Archaea possess a remarkably diverse mobilome, but for many archaeal viruses and plasmids, even the basic processes, such as genome replication, remain poorly understood. Here, we characterize a previously uncharacterized family of putative rolling-circle replication endonucleases, termed Rep-Arvir, widespread among viruses and plasmids associated with phylogenetically diverse archaea, including halophiles, methanogens, and hyperthermophiles. We show that RepSNJ2, encoded by the temperate pleomorphic virus SNJ2, a model member of the Pleolipoviridae family, is essential for driving autonomous replication. Moreover, a conserved hairpin-forming DNA element downstream of repsnj2 likely functions as the recognition site for RepSNJ2 and origin of replication of SNJ2. Notably, the functional replication operon is restored only following the excision and circularization of the SNJ2 viral genome, representing an elegant regulatory mechanism controlling the lysogeny-replication switch. Leveraging this system, we constructed SNJ2-based shuttle vectors that enable stable gene expression and are compatible with other Natrinema plasmids. Structural modeling revealed that the Rep-Arvir family is distantly related to the bacterial Rep_trans family endonucleases, a relationship not recognizable at the sequence level. These findings provide evidence for a previously unrecognized replication mechanism in archaea, highlight deep evolutionary links between archaeal and bacterial replicons, and provide a versatile genetic platform for studying virus–host interactions in hypersaline environments.

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/.

Most new genomes lack annotation, automated methods are error-prone, and few genomes are ever manually curated due to time and cost. Protein structure predictions may offer a new route to assess and improve gene models without requiring experimental data. Here, we explore whether scores from protein structure prediction can aid in scoring gene model quality. We chose three species (Fusarium graminearum, Toxoplasma gondii, and Aspergillus fumigatus) from the VEuPathDB database that have collectively undergone more than 1000 manual curation events. We modelled translations of the gene models with AlphaFold 3, before and after curation, collecting various scores. Then we carried out structure searching of the PDB with Foldseek and sequence-based domain identification using InterProScan. We profiled the scores produced by these methods to identify those best for gene model assessment. AlphaFold 3 scores strongly favored manually improved over pre-improvement gene models, supporting 65–84% of manually-curated changes. Combining scores across multiple tools (AlphaFold 3, Foldseek and InterProScan) provided further improvements in model scoring. Overall, the most discriminative scores combined the outputs of AlphaFold 3 and Foldseek. Importantly, we find that scores from the much faster Protenix-Mini retain the same discriminatory power as those from AlphaFold 3. Our results, therefore, highlight the potential of scores derived from deep learning-based protein structure prediction for scoring gene models in the absence of experimental data.

Iron is a limiting micronutrient in various environments, and its scarcity orchestrates microbial interactions across diverse ecosystems. The cheese surface, which is oxic, iron-limited, and a host of moderately complex ecosystems, can serve as a model system to study iron-mediated microbial interactions. In this work, we focused on two ripening bacteria isolated from cheese, Hafnia alvei and Brevibacterium aurantiacum. We combine growth measurements, transcriptomics, proteomics, and metabolomics to examine the role of iron in their interactions within a synthetic medium designed to mimic late cheese-ripening conditions, using mono and coculture systems under iron limitation. Coculturing resulted in significant differences in the physiology of both strains, with a more notable effect on H. alvei. H. alvei, the only siderophore producer of the two, appeared to experience iron limitation in the coculture. This is partially attributed to sharing siderophores, and thus, iron, with B. aurantiacum. Multi-omics analysis points to several key exchanges. First, putrescine acts as a cross-fed metabolite, where B. aurantiacum synthesizes it and H. alvei uses it as an energy source. Next, we found evidence for the activity of quorum sensing and potential quorum quenching mechanisms, previously implicated in siderophore biosynthesis. Additionally, coculturing led to increased production of volatile sulfur compounds, contributing to positive organoleptic characteristics of cheese. Our model system reveals the modifications of C, N, S metabolisms in response to an abiotic stress and provides a framework to study such responses in numerous iron-limited ecosystems.

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.