Optimization of storage conditions for lipid nanoparticle-formulated self-replicating RNA vaccines.

The recent clinical success of multiple mRNA-based SARS-CoV-2 vaccines has proven the potential of RNA formulated in lipid nanoparticles (LNPs) in humans, and products based on base-modified RNA, sequence-optimized RNA, and self-replicating RNAs formulated in LNPs are all in various stages of clinical development. However, much remains to be learned about critical parameters governing the manufacturing and use of LNP-RNA formulations. One important issue that has received limited attention in the literature to date is the identification of optimal storage conditions for LNP-RNA that preserve long-term activity of the formulations.

Uncovering Transcriptional Regulators and Targets of sRNAs Using an Integrative Data-Mining Approach: H-NS-Regulated RseX as a Case Study.

Bacterial small RNAs (sRNAs) play a vital role in pathogenesis by enabling rapid, efficient networks of gene attenuation during infection. In recent decades, there has been a surge in the number of proposed and biochemically-confirmed sRNAs in both Gram-positive and Gram-negative pathogens. However, limited homology, network complexity, and condition specificity of sRNA has stunted complete characterization of the activity and regulation of these RNA regulators.

Bioinformatic Application of Fluorescence-Based In Vivo RNA Regional Accessibility Data to Identify Novel sRNA Targets.

Data from fluorescence-based methods that measure in vivo hybridization efficacy of unique RNA regions can be used to infer regulatory activity and to identify novel RNA: RNA interactions. Here, we document the step-by-step analysis of fluorescence data collected using an in vivo regional RNA structural sensing system (iRS) for the purpose of identifying potential functional sites that are likely to be involved in regulatory interactions. We also detail a step-by-step protocol that couples this in vivo accessibility data with computational mRNA target predictions to inform the selection of potentially true targets from long lists of thermodynamic predictions.