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Background Alternative splicing, which generates multiple mRNA isoforms from single genes, is crucial for the regulation of eukaryotic gene expression. The flux through competing splicing pathways cannot be determined by traditional RNA-Seq, however, because different mRNA isoforms can have widely differing decay rates. Indeed, some mRNA isoforms with extremely short half-lives, such as those subject to translation-dependent nonsense-mediated decay (AS-NMD), may be completely overlooked in even the most extensive RNA-Seq analyses. Results RNA immunoprecipitation in tandem (RIPiT) of exon junction complex components allows for purification of post-splicing mRNA-protein particles (mRNPs) not yet subject to translation (pre-translational mRNPs) and, therefore, translation-dependent mRNA decay. Here we compare exon junction complex RIPiT-Seq to whole cell RNA-Seq data from HEK293 cells. Consistent with expectation, the flux through known AS-NMD pathways is substantially higher than that captured by RNA-Seq. Our RIPiT-Seq also definitively demonstrates that the splicing machinery itself has no ability to detect reading frame. We identify thousands of previously unannotated splicing events; while many can be attributed to splicing noise, others are evolutionarily conserved events that produce new AS-NMD isoforms likely involved in maintenance of protein homeostasis. Several of these occur in genes whose overexpression has been linked to poor cancer prognosis. Conclusions Deep sequencing of RNAs in post-splicing, pre-translational mRNPs provides a means to identify and quantify splicing events without the confounding influence of differential mRNA decay. For many known AS-NMD targets, the nonsense-mediated decay-linked alternative splicing pathway predominates. Exon junction complex RIPiT-Seq also revealed numerous conserved but previously unannotated AS-NMD events.

mRNA medicine is a new and rapidly developing field in which the delivery of genetic information in the form of mRNA is used to direct therapeutic protein production in humans. This approach, which allows for the quick and efficient identification and optimization of drug candidates for both large populations and individual patients, has the potential to revolutionize the way we prevent and treat disease. A key feature of mRNA medicines is their high degree of designability, although the design choices involved are complex. Maximizing the production of therapeutic proteins from mRNA medicines requires a thorough understanding of how nucleotide sequence, nucleotide modification and RNA structure interplay to affect translational efficiency and mRNA stability. In this Review, we describe the principles that underlie the physical stability and biological activity of mRNA and emphasize their relevance to the myriad considerations that factor into therapeutic mRNA design.The recent success of mRNA vaccines has boosted the prospects for the development of a new class of designer medicines based on mRNA. This Review discusses the multiple design parameters that need to be carefully considered to create highly effective mRNA medicines.

A vaccine for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is needed to control the coronavirus disease 2019 (COVID-19) global pandemic. Structural studies have led to the development of mutations that stabilize Betacoronavirus spike proteins in the prefusion state, improving their expression and increasing immunogenicity 1 . This principle has been applied to design mRNA-1273, an mRNA vaccine that encodes a SARS-CoV-2 spike protein that is stabilized in the prefusion conformation. Here we show that mRNA-1273 induces potent neutralizing antibody responses to both wild-type (D614) and D614G mutant 2 SARS-CoV-2 as well as CD8 + T cell responses, and protects against SARS-CoV-2 infection in the lungs and noses of mice without evidence of immunopathology. mRNA-1273 is currently in a phase III trial to evaluate its efficacy. mRNA-1273, an mRNA vaccine that encodes a stabilized prefusion-state severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein, elicits robust immune responses and protects mice against replication of SARS-CoV-2 in the upper and lower airways.

Cellular automata (CA) are discrete-time dynamical systems with local update rules on a lattice. Despite their elementary definition, CA support a wide spectrum of macroscopic phenomena central to statistical physics: equilibrium and nonequilibrium phase transitions, transport and hydrodynamic limits, kinetic roughening, self-organized criticality, and complex spatiotemporal correlations. This survey focuses on three tightly connected themes. \\emph{(i)} We present a structural view of CA as shift-commuting maps on configuration spaces, emphasizing rule complexity, reversibility, and conservation laws (including discrete continuity equations). \\emph{(ii)} We organize transport in CA into ballistic, diffusive, and anomalous regimes, and connect microscopic currents to macroscopic laws through Green--Kubo formulas, scaling theory, and universality classes. \\emph{(iii)} We develop correlation-based methods -- from structure factors and response formulas to computational mechanics and data-driven inference -- that diagnose regimes and enable coarse-graining.

Random walks in random environments (RWRE) model transport in quenched disorder, incorporating spatial heterogeneity, trapping, random drift, and random geometry. This paper summarizes discrete and continuous time formulations, identifies principal transport regimes through quantitative observables (velocity, diffusivity, mean-square displacement, first-passage, large deviations, aging), and reviews core methods in one dimension (potential/valley mechanisms) and in higher dimensions (environment-seen-from-the-particle, correctors/homogenization, regeneration and ballisticity criteria).

Cellular automata (CA) are discrete-time dynamical systems with local update rules on a lattice. Despite their elementary definition, CA support a wide spectrum of macroscopic phenomena central to statistical physics: equilibrium and nonequilibrium phase transitions, transport and hydrodynamic limits, kinetic roughening, self-organized criticality, and complex spatiotemporal correlations. This survey focuses on three tightly connected themes. \\emph{(i)} We present a structural view of CA as shift-commuting maps on configuration spaces, emphasizing rule complexity, reversibility, and conservation laws (including discrete continuity equations). \\emph{(ii)} We organize transport in CA into ballistic, diffusive, and anomalous regimes, and connect microscopic currents to macroscopic laws through Green--Kubo formulas, scaling theory, and universality classes. \\emph{(iii)} We develop correlation-based methods -- from structure factors and response formulas to computational mechanics and data-driven inference -- that diagnose regimes and enable coarse-graining.

The success of COVID-19 mRNA vaccines has made optimizing mRNAs for in-vial stability a key objective. However, we still lack a complete understanding of the sequence metrics that influence mRNA stability in solution. RNA secondary structure plays a central role in protecting against hydrolysis, the primary degradation pathway under storage conditions. Yet, the structural metrics that best guide stability-focused mRNA design remain unclear. Global metrics like minimum free energy and average unpaired probability have improved mRNA stability but fail to capture local structural variation relevant to degradation. We show that base-pairing probability, in terms of log odds, provide fine-scale, orthogonal insight that complements global metrics and improves stability modeling. By combining local and global features into a four-feature regression model, dubbed STRAND (Stability Regression Analysis using Nucleotide-Derived features), we achieve substantial gains in predictive performance over current methods. This compact and interpretable model provides a practical framework for designing mRNAs with enhanced in-solution stability.

mRNA technology has revolutionized vaccine development, protein replacement therapies, and cancer immunotherapies, offering rapid production and precise control over sequence and efficacy. However, the inherent instability of mRNA poses significant challenges for drug storage and distribution, particularly in resource-limited regions. Co-optimizing RNA structure and codon choice has emerged as a promising strategy to enhance mRNA stability while preserving efficacy. Given the vast sequence and structure design space, specialized algorithms are essential to achieve these qualities. Recently, several effective algorithms have been developed to tackle this challenge that all use similar underlying principles. We call these specialized algorithms \"mRNA folding\" algorithms as they generalize classical RNA folding algorithms. A comprehensive analysis of their underlying principles, performance, and limitations is lacking. This review aims to provide an in-depth understanding of these algorithms, identify opportunities for improvement, and benchmark existing software implementations in terms of scalability, correctness, and feature support.

The success of COVID-19 mRNA vaccines has made the in-solution stability optimization of mRNAs a key objective. However, we still lack a complete understanding of sequence metrics that influence mRNA in-solution stability. RNA secondary structure plays a critical role in protecting against hydrolysis, the primary degradation pathway under storage conditions. Yet, the structural metrics that best guide stability-focused mRNA design remain incompletely defined. Global metrics like minimum free energy and average unpaired probability have improved mRNA stability but fail to capture local structural variation relevant to RNA degradation. We demonstrate that base-pairing log odds provide fine-scale, orthogonal insight that complements global metrics and improves stability modeling. Further, by combining local and global features into a parsimonious four-feature regression model, dubbed Stability Regression Analysis using Nucleotide-Derived features (STRAND), we achieve a greater than two-fold reduction in prediction error compared to existing machine learning and deep learning approaches and demonstrate robust generalization across diverse transcript contexts. This compact and interpretable model provides an accurate and reliable framework for predicting mRNAs in-solution stability.

Co-optimizing mRNA sequences for both codon optimality and secondary structure is crucial for producing stable and efficacious mRNA therapeutics. Codon optimization, which adjusts nucleotide sequences to enhance translational efficiency, inherently influences mRNA secondary structure - a key determinant of molecular stability both in-vial and in-cell. Because both properties are governed by the same underlying sequence, optimizing one directly impacts the other. To address this interdependence, we introduce a novel variational framework that simultaneously optimizes codon usage and secondary structure. Our method employs a dual-objective function that balances the codon adaptation index (CAI) and minimum free energy (MFE), incorporating variational parameters for codon selection. Leveraging a hybrid quantum-classical computational strategy and building on prior advancements in quantum algorithms for secondary structure prediction, we effectively navigate this complex optimization space. We demonstrate the feasibility of executing this end-to-end workflow on real quantum hardware, using IBM's 127-qubit Eagle processor. We validate our approach through both simulations and quantum hardware experiments on sequences of up to 30 nucleotides. These results highlight the potential of our framework to accelerate the design of optimal mRNA constructs for therapeutic and research applications.