Serotyping identifies bacterial variants based on surface antigens, traditionally using antibody-based assays, but has been increasingly replaced by in silico methods that infer serotypes from genomic sequences for faster, scalable and more reproducible analyses. However, traditional E. coli capsule serotyping has largely fallen out of use since the 1990s, leaving gaps in our knowledge of capsule genetics, diversity, distribution and epidemiology. As capsules influence bacterial interactions with phages, host immune systems and the environment, this gap limits our understanding of E. coli ecology and pathogenicity as well as vaccine and diagnostic development. Here we established a definitive genotype–serotype map for 35 serologically identified and structurally characterized transporter-dependent capsules. We then surveyed 37,723 E. coli genomes, cataloguing 85 transporter-dependent capsule types (K-types), including 55 types that were not part of the reference collection. We leveraged this catalogue to develop a hidden Markov model-based in silico serotyping tool, kTYPr, and applied it to curated sets of 24,015 E. coli genomes and 2,762 metagenome-assembled genomes spanning diverse environmental and clinical sources. We found previously uncharacterized K-types enriched in undersampled environments and associated with E. coli disease. This study expands our understanding of E. coli surface structures, supporting efforts for precision targeting with phage therapy or vaccines. Escherichia coli ABC transporter-dependent capsule gene analysis alongside the development and application of the kTYPr in silico capsule typing tool uncovers E. coli capsule diversity across environments.

Determining the host range is a fundamental step in characterizing newly isolated phages, as it not only guides their use in therapy and in biocontrol applications but also helps in managing their impact on bioprocesses. Host specificity is typically evaluated through experimental assessment of host cell lysis, but the underlying molecular mechanisms that define host range are rarely explored in a comprehensive manner. Interactions between viral proteins and bacterial surface receptors during adsorption have historically been viewed as the primary determinants of host specificity. However, advances in cataloguing the diversity of bacterial defence systems emphasize that host range is shaped by a complex interplay of phage–host interactions, phage–phage interactions and environmental factors. Moreover, host range is dynamic, being influenced by ongoing coevolution between phages and bacteria as well as by phenotypic variability. As tools are now being developed to predict phage host range at the strain level from genomic data, a deep understanding of the diverse, dynamic factors that shape this host range is essential. In this Review, we provide an integrated perspective on the molecular and ecological determinants of phage host range, explore their dynamics and discuss the implications for phage-based applications. Comprehensive assessment of the host range of phages is a prerequisite for their safe and effective use in multiple applications but is rarely undertaken. This Review outlines the molecular and ecological determinants of phage host range and discusses the importance of these determinants for developing phage-based applications.

Metagenomic sequencing has transformed virus discovery; however, downstream bioinformatic analyses for viral identification, classification, and host prediction remain fragmented across multiple tools. Here, we present PhaBOX2, a major upgrade that extends the platform from a specialized bacteriophage identification tool to a comprehensive and integrated suite for viral sequence analysis. PhaBOX2 broadens its detection, taxonomic, and host prediction scope beyond phages to enable the characterization of archaeal and eukaryotic viruses. The updated workflow incorporates rigorous quality control and quantitative analyses, automatically removes host contamination, clusters sequences into viral operational taxonomic units, and performs phylogenetic analysis based on marker genes. In contrast to traditional “black–box” deep learning approaches, PhaBOX2 combines alignment–based strategies with machine–learning models under a “glass–box” design philosophy, providing interpretable intermediate evidence alongside final predictions to improve transparency and biological interpretability. Powered by a dedicated high–performance computing infrastructure, the server delivers a fully automated, end–to–end workflow, while achieving an ~80% reduction in processing time. PhaBOX2 thus provides a robust and user–friendly ecosystem for viral metagenomic analysis and is freely available at https://phage.ee.cityu.edu.hk/.

Soils are critical reservoirs of antibiotic-resistance genes (ARGs) which are strongly shaped by microbial interactions and environmental conditions and are therefore highly sensitive to disturbance. Although climate warming is recognized as one of the most significant disturbances to microbial communities and their functions, its impacts on soil resistomes remain poorly understood. Here we investigated the effects of decade-long experimental warming on ARGs in grassland soils using integrated experimental and computational approaches. Our results revealed that ARG abundance substantially increased (23.9%) under warming—particularly glycopeptide- and rifamycin-resistance genes. Warming specifically enriched Actinomycetota hosts, including various potential plant pathogens, and enhanced ARG mobility. Large-scale unprecedented isolates-based phenotypic analyses also validated that warming increased bacterial resistance to multiple antibiotics. Further mechanistic analyses revealed that warming increased ARG abundance primarily through co-selection of resistance genes physically linked to adaptive traits (for example, thermal tolerance and nitrogen assimilation) and positive selection for thermal tolerance genes, which could be further amplified via horizontal gene transfer. Together, these findings convincingly demonstrate that climate warming substantially accelerates soil antibiotic resistance at genomic, ecological and evolutionary levels, with broad implications for public health and environmental sustainability in a warming world. Decade-long warming increased soil antibiotic-resistance genes by about 24%, enriching resistant microbes and gene transfer, driven by selection for thermal tolerance and linked traits, with implications for public health.

A giant mimivirus encodes its own cap-binding translation initiation complex, revealing an unexpected level of viral autonomy in rewiring host translation towards viral protein production.

RNA secondary structure plays a critical role in gene regulation, yet existing computational and experimental tools for structure analysis are often fragmented across prediction, ensemble modeling, and functional interpretation workflows. Here, we present ShapeRNA, a user-friendly web server for integrated RNA secondary structure prediction, ensemble inference, and structure-aware regulatory annotation. ShapeRNA supports three complementary analytical workflows, including sequence-based structure prediction, reactivity-guided modeling using SHAPE or DMS data, and sequencing–guided ensemble inference from high-throughput probing experiments. The platform integrates multiple established prediction algorithms and provides standardized data processing, ensemble clustering, and visualization. In addition, ShapeRNA enables mapping of RNA modification sites, microRNA target regions, and RNA-binding protein interaction motifs onto predicted RNA structures and representative ensemble conformations. We demonstrate the utility of ShapeRNA through applications including analysis of mutation-associated structural changes in MAPT exon 10, characterization of conformational heterogeneity in the HIV-1 Rev Response Element, and regulatory annotation of the oncogenic long non-coding RNA HULC. ShapeRNA provides an accessible and extensible platform for investigating RNA structural heterogeneity and regulatory mechanisms. This website is free and open to all users, and there is no login requirement. https://shaperna.com.

Precise, site-specific insertion of large gene sequences holds great promise for the treatment of diverse genetic disorders. Although prime editing using paired guide RNAs (pegRNAs) can mediate targeted integration, insertion efficiency drops sharply for payloads exceeding 300 base pairs. Here we present a rationally designed quadruple pegRNA strategy (QuadPE) for efficient and programmable insertion of large DNA fragments. Through screening different designs, we identified that combinations of two genome-targeting pegRNAs in a PAM-out or PAM-in orientation, when paired with two donor-targeting pegRNAs in linear or circular form, yield optimal efficiency. Using QuadPE, we achieved stable integration efficiency of DNA fragments ranging from 1.6 to 26 kb, with efficiencies of around 40% at multiple loci with minimal off-target insertion activity. QuadPE substantially outperformed recombinase-mediated (PASSIGE and PASTE) and transposase-mediated (CAST) insertion systems, particularly for larger payloads, showing a 11-fold, 61-fold and 12-fold improvement for a 9.5 kb insertion, respectively. Notably, QuadPE was effective in both dividing and non-dividing primary cells such as human primary T cells and post-mitotic neurons, establishing QuadPE as a powerful and precise platform for large-fragment gene insertion without the need for double-stranded breaks or recombinases. QuadPE—a quadruple paired prime editing guide RNA strategy—enables efficient and programmable insertion of large DNA fragments.