow cytometry-based activity measurements for selected tetracycline-responsive (c) and neomycin-responsive (d) switches identified from the NGS analysis. Median GFP/mCherry ratios are reported for cells harboring the indicated switches and controls grown in the absence (red) and presence (blue; tetracycline: 1 mM, neomycin: 0.1 mM) of target. Error bars represent the standard error of the mean over at least three biological replicates. Activation ratios are reported above each construct. Vertical dotted lines separate ribozyme/controls, secondary structure switches, and tertiary structure switches. Additional compensation was performed for samples containing tetracycline (see Online Methods) due to fluorescence properties of this molecule.