🧬 PubMed RNA Editing Daily Digest

RNA editing is a way to diversify, regulate expression, and expand the cell transcriptome. The most common RNA editing is the reversible conversion of adenosine (A) to inosine (I) driven by double-stranded RNA-binding adenosine deaminases (ADARs). As inosine is recognized as guanosine (G) during translation, the RNA editing may result in non-synonymous codon changes. For this reason, ADARs have gained attention as promising enzymes to rewrite mRNA. Many efforts were undertaken to engineer a precise, effective, and controllable ADAR-based system to target certain Adenines on RNA to repair pathological mutations. This review summarizes the advances in ADAR-mediated RNA editing, evolving from systems using antisense oligonucleotides as guide RNA to recruit endogenous or overexpressed ADARs, through more complex setups additionally expressing other RNA-binding proteins, to rational designs harnessing ADARs to convert other nucleotides and amplify the low initial signal. Increasing the specificity and yield of RNA editing, expanding the number of targetable sites, and reducing off-target and bystander activity remain key challenges for these technologies. Improving delivery efficiency across a broad range of cell types, as well as optimizing delivery routes in in vivo studies are also critical to harness them as advantageous tools for both research and therapy.

The emergence of CRISPR-Cas systems has transformed nucleic acid detection and manipulation. Cas13, a type VI CRISPR effector, targets RNA with high sensitivity through both cis (target RNA) and trans (collateral RNA) cleavage. This property enables the use of fluorescent reporters for sensitive diagnostics. However, Cas13's heightened sensitivity also leads to reduced specificity due to its susceptibility to single-nucleotide mismatches, potentially causing off-target effects. To overcome this limitation, we developed the first Dual-guide RNA system for Cas13 that improves mismatch discrimination and enhances target specificity. This system employs two distinct RNAs-dcrRNA and dtracrRNA-which cooperatively recognize the target and reduce off-target activity. In vitro experiments demonstrated robust cis- and trans-RNase activity, indicating efficient and specific cleavage. The system accurately detected SARS-CoV-2 RNA, distinguished KRAS G12D and G12C mutations, and differentiated mucocutaneous from cutaneous Leishmania sequences in analytical assays, with clinical validation confirming accurate detection of positive and negative samples. These results highlight the Dual-guide Cas13 platform's potential for precise, rapid, and reliable RNA detection. Overall, this approach represents a substantial advance over conventional Cas13 systems, offering improved specificity while maintaining clinically relevant sensitivity, and provides a generalizable tool for next-generation molecular diagnostics and precision RNA targeting and regulation.

Cancer is a global health challenge. The initiation and progression of cancer are correlated with dynamic dysregulation of RNA regulatory networks. This review systematically explains how contending RNAs (including mRNA, miRNA, lncRNA, circRNA, etc.) remold gene expression programs across multiple dimensions. They do this primarily through the competing endogenous RNA sponge effect, RNA-protein complex assembly, RNA editing (A-to-I editing, m6A modification, etc.), tumorigenesis, heterogeneous evolution, and therapeutic resistance. RNA regulatory networks do not only help one to decode cancer biology but because they are dynamic in nature, they are now also being looked at as good precision targets for diagnosis and treatment. This article integrates recent findings on the emerging functions of RNA networks in tumor metabolic reprogramming, tumor immune microenvironment shaping, and cancer stem cell property maintenance, while highlighting their clinical application prospects as liquid biopsy biomarkers. Our therapies focus on assessing the potential and clinical translation bottlenecks of novel RNA-targeted interventions, including antisense oligonucleotides, RNA aptamers, and the CRISPR-Cas13 system. A dynamic adjustability made the RNA-targeted therapies promising intervention nodes in precision medicine even if most of them are still in a preclinical state.

Although prime editing (PE) can effect virtually any specified local change to genomic DNA in living systems, its efficient application currently requires extensive optimization of prime editing guide RNA (pegRNA) sequences. We present OptiPrime, a machine learning model of PE efficiency based on our current understanding of the mechanism of prime editing. OptiPrime achieves state-of-the-art accuracy on PE efficiency prediction and also enables prediction of nicking guide RNA (PE3) and dual pegRNA (twinPE) outcomes. We validated that OptiPrime has learned the determinants of mammalian mismatch repair (MMR), and is therefore well suited for nominating MMR-evasive silent edits that improve PE efficiency. We demonstrate the utility of OptiPrime in a variety of prospective therapeutic contexts, including in primary human and mouse cells. Finally, we show how OptiPrime can be used to achieve highly streamlined and efficient

Adenosine-to-inosine (A-to-I) RNA editing, predominantly catalyzed by the enzyme adenosine deaminase acting on RNA 1 (ADAR1), has attracted interest due to its essential functions in regulating immune response and cancer progression. This research investigates ADAR1 inhibition as a promising strategy aimed at improving immunotherapy efficacy in lung adenocarcinoma (LUAD) and explores the underlying mechanisms. Findings from murine models demonstrate that ADAR1 suppression within tumors notably improves the immune microenvironment, marked by increased PD-L1 expression and enhanced CD8