Roderic Guigo

Correction: Multi-homosubunit dsRNA/ dsDNA virus genome packaging nanomotors with sequential revolving mechanisms for potent drug development

Frontiers Production Office

Multi-homosubunit dsRNA/dsDNA virus genome packaging nanomotors with sequential revolving mechanisms for potent drug development

Margaret Bohmer, Justin Gnarra, Daniel W. Binzel, Tao Weitao, Peixuan Guo

The emergence of new viral diseases and viral drug resistance presents a critical challenge in human health. Recent structural and mechanistic studies have revealed remarkable parallels and structural similarities among viral dsDNA- and dsRNA-packaging motors, ATPases, ATP-binding cassette drug transporters (ABCs), and ATP-generating machines, particularly in their asymmetrical, multi-homosubunit hexameric arrangement and sequential revolving mechanisms. Both dsDNA and dsRNA viruses package their genomes into preformed hexameric ATPase motors. This review examines how understanding these evolutionarily convergent nanomotors can inform novel therapeutic strategies. We first review the structures and mechanisms of representative ds-genome packaging motors from bacteriophage phi29, human herpesvirus, dsRNA viruses, cystoviruses, and other systems. We systematically compare viral DNA packaging motors with ABC transporters, cataloguing over 30 major ATPase systems across eight functional categories. Their commonalities—multi-homosubunit architecture with sequential revolving mechanisms regulated by ATP—are summarized and classified by therapeutic potential. We then describe a novel drug development method targeting multi-homosubunit biomachines through the “Series-Circuit-Christmas-Bulb” mechanism, where Z > 1 (subunit stoichiometry) and K = 1 (drugged subunits needed for inhibition). Blocking one subunit disrupts the entire sequential chain, analogous to one single series-circuit Christmas lightbulb failure, disabling the entire strand. Mathematical validation using phi29 systems demonstrates 10 7 -10 8 fold inhibition efficiency. Practical examples include FDA-approved bedaquiline targeting bacterial F 1 F 0 ATPase for tuberculosis treatment and RNA nanoparticle-based delivery systems targeting P-glycoprotein for cancer drug resistance. The convergence of nano-structural insights, computational methods, and advanced delivery systems presents a unique framework for developing potent therapies against viral infections and drug resistance mechanisms.

Protein-side-chain-templated DNA synthesis in an antiphage reverse transcriptase: implications for noncoding RNA function

Muzafar Riyaz

Reading between the bases: technological advances in RNA modification detection

Maxim Bouvet, Isaia Barbieri

Despite the rapid expansion of epitranscriptomics, accurately detecting and mapping RNA modifications remains a major technical challenge that limits our understanding of their functional and therapeutic relevance. More than 170 chemical RNA modifications have been identified, collectively shaping the epitranscriptome through dynamic regulation of RNA processing, stability, localization, and translation. Among these, m 6 A, m 5 C, pseudouridine (Ψ), m 1 A, m7G, 2′-O-Me and A-to-I editing are among the best characterized marks and have emerged as crucial regulators of RNA functions affecting a broad range of cellular functions, whose dysregulation is broadly implicated in disease. The growing therapeutic interest in targeting epitranscriptomic pathways, particularly in cancer, has intensified the need for robust, quantitative, and modification-specific detection strategies. In this mini-review, we critically examine the principal technologies currently used for epitranscriptomic profiling, including immunoprecipitation-based sequencing, chemical and enzymatic probing, RNA mass spectrometry, and direct RNA sequencing. Rather than treating these approaches as isolated methodologies, we emphasize the methodological complementarities required for rigorous epitranscriptomic mapping across distinct RNA modification classes. While every approach involves trade-offs among resolution, throughput, and systematic bias, hybrid strategies integrating native multimodal sequencing, orthogonal chemical reactivity, and genetic validation are improving detection accuracy and specificity. Persistent challenges in stoichiometric quantification, rare mark detection, and causal inference remain, but emerging advances in AI-driven de novo detection may overcome these limitations. Together with advances in single-cell spatial transcriptomics and large-scale CRISPR-epitranscriptomic screening, these technologies are poised to accelerate the functional decoding of RNA modifications and their translation into therapeutic applications.

Beyond mutations: personalising oncology via RNA-editing

Ivo Fierro-Monti

Adenosine-to-inosine (A-to-I) RNA-editing, catalysed by ADAR enzymes, is a dynamic layer of post-transcriptional regulation that significantly diversifies the cancer transcriptome and proteome. A-to-I editing, the most frequent and well-established form of editing in human cancer, holds greater immediate relevance for personalised oncology than the less mapped C-to-U “editome” landscape. By recoding oncogenes and tumour suppressors (e.g., AZIN1, NEIL1), modulating drug response, and generating immunogenic RNA-editing-derived neoantigens, A-to-I events directly influence tumour progression, heterogeneity, and immune recognition. These roles establish RNA-editing profiles as promising biomarkers for patient stratification and prognosis. Furthermore, the integration of editing data with multi-omics and machine learning is accelerating the development of personalised therapeutic strategies. These include ADAR-guided RNA correction, mRNA vaccines encoding edited neoantigens, and RNA interference, offering reversible and tailored intervention points. Despite challenges in delivery, specificity, and clinical translation, A-to-I RNA-editing represents both a fundamental cancer mechanism and a versatile toolkit, driving the next generation of precision oncology beyond the constraints of the genomic blueprint.