The structural transitions RNAs undergo during trafficking are not well-understood. Here, we used the well-developed yeast Ty1 retrotransposon to provide the first structural model of genome (g) RNA in the nucleus from a retrovirus-like transposon. Through a detailed comparison of nuclear Ty1 gRNA structure with those established in the cytoplasm, virus-like particles (VLPs), and synthesized in vitro, we detected Ty1 gRNA structural alternations that occur during retrotransposition. Full-length Ty1 gRNA serves as the mRNA for Gag and Gag-Pol proteins and as the genome that is reverse transcribed within VLPs. We show that about 60% of base pairs predicted for the nuclear Ty1 gRNA appear in the cytoplasm, and active translation does not account for such structural differences. Most of the shared base pairs are represented by short-range interactions, while the long-distance pairings seem unique for each compartment. Highly structured motifs tend to be preserved after nuclear export of Ty1 gRNA. In addition, our study highlights the important role of Ty1 Gag in mediating critical RNA:RNA interactions required for retrotransposition.

In the last decade, the genetic engineering world has been shaken up by a relatively new genetic editing tool based on RNA-guided Nucleases (RGNs): the CRISPR/Cas9 system. Since the first report in 1987 and its characterization in 2007 as a bacterial defense mechanism, the interest and research on this system have grown exponentially. CRISPR systems provide immunity to bacteria against invading genetic material; however, with specific modifications in sequence and structure, it becomes a precise editing system that makes it possible to genetically modify almost any organism. There are diverse approaches regarding the refinement of these modifications, such as constructing more accurate nucleases, understanding the cellular context and facing the epigenetic conditions, or re-designing guide RNAs (gRNAs). Considering the critical importance for the correct CRISPR/Cas9 systems performance, our scope will emphasize in the latter approach. Hence, we present an overview of the past and the most recent guide RNA web-based design tools, highlighting their computational architecture and gRNA characteristics evolution through the years. Our study concisely explains the computational approaches that use machine learning techniques, deep neural networks, and large datasets of gRNA/target interactions to make possible both predictions and classifications directed to design, optimize, and create promising gRNAs suitable for future gene therapies.

Gene or genome editing (GE) revises, removes, or replaces a mutated gene at the DNA level; it is a tool. Gene therapy (GT) offsets mutations by introducing a "normal" version of the gene into the body while the diseased gene remains in the genome; it is a medicine. So far, no in vivo GE product has been approved, as opposed to 22 GT products approved by the FDA. However, several are under development, and the FDA anticipates their fast entry, as evidenced by the FDA's actions of issuing GE-specific guidelines. The potential of GE in treating diseases far supersedes any other modality due to the recognized role of genes in most human diseases. While GT products add missing genes, GE tools can overcome many aberrations. However, GE presents many safety challenges, including off-target impact, delivery consistency, and long-term effects of gene-fixing. However, most of these concerns will be resolved once the FDA begins approving them, leading to hundreds of products capable of treating untreatable diseases.

SARS-CoV-2 infection remains a global burden. Despite intensive research, the mechanism and dynamics of early viral replication are not completely understood including the kinetics of formation of genomic RNA (gRNA), sub-genomic RNA (sgRNA) and replication centers/organelles (ROs). We employed single-molecule RNA-fluorescence in situ hybridization (smRNA-FISH) to simultaneously detect viral gRNA and sgRNA, immunofluorescence to detect nsp3 protein, a marker for the formation RO, and carried out a time course analysis. We found that single molecules of gRNA are visible within the cytoplasm at 30 minutes post-infection (p.i.). Starting from 2 hr. p.i., most of the viral RNA existed in clusters/speckles, some of which were surrounded by single molecules of sgRNA. These speckles associated with nsp3 protein starting at 3 hr. p.i., indicating that these were precursors to ROs. Furthermore, RNA replication was asynchronous as cells with RNA at all stages of replication were found at any given time point. Our probes detected the SARS-COV-2 variants of concern, and BA.1 strain exhibited slower rate of replication kinetics than the WA1 strain. Our results provide insights into the kinetics of SARS-CoV-2 early post-entry events, which will facilitate identification of new therapeutic targets to early stage replication to combat COVID-19.