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. Here, we analyzed the physical structure, in vivo expression characteristics, and functional activity of alphavirus-derived self-replicating RNA (repRNA)-loaded LNPs encoding HIV vaccine antigens following storage in varying temperatures, buffers, and in the presence or absence of cryoprotectants. We found that for lipid nanoparticles with compositions similar to clinically-used LNPs, storage in RNAse-free PBS containing 10% (w/v) sucrose at -20 °C was able to maintain vaccine stability and in vivo potency at a level equivalent to freshly prepared vaccines following 30 days of storage. LNPs loaded with repRNA could also be lyophilized with retention of bioactivity.

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. To streamline the discovery of the expression of sRNAs, and their post-transcriptional activities, we propose an integrative in vivo data-mining approach that couples DNA protein occupancy, RNA-seq, and RNA accessibility data with motif identification and target prediction algorithms. We benchmark the approach against a subset of well-characterized E. coli sRNAs for which a degree of in vivo transcriptional regulation and post-transcriptional activity has been previously reported, finding support for known regulation in a large proportion of this sRNA set. We showcase the abilities of our method to expand understanding of sRNA RseX, a known envelope stress-linked sRNA for which a cellular role has been elusive due to a lack of native expression detection. Using the presented approach, we identify a small set of putative RseX regulators and targets for experimental investigation. These findings have allowed us to confirm native RseX expression under conditions that eliminate H-NS repression as well as uncover a post-transcriptional role of RseX in fimbrial regulation. Beyond RseX, we uncover 163 putative regulatory DNA-binding protein sites, corresponding to regulation of 62 sRNAs, that could lead to new understanding of sRNA transcription regulation. For 32 sRNAs, we also propose a subset of top targets filtered by engagement of regions that exhibit binding site accessibility behavior in vivo. We broadly anticipate that the proposed approach will be useful for sRNA-reliant network characterization in bacteria. Such investigations under pathogenesis-relevant environmental conditions will enable us to deduce complex rapid-regulation schemes that support infection.

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 (iRS3) 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.