Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

Abstract

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop a new RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that "superfolder" mRNAs can be designed to improve both stability and expression that are further enhanced through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

Figure 1.. PERSIST-seq overview and illustrative ribosome load insights.

(A) Overview of the mRNA optimization workflow. Literature mined and rationally designed 5’ and 3’ UTRs were combined with Eterna and algorithmically designed coding sequences. All sequences were then experimentally tested in parallel for in-solution and in-cell stability as well as ribosome load. The mRNA design included unique, 6–9 nt barcodes in the 3’ UTR for tag counting by short-read sequencing. (B) Experimental design for testing in-solution and in-cell stability and ribosome load in parallel. mRNAs were in vitro transcribed, 5’ capped, and polyadenylated in a pooled format before transfection into HEK293T cells or being subjected to in-solution degradation. Transfected cells were then harvested for sucrose gradient fractionation or in-cell degradation analysis. (C) Polysome trace from transfected HEK293T cells with 233-mRNA pool. (D) 5’ UTR variants display a higher variance in mean ribosome load per construct as determined from polysome sequencing. The formula for ribosome load is given. (E) Heatmaps from polysome profiles of mRNA designs selected from the top, middle, and bottom five mRNAs (by ribosome load) from each design category. (F) Secondary structure model of the SARS-CoV-2 5’ UTR. Introduced mutations and substitutions are highlighted. (G) Heatmaps of SARS-CoV-2 5’ UTR variants’ polysome profiles sorted by ribosome load.

Figure 2.. In-cell RNA stability drives downstream protein expression levels.

(A) In-cell half-life of each mRNA design in HEK293T cells. (B) Higher polysome load correlates with decreased in-cell half-life. Correlation between in-cell half-life and mean ribosome load across the entire profile (left), monosome-to-free subunit ratio (center), or polysome-to-monosome ratio (right). (C) In-cell half-life and mean ribosome load for individual mRNA designs with varying UTRs. (D) Kinetic model for predicting protein expression from mRNA half-life and ribosome load. P(t) is protein quantity at time t; m₀ is the mass of mRNA present at t=0; l is mRNA length; k_t is translation rate; and k_m and k_p are rates of mRNA and protein decay, respectively. (E) Protein expression predicted using the kinetic model in (D) on the basis of mRNA half-life and ribosome load. Top: predicted protein expression of each UTR variant; note closer similarity to in-cell half-life data than to ribosome load in (C). Bottom: predicted protein expression normalized by mRNA length (corresponding to transfecting equal masses of each mRNA). (F) Correlation of predicted protein expression and Nluc/Fluc activity in HEK293T cells. (G) In-solution half-life of various mRNA design variants. mRNA lifetimes are strongly dependent on mRNA length and designed structures, revealed by time courses of mRNA degradation under accelerated aging conditions (10 mM MgCl₂, 50 mM Na-CHES, pH 10.0). (H) Nucleotide-resolution in vitro DMS mapping confirms large differences in structural accessibility between a highly structured JEV-HA-Nluc mRNA construct, “LinearDesign-1” and a highly unstructured construct “Yellowstone”. The 5’ and 3’ UTRs (hHBB) were kept constant between designs. (I) Nucleotide DMS accessibility mapped on to structures from DMS-directed structure prediction.

Figure 3.. High-throughput in-line hydrolysis uncovers principles of in-solution RNA degradation.

(A) Eterna participants were asked to design 68-nucleotide RNA fragments maximizing sequence and structure diversity. 3030 constructs were characterized and probed using high-throughput in-line degradation (In-line-seq). (B) Nucleotide-resolution degradation of 2165 68-nt RNA sequences (filtered for signal quality), probed by In-line-seq, sorted by hierarchical clustering on degradation profiles. (C) Sequences span a diverse set of secondary structure motifs, revealing patterns in degradation based on both sequence (i.e., linkages ending at 3’ uridine are particularly reactive) and structure (symmetric internal loops, circled, have suppressed hydrolytic degradation compared to asymmetric internal loops). (D) The ridge regression model “DegScore” was trained to predict per-nucleotide degradation from sequence and loop assignment information. Coefficients with the largest magnitude corresponded to sequence identity immediately after the link, with U being most disfavored. (E) DegScore showed improved predictive power on mRNAs over two other metrics previously posited to predict RNA stability. (F) Introduction of pseudouridine (ψ) modifications stabilizes selected short RNAs at U nucleotides in both loop motifs and in fully unstructured RNAs. (G) Capillary electrophoresis characterization of fragmentation time courses of Nluc mRNA molecules designed with extensive structure (LinearDesign-1) and relatively less structure (Yellowstone), synthesized with standard nucleotides and with ψ modifications. The full-length mRNA band is indicated with a red asterisk. The Tetrahymena ribozyme P4-P6 domain RNA was included after degradation as a control. (H) Exponential fits of capillary electrophoresis measurements of intact RNA over ten time points confirm >3x difference between in-solution lifetimes of LinearDesign-1 and Yellowstone Nluc mRNAs. Inset: Calculated half-lives. Error bars represent standard deviations from bootstrapped exponential fits (n = 1000).

Figure 4.. Integration of 5’/3’ UTRs, structure-optimized CDSs, and pseudouridine (ψ) together enhance mRNA stability and translational output.

(A) CDS and 5’/3’UTR combinations differentially impact protein synthesis. Six mRNA constructs were in vitro synthesized and luciferase activity was measured 6 or 24 hrs post-transfection. Inclusion of ψ was tested on two selected constructs. (B) Workflow for different approaches to design the CDS variants tested in (C). (C) Variations in CDS design facilitates high in-solution stability and differential protein expression. In vitro transcribed mRNAs (24 in total) were subjected to in-solution degradation or transfected into HEK293T cells for 6 and 24 hrs. In-solution half-lives and luciferase activity are normalized to the Nluc START reference construct. Predicted secondary structures are shown for select constructs with colors indicating DegScore at each nucleotide. Designs derived from LinearDesign solutions are marked with a purple triangle. (D) Predicted secondary structure overview of Ribotree_LinearDesign_degscoreall_1. Zoomed boxes indicate sequence optimizations and subsequent structural changes made by DegScore to the reference LinearDesign construct. (E) Increased in-solution half-life and enhanced luciferase expression at 24 hrs, but not 6 hrs, correlate with DegScore. (F) Schematic for testing the synergy between RNA modifications and mRNA design rules on downstream stability and protein output. mRNAs were in vitro synthesized with or without ψ and subjected to degradation conditions. Samples were collected overtime and the RNA was purified before being transfected into HEK293T cells. Luciferase activity was measured 24 hrs after transfection. (G) Luciferase activity of the reference Nluc sequence and DegScore-optimized CDS with or without ψ after being subjected to in-solution degradation.