🧬 PubMed RNA Editing Daily Digest

Prokaryotic Argonaute proteins (pAgos) are nucleic acid-guided endonucleases with diverse functions. Mucilaginibacter paludis Argonaute (MbpAgo) is unusual in using guide DNA (gDNA) to cleave target RNA (tgRNA), but the structural basis for this activity has been unclear. Here we present cryo-electron microscopy structures of MbpAgo in apo, binary, and ternary states at up to 2.55 Å resolution. The apo structure reveals a conserved bilobal scaffold with unique insertions in the PIWI and MID domains that stabilize the catalytic conformation. Upon gDNA binding, MbpAgo forms a dimer stabilized by multiple protein-protein interfaces and an auxiliary nucleic acid-like density bridging the PAZ-MID lobes. The auxiliary nucleic acid interactions coordinate gDNA binding and dimer stabilization to support MbpAgo activity, with dimerization becoming particularly important for efficient cleavage with double-stranded DNA (dsDNA) guides. Binding of tgRNA induces a DNA-RNA hybrid duplex and conformational changes that destabilize the dimer, reverting MbpAgo to an active monomer capable of cleaving structured viral RNAs such as the SARS-CoV-2 5'UTR and HIV-1 CES. These findings suggest a dynamic monomer-dimer transition as both the regulatory mechanism of MbpAgo and an evolutionary adaptation for processing dsDNA-derived guides, providing a structural framework for programmable RNA targeting.

RNA sequencing (RNA-seq) has become a powerful technology for capturing transcriptomic variation, enabling the detection of expressed genetic variants that influence gene regulation, disease mechanisms, and therapeutic response. Unlike DNA sequencing, variant calling from RNA-seq presents unique challenges, including alternative splicing, allele-specific expression, RNA editing, and variable transcript abundance. To address these complexities, diverse computational strategies have been developed, ranging from classical statistical models to advanced and deep learning approaches. Early methods such as SAMtools, VarScan2, and bcftools relied on pileup-based binomial and Bayesian likelihood models, offering computational efficiency but limited sensitivity to RNA-specific artifacts. More sophisticated tools, such as GATK HaplotypeCaller and Octopus, incorporate pair-Hidden Markov Models and de Bruijn graph assemblies, enabling haplotype-aware detection with improved accuracy across spliced regions. Machine learning-based frameworks, including RNA-SNPhunter (random forest) and RVboost (gradient boosting), leverage alignment and sequence features to enhance variant classification, while deep learning methods such as DeepVariant employ convolutional neural networks to learn complex error signatures directly from sequencing data. Complementary advances in splice-aware alignment, variant filtering, and functional annotation further strengthen accuracy and biological interpretation. Benchmarking studies highlight that the best-performing pipelines often integrate statistical rigor with AI-driven adaptability, balancing precision and recall across variant types and expression levels. Collectively, these methodological innovations are reshaping RNA-seq variant calling, advancing its role from a specialized challenge to a core component of multi-omic integration and precision medicine.

The plant mitochondrial genome has become a current research hotspot as an independent genetic model. Nevertheless, mitochondrial genome information for most