Mini-Metagenomics and Nucleotide Composition Aid the Identification and Host Association of Novel Bacteriophage Sequences

Tools and methodology to in silico phage discovery in freshwater environments

Freshwater availability is essential, and its maintenance has become an enormous challenge. Due to population growth and climate changes, freshwater sources are becoming scarce, imposing the need for strategies for its reuse. Currently, the constant discharge of waste into water bodies from human activities leads to the dissemination of pathogenic bacteria, negatively impacting water quality from the source to the infrastructure required for treatment, such as the accumulation of biofilms. Current water treatment methods cannot keep pace with bacterial evolution, which increasingly exhibits a profile of multidrug resistance to antibiotics. Furthermore, using more powerful disinfectants may affect the balance of aquatic ecosystems. Therefore, there is a need to explore sustainable ways to control the spreading of pathogenic bacteria. Bacteriophages can infect bacteria and archaea, hijacking their host machinery to favor their replication. They are widely abundant globally and provide a biological alternative to bacterial treatment with antibiotics. In contrast to common disinfectants and antibiotics, bacteriophages are highly specific, minimizing adverse effects on aquatic microbial communities and offering a lower cost-benefit ratio in production compared to antibiotics. However, due to the difficulty involving cultivating and identifying environmental bacteriophages, alternative approaches using NGS metagenomics in combination with some bioinformatic tools can help identify new bacteriophages that can be useful as an alternative treatment against resistant bacteria. In this review, we discuss advances in exploring the virome of freshwater, as well as current applications of bacteriophages in freshwater treatment, along with current challenges and future perspectives.

Benchmarking informatics approaches for virus discovery: caution is needed when combining in silico identification methods

Understanding the ecological impacts of viruses on natural and engineered ecosystems relies on the accurate identification of viral sequences from community sequencing data. To maximize viral recovery from metagenomes, researchers frequently combine viral identification tools. However, the effectiveness of this strategy is unknown. Here, we benchmarked combinations of six widely used informatics tools for viral identification and analysis (VirSorter, VirSorter2, VIBRANT, DeepVirFinder, CheckV, and Kaiju), called "rulesets." Rulesets were tested against mock metagenomes composed of taxonomically diverse sequence types and diverse aquatic metagenomes to assess the effects of the degree of viral enrichment and habitat on tool performance. We found that six rulesets achieved equivalent accuracy [Matthews Correlation Coefficient (MCC) = 0.77, Padj ≥ 0.05]. Each contained VirSorter2, and five used our "tuning removal" rule designed to remove non-viral contamination. While DeepVirFinder, VIBRANT, and VirSorter were each found once in these high-accuracy rulesets, they were not found in combination with each other: combining tools does not lead to optimal performance. Our validation suggests that the MCC plateau at 0.77 is partly caused by inaccurate labeling within reference sequence databases. In aquatic metagenomes, our highest MCC ruleset identified more viral sequences in virus-enriched (44%-46%) than in cellular metagenomes (7%-19%). While improved algorithms may lead to more accurate viral identification tools, this should be done in tandem with careful curation of sequence databases. We recommend using the VirSorter2 ruleset and our empirically derived tuning removal rule. Our analysis provides insight into methods for in silico viral identification and will enable more robust viral identification from metagenomic data sets.

IMPORTANCE

The identification of viruses from environmental metagenomes using informatics tools has offered critical insights in microbial ecology. However, it remains difficult for researchers to know which tools optimize viral recovery for their specific study. In an attempt to recover more viruses, studies are increasingly combining the outputs from multiple tools without validating this approach. After benchmarking combinations of six viral identification tools against mock metagenomes and environmental samples, we found that these tools should only be combined cautiously. Two to four tool combinations maximized viral recovery and minimized non-viral contamination compared with either the single-tool or the five- to six-tool ones. By providing a rigorous overview of the behavior of in silico viral identification strategies and a pipeline to replicate our process, our findings guide the use of existing viral identification tools and offer a blueprint for feature engineering of new tools that will lead to higher-confidence viral discovery in microbiome studies.

Horizontal transfer potential of antibiotic resistance genes in wastewater treatment plants unraveled by microfluidic-based mini-metagenomics

Wastewater treatment plants (WWTPs) are known to harbor antibiotic resistance genes (ARGs), which can potentially spread to the environment and human populations. However, the extent and mechanisms of ARG transfer in WWTPs are not well understood due to the high microbial diversity and limitations of molecular techniques. In this study, we used a microfluidic-based mini-metagenomics approach to investigate the transfer potential and mechanisms of ARGs in activated sludge from WWTPs. Our results show that while diverse ARGs are present in activated sludge, only a few highly similar ARGs are observed across different taxa, indicating limited transfer potential. We identified two ARGs, ermF and tla-1, which occur in a variety of bacterial taxa and may have high transfer potential facilitated by mobile genetic elements. Interestingly, genes that are highly similar to the sequences of these two ARGs, as identified in this study, display varying patterns of abundance across geographic regions. Genes similar to ermF found are widely found in Asia and the Americas, while genes resembling tla-1 are primarily detected in Asia. Genes similar to both genes are barely detected in European WWTPs. These findings shed light on the limited horizontal transfer potential of ARGs in WWTPs and highlight the importance of monitoring specific ARGs in different regions to mitigate the spread of antibiotic resistance.

Gauge your phage: benchmarking of bacteriophage identification tools in metagenomic sequencing data

BACKGROUND

The prediction of bacteriophage sequences in metagenomic datasets has become a topic of considerable interest, leading to the development of many novel bioinformatic tools. A comparative analysis of ten state-of-the-art phage identification tools was performed to inform their usage in microbiome research.

METHODS

Artificial contigs generated from complete RefSeq genomes representing phages, plasmids, and chromosomes, and a previously sequenced mock community containing four phage species, were used to evaluate the precision, recall, and F1 scores of the tools. We also generated a dataset of randomly shuffled sequences to quantify false-positive calls. In addition, a set of previously simulated viromes was used to assess diversity bias in each tool's output.

RESULTS

VIBRANT and VirSorter2 achieved the highest F1 scores (0.93) in the RefSeq artificial contigs dataset, with several other tools also performing well. Kraken2 had the highest F1 score (0.86) in the mock community benchmark by a large margin (0.3 higher than DeepVirFinder in second place), mainly due to its high precision (0.96). Generally, k-mer-based tools performed better than reference similarity tools and gene-based methods. Several tools, most notably PPR-Meta, called a high number of false positives in the randomly shuffled sequences. When analysing the diversity of the genomes that each tool predicted from a virome set, most tools produced a viral genome set that had similar alpha- and beta-diversity patterns to the original population, with Seeker being a notable exception.

CONCLUSIONS

This study provides key metrics used to assess performance of phage detection tools, offers a framework for further comparison of additional viral discovery tools, and discusses optimal strategies for using these tools. We highlight that the choice of tool for identification of phages in metagenomic datasets, as well as their parameters, can bias the results and provide pointers for different use case scenarios. We have also made our benchmarking dataset available for download in order to facilitate future comparisons of phage identification tools. Video Abstract.

Advances in the field of phage-based therapy with special emphasis on computational resources

In the current era, one of the major challenges is to manage the treatment of drug/antibiotic-resistant strains of bacteria. Phage therapy, a century-old technique, may serve as an alternative to antibiotics in treating bacterial infections caused by drug-resistant strains of bacteria. In this review, a systematic attempt has been made to summarize phage-based therapy in depth. This review has been divided into the following two sections: general information and computer-aided phage therapy (CAPT). In the case of general information, we cover the history of phage therapy, the mechanism of action, the status of phage-based products (approved and clinical trials) and the challenges. This review emphasizes CAPT, where we have covered primary phage-associated resources, phage prediction methods and pipelines. This review covers a wide range of databases and resources, including viral genomes and proteins, phage receptors, host genomes of phages, phage-host interactions and lytic proteins. In the post-genomic era, identifying the most suitable phage for lysing a drug-resistant strain of bacterium is crucial for developing alternate treatments for drug-resistant bacteria and this remains a challenging problem. Thus, we compile all phage-associated prediction methods that include the prediction of phages for a bacterial strain, the host for a phage and the identification of interacting phage-host pairs. Most of these methods have been developed using machine learning and deep learning techniques. This review also discussed recent advances in the field of CAPT, where we briefly describe computational tools available for predicting phage virions, the life cycle of phages and prophage identification. Finally, we describe phage-based therapy's advantages, challenges and opportunities.

Moisture modulates soil reservoirs of active DNA and RNA viruses

Soil is known to harbor viruses, but the majority are uncharacterized and their responses to environmental changes are unknown. Here, we used a multi-omics approach (metagenomics, metatranscriptomics and metaproteomics) to detect active DNA viruses and RNA viruses in a native prairie soil and to determine their responses to extremes in soil moisture. The majority of transcribed DNA viruses were bacteriophage, but some were assigned to eukaryotic hosts, mainly insects. We also demonstrated that higher soil moisture increased transcription of a subset of DNA viruses. Metaproteome data validated that the specific viral transcripts were translated into proteins, including chaperonins known to be essential for virion replication and assembly. The soil viral chaperonins were phylogenetically distinct from previously described marine viral chaperonins. The soil also had a high abundance of RNA viruses, with highest representation of Reoviridae. Leviviridae were the most diverse RNA viruses in the samples, with higher amounts in wet soil. This study demonstrates that extreme shifts in soil moisture have dramatic impacts on the composition, activity and potential functions of both DNA and RNA soil viruses.