Coral reefs are declining globally due in part to bacterial overgrowth, a process known as microbialization. However, the role of bacteriophages that may inhibit microbialization by infecting and killing these bacteria remains poorly understood, especially their metabolic impacts on bacterial proliferation. To address this, we analyzed central carbon metabolism gene frequencies in viral communities from healthy (lytic-dominated) and degraded (temperate-dominated) Central Pacific coral reefs. We found that viral metabolism shifted broadly from being dominated by metabolism that builds up pools of central intermediates on degraded reefs dominated by temperate viral infection ('anaplerotic' reactions) to metabolism that consumes these pools to prioritize production of metabolic precursors for virion construction on healthy reefs dominated by lytic infection ('cataplerotic' reactions). This switch was shown by the over-representation of Entner-Doudoroff (ED) glycolysis genes on degraded, temperate-dominated reefs and of pentose phosphate pathway (PPP) and reductive tricarboxylic acid cycle (TCA) genes on healthy, lytic-dominated reefs. As a result of this metabolic dichotomy, our qualitative compartment modeling revealed two distinct ecosystem states: (i) healthy reefs, where lytic viral metabolism enhances viral production and suppresses bacterial overgrowth, and (ii) degraded reefs, where temperate viral metabolism accelerates bacterial proliferation. Because viral switching between lytic and temperate lifestyles is a known function of host physiological state, these findings position viral metabolism as both a driver of reef decline and a conservation lever, with metabolically mediated 're-viralization' offering a novel strategy to restore reef resilience.

Motivation:Phage therapy offers a viable alternative for bacterial infections amid rising antimicrobial resistance. Its success relies on selecting safe and effective phage candidates that require comprehensive genomic screening to identify potential risks. However, this process is often labor intensive and time-consuming, hindering rapid clinical deployment. Results:We developed Sphae, an automated bioinformatics pipeline designed to streamline the therapeutic potential of a phage in under 10 minutes. Using Snakemake workflow manager, Sphae integrates tools for quality control, assembly, genome assessment, and annotation tailored specifically for phage biology. Sphae automates the detection of key genomic markers, including virulence factors, antimicrobial resistance genes, and lysogeny indicators such as integrase, recombinase, and transposase, which could preclude therapeutic use. Among the 65 phage sequences analyzed, 28 showed therapeutic potential, 8 failed due to low sequencing depth, 22 contained prophage or virulent markers, and 23 had multiple phage genomes. This workflow produces a report to assess phage safety and therapy suitability quickly. Sphae is scalable and portable, facilitating efficient deployment across most high-performance computing and cloud platforms, accelerating the genomic evaluation process. Availability and implementation:Sphae source code is freely available at https://github.com/linsalrob/sphae, with installation supported on Conda, PyPi, Docker containers.

Phages, the viruses of bacteria, harbor an incredibly diverse repertoire of proteins capable of manipulating their bacterial hosts, inspiring many medical and biotechnological applications. However, to date, only a limited subset of that repertoire can be exploited, due to the difficulties in functionally elucidating these proteins. In this study, we investigated several structure-informed approaches to annotate hypothetical proteins from Pseudomonas infecting phages. We curated a representative dataset of over 10,000 proteins derived from NCBI, for which we predicted protein structures with ColabFold and assessed structural similarity via FoldSeek against the PDB, AlphaFold, and Phold databases. We evaluated multiple annotation strategies, including sequence-based (Pharokka), and structure-based (FoldSeek, Phold) methods. Our results show that up to 43 % of truly unannotated proteins can be functionally annotated when combining structure-informed approaches with UniProt-derived annotations. We highlight the complementarity of different databases and the importance of annotation quality filtering. This work provides a valuable resource of predicted structures and annotations, and offers insights into optimizing structure-based annotation pipelines for viral proteins, paving the way for deeper exploration of phage biology and its applications.

Prophages-viruses that integrate into bacterial genomes-are ubiquitous in the microbial realm. Prophages contribute significantly to horizontal gene transfer, including the potential spread of antimicrobial resistance (AMR) genes, because they can collect host genes. Understanding their role in the human microbiome is essential for fully understanding AMR dynamics and possible clinical implications. We analysed almost 15,000 bacterial genomes for prophages and AMR genes. The bacteria were isolated from diverse human body sites and geographical regions, and their genomes were retrieved from GenBank. AMR genes were detected in 6.6% of bacterial genomes, with a higher prevalence in people with symptomatic diseases. We found a wide variety of AMR genes combating multiple drug classes. We discovered AMR genes previously associated with plasmids, such as blaOXA-23 in Acinetobacter baumannii prophages or genes found in prophages in species they had not been previously described in, such as mefA-msrD in Gardnerella prophages, suggesting prophage-mediated gene transfer of AMR genes. Prophages encoding AMR genes were found at varying frequencies across body sites and geographical regions, with Asia showing the highest diversity of AMR genes. Importance:Antimicrobial resistance (AMR) is a growing threat to public health, and understanding how resistance genes spread between bacteria is essential for controlling their dissemination. Bacteriophages, viruses that infect bacteria, have been recognised as potential vehicles for transferring these resistance genes, but their role in the human microbiome remains poorly understood. We examined nearly 15,000 bacterial genomes from various human body sites and regions worldwide to investigate how often prophages carry AMR genes in the human microbiome. Although AMR genes were uncommon in prophages, we identified diverse resistance genes across multiple bacterial species and drug classes, including some typically associated with plasmids. These findings reveal that prophages may contribute to the spread of resistance genes, highlighting an overlooked mechanism in the dynamics of AMR transmission. Ongoing monitoring of prophages is critical to fully understanding the pathways through which resistance genes move within microbial communities and impact human health.

Picobirnaviruses (PBVs) are double-stranded RNA viruses detected in various environments and host-associated samples, including those from humans, non-human animals, invertebrates and birds. First described in human fecal material, PBVs were initially hypothesized to be human enteric pathogens. However, no definitive association with disease has been established. Their pathogenic potential remains unclear, therefore, their presence in clinical or environmental samples may reflect asymptomatic colonization, indirect association or infection of a non-human host. The PBV genome exhibits remarkably high genetic diversity both within and across its genomic segments, as well as notable variability in genetic code usage. Some PBV genomes use alternative codon assignments, raising the possibility that they infect prokaryotic or otherwise unconventional hosts. This review critically examines the experimental and bioinformatic methods used to detect PBVs and infer their host range. We distinguish between methods used for PBV genome identification (e.g. PCR, metagenomic sequencing) and those aimed at host determination (e.g. culturing attempts, codon usage bias, cloning into model systems). We also evaluate the challenges and limitations associated with each approach. Elucidating PBVs' host range is essential to understanding their biological roles and ecological significance, including potential implications for human and animal health and microbial community dynamics across ecosystems.

SUMMARYUnderstanding protein functions is crucial for interpreting microbial life; however, reliable function annotation remains a major challenge in computational biology. Despite significant advances in bioinformatics methods, ~30% of all bacterial and ~65% of all bacteriophage (phage) protein sequences cannot be confidently annotated. In this review, we examine state-of-the-art bioinformatics tools and methodologies for annotating bacterial and phage proteins, particularly those of unknown or poorly characterized function. We describe the process of identifying protein-coding regions and the systems to classify protein functionalities. Additionally, we explore a range of protein annotation methods, from traditional homology-based methods to cutting-edge machine learning models. In doing so, we provide a toolbox for confidently annotating previously unknown bacterial and phage proteins, advancing the discovery of novel functions and our understanding of microbial systems.

Large-scale metagenomic and data-mining efforts have revealed an expansive diversity of bacteriophages (phages) within the human gut1-3. However, functional understanding of phage-host interactions within this complex environment is limited, largely due to a lack of cultured isolates available for experimental validation. Here we characterize 134 inducible prophages originating from 252 human gut bacterial isolates using 10 different induction conditions to expand the experimentally validated temperate phage-host pairs originating from the human gut. Importantly, only 18% of computationally predicted prophages could be induced in pure cultures. Moreover, we construct a 78-member synthetic microbiome that, when co-cultured in the presence of human colonic cells (Caco2), led to the induction of 35% phage species. Using cultured isolates, we demonstrate that human host-associated cellular products may act as induction agents, providing a possible link between gastrointestinal cell lysis and temperate phage populations4,5. We provide key insights into prophage diversity and genetics, including a genetic pathway for domestication, finding that polylysogeny was common and resulted in coordinated prophage induction, and that differential induction can be influenced by divergent prophage integration sites. More broadly, our study highlights the importance of culture-based techniques, alongside experimental validation, genomics and computational prediction, to understand the biology and function of temperate phages in the human gut microbiome. These culture-based approaches will enable applications across synthetic biology, biotechnology and microbiome fields.

Multidrug-resistant (MDR) and extensively drug-resistant (XDR) ESKAPE pathogens pose a significant global health threat due to their ability to evade antibiotics through intrinsic and acquired mechanisms. These bacteria, including Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Escherichia coli, and Enterobacter species, evade antibiotics through intrinsic and adaptive mechanisms. Common strategies include capsule formation, biofilm, β-lactamase production, and efflux activity. Using these mechanisms, bacteria can evade the effects of antibiotics, leading to persistent and difficult-to-treat infections. Understanding the mechanisms of resistance is crucial in developing effective strategies to combat MDR and XDR ESKAPEE pathogens. A promising approach is the development of alternative treatments targeting specific resistance mechanisms in these pathogens. Bacteriophages (phages), which co-evolve with bacterial hosts, offer a dynamic therapeutic alternative by targeting pathogenic bacteria using precision-based strategies. This targeted approach can overcome antibiotic resistance and reduce the risk of damaging the beneficial microbiota. Phages can restore susceptibility in previously untreatable infections by enhancing antibiotic uptake and imposing fitness costs on resistant strains. However, therapeutic deployment faces challenges such as rapid evolution of phage resistance, inconsistent production standards, and limited regulatory pathways. This review examines the mechanistic insights into phage-antibiotic synergy, with a focus on efflux pump-mediated resistance. It discusses emerging therapeutic strategies, current clinical applications, and the translational frameworks needed to integrate phage therapy into mainstream medicine and transform the clinical management of drug-resistant ESKAPEE infections.

Intrahepatic cholestasis of pregnancy (ICP) is the most common liver-specific disorder affecting pregnant women, with an Australian incidence of 0.6% pa and recurring in 70% of those affected. ICP causes mild to severe pruritus, often resulting in considerable skin excoriations, profound sleep disturbances, and severe anxiety, and yet the aetiology and optimal treatment or management of this condition remains unknown. In this review, we consider the role of viruses in causing or exacerbating ICP and discuss viruses that have been most closely implicated in the disease, including the role of Hepatitis B and Hepatitis C viruses in ICP.

Metagenomics involves the study of genetic material obtained directly from communities of microorganisms living in natural environments. The field of metagenomics has provided valuable insights into the structure, diversity and ecology of microbial communities. Once an environmental sample is sequenced and processed, metagenomic binning clusters the sequences into bins representing different taxonomic groups such as species, genera, or higher levels. Several computational tools have been developed to automate the process of metagenomic binning. These tools have enabled the recovery of novel draft genomes of microorganisms allowing us to study their behaviors and functions within microbial communities. This review classifies and analyzes different approaches of metagenomic binning and different refinement, visualization, and evaluation techniques used by these methods. Furthermore, the review highlights the current challenges and areas of improvement present within the field of research.

It is now possible to assemble near-perfect bacterial genomes using Oxford Nanopore Technologies (ONT) long reads, but short-read polishing is usually required for perfection. However, the effect of short-read depth on polishing performance is not well understood. Here, we introduce Pypolca (with default and careful parameters) and Polypolish v0.6.0 (with a new careful parameter). We then show that: (1) all polishers other than Pypolca-careful, Polypolish-default and Polypolish-careful commonly introduce false-positive errors at low read depth; (2) most of the benefit of short-read polishing occurs by 25× depth; (3) Polypolish-careful almost never introduces false-positive errors at any depth; and (4) Pypolca-careful is the single most effective polisher. Overall, we recommend the following polishing strategies: Polypolish-careful alone when depth is very low (<5×), Polypolish-careful and Pypolca-careful when depth is low (5–25×), and Polypolish-default and Pypolca-careful when depth is sufficient (>25×).

Background One of the stranger phenomena that can occur during gene translation is where, as a ribosome reads along the mRNA, various cellular and molecular properties contribute to stalling the ribosome on a slippery sequence and shifting the ribosome into one of the other two alternate reading frames. The alternate frame has different codons, so different amino acids are added to the peptide chain. More importantly, the original stop codon is no longer in-frame, so the ribosome can bypass the stop codon and continue to translate the codons past it. This produces a longer version of the protein, a fusion of the original in-frame amino acids, followed by all the alternate frame amino acids. There is currently no automated software to predict the occurrence of these programmed ribosomal frameshifts (PRF), and they are currently only identified by manual curation. Results Here we present PRFect, an innovative machine-learning method for the detection and prediction of PRFs in coding genes of various types. PRFect combines advanced machine learning techniques with the integration of multiple complex cellular properties, such as secondary structure, codon usage, ribosomal binding site interference, direction, and slippery site motif. Calculating and incorporating these diverse properties posed significant challenges, but through extensive research and development, we have achieved a user-friendly approach. The code for PRFect is freely available, open-source, and can be easily installed via a single command in the terminal. Our comprehensive evaluations on diverse organisms, including bacteria, archaea, and phages, demonstrate PRFect’s strong performance, achieving high sensitivity, specificity, and an accuracy exceeding 90%. The code for PRFect is freely available and installs with a single terminal command. Conclusion PRFect represents a significant advancement in the field of PRF detection and prediction, offering a powerful tool for researchers and scientists to unravel the intricacies of programmed ribosomal frameshifting in coding genes.

Coral reef health is tightly connected to the coral holobiont, which is the association between the coral animal and a diverse microbiome functioning as a unit. The coral holobiont depends on key services such as nitrogen and sulfur cycling mediated by the associated bacteria. However, these microbial services may be impaired in response to environmental changes, such as thermal stress. A perturbed microbiome may lead to coral bleaching and disease outbreaks, which have caused an unprecedented loss in coral cover worldwide, particularly correlated to a warming ocean. The response mechanisms of the coral holobiont under high temperatures are not completely understood, but the associated microbial community is a potential source of acquired heat-tolerance. Here we investigate the effects of increased temperature on the taxonomic and functional profiles of coral surface mucous layer (SML) microbiomes in relationship to coral–algal physiology. We used shotgun metagenomics in an experimental setting to understand the dynamics of microbial taxa and genes in the SML microbiome of the coral Pseudodiploria strigosa under heat treatment. The metagenomes of corals exposed to heat showed high similarity at the level of bacterial genera and functional genes related to nitrogen and sulfur metabolism and stress response. The coral SML microbiome responded to heat with an increase in the relative abundance of taxa with probiotic potential, and functional genes for nitrogen and sulfur acquisition. Coral–algal physiology significantly explained the variation in the microbiome at taxonomic and functional levels. These consistent and specific microbial taxa and gene functions that significantly increased in proportional abundance in corals exposed to heat are potentially beneficial to coral health and thermal resistance.

Improvements in the accuracy and availability of long-read sequencing mean that complete bacterial genomes are now routinely reconstructed using hybrid (i.e. short- and long-reads) assembly approaches. Complete genomes allow a deeper understanding of bacterial evolution and genomic variation beyond single nucleotide variants. They are also crucial for identifying plasmids, which often carry medically significant antimicrobial resistance genes. However, small plasmids are often missed or misassembled by long-read assembly algorithms. Here, we present Hybracter which allows for the fast, automatic and scalable recovery of near-perfect complete bacterial genomes using a long-read first assembly approach. Hybracter can be run either as a hybrid assembler or as a long-read only assembler. We compared Hybracter to existing automated hybrid and long-read only assembly tools using a diverse panel of samples of varying levels of long-read accuracy with manually curated ground truth reference genomes. We demonstrate that Hybracter as a hybrid assembler is more accurate and faster than the existing gold standard automated hybrid assembler Unicycler. We also show that Hybracter with long-reads only is the most accurate long-read only assembler and is comparable to hybrid methods in accurately recovering small plasmids.

The majority of bacteriophage diversity remains uncharacterized, and new intriguing mechanisms of their biology are being continually described. Members of some phage lineages, such as the Crassvirales, repurpose stop codons to encode an amino acid by using alternate genetic codes. Here, we investigated the prevalence of stop codon reassignment in phage genomes and its subsequent impacts on functional annotation. We predicted 76 genomes within INPHARED and 712 vOTUs from the Unified Human Gut Virome Catalogue (UHGV) that repurpose a stop codon to encode an amino acid. We re-annotated these sequences with modified versions of Pharokka and Prokka, called Pharokka-gv and Prokka-gv, to automatically predict stop codon reassignment prior to annotation. Both tools significantly improved the quality of annotations, with Pharokka-gv performing best. For sequences predicted to repurpose TAG to glutamine (translation table 15), Pharokka-gv increased the median gene length (median of per genome median) from 287 to 481 bp for UHGV sequences (67.8% increase) and from 318 to 550 bp for INPHARED sequences (72.9% increase). The re-annotation increased median coding capacity from 66.8% to 90.0% and from 69.0% to 89.8% for UHGV and INPHARED sequences predicted to use translation table 15. Furthermore, the proportion of genes that could be assigned functional annotation increased, including an increase in the number of major capsid proteins that could be identified. We propose that automatic prediction of stop codon reassignment before annotation is beneficial to downstream viral genomic and metagenomic analyses.

BACKGROUND:Modern sequencing technologies offer extraordinary opportunities for virus discovery and virome analysis. Annotation of viral sequences from metagenomic data requires a complex series of steps to ensure accurate annotation of individual reads and assembled contigs. In addition, varying study designs will require project-specific statistical analyses. FINDINGS:Here we introduce Hecatomb, a bioinformatic platform coordinating commonly used tasks required for virome analysis. Hecatomb means "a great sacrifice." In this setting, Hecatomb is "sacrificing" false-positive viral annotations using extensive quality control and tiered-database searches. Hecatomb processes metagenomic data obtained from both short- and long-read sequencing technologies, providing annotations to individual sequences and assembled contigs. Results are provided in commonly used data formats useful for downstream analysis. Here we demonstrate the functionality of Hecatomb through the reanalysis of a primate enteric and a novel coral reef virome. CONCLUSION:Hecatomb provides an integrated platform to manage many commonly used steps for virome characterization, including rigorous quality control, host removal, and both read- and contig-based analysis. Each step is managed using the Snakemake workflow manager with dependency management using Conda. Hecatomb outputs several tables properly formatted for immediate use within popular data analysis and visualization tools, enabling effective data interpretation for a variety of study designs. Hecatomb is hosted on GitHub (github.com/shandley/hecatomb) and is available for installation from Bioconda and PyPI.

AbstractButyrate‐producing bacteria are found in many outdoor ecosystems and host organisms, including humans, and are vital to ecosystem functionality and human health. These bacteria ferment organic matter, producing the short‐chain fatty acid butyrate. However, the macroecological influences on their biogeographical distribution remain poorly resolved. Here we aimed to characterise their global distribution together with key explanatory climatic, geographical and physicochemical variables. We developed new normalised butyrate production capacity (BPC) indices derived from global metagenomic (n = 13,078) and Australia‐wide soil 16S rRNA (n = 1331) data, using Geographic Information System (GIS) and modelling techniques to detail their ecological and biogeographical associations. The highest median BPC scores were found in anoxic and fermentative environments, including the human (BPC = 2.99) and non‐human animal gut (BPC = 2.91), and in some plant–soil systems (BPC = 2.33). Within plant–soil systems, roots (BPC = 2.50) and rhizospheres (BPC = 2.34) had the highest median BPC scores. Among soil samples, geographical and climatic variables had the strongest overall effects on BPC scores (variable importance score range = 0.30–0.03), with human population density also making a notable contribution (variable importance score = 0.20). Higher BPC scores were in soils from seasonally productive sandy rangelands, temperate rural residential areas and sites with moderate‐to‐high soil iron concentrations. Abundances of butyrate‐producing bacteria in outdoor soils followed complex ecological patterns influenced by geography, climate, soil chemistry and hydrological fluctuations. These new macroecological insights further our understanding of the ecological patterns of outdoor butyrate‐producing bacteria, with implications for emerging microbially focused ecological and human health policies.

Despite mounting evidence of their importance in human health and ecosystem functioning, the definition and measurement of 'healthy microbiomes' remain unclear. More advanced knowledge exists on health associations for compounds used or produced by microbes. Environmental microbiome exposures (especially via soils) also help shape, and may supplement, the functional capacity of human microbiomes. Given the synchronous interaction between microbes, their feedstocks, and micro-environments, with functional genes facilitating chemical transformations, our objective was to examine microbiomes in terms of their capacity to process compounds relevant to human health. Here we integrate functional genomics and biochemistry frameworks to derive new quantitative measures of in silico potential for human gut and environmental soil metagenomes to process a panel of major compound classes (e.g., lipids, carbohydrates) and selected biomolecules (e.g., vitamins, short-chain fatty acids) linked to human health. Metagenome functional potential profile data were translated into a universal compound mapping 'landscape' based on bioenergetic van Krevelen mapping of function-level meta-compounds and corresponding functional relative abundances, reflecting imprinted genetic capacity of microbiomes to metabolize an array of different compounds. We show that measures of 'compound processing potential' associated with human health and disease (examining atherosclerotic cardiovascular disease, colorectal cancer, type 2 diabetes and anxious-depressive behavior case studies), and displayed seemingly predictable shifts along gradients of ecological disturbance in plant-soil ecosystems (three case studies). Ecosystem quality explained 60-92 % of variation in soil metagenome compound processing potential measures in a post-mining restoration case study dataset. With growing knowledge of the varying proficiency of environmental microbiota to process human health associated compounds, we might design environmental interventions or nature prescriptions to modulate our exposures, thereby advancing microbiota-oriented approaches to human health. Compound processing potential offers a simplified, integrative approach for applying metagenomics in ongoing efforts to understand and quantify the role of microbiota in environmental- and human-health.

Soil microbiota underpin ecosystem functionality yet are rarely targeted during ecosystem restoration. Soil microbiota recovery following native plant revegetation can take years to decades, while the effectiveness of soil inoculation treatments on microbiomes remains poorly explored. Therefore, innovative restoration treatments that target soil microbiota represent an opportunity to accelerate restoration outcomes. Here, we introduce the concept of ecological phage therapy—the application of phage for the targeted reduction of the most abundant and dominant bacterial taxa present in degraded ecosystems. We propose that naturally occurring bacteriophages—viruses that infect bacteria—could help rapidly shift soil microbiota towards target communities. Bacteriophages sculpt the microbiome by lysis of specific bacteria, and if followed by the addition of reference soil microbiota, such treatments could facilitate rapid reshaping of soil microbiota. Here, we experimentally tested this concept in a pilot study. We collected five replicate pre‐treatment degraded soil samples, then three replicate soil samples 48 hours after phage, bacteria, and control treatments. Bacterial 16S rDNA sequencing showed that phage‐treated soils had reduced bacterial diversity; however, when we combined ecological phage therapy with reference soil inoculation, we did not see a shift in soil bacterial community composition from degraded soil towards a reference‐like community. Our pilot study provides early evidence that ecological phage therapy could help accelerate the reshaping of soil microbiota with the ultimate aim of reducing timeframes for ecosystem recovery. We recommend the next steps for ecological phage therapy be (a) developing appropriate risk assessment and management frameworks, and (b) focussing research effort on its practical application to maximise its accessibility to restoration practitioners.