Taxonomy Of Viruses Research Articles

Unveiling bat-borne viruses: a comprehensive classification and analysis of virome evolution.

Bats (Order Chiroptera) are an important reservoir of emerging zoonotic microbes, including viruses of public health concern such as henipaviruses, lyssaviruses, and SARS-related coronaviruses. Despite the continued discovery of new viruses in bat populations, a significant proportion of these viral agents remain uncharacterized, highlighting the imperative for additional research aimed at elucidating their evolutionary relationship and taxonomic classification. In order to delve deeper into the viral reservoir hosted by bats, the present study employed Next Generation Sequencing (NGS) technology to analyze 13,105 swab samples obtained from various locations in China. Analysis of 378 sample pools revealed the presence of 846 vertebrate-associated viruses. Subsequent thorough examination, adhering to the International Committee on Taxonomy of Viruses (ICTV) criteria for virus classification, identified a total of 120 putative viral species with the potential to emerge as novel viruses, comprising a total of 294 viral strains. Phylogenetic analysis of conserved genomic regions indicated the novel virus exhibited a diverse array of viral lineages and branches, some of which displayed close genetic relationships to known human and livestock pathogens, such as poxviruses and pestiviruses. This study investigates the breadth of DNA and RNA viruses harbored by bats, delineating several novel evolutionary lineages and offering significant contributions to virus taxonomy. Furthermore, the identification of hitherto unknown viruses with relevance to human and livestock health underscores the importance of this study in encouraging infectious disease monitoring and management efforts in both public health and veterinary contexts. Video Abstract.

VISTA: A Tool for Fast Taxonomic Assignment of Viral Genome Sequences.

The rapid expansion of the number of viral genome sequences in public databases necessitates a scalable, universal, and automated preliminary taxonomic framework for comprehensive virus studies. Here, we introduce VISTA (Virus Sequence-based Taxonomy Assignment), a computational tool that employs a novel pairwise sequence comparison system and an automatic demarcation threshold identification framework for virus taxonomy. Leveraging physio-chemical property sequences, k-mer profiles, and machine learning techniques, VISTA constructs a robust distance-based framework for taxonomic assignment. Functionally similar to PASC (Pairwise Sequence Comparison), a widely used virus assignment tool based on pairwise sequence comparison, VISTA demonstrates superior performance by providing significantly improved separation for taxonomic groups, more objective taxonomic demarcation thresholds, greatly enhanced speed, and a wider application scope. We successfully applied VISTA to 38 virus families, as well as to the class Caudoviricetes. This demonstrates VISTA's scalability, robustness, and ability to automatically and accurately assign taxonomy to both prokaryotic and eukaryotic viruses. Furthermore, the application of VISTA to 679 unclassified prokaryotic virus genomes recovered from metagenomic data identified 46 novel virus families. VISTA is available as both a command line tool and a user-friendly web portal at https://ngdc.cncb.ac.cn/vista.

Changes to virus taxonomy and the ICTV Statutes ratified by the International Committee on Taxonomy of Viruses (2024).

This article reports changes to virus taxonomy and taxon nomenclature that were approved and ratified by the International Committee on Taxonomy of Viruses (ICTV) in April 2024. The entire ICTV membership was invited to vote on 203 taxonomic proposals that had been approved by the ICTV Executive Committee (EC) in July 2023 at the 55th EC meeting in Jena, Germany, or in the second EC vote in November 2023. All proposals were ratified by online vote. Taxonomic additions include one new phylum (Ambiviricota), one new class, nine new orders, three new suborders, 51 new families, 18 new subfamilies, 820 new genera, and 3547 new species (excluding taxa that have been abolished). Proposals to complete the process of species name replacement to the binomial (genus + species epithet) format were ratified. Currently, a total of 14,690 virus species have been established.

ICTV Virus Taxonomy Profile: Peribunyaviridae 2024.

Peribunyavirids produce enveloped virions with three negative-sense RNA segments comprising 10.7-12.5 kb in total. The family includes globally distributed viruses in multiple genera. While most peribunyavirids are maintained in geographically restricted vertebrate-arthropod transmission cycles, others are arthropod-specific or do not have a known vector. Arthropods can be persistently infected. Human and other vertebrate animal infections occur through blood feeding by an infected vector arthropod, resulting in diverse human and veterinary clinical outcomes in a strain-specific manner. Reassortment can occur between members of the same genus. This is a summary of the International Committee on Taxonomy of Viruses (ICTV) Report on the family Peribunyaviridae, which is available at ictv.global/report/peribunyaviridae.

Comparison of machine learning algorithms for dengue virus (DENV) classification

Dengue, transmitted by the Aedes aegypti mosquito, necessitates accurate case classification for effective management. The research focuses on leveraging machine learning algorithms to enhance diagnosis and streamline control strategies. Utilizing a dataset from a public hospital, encompassing 21,157 cases categorized by period, outcome, gender, age, symptoms, and origin (autochthonous or imported), the study conducted a comparative analysis of Support Vector Machine, Random Forest, and Artificial Neural Network algorithms. The dataset was divided into 70% (14,809 cases) for training and 30% (6,348 cases) for testing. Results unveiled the Artificial Neural Network as the frontrunner, exhibiting an impressive 86.47% accuracy and a robust 92.91% recall in the classification of dengue-related cases. This underscores the potential of machine learning in refining dengue diagnosis and facilitating more efficient control strategies, offering a promising avenue for combating this vector-borne disease.

Evaluation of the Cytotoxicity Effects of Bacteriophage VbɸPA-1 on the Virulent Pseudomonas aeruginosa, A375 Human Skin Melanoma Epithelial, and HFSF PI3 Human Skin Fibroblast Cells

Background: Gram-negative bacteria are the most common infectious bacteria and are life-threatening in burn patients. Phage therapy studies can help the hospital community eliminate these bacterial pathogens. Objectives: This study aimed to evaluate the effects of the isolated bacteriophages from hospital wastewater against Gram-negative pathogenic bacteria and investigate their cytotoxicity on two human skin cell lines. Methods: The bacterial strains were isolated from burn wound infections. Bacteriophages were isolated from the hospital wastewater treatment plant in Isfahan, Iran. Transmission electron microscopy, spot tests, and restriction enzyme digestion were considered for phage identification. Finally, the lactate dehydrogenase (LDH) and tetrazolium-based MTT assays were used to realize the cytotoxicity impacts of the isolated phages. The statistical analysis of cell viability was performed using a two-way analysis of variance (ANOVA). Results: The results showed that among the isolated strains, P. aeruginosa strain NEG_RA1300 (GenBank accession number: MW845642) was sensitive to the isolated phage VbɸPA-1. The TEM results showed a possible viral taxonomy of VbɸPA-1 based on its morphology, which belonged to the Myoviridae (T4-like phages). The MTT and LDH experiments showed no cytotoxicity of VbɸPA-1 on two tested cell lines. Conclusions: Based on the results, the inhibitory effect of isolated phage on P. aeruginosa isolated from burn wounds with no toxic effect on cell lines was very significant. This phage can also be an acceptable selection against burn wound infections.

Support for the use of Binomial Nomenclature in the Taxonomy of Viruses

Two contrasting opinions about virus taxonomy or, more precisely, about virus nomenclature, appeared in the first 2004 issue of Emerging Infectious Diseases [1,2]. These differring ideas about scientific (vs. common) names of viruses are part of a long disagreement among virologists that can be tracked to the early 1950s [3,4].

Promotion of order Bunyavirales to class Bunyaviricetes to accommodate a rapidly increasing number of related polyploviricotine viruses.

Prior to 2017, the family Bunyaviridae included five genera of arthropod and rodent viruses with tri-segmented negative-sense RNA genomes related to the Bunyamwera virus. In 2017, the International Committee on Taxonomy of Viruses (ICTV) promoted the family to order Bunyavirales and subsequently greatly expanded its composition by adding multiple families for non-segmented to polysegmented viruses of animals, fungi, plants, and protists. The continued and accelerated discovery of bunyavirals highlighted that an order would not suffice to depict the evolutionary relationships of these viruses. Thus, in April 2024, the order was promoted to class Bunyaviricetes. This class currently includes two major orders, Elliovirales (Cruliviridae, Fimoviridae, Hantaviridae, Peribunyaviridae, Phasmaviridae, Tospoviridae, and Tulasviridae) and Hareavirales (Arenaviridae, Discoviridae, Konkoviridae, Leishbuviridae, Mypoviridae, Nairoviridae, Phenuiviridae, and Wupedeviridae), for hundreds of viruses, many of which are pathogenic for humans and other animals, plants, and fungi.

VirID: Beyond Virus Discovery-An Integrated Platform for Comprehensive RNA Virus Characterization.

RNA viruses exhibit vast phylogenetic diversity and can significantly impact public health and agriculture. However, current bioinformatics tools for viral discovery from metagenomic data frequently generate false positive virus results, overestimate viral diversity, and misclassify virus sequences. Additionally, current tools often fail to determine virus-host associations, which hampers investigation of the potential threat posed by a newly detected virus. To address these issues we developed VirID, a software tool specifically designed for the discovery and characterization of RNA viruses from metagenomic data. The basis of VirID is a comprehensive RNA-dependent RNA polymerase database to enhance a workflow that includes RNA virus discovery, phylogenetic analysis, and phylogeny-based virus characterization. Benchmark tests on a simulated data set demonstrated that VirID had high accuracy in profiling viruses and estimating viral richness. In evaluations with real-world samples, VirID was able to identify RNA viruses of all types, but also provided accurate estimations of viral genetic diversity and virus classification, as well as comprehensive insights into virus associations with humans, animals, and plants. VirID therefore offers a robust tool for virus discovery and serves as a valuable resource in basic virological studies, pathogen surveillance, and early warning systems for infectious disease outbreaks.

Juruaça virus taxonomy, tolerance and resistance to infection, and inflammatory response modulation in murine model

Juruaça virus (JUAV), previously unclassified, was isolated from bats and administered to neonatal and adult BALB/c mice to investigate acute and chronic disease progression. In this study, we conducted genomic sequencing to achieve taxonomic classification and utilized these models to explore the inflammatory response and sickness behavior in both neonatal and adult mice. Neonates received a single intranasal instillation of infected brain homogenate (20 µL), whereas 31-day-old mice were given the same volume intranasally for three consecutive days. Control groups were administered equal volumes of uninfected brain homogenate. Our findings reveal that intranasal JUAV infection-induced acute meningoencephalitis and death in neonates, while adult mice exhibited chronic infection with variable clinical signs, inflammatory mediator production, histopathological changes, and neuropathological features. Interestingly, only some adult mice showed sickness behavior post-infection, and among these, a subset continued to decline and die. The differential tissue damage observed in mice with and without overt disease symptoms suggests mechanisms of resistance or tolerance, where exceeding tolerance capacity resulted in pathological outcomes, including chronic dysfunction or death. This study provides the first evidence of JUAV’s capability to infect mammals, demonstrating its distinct impact on bats and variable effects in neonatal and adult mice. We provisionally classified JUAV as closely related to the clade containing tombus-like virus 6 found in mute swan feces. Our research highlights the importance of understanding viral–host interactions and the inflammatory responses that contribute to disease variability, offering insights into tolerance and resistance mechanisms based on inflammatory response modulation.

MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes.

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.IMPORTANCEThe significance of our work lies in the development of Modular Viromics Pipeline (MVP), an integrated and user-friendly pipeline tailored exclusively for viromics analyses. MVP stands out due to its modular design, which ensures easy installation, execution, and integration of new tools and databases. By combining state-of-the-art tools such as geNomad and CheckV, MVP provides high-quality viral genome recovery and taxonomy and host assignment, and functional annotation, addressing the limitations of existing pipelines. MVP's ability to handle diverse sample types, including environmental, human microbiome, and plant-associated samples, makes it a versatile tool for the broader microbiome research community. By standardizing the analysis process and providing easily interpretable results, MVP enables researchers to perform comprehensive studies of viral communities, significantly advancing our understanding of viral ecology and its impact on various ecosystems.

VirTAXA: enhancing RNA virus taxonomic classification with remote homology search and tree-based validation.

RNA viruses are ubiquitous across a broad spectrum of ecosystems. Therefore, beyond their significant implications for public health, RNA viruses are also key players in ecological processes. High-through sequencing has accelerated the discovery of RNA viruses. Nevertheless, many of these viruses lack taxonomic annotation, posing a challenge to functional inference and evolutionary study. In particular, virus classification at the genus level remains difficult due to the limited reference data and ambiguous boundaries between some closely related genera. We introduce VirTAXA, a robust classification tool that combines remote homology search and tree-based validation to enhance the genus-level taxonomic classification of RNA viruses. VirTAXA is able to predict the genus label of an assembled viral contig and provide evidence type for each prediction. It achieves comparable accuracy to state-of-the-art methods while assigning genus labels to a greater number of sequences. Specifically, on the Global Ocean RNA metatranscriptomic data, VirTAXA can assign genus labels for 18% more contigs than the second-best classification tool. Furthermore, we demonstrated that VirTAXA can be conveniently extended to other types of viruses. The source code and data of VirTAXA are available via https://github.com/JudithEllyn/VirTAXA.

Characterization and Genomic Analyses of dsDNA Vibriophage vB_VpaM_XM1, Representing a New Viral Family.

A novel vibriophage vB_VpaM_XM1 (XM1) was described in the present study. Morphological analysis revealed that phage XM1 had Myovirus morphology, with an oblate icosahedral head and a long contractile tail. The genome size of XM1 is 46,056 bp, with a G + C content of 42.51%, encoding 69 open reading frames (ORFs). Moreover, XM1 showed a narrow host range, only lysing Vibrio xuii LMG 21346 (T) JL2919, Vibrio parahaemolyticus 1.1997, and V. parahaemolyticus MCCC 1H00029 among the tested bacteria. One-step growth curves showed that XM1 has a 20-min latent period and a burst size of 398 plaque-forming units (PFU)/cell. In addition, XM1 exhibited broad pH, thermal, and salinity stability, as well as strong lytic activity, even at a multiplicity of infection (MOI) of 0.001. Multiple genome comparisons and phylogenetic analyses showed that phage XM1 is grouped in a clade with three other phages, including Vibrio phages Rostov 7, X29, and phi 2, and is distinct from all known viral families that have ratified by the standard genomic analysis of the International Committee on Taxonomy of Viruses (ICTV). Therefore, the above four phages might represent a new viral family, tentatively named Weiviridae. The broad physiological adaptability of phage XM1 and its high lytic activity and host specificity indicated that this novel phage is a good candidate for being used as a therapeutic bioagent against infections caused by certain V. parahaemolyticus strains.

Dilated multilevel fused network for virus classification using transmission electron microscopy images

Previous studies have demonstrated significant performance in the field of virus classification; however, they focused on the classification of a small number of virus classes, with a maximum of 16 classes. To address this limitation, this study aims to create a deep learning-based network that outperforms the state-of-the-art (SOTA) models for the classification of 22 different virus classes with the fewest possible trainable parameters. We introduce an automatic identification system for virus classes based on our classification-driven retrieval framework. The proposed dilated multilevel fused network (DMLF-Net) utilizes the multilevel feature fusion concept within a network to exploit more abstract features for microscopic data analysis. A multi-stage training strategy was applied to achieve optimal model convergence without overfitting the training data.We evaluated the performance of the DMLF-Net on three open databases including two virus datasets and one bacteria species dataset. The results demonstrated an accuracy of 89.89%, a weighted harmonic mean of precision and recall (F1-score) of 83.39%, and an area under the curve (AUC) of 92.50% for the 1st virus dataset. For the 2nd virus dataset, the accuracy was 80.70%, the F1-score was 81.20%, and the AUC was 86.20%. For the 3rd bacteria species dataset, the accuracy was 95.93% and the F1-score was 96.24%. DMLF-Net outperforms SOTA methods in terms of classification accuracy while utilizing nearly 5.3 times fewer trainable parameters (25.5 million) compared to the second-best model, visual geometry group (VGG)16 (134.3 million).

Extensive Diversity of Viruses in Millipedes Collected in the Dong Nai Biosphere Reserve (Vietnam).

Advances in sequencing technologies and bioinformatics have led to breakthroughs in the study of virus biodiversity. Millipedes (Diplopoda, Myriapoda, Arthropoda) include more than 12,000 extant species, yet data on virus diversity in Diplopoda are scarce. This study aimed to explore the virome of the millipedes collected in the Dong Nai Biosphere Reserve in Vietnam. We studied 14 species of millipedes and managed to assemble and annotate the complete coding genomes of 16 novel viruses, the partial coding genomes of 10 more viruses, and several fragmented viral sequences, which may indicate the presence of about 54 more viruses in the studied samples. Among the complete and partial genomes, 27% were putative members of the order Picornavirales. Most of the discovered viruses were very distant from the viruses currently present in the relevant databases. At least eight viruses meet the criteria to be recognized as a new species by the International Committee on Taxonomy of Viruses, and, for two of them, a higher taxonomic status (genus and even family) can be suggested.

A new lineage nomenclature to aid genomic surveillance of dengue virus.

Dengue virus (DENV) is currently causing epidemics of unprecedented scope in endemic settings and expanding to new geographical areas. It is therefore critical to track this virus using genomic surveillance. However, the complex patterns of viral genomic diversity make it challenging to use the existing genotype classification system. Here, we propose adding 2 sub-genotypic levels of virus classification, named major and minor lineages. These lineages have high thresholds for phylogenetic distance and clade size, rendering them stable between phylogenetic studies. We present assignment tools to show that the proposed lineages are useful for regional, national, and subnational discussions of relevant DENV diversity. Moreover, the proposed lineages are robust to classification using partial genome sequences. We provide a standardized neutral descriptor of DENV diversity with which we can identify and track lineages of potential epidemiological and/or clinical importance. Information about our lineage system, including methods to assign lineages to sequence data and propose new lineages, can be found at: dengue-lineages.org.

Studies on the taxonomy and ecology of viruses infecting fruit trees in the South-West region, Japan

Studies on the taxonomy and ecology of viruses infecting fruit trees in the South-West region, Japan

SCANellome V2: Update of the Primate Anellovirus Reference Sequences Database.

Anelloviruses are ubiquitous in humans and represent a major component of the human virome. Its best-known representative is Torque teno virus (i.e., the Alphatorquevirus genus), which is considered a potential immunity biomarker. Recent metagenomic investigations revealed not only the extraordinary genomic diversity of anellovirus sequences, but also that co-detection of genera, genotypes, or species seems to be the rule in humans. SCANellome was developed to represent a user-friendly tool to analyze the primate (both human and non-human) anellovirus composition at the genus, species, and genotype level from metagenomics data based on an up-to-date database. This SCANellome update includes >900 additional reference sequences from GenBank. Using a clustering at 90% identity, the FASTA database was updated and generated 134 new representative sequences. Based on ORF1, the analysis of these new sequences indicates the presence of 206 potential new species, including four nonhuman primates, and adds four new non-human primate species which will be the subject of a proposal to the International Committee on Taxonomy of Viruses (ICTV). In addition, SCANellome V2 provides now the user with an interactive up-to-date phylogenetic analysis (of ORF1) to show the distribution among the 12 human and nonhuman primate genera of these new potential species. Finally, the Anelloviridae taxonomy was updated to rename species names in binomial format as required by the ICTV.

2024 Smacoviridae family update: 59 new species in seven genera.

Family Smacoviridae (order Cremevirales, class Arfiviricetes, phylum Cressdnaviricota) comprises viruses with small circular genomes of ~2300-3000 nt in length that encode at least two proteins, the rolling-circle replication associated protein (Rep) and the capsid protein (CP). Smacovirids have been discovered in fecal samples of various animals and display remarkable sequence diversity. Here, we provide an overview of the genomic properties of classified smacovirids and report on the latest taxonomy update in the family Smacoviridae. The family has been expanded by 59 new species in the genera Porprismacovirus (n = 25), Inpeasmacovirus (n = 1), Felismacovirus (n = 22), Drosmacovirus (n = 4), Dragsmacovirus (n = 2), Bovismacovirus (n = 4), and Bonzesmacovirus (n = 1) and currently includes 12 genera with 143 species officially recognized by the International Committee on Taxonomy of Viruses (ICTV).

Studies on the taxonomy and ecology of viruses infecting fruit trees in the South-West region, Japan

Studies on the taxonomy and ecology of viruses infecting fruit trees in the South-West region, Japan