Synthetic mRNA produced by in vitro transcription (ivt mRNA) is the active pharmaceutical ingredient of approved anti-COVID-19 vaccines and of many drugs under development. Such synthetic mRNA typically contains several hundred bases of non-coding \"untranslated\" regions (UTRs) that are involved in the stabilization and translation of the mRNA. However, UTRs are often complex structures, which may complicate the entire production process. To eliminate this obstacle, we managed to reduce the total amount of nucleotides in the UTRs to only four bases. In this way, we generate minimal ivt mRNA (\"minRNA\"), which is less complex than the usual optimized ivt mRNAs that are contained, for example, in approved vaccines. We have compared the efficacy of minRNA to common augmented mRNAs (with UTRs of globin genes or those included in licensed vaccines) in vivo and in vitro and could demonstrate equivalent functionalities. Our minimal mRNA design will facilitate the further development and implementation of ivt mRNA-based vaccines and therapies.

All mRNA products are currently manufactured in in vitro transcription (IVT) reactions that utilize single-subunit RNA polymerase (RNAP) biocatalysts. Although it is known that discrete polymerases exhibit highly variable bioproduction phenotypes, including different relative processivity rates and impurity generation profiles, only a handful of enzymes are generally available for mRNA biosynthesis. This limited RNAP toolbox restricts strategies to design and troubleshoot new mRNA manufacturing processes, which is particularly undesirable given the continuing diversification of mRNA product lines toward larger and more complex molecules. Herein, we describe development of a high-throughput RNAP screening platform, comprising complementary in silico and in vitro testing modules, that enables functional characterization of large enzyme libraries. Utilizing this system, we identified eight novel sequence-diverse RNAPs, with associated active cognate promoters, and subsequently validated their performance as recombinant enzymes in IVT-based mRNA production processes. By increasing the number of available characterized functional RNAPs by more than 130% and providing a platform to rapidly identify further potentially useful enzymes, this work significantly expands the RNAP biocatalyst solution space for mRNA manufacture, thereby enhancing the capability for application-specific and molecule-specific optimization of both product yield and quality.

Bacterial chromosomal DNA is structured and compacted by proteins known as bacterial chromatin proteins (i.e., nucleoid-associated proteins or NAPs). DNA-dependent RNA polymerase (RNAP) must frequently interact with bacterial chromatin proteins because they often bind DNA genome-wide. In some cases, RNAP must overcome barriers bacterial chromatin proteins impose on transcription. One key bacterial chromatin protein in Escherichia coli that influences transcription is the histone-like nucleoid structuring protein, H-NS. H-NS binds to DNA and forms nucleoprotein filaments. To investigate the effect of H-NS filaments on RNAP elongation, we developed an in vitro transcription assay to monitor RNAP progression on a DNA template bound by H-NS. In this method, initiation and elongation by RNAP are uncoupled by first initiating transcription in the presence of only three ribonucleoside triphosphates (rNTPs) to halt elongation just downstream of the promoter. Before elongation is restarted by addition of the fourth NTP, an H-NS filament is formed on the DNA so that transcript elongation occurs on an H-NS nucleoprotein filament template. Here, we provide detailed protocols for performing in vitro transcription through H-NS filaments, analysis of the transcription products, and visualization of H-NS filament formation on DNA by electrophoretic mobility shift assay (EMSA). These methods enable insight into how H-NS affects RNAP transcript elongation and provide a starting point to determine effects of other bacterial chromatin proteins on RNAP elongation.

In vitro transcription (IVT) reaction is an RNA polymerase-catalyzed production of messenger RNA (mRNA) from DNA template, and the unit operation with highest cost of goods in the mRNA drug substance production process. To decrease the cost of mRNA production, reagents should be optimally utilized. Due to the catalytic, multicomponent nature of the IVT reaction, optimization is a multi-factorial problem, ideally suited to design-of-experiment approach for optimization and identification of design space. We derived a data-driven model of the IVT reaction and explored factors that drive process yield (in g/L), including impact of nucleoside triphosphate (NTP) concentration and Mg:NTP ratio on reaction yield and how to optimize the main cost drivers RNA polymerase and DNA template, while minimizing dsRNA formation, a critical quality attribute in mRNA products. We report a methodological approach to derive an optimum reaction design, with which cost efficiency of the reaction was improved by 44%. We demonstrate the validity of the model on mRNA construct of different lengths. Finally, we maximized the yield of the IVT reaction to 24.9 ± 1.5 g/L in batch, thus doubling the highest ever reported IVT yield.

SARS-CoV-2, the causative virus of the COVID-19 pandemic, follows SARS and MERS as recent zoonotic coronaviruses causing severe respiratory illness and death in humans. The recurrent impact of zoonotic coronaviruses demands a better understanding of their fundamental molecular biochemistry. Nucleoside modifications, which modulate many steps of the RNA lifecycle, have been found in SARS-CoV-2 RNA, although whether they confer a pro- or anti-viral effect is unknown. Regardless, the viral RNA-dependent RNA polymerase will encounter these modifications as it transcribes through the viral genomic RNA. We investigated the functional consequences of nucleoside modification on the pre-steady state kinetics of SARS-CoV-2 RNA-dependent RNA transcription using an in vitro reconstituted transcription system with modified RNA templates. Our findings show that N6-methyladenosine and 2\'O-methyladenosine modifications slow the rate of viral transcription at magnitudes specific to each modification, which has the potential to impact SARS-CoV-2 genome maintenance.

Single-cell RNA sequencing (scRNA-seq) stands as a cutting-edge technology widely used in biological and biomedical research. Existing scRNA-seq methods rely on reverse transcription (RT) and second-strand synthesis (SSS) to convert RNA to cDNA before amplification. However, these methods often suffer from limited RT/SSS efficiency, which compromises the sensitivity of RNA detection. Here, we develop a new method, linearly amplified single-stranded RNA-derived transcriptome sequencing (LAST-seq), which directly amplifies the original single-stranded RNA without prior RT and SSS and offers high-sensitivity RNA detection and a low level of technical noise in single-cell transcriptome analysis. LAST-seq has been applied to quantify transcriptional bursting kinetics in human cells, advancing our understanding of chromatin organization\'s role in regulating gene expression. Key features • An RNase H/DNA polymerase-based strategy to attach the T7 promoter to single-stranded RNA. • T7 promoter mediated IVT on single stranded RNA template at single cell level.

Visualizing RNA-protein interactions through structural approaches requires the use of RNA molecules purified to homogeneity. We describe here a simple and effective method, free of acrylamide contamination and without using UV radiation, to separate in vitro synthesized, heterogeneous RNA transcripts (up to ∼15 nucleotides) at single-nucleotide resolution by quaternary-amine anion exchange chromatography. The quality of short RNAs isolated through this method is validated by gel electrophoresis, mass spectrometry, and crystallization with a protein-binding partner.

Quality by digital design (QbDD) utilizes data-driven, mechanistic, or hybrid models to define and optimize a manufacturing design space. It improves upon the QbD approach used extensively in the pharmaceutical industry. The computational models developed in this approach identify and quantify the relationship between the product\'s critical quality attributes (CQAs) and the critical process parameters (CPPs) of unit operations within the manufacturing process. This chapter discusses the QbDD approach in developing and optimizing unit operations such as in vitro transcription, tangential flow filtration, affinity chromatography, and lipid nanoparticle (LNP) formulation in mRNA vaccine manufacturing. QbDD can be an efficient framework for developing a production process for a disease-agnostic product that requires extensive experimental and model-based process-product interaction characterization during the early process development phase.

Here we describe the in vitro preparation of mRNA from DNA templates, including setting up the transcription reaction, mRNA capping, and mRNA labeling. We then describe methods used for mRNA characterization, including UV and fluorescence spectrophotometry, as well as gel electrophoresis. Moreover, characterization of the in vitro transcribed RNA using the Bioanalyzer instrument is described, allowing a higher resolution analysis of the target molecules. For the in vitro testing of the mRNA molecules, we include protocols for the transfection of various primary cell cultures and the confirmation of translation by intracellular staining and western blotting.

Double-stranded RNA is an immunogenic byproduct present in RNA synthesized with in vitro transcription. dsRNA byproducts engage virus-sensing innate immunity receptors and cause inflammation. Removing dsRNA from in vitro transcribed messenger RNA (mRNA) reduces immunogenicity and improves protein translation. Levels of dsRNA are typically 0.1%-0.5% of total transcribed RNA. Because they form such a minor fraction of the total RNA in transcription reactions, it is difficult to confidently identify discrete bands on agarose gels that correspond to the dsRNA byproducts. Thus, the sizes of dsRNA byproducts are largely unknown. Total levels of dsRNA are typically assayed with dsRNA-specific antibodies in ELISA and immuno dot-blot assays. Here we report a dsRNA-specific immuno-northern blot technique that provides a clear picture of the dsRNA size distributions in transcribed RNA. This technique could complement existing dsRNA analytical methods in studies of dsRNA byproduct synthesis, dsRNA removal, and characterization of therapeutic RNA drug substances.