🧬 PubMed RNA Editing Daily Digest

Prime editing is a versatile genome editing technology that enables precise genetic modifications without inducing DNA double-strand breaks. Owing to numerous variables in the prime editing guide RNA (pegRNA) design, experimentally identifying the most efficient pegRNA for a specific locus and edit is laborious. Therefore, we have developed computational tools to streamline this process. Here we present a comprehensive protocol detailing how to use PRIDICT2.0 and ePRIDICT, machine-learning models that assess the influence of the pegRNA design and chromatin context on prime editing. PRIDICT2.0 is an ensemble of attention-based bidirectional recurrent neural networks that predicts pegRNA efficiencies for replacements, insertions or deletions in different cellular contexts. Compared with other pegRNA design tools, PRIDICT2.0 accommodates larger edits-up to 40 base pairs-across diverse edit types, also allowing the introduction of silent bystander edits that can enhance editing efficiency. ePRIDICT, a gradient-boosting algorithm, further accounts for the local chromatin environments and assesses how the genomic location of the target site affects prime editing rates. Both tools are available at www.pridict.it for individual predictions or can be installed locally for batch processing of multiple edits and target sites. The protocol provides step-by-step instructions on using PRIDICT2.0 and ePRIDICT, covering sequence input, prediction generation and interpretation. Web-based predictions take under a minute, while local installation and batch processing may take up to several hours, depending on the dataset size. By streamlining pegRNA selection and chromatin context analysis, these tools promote the adoption of prime editing in basic and translational research.

CRISPR-Cas9 technology has transformed the study of gene function, enabling the systematic investigation of host-virus interactions. However, most CRISPR-based screens in the context of viral infections rely on cell survival as a readout, which limits their sensitivity and biases results toward early infection stages. To address these challenges, we developed the virus-encoded CRISPR-based direct readout system (VECOS), a virus-centric approach in which human cytomegalovirus is engineered to express single-guide RNA (sgRNA) libraries directly from its genome. This system allows sgRNA abundance, embedded in the viral genome, to serve as a direct and quantitative readout of gene-perturbation effects on viral propagation. By tracking sgRNA levels at distinct stages of the viral infection cycle, VECOS enables a detailed, multidimensional analysis of virus-host interactions. Here we present a modular detailed Protocol for (1) constructing and reconstituting complex sgRNA libraries in double-stranded DNA viruses using bacterial artificial chromosomes, (2) performing multipassage screens to investigate perturbation effects on various stages of viral infection and (3) analyzing the multipassage and multistage sgRNA abundance measurements utilizing a comprehensive framework for data analysis. Successful implementation of this full Protocol takes 14-22 weeks and requires proficiency in molecular biology, as well as basic familiarity with Unix-based computing and programming in R for data processing. This Protocol offers researchers a robust tool for uncovering the molecular mechanisms that drive viral propagation and host-virus interactions.

Multi-kilobase knock-ins (KIs) are a necessary, yet challenging type of genome editing to create and characterize in cell lines and animals. The combination of rAAV donor transduction and electroporation of single-cell mouse embryos with Cas9/gRNA ribonucleoprotein complex enables highly efficient KI, but the insert size is limited by the viral packaging capacity. Here, we report the creation of up to 6.7 kb precise KI achieved in one step by using three rAAVs designed to insert one after the other. To fully characterize the edited genome with large KIs, we developed LOCK-seq (LOng-read sequencing of Captured Kilo-base targets), where relevant genomic regions are enriched via hybridization, achieving over 100-fold greater coverage compared with other long-read methods with enrichment. LOCK-seq simultaneously detects the presence of precise KI alleles, imprecision in the insert and donor concatenation, genotypes of non-KI alleles, and more importantly, uniquely identifies and localizes random integration of the full or partial donor(s). Additionally, the multi-rAAV donor approach is successfully applied to cell lines, including lines intolerant of plasmid DNA, whereas LOCK-seq reliably and efficiently screens for KI clones. Together, the two approaches significantly improve the creation and precision of knock-in models.

The genus Zingiber possesses substantial economic and medicinal value, yet its mitochondrial genome (mitogenome) has remained entirely unexplored. Here, we present the first complete mitogenome assembly of ginger (Zingiber officinale), obtained through a hybrid sequencing (Illumina and PacBio) strategy. The genome features a complex, multi-branched structure totaling 8,977,507 bp and a GC content of 45.27%. It harbors 40 protein-coding genes (PCGs), 31 tRNAs, and three rRNAs. We identified abundant repetitive sequences, including 2954 SSRs and 54,887 dispersed repeats driving this genomic expansion, alongside widespread evidence of intracellular DNA transfer, with 273 plastid-derived fragments integrated into the mitogenome. Codon usage analysis revealed a pronounced bias toward A/U-ending synonymous codons. Importantly, Deepred-Mt predictions of extensive C-to-U RNA editing sites were experimentally corroborated via Sanger sequencing, confirming their critical role in maintaining the hydrophobicity of respiratory chain genes. Comparative analyses revealed a striking evolutionary decoupling: highly conserved nucleotide substitution rates contrasting with extreme structural rearrangements relative to related Curcuma species, and phylogenetic reconstruction strongly supported the sister relationship between Z. officinale and the Curcuma clade. This study provides a foundational genomic resource for further research on cytoplasmic inheritance, genome gigantism, molecular breeding, and the links between mitogenomic variation and key agronomic or medicinal traits in Z. officinale.