Informative Regions In Viral Genomes

Large-scale genomic survey with deep learning-based method reveals strain-level phage specificity determinants

BACKGROUND

Phage therapy, reemerging as a promising approach to counter antimicrobial-resistant infections, relies on a comprehensive understanding of the specificity of individual phages. Yet the significant diversity within phage populations presents a considerable challenge. Currently, there is a notable lack of tools designed for large-scale characterization of phage receptor-binding proteins, which are crucial in determining the phage host range.

RESULTS

In this study, we present SpikeHunter, a deep learning method based on the ESM-2 protein language model. With SpikeHunter, we identified 231,965 diverse phage-encoded tailspike proteins, a crucial determinant of phage specificity that targets bacterial polysaccharide receptors, across 787,566 bacterial genomes from 5 virulent, antibiotic-resistant pathogens. Notably, 86.60% (143,200) of these proteins exhibited strong associations with specific bacterial polysaccharides. We discovered that phages with identical tailspike proteins can infect different bacterial species with similar polysaccharide receptors, underscoring the pivotal role of tailspike proteins in determining host range. The specificity is mainly attributed to the protein's C-terminal domain, which strictly correlates with host specificity during domain swapping in tailspike proteins. Importantly, our dataset-driven predictions of phage-host specificity closely match the phage-host pairs observed in real-world phage therapy cases we studied.

CONCLUSIONS

Our research provides a rich resource, including both the method and a database derived from a large-scale genomics survey. This substantially enhances understanding of phage specificity determinants at the strain level and offers a valuable framework for guiding phage selection in therapeutic applications.

Viromes vs. mixed community metagenomes: choice of method dictates interpretation of viral community ecology

Background

Viruses, the majority of which are uncultivated, are among the most abundant biological entities on Earth. From altering microbial physiology to driving community dynamics, viruses are fundamental members of microbiomes. While the number of studies leveraging viral metagenomics (viromics) for studying uncultivated viruses is growing, standards for viromics research are lacking. Viromics can utilize computational discovery of viruses from total metagenomes of all community members (hereafter metagenomes) or use physical separation of virus-specific fractions (hereafter viromes). However, differences in the recovery and interpretation of viruses from metagenomes and viromes obtained from the same samples remain understudied.

Results

Here, we compare viral communities from paired viromes and metagenomes obtained from 60 diverse samples across human gut, soil, freshwater, and marine ecosystems. Overall, viral communities obtained from viromes were more abundant and species rich than those obtained from metagenomes, although there were some exceptions. Despite this, metagenomes still contained many viral genomes not detected in viromes. We also found notable differences in the predicted lytic state of viruses detected in viromes vs metagenomes at the time of sequencing. Other forms of variation observed include genome presence/absence, genome quality, and encoded protein content between viromes and metagenomes, but the magnitude of these differences varied by environment.

Conclusions

Overall, our results show that the choice of method can lead to differing interpretations of viral community ecology. We suggest that the choice of whether to target a metagenome or virome to study viral communities should be dependent on the environmental context and ecological questions being asked. However, our overall recommendation to researchers investigating viral ecology and evolution is to pair both approaches to maximize their respective benefits.

VIRify: An integrated detection, annotation and taxonomic classification pipeline using virus-specific protein profile hidden Markov models

The study of viral communities has revealed the enormous diversity and impact these biological entities have on various ecosystems. These observations have sparked widespread interest in developing computational strategies that support the comprehensive characterisation of viral communities based on sequencing data. Here we introduce VIRify, a new computational pipeline designed to provide a user-friendly and accurate functional and taxonomic characterisation of viral communities. VIRify identifies viral contigs and prophages from metagenomic assemblies and annotates them using a collection of viral profile hidden Markov models (HMMs). These include our manually-curated profile HMMs, which serve as specific taxonomic markers for a wide range of prokaryotic and eukaryotic viral taxa and are thus used to reliably classify viral contigs. We tested VIRify on assemblies from two microbial mock communities, a large metagenomics study, and a collection of publicly available viral genomic sequences from the human gut. The results showed that VIRify could identify sequences from both prokaryotic and eukaryotic viruses, and provided taxonomic classifications from the genus to the family rank with an average accuracy of 86.6%. In addition, VIRify allowed the detection and taxonomic classification of a range of prokaryotic and eukaryotic viruses present in 243 marine metagenomic assemblies. Finally, the use of VIRify led to a large expansion in the number of taxonomically classified human gut viral sequences and the improvement of outdated and shallow taxonomic classifications. Overall, we demonstrate that VIRify is a novel and powerful resource that offers an enhanced capability to detect a broad range of viral contigs and taxonomically classify them.

Computational Tools for the Analysis of Uncultivated Phage Genomes

Over a century of bacteriophage research has uncovered a plethora of fundamental aspects of their biology, ecology, and evolution. Furthermore, the introduction of community-level studies through metagenomics has revealed unprecedented insights on the impact that phages have on a range of ecological and physiological processes. It was not until the introduction of viral metagenomics that we began to grasp the astonishing breadth of genetic diversity encompassed by phage genomes. Novel phage genomes have been reported from a diverse range of biomes at an increasing rate, which has prompted the development of computational tools that support the multilevel characterization of these novel phages based solely on their genome sequences. The impact of these technologies has been so large that, together with MAGs (Metagenomic Assembled Genomes), we now have UViGs (Uncultivated Viral Genomes), which are now officially recognized by the International Committee for the Taxonomy of Viruses (ICTV), and new taxonomic groups can now be created based exclusively on genomic sequence information. Even though the available tools have immensely contributed to our knowledge of phage diversity and ecology, the ongoing surge in software programs makes it challenging to keep up with them and the purpose each one is designed for. Therefore, in this review, we describe a comprehensive set of currently available computational tools designed for the characterization of phage genome sequences, focusing on five specific analyses: (i) assembly and identification of phage and prophage sequences, (ii) phage genome annotation, (iii) phage taxonomic classification, (iv) phage-host interaction analysis, and (v) phage microdiversity.

Biological foundations of successful bacteriophage therapy

Bacteriophages (phages) are selective viral predators of bacteria. Abundant and ubiquitous in nature, phages can be used to treat bacterial infections (phage therapy), including refractory infections and those resistant to antibiotics. However, despite an abundance of anecdotal evidence of efficacy, significant hurdles remain before routine implementation of phage therapy into medical practice, including a dearth of robust clinical trial data. Phage-bacterium interactions are complex and diverse, characterized by co-evolution trajectories that are significantly influenced by the environments in which they occur (mammalian body sites, water, soil, etc.). An understanding of the molecular mechanisms underpinning these dynamics is essential for successful clinical translation. This review aims to cover key aspects of bacterium-phage interactions that affect bacterial killing by describing the most relevant published literature and detailing the current knowledge gaps most likely to influence therapeutic success.

Perspective on taxonomic classification of uncultivated viruses

Historically, virus taxonomy has been limited to describing viruses that were readily cultivated in the laboratory or emerging in natural biomes. Metagenomic analyses, single-particle sequencing, and database mining efforts have yielded new sequence data on an astounding number of previously unknown viruses. As metagenomes are relatively free of biases, these data provide an unprecedented insight into the vastness of the virosphere, but to properly value the extent of this diversity it is critical that the viruses are taxonomically classified. Inclusion of uncultivated viruses has already improved the process as well as the understanding of the taxa, viruses, and their evolutionary relationships. The continuous development and testing of computational tools will be required to maintain a dynamic virus taxonomy that can accommodate the new discoveries.