NCBI viral genomes resource

Comprehensive human amniotic fluid metagenomics supports the sterile womb hypothesis

As metagenomic approaches for detecting infectious agents have improved, each tissue that was once thought to be sterile has been found to harbor a variety of microorganisms. Controversy still exists over the status of amniotic fluid, which is part of an immunologically privileged zone that is required to prevent maternal immune system rejection of the fetus. Due to this privilege, the exclusion of microbes has been proposed to be mandatory, leading to the sterile womb hypothesis. Since nucleic acid yields from amniotic fluid are very low, contaminating nucleic acid found in water, reagents and the laboratory environment frequently confound attempts to address this hypothesis. Here we present metagenomic criteria for microorganism detection and a metagenomic method able to be performed with small volumes of starting material, while controlling for exogenous contamination, to circumvent these and other pitfalls. We use this method to show that human mid-gestational amniotic fluid has no detectable virome or microbiome, supporting the sterile womb hypothesis.

An overview of dinucleotide and codon usage in all viruses

Viruses are, by far, the most abundant biological entities on earth. They are found in all known ecological niches and are the causative agents of many important diseases in plants and animals. From an evolutionary point of view, since viruses do not share any orthologous genes, there is a general consensus that they are polyphyletic; that is, they do not have a common ancestor. This means that they appeared several times during the course of evolution. For their life cycle, they are always obligate parasites of a free cellular life form, which can be bacteria, archaea, or eukaryotes. More complexity is added to these entities by the fact that their genetic material can be DNA or RNA (double- or single-stranded) or retrotranscribed. Given these features, we wondered if some general rules can be inferred when studying two basic genomic signatures-dinucleotides and codon usage-analyzing all available complete and non-redundant viral sequences. In spite of the obviously biased sample of sequences available, some general features appear to emerge.

AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning

BACKGROUND

The widely spreading coronavirus disease (COVID-19) has three major spreading properties: pathogenic mutations, spatial, and temporal propagation patterns. We know the spread of the virus geographically and temporally in terms of statistics, i.e., the number of patients. However, we are yet to understand the spread at the level of individual patients. As of March 2021, COVID-19 is wide-spread all over the world with new genetic variants. One important question is to track the early spreading patterns of COVID-19 until the virus has got spread all over the world.

RESULTS

In this work, we proposed AutoCoV, a deep learning method with multiple loss object, that can track the early spread of COVID-19 in terms of spatial and temporal patterns until the disease is fully spread over the world in July 2020. Performances in learning spatial or temporal patterns were measured with two clustering measures and one classification measure. For annotated SARS-CoV-2 sequences from the National Center for Biotechnology Information (NCBI), AutoCoV outperformed seven baseline methods in our experiments for learning either spatial or temporal patterns. For spatial patterns, AutoCoV had at least 1.7-fold higher clustering performances and an F1 score of 88.1%. For temporal patterns, AutoCoV had at least 1.6-fold higher clustering performances and an F1 score of 76.1%. Furthermore, AutoCoV demonstrated the robustness of the embedding space with an independent dataset, Global Initiative for Sharing All Influenza Data (GISAID).

CONCLUSIONS

In summary, AutoCoV learns geographic and temporal spreading patterns successfully in experiments on NCBI and GISAID datasets and is the first of its kind that learns virus spreading patterns from the genome sequences, to the best of our knowledge. We expect that this type of embedding method will be helpful in characterizing fast-evolving pandemics.

The Chronic Wound Phageome: Phage Diversity and Associations with Wounds and Healing Outcomes

Two leading impediments to chronic wound healing are polymicrobial infection and biofilm formation. Recent studies have characterized the bacterial fraction of these microbiomes and have begun to elucidate compositional correlations to healing outcomes. However, the factors that drive compositional shifts are still being uncovered. The virome may play an important role in shaping bacterial community structure and function. Previous work on the skin virome determined that it was dominated by bacteriophages, viruses that infect bacteria. To characterize the virome, we enrolled 20 chronic wound patients presenting at an outpatient wound care clinic in a microbiome survey, collecting swab samples from healthy skin and chronic wounds (diabetic, venous, arterial, or pressure) before and after a single, sharp debridement procedure. We investigated the virome using a virus-like particle enrichment procedure, shotgun metagenomic sequencing, and a k-mer-based, reference-dependent taxonomic classification method. Taxonomic composition, diversity, and associations with covariates are presented. We find that the wound virome is highly diverse, with many phages targeting known pathogens, and may influence bacterial community composition and functionality in ways that impact healing outcomes.

IMPORTANCE Chronic wounds are an increasing medical burden. These wounds are known to be rich in microbial content, including both bacteria and bacterial viruses (phages). The viruses may play an important role in shaping bacterial community structure and function. We analyzed the virome and bacterial composition of 20 patients with chronic wounds. The viruses found in wounds are highly diverse compared to normal skin, unlike the bacterial composition, where diversity is decreased. These data represent an initial look at this relatively understudied component of the chronic wound microbiome and may help inform future phage-based interventions.

Virome characterization of game animals in China reveals a spectrum of emerging pathogens

Game animals are wildlife species traded and consumed as food and are potential reservoirs for SARS-CoV and SARS-CoV-2. We performed a meta-transcriptomic analysis of 1,941 game animals, representing 18 species and five mammalian orders, sampled across China. From this, we identified 102 mammalian-infecting viruses, with 65 described for the first time. Twenty-one viruses were considered as potentially high risk to humans and domestic animals. Civets (Paguma larvata) carried the highest number of potentially high-risk viruses. We inferred the transmission of bat-associated coronavirus from bats to civets, as well as cross-species jumps of coronaviruses from bats to hedgehogs, from birds to porcupines, and from dogs to raccoon dogs. Of note, we identified avian Influenza A virus H9N2 in civets and Asian badgers, with the latter displaying respiratory symptoms, as well as cases of likely human-to-wildlife virus transmission. These data highlight the importance of game animals as potential drivers of disease emergence.

Benchmarking metagenomics classifiers on ancient viral DNA: a simulation study

Owing to technological advances in ancient DNA, it is now possible to sequence viruses from the past to track down their origin and evolution. However, ancient DNA data is considerably more degraded and contaminated than modern data making the identification of ancient viral genomes particularly challenging. Several methods to characterise the modern microbiome (and, within this, the virome) have been developed; in particular, tools that assign sequenced reads to specific taxa in order to characterise the organisms present in a sample of interest. While these existing tools are routinely used in modern data, their performance when applied to ancient microbiome data to screen for ancient viruses remains unknown. In this work, we conducted an extensive simulation study using public viral sequences to establish which tool is the most suitable to screen ancient samples for human DNA viruses. We compared the performance of four widely used classifiers, namely Centrifuge, Kraken2, DIAMOND and MetaPhlAn2, in correctly assigning sequencing reads to the corresponding viruses. To do so, we simulated reads by adding noise typical of ancient DNA to a set of publicly available human DNA viral sequences and to the human genome. We fragmented the DNA into different lengths, added sequencing error and C to T and G to A deamination substitutions at the read termini. Then we measured the resulting sensitivity and precision for all classifiers. Across most simulations, more than 228 out of the 233 simulated viruses were recovered by Centrifuge, Kraken2 and DIAMOND, in contrast to MetaPhlAn2 which recovered only around one third. Overall, Centrifuge and Kraken2 had the best performance with the highest values of sensitivity and precision. We found that deamination damage had little impact on the performance of the classifiers, less than the sequencing error and the length of the reads. Since Centrifuge can handle short reads (in contrast to DIAMOND and Kraken2 with default settings) and since it achieve the highest sensitivity and precision at the species level across all the simulations performed, it is our recommended tool. Regardless of the tool used, our simulations indicate that, for ancient human studies, users should use strict filters to remove all reads of potential human origin. Finally, we recommend that users verify which species are present in the database used, as it might happen that default databases lack sequences for viruses of interest.

Cross-cohort gut microbiome associations with immune checkpoint inhibitor response in advanced melanoma

The composition of the gut microbiome has been associated with clinical responses to immune checkpoint inhibitor (ICI) treatment, but there is limited consensus on the specific microbiome characteristics linked to the clinical benefits of ICIs. We performed shotgun metagenomic sequencing of stool samples collected before ICI initiation from five observational cohorts recruiting ICI-naive patients with advanced cutaneous melanoma (n = 165). Integrating the dataset with 147 metagenomic samples from previously published studies, we found that the gut microbiome has a relevant, but cohort-dependent, association with the response to ICIs. A machine learning analysis confirmed the link between the microbiome and overall response rates (ORRs) and progression-free survival (PFS) with ICIs but also revealed limited reproducibility of microbiome-based signatures across cohorts. Accordingly, a panel of species, including Bifidobacterium pseudocatenulatum, Roseburia spp. and Akkermansia muciniphila, associated with responders was identified, but no single species could be regarded as a fully consistent biomarker across studies. Overall, the role of the human gut microbiome in ICI response appears more complex than previously thought, extending beyond differing microbial species simply present or absent in responders and nonresponders. Future studies should adopt larger sample sizes and take into account the complex interplay of clinical factors with the gut microbiome over the treatment course.

Using networks to analyze and visualize the distribution of overlapping genes in virus genomes

Gene overlap occurs when two or more genes are encoded by the same nucleotides. This phenomenon is found in all taxonomic domains, but is particularly common in viruses, where it may increase the information content of compact genomes or influence the creation of new genes. Here we report a global comparative study of overlapping open reading frames (OvRFs) of 12,609 virus reference genomes in the NCBI database. We retrieved metadata associated with all annotated open reading frames (ORFs) in each genome record to calculate the number, length, and frameshift of OvRFs. Our results show that while the number of OvRFs increases with genome length, they tend to be shorter in longer genomes. The majority of overlaps involve +2 frameshifts, predominantly found in dsDNA viruses. Antisense overlaps in which one of the ORFs was encoded in the same frame on the opposite strand (−0) tend to be longer. Next, we develop a new graph-based representation of the distribution of overlaps among the ORFs of genomes in a given virus family. In the absence of an unambiguous partition of ORFs by homology at this taxonomic level, we used an alignment-free k-mer based approach to cluster protein coding sequences by similarity. We connect these clusters with two types of directed edges to indicate (1) that constituent ORFs are adjacent in one or more genomes, and (2) that these ORFs overlap. These adjacency graphs not only provide a natural visualization scheme, but also a novel statistical framework for analyzing the effects of gene- and genome-level attributes on the frequencies of overlaps.

DETECCIÓN Y CARACTERIZACIÓN MOLECULAR DEL POTATO VIRUS B (PVB) EN CULTIVOS DE PAPA CRIOLLA (Solanum phureja) EN ANTIOQUIA

La papa criolla (Solanum phureja Juz. & Bukasov) es uno de los principales productos agrícolas de la región Andina de Colombia. Su siembra ocurre principalmente en pequeñas parcelas con deficiencias técnicas y bajos rendimientos. Las enfermedades de origen viral son una de las principales limitantes de este cultivo, siendo los virus PYVV, PVS, PLRV y PVV algunos de los más importantes. El nivel de conocimiento que se tiene del viroma de la papa criolla en Colombia es incipiente. En este estudio utilizando técnicas moleculares como secuenciación de alto rendimiento (HTS) y RT-PCR en tiempo real (RT-qPCR), a partir de muestras de tejido foliar de plantas procedentes de diferentes lotes de papa criolla en Antioquia (Colombia), se detectó por primera vez para el país la infección del Potato virus B (PVB), un nepovirus hasta ahora sólo registrado en Perú. Mediante análisis bioinformáticos fue posible el ensamblaje de la mayor parte del genoma de PVB, consistente de dos segmentos de 7.126 nt (ARN1) y 4.298 nt (ARN2) que codifican para dos poliproteínas (P1 y P2). Con las secuencias obtenidas se diseñaron primers específicos para la detección del PVB por RT-qPCR y RT-PCR convencional. Los resultados indicaron niveles medios de prevalencia (35%) del PVB en los cultivos de papa criolla evaluados y su ausencia en las 20 muestras evaluadas de S. tuberosum var. Diacol-Capiro. Con las técnicas aquí empleadas, se sugiere el establecimiento de un programa de seguimiento epidemiológico de PVB en Colombia y otros países andinos.

Combination of antiviral drugs inhibits SARS-CoV-2 polymerase and exonuclease and demonstrates COVID-19 therapeutic potential in viral cell culture

SARS-CoV-2 has an exonuclease-based proofreader, which removes nucleotide inhibitors such as Remdesivir that are incorporated into the viral RNA during replication, reducing the efficacy of these drugs for treating COVID-19. Combinations of inhibitors of both the viral RNA-dependent RNA polymerase and the exonuclease could overcome this deficiency. Here we report the identification of hepatitis C virus NS5A inhibitors Pibrentasvir and Ombitasvir as SARS-CoV-2 exonuclease inhibitors. In the presence of Pibrentasvir, RNAs terminated with the active forms of the prodrugs Sofosbuvir, Remdesivir, Favipiravir, Molnupiravir and AT-527 were largely protected from excision by the exonuclease, while in the absence of Pibrentasvir, there was rapid excision. Due to its unique structure, Tenofovir-terminated RNA was highly resistant to exonuclease excision even in the absence of Pibrentasvir. Viral cell culture studies also demonstrate significant synergy using this combination strategy. This study supports the use of combination drugs that inhibit both the SARS-CoV-2 polymerase and exonuclease for effective COVID-19 treatment.

Unique mobile elements and scalable gene flow at the prokaryote-eukaryote boundary revealed by circularized Asgard archaea genomes

Eukaryotic genomes are known to have garnered innovations from both archaeal and bacterial domains but the sequence of events that led to the complex gene repertoire of eukaryotes is largely unresolved. Here, through the enrichment of hydrothermal vent microorganisms, we recovered two circularized genomes of Heimdallarchaeum species that belong to an Asgard archaea clade phylogenetically closest to eukaryotes. These genomes reveal diverse mobile elements, including an integrative viral genome that bidirectionally replicates in a circular form and aloposons, transposons that encode the 5,000 amino acid-sized proteins Otus and Ephialtes. Heimdallaechaeal mobile elements have garnered various genes from bacteria and bacteriophages, likely playing a role in shuffling functions across domains. The number of archaea- and bacteria-related genes follow strikingly different scaling laws in Asgard archaea, exhibiting a genome size-dependent ratio and a functional division resembling the bacteria- and archaea-derived gene repertoire across eukaryotes. Bacterial gene import has thus likely been a continuous process unaltered by eukaryogenesis and scaled up through genome expansion. Our data further highlight the importance of viewing eukaryogenesis in a pan-Asgard context, which led to the proposal of a conceptual framework, that is, the Heimdall nucleation-decentralized innovation-hierarchical import model that accounts for the emergence of eukaryotic complexity.

Feature-extraction and analysis based on spatial distribution of amino acids for SARS-CoV-2 Protein sequences

BACKGROUND AND OBJECTIVE

The world is currently facing a global emergency due to COVID-19, which requires immediate strategies to strengthen healthcare facilities and prevent further deaths. To achieve effective remedies and solutions, research on different aspects, including the genomic and proteomic level characterizations of SARS-CoV-2, are critical. In this work, the spatial representation/composition and distribution frequency of 20 amino acids across the primary protein sequences of SARS-CoV-2 were examined according to different parameters.

METHOD

To identify the spatial distribution of amino acids over the primary protein sequences of SARS-CoV-2, the Hurst exponent and Shannon entropy were applied as parameters to fetch the autocorrelation and amount of information over the spatial representations. The frequency distribution of each amino acid over the protein sequences was also evaluated. In the case of a one-dimensional sequence, the Hurst exponent (HE) was utilized due to its linear relationship with the fractal dimension (D), i.e. D+HE=2, to characterize fractality. Moreover, binary Shannon entropy was considered to measure the uncertainty in a binary sequence then further applied to calculate amino acid conservation in the primary protein sequences.

RESULTS AND CONCLUSION

Fourteen (14) SARS-CoV protein sequences were evaluated and compared with 105 SARS-CoV-2 proteins. The simulation results demonstrate the differences in the collected information about the amino acid spatial distribution in the SARS-CoV-2 and SARS-CoV proteins, enabling researchers to distinguish between the two types of CoV. The spatial arrangement of amino acids also reveals similarities and dissimilarities among the important structural proteins, E, M, N and S, which is pivotal to establish an evolutionary tree with other CoV strains.

The Neglected Gut Microbiome: Fungi, Protozoa, and Bacteriophages in Inflammatory Bowel Disease

The gut microbiome has been implicated in the pathogenesis of inflammatory bowel disease (IBD). Studies suggest that the IBD gut microbiome is less diverse than that of the unaffected population, a phenomenon often referred to as dysbiosis. However, these studies have heavily focused on bacteria, while other intestinal microorganisms-fungi, protozoa, and bacteriophages-have been neglected. Of the nonbacterial microbes that have been studied in relation to IBD, most are thought to be pathogens, although there is evidence that some of these species may instead be harmless commensals. In this review, we discuss the nonbacterial gut microbiome of IBD, highlighting the current biases, limitations, and outstanding questions that can be addressed with high-throughput DNA sequencing methods. Further, we highlight the importance of studying nonbacterial microorganisms alongside bacteria for a comprehensive view of the whole IBD biome and to provide a more precise definition of dysbiosis in patients. With the rise in popularity of microbiome-altering therapies for the treatment of IBD, such as fecal microbiota transplantation, it is important that we address these knowledge gaps to ensure safe and effective treatment of patients.

Bacteriophages Roam the Wheat Phyllosphere

The phyllosphere microbiome plays an important role in plant fitness. Recently, bacteriophages have been shown to play a role in shaping the bacterial community composition of the phyllosphere. However, no studies on the diversity and abundance of phyllosphere bacteriophage communities have been carried out until now. In this study, we extracted, sequenced, and characterized the dsDNA and ssDNA viral community from a phyllosphere for the first time. We sampled leaves from winter wheat (Triticum aestivum), where we identified a total of 876 virus operational taxonomic units (vOTUs), mostly predicted to be bacteriophages with a lytic lifestyle. Remarkably, 848 of these vOTUs corresponded to new viral species, and we estimated a minimum of 2.0 × 10⁶ viral particles per leaf. These results suggest that the wheat phyllosphere harbors a large and active community of novel bacterial viruses. Phylloviruses have potential applications as biocontrol agents against phytopathogenic bacteria or as microbiome modulators to increase plant growth-promoting bacteria.

Epistatic models predict mutable sites in SARS-CoV-2 proteins and epitopes

During the COVID pandemic, new severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants emerge and spread, some being of major concern due to their increased infectivity or capacity to reduce vaccine efficiency. Anticipating mutations, which might give rise to new variants, would be of great interest. We construct sequence models predicting how mutable SARS-CoV-2 positions are, using a single SARS-CoV-2 sequence and databases of other coronaviruses. Predictions are tested against available mutagenesis data and the observed variability of SARS-CoV-2 proteins. Interestingly, predictions agree increasingly with observations, as more SARS-CoV-2 sequences become available. Combining predictions with immunological data, we find an overrepresentation of mutations in current variants of concern. The approach may become relevant for potential outbreaks of future viral diseases.

The emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therapeutic interventions. Here, we predict the mutability of all positions in SARS-CoV-2 protein domains to forecast the appearance of unseen variants. Using sequence data from other coronaviruses, preexisting to SARS-CoV-2, we build statistical models that not only capture amino acid conservation but also more complex patterns resulting from epistasis. We show that these models are notably superior to conservation profiles in estimating the already observable SARS-CoV-2 variability. In the receptor binding domain of the spike protein, we observe that the predicted mutability correlates well with experimental measures of protein stability and that both are reliable mutability predictors (receiver operating characteristic areas under the curve ∼0.8). Most interestingly, we observe an increasing agreement between our model and the observed variability as more data become available over time, proving the anticipatory capacity of our model. When combined with data concerning the immune response, our approach identifies positions where current variants of concern are highly overrepresented. These results could assist studies on viral evolution and future viral outbreaks and, in particular, guide the exploration and anticipation of potentially harmful future SARS-CoV-2 variants.

Discovery, diversity, and functional associations of crAss-like phages in human gut metagenomes from four Dutch cohorts

The crAss-like phages are a diverse group of related viruses that includes some of the most abundant viruses of the human gut. To explore their diversity and functional role in human population and clinical cohorts, we analyze gut metagenomic data collected from 1,950 individuals from the Netherlands. We identify 1,556 crAss-like phage genomes, including 125 species-level and 32 genus-level clusters absent from the reference databases used. Analysis of their genomic features shows that closely related crAss-like phages can possess strikingly divergent regions responsible for transcription, presumably acquired through recombination. Prediction of crAss-like phage hosts points primarily to bacteria of the phylum Bacteroidetes, consistent with previous reports. Finally, we explore the temporal stability of crAss-like phages over a 4-year period and identify associations between the abundance of crAss-like phages and several human phenotypes, including depletion of crAss-like phages in inflammatory bowel disease patients.

Conserved Motifs and Domains in Members of Pospiviroidae

In 1985, Keese and Symons proposed a hypothesis on the sequence and secondary structure of viroids from the family Pospiviroidae: their secondary structure can be subdivided into five structural and functional domains and “viroids have evolved by rearrangement of domains between different viroids infecting the same cell and subsequent mutations within each domain”; this article is one of the most cited in the field of viroids. Employing the pairwise alignment method used by Keese and Symons and in addition to more recent methods, we tried to reproduce the original results and extent them to further members of Pospiviroidae which were unknown in 1985. Indeed, individual members of Pospiviroidae consist of a patchwork of sequence fragments from the family but the lengths of fragments do not point to consistent points of rearrangement, which is in conflict with the original hypothesis of fixed domain borders.

Characterization and in vitro testing of newly isolated lytic bacteriophages for the biocontrol of Pseudomonas aeruginosa

Aim: Two lytic phages were isolated using P. aeruginosa DSM19880 as host and fully characterized. Materials & methods: Phages were characterized physicochemically, biologically and genomically. Results & conclusion: Host range analysis revealed that the phages also infect some multidrug-resistant (MDR) P. aeruginosa clinical isolates. Increasing MOI from 1 to 1000 significantly increased phage efficiency and retarded bacteria regrowth, but phage ph0034 (reduction of 7.5 log CFU/ml) was more effective than phage ph0031 (reduction of 5.1 log CFU/ml) after 24 h. Both phages belong to Myoviridae family. Genome sequencing of phages ph0031 and ph0034 showed that they do not carry toxin, virulence, antibiotic resistance and integrase genes. The results obtained are highly relevant in the actual context of bacterial resistance to antibiotics.

Development of a Genomics-Based Approach To Identify Putative Hypervirulent Nontyphoidal Salmonella Isolates: Salmonella enterica Serovar Saintpaul as a Model

While differences in human virulence have been reported across nontyphoidal Salmonella (NTS) serovars and associated subtypes, a rational and scalable approach to identify Salmonella subtypes with differential ability to cause human diseases is not available. Here, we used NTS serovar Saintpaul (S. Saintpaul) as a model to determine if metadata and associated whole-genome sequence (WGS) data in the NCBI Pathogen Detection (PD) database can be used to identify (i) subtypes with differential likelihoods of causing human diseases and (ii) genes and single nucleotide polymorphisms (SNPs) potentially responsible for such differences. S. Saintpaul SNP clusters (n = 211) were assigned different epidemiology types (epi-types) based on statistically significant over- or underrepresentation of human clinical isolates, including human associated (HA; n = 29), non-human associated (NHA; n = 23), and other (n = 159). Comparative genomic analyses identified 384 and 619 genes overrepresented among isolates in 5 HA and 4 NHA SNP clusters most significantly associated with the respective isolation source. These genes included 5 HA-associated virulence genes previously reported to be present on Gifsy-1/Gifsy-2 prophages. Additionally, premature stop codons in 3 and 7 genes were overrepresented among the selected HA and NHA SNP clusters, respectively. Tissue culture experiments with strains representing 4 HA and 3 NHA SNP clusters did not reveal evidence for enhanced invasion or intracellular survival for HA strains. However, the presence of sodCI (encoding a superoxide dismutase), found in 4 HA and 1 NHA SNP clusters, was positively correlated with intracellular survival in macrophage-like cells. Post hoc analyses also suggested a possible difference in intracellular survival among S. Saintpaul lineages. IMPORTANCE Not all Salmonella isolates are equally likely to cause human disease, and Salmonella control strategies may unintentionally focus on serovars and subtypes with high prevalence in source populations but are rarely associated with human clinical illness. We describe a framework leveraging WGS data in the NCBI PD database to identify Salmonella subtypes over- and underrepresented among human clinical cases. While we identified genomic signatures associated with HA/NHA SNP clusters, tissue culture experiments failed to identify consistent phenotypic characteristics indicative of enhanced human virulence of HA strains. Our findings illustrate the challenges of defining hypo- and hypervirulent S. Saintpaul and potential limitations of phenotypic assays when evaluating human virulence, for which in vivo experiments are essential. Identification of sodCI, an HA-associated virulence gene associated with enhanced intracellular survival, however, illustrates the potential of the framework and is consistent with prior work identifying specific genomic features responsible for enhanced or reduced virulence of nontyphoidal Salmonella.

OUP accepted manuscript

Species A rotaviruses (RVAs) are important aetiological agents of severe diarrhoea in young children. They are also widely distributed in mammals and birds, and increasing evidence indicates the possibility of zoonotic transmission of RVA strains between animals and humans. Moreover, reassortment of the eleven segments of the RVA genome can result in rapid biological changes and may influence pathogenic properties. Here, the nearly complete genome of an RVA strain from a common shrew (Sorex araneus) was sequenced, which showed high nucleotide sequence similarity to additionally determined partial sequences from common shrew RVAs but only very low identity (below 68 per cent) to RVAs from other animal species and humans. New genotypes were assigned to most genome segments of the novel common shrew RVA strain KS14/269, resulting in the genome constellation G39-P[55]-I27-R26-C22-M22-A37-N26-T26-E30-H26. Phylogenetic analyses clustered the common shrew RVAs as ancestral branches of other mammalian and avian RVAs for most of the genome segments, which is in contrast to the phylogeny of the hosts. Nevertheless, conserved sequences typical for all RVAs were identified at the 5ʹ- and 3ʹ- non-coding segment termini. To explore whether the common shrew RVA can exchange genetic material with other mammalian RVAs by reassortment, a reverse genetics system based on the simian RVA strain SA11 was used. However, no viable reassortants could be rescued by exchanging the VP4-, VP6-, or VP7-encoding genome segment alone or in combinations. It can be concluded that highly divergent RVAs are present in common shrews, indicating an evolution of these viruses largely separated from other mammalian and avian RVAs. The zoonotic potential of the virus seems to be low but needs to be further analysed in future.

FnCas9 Editor Linked Uniform Detection Assay for COVID-19

The recent COVID-19 outbreak and pandemic of 2020 and its surveillance were implemented by quickly adapting the existing diagnostic methods to detect the SARS-CoV-2 RNA. While traditional methods for detecting pathogenic DNA and RNA have relied heavily on gold standard quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and sequencing-based methods, their shortcomings under resource-limited settings have emphasized the need of developing point-of-care (POC) diagnostics. Clustered regularly interspaced short palindromic repeats (CRISPR)-based detection systems provide a rapid and accurate alternative. Here, we describe a CRISPR-Cas9-based detection system FnCas9 Editor Linked Uniform Detection Assay (FELUDA) using a lateral flow test that can detect nucleobase and nucleotide sequences depending upon the stoichiometric-based binding of FnCas9 ribonucleoprotein complex (RNP)-target sequences. The assay has been optimized to be conducted within 1 h and shows 100% sensitivity and 97% specificity in clinical samples across a range of viral loads. The lateral strip results are read using the True Outcome Predicted via Strip Evaluation (TOPSE) smartphone application. This assay is versatile and can be optimized and adjusted to target various diseases.

Deconvoluting virome-wide antibody epitope reactivity profiles

BACKGROUND

Comprehensive characterization of exposures and immune responses to viral infections is critical to a basic understanding of human health and disease. We previously developed the VirScan system, a programmable phage-display technology for profiling antibody binding to a library of peptides designed to span the human virome. Previous VirScan analytical approaches did not carefully account for antibody cross-reactivity among sequences shared by related viruses or for the disproportionate representation of individual viruses in the library.

METHODS

Here we present the AntiViral Antibody Response Deconvolution Algorithm (AVARDA), a multi-module software package for analyzing VirScan datasets. AVARDA provides a probabilistic assessment of infection with species-level resolution by considering sequence alignment of all library peptides to each other and to all human viruses. We employed AVARDA to analyze VirScan data from a cohort of encephalitis patients with either known viral infections or undiagnosed etiologies. We further assessed AVARDA's utility in associating viral infection with type 1 diabetes and lupus.

FINDINGS

By comparing acute and convalescent sera, AVARDA successfully confirmed or detected encephalitis-associated responses to human herpesviruses 1, 3, 4, 5, and 6, improving the rate of diagnosing viral encephalitis in this cohort by 44%. AVARDA analyses of VirScan data from the type 1 diabetes and lupus cohorts implicated enterovirus and herpesvirus infections, respectively.

INTERPRETATION

AVARDA, in combination with VirScan and other pan-pathogen serological techniques, is likely to find broad utility in the epidemiology and diagnosis of infectious diseases.

FUNDING

This work was made possible by support from the National Institutes of Health (NIH), the US Army Research Office, the Singapore Infectious Diseases Initiative (SIDI), the Singapore Ministry of Health's National Medical Research Council (NMRC) and the Singapore National Research Foundation (NRF).

VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares

BACKGROUND

A new coronavirus disease named COVID-19, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide. However, there is currently no effective drug to fight COVID-19.

METHODS

In this study, we developed a Virus-Drug Association (VDA) identification framework (VDA-RWLRLS) combining unbalanced bi-Random Walk, Laplacian Regularized Least Squares, molecular docking, and molecular dynamics simulation to find clues for the treatment of COVID-19. First, virus similarity and drug similarity are computed based on genomic sequences, chemical structures, and Gaussian association profiles. Second, an unbalanced bi-random walk is implemented on the virus network and the drug network, respectively. Third, the results of the random walks are taken as the input of Laplacian regularized least squares to compute the association score for each virus-drug pair. Fourth, the final associations are characterized by integrating the predictions from the virus network and the drug network. Finally, molecular docking and molecular dynamics simulation are implemented to measure the potential of screened anti-COVID-19 drugs and further validate the predicted results.

RESULTS

In comparison with six state-of-the-art association prediction models (NGRHMDA, SMiR-NBI, LRLSHMDA, VDA-KATZ, VDA-RWR, and VDA-BiRW), VDA-RWLRLS demonstrates superior VDA prediction performance. It obtains the best AUCs of 0.885 8, 0.835 5, and 0.862 5 on the three VDA datasets. Molecular docking and dynamics simulations demonstrated that remdesivir and ribavirin may be potential anti-COVID-19 drugs.

CONCLUSIONS

Integrating unbalanced bi-random walks, Laplacian regularized least squares, molecular docking, and molecular dynamics simulation, this work initially screened a few anti-SARS-CoV-2 drugs and may contribute to preventing COVID-19 transmission.

The United States Swine Pathogen Database: integrating veterinary diagnostic laboratory sequence data to monitor emerging pathogens of swine

Veterinary diagnostic laboratories derive thousands of nucleotide sequences from clinical samples of swine pathogens such as porcine reproductive and respiratory syndrome virus (PRRSV), Senecavirus A and swine enteric coronaviruses. In addition, next generation sequencing has resulted in the rapid production of full-length genomes. Presently, sequence data are released to diagnostic clients but are not publicly available as data may be associated with sensitive information. However, these data can be used for field-relevant vaccines; determining where and when pathogens are spreading; have relevance to research in molecular and comparative virology; and are a component in pandemic preparedness efforts. We have developed a centralized sequence database that integrates private clinical data using PRRSV data as an exemplar, alongside publicly available genomic information. We implemented the Tripal toolkit, a collection of Drupal modules that are used to manage, visualize and disseminate biological data stored within the Chado database schema. New sequences sourced from diagnostic laboratories contain: genomic information; date of collection; collection location; and a unique identifier. Users can download annotated genomic sequences using a customized search interface that incorporates data mined from published literature; search for similar sequences using BLAST-based tools; and explore annotated reference genomes. Additionally, custom annotation pipelines have determined species, the location of open reading frames and nonstructural proteins and the occurrence of putative frame shifts. Eighteen swine pathogens have been curated. The database provides researchers access to sequences discovered by veterinary diagnosticians, allowing for epidemiological and comparative virology studies. The result will be a better understanding on the emergence of novel swine viruses and how these novel strains are disseminated in the USA and abroad. Database URLhttps://swinepathogendb.org.

Seq12, Seq12m, and Seq13m, peptide analogues of the spike glycoprotein shows antiviral properties against SARS-CoV-2: An in silico study through molecular docking, molecular dynamics simulation, and MM-PB/GBSA calculations

At the very beginning of the new decade, the COVID-19 pandemic has badly hit modern human societies. SARS-CoV-2, the causative agent of COVID-19 acquiring mutations and circulating as new variants. Herein, we have found three new antiviral peptides (AVPs) against the SARS-CoV-2. These AVPs are analogous to the spike glycoprotein of the SARS-CoV-2. Antiviral peptides, i.e., Seq12, Seq12m, and Seq13m, can block the receptor-binding domain (RBD) of the SARS-CoV-2, which is necessary for communicating with the angiotensin-converting enzyme 2 (ACE2). Also, these AVPs sustain their antiviral properties, even after the insertion of 25 mutations in the RBD (Rosetta and FoldX based). Further, Seq12 and Seq12m showed negligible cytotoxicity. Besides, the binding free energies calculated using MM-PB/GBSA method are also in agreement with the molecular docking studies. The molecular interactions between AVPs and the viral membrane protein (M) also showed a favorable interaction suggesting it could inhibit the viral re-packaging process. In conclusion, this study suggests Seq12, Seq12m, and Seq13m could be helpful to fight against SARS-CoV-2. These AVPs could also aid virus diagnostic tools and nasal spray against SARS-CoV-2 in the future.

Bioinformatics of virus taxonomy: foundations and tools for developing sequence-based hierarchical classification

The genome sequence is the only characteristic readily obtainable for all known viruses, underlying the growing role of comparative genomics in organizing knowledge about viruses in a systematic evolution-aware way, known as virus taxonomy. Overseen by the International Committee on Taxonomy of Viruses (ICTV), development of virus taxonomy involves taxa demarcation at 15 ranks of a hierarchical classification, often in host-specific manner. Outside the ICTV remit, researchers assess fitting numerous unclassified viruses into the established taxa. They employ different metrics of virus clustering, basing on conserved domain(s), separation of viruses in rooted phylogenetic trees and pair-wise distance space. Computational approaches differ further in respect to methodology, number of ranks considered, sensitivity to uneven virus sampling, and visualization of results. Advancing and using computational tools will be critical for improving taxa demarcation across the virosphere and resolving rank origins in research that may also inform experimental virology.

A vertically transmitted amalgavirus is present in certain accessions of the bryophyte Physcomitrium patens

In the last few years, next-generation sequencing techniques have started to be used to identify new viruses infecting plants. This has allowed to rapidly increase our knowledge on viruses other than those causing symptoms in economically important crops. Here we used this approach to identify a virus infecting Physcomitrium patens that has the typical structure of the double-stranded RNA endogenous viruses of the Amalgaviridae family, which we named Physcomitrium patens amalgavirus 1, or PHPAV1. PHPAV1 is present only in certain accessions of P. patens, where its RNA can be detected throughout the cell cycle of the plant. Our analysis demonstrates that PHPAV1 can be vertically transmitted through both paternal and maternal germlines, in crosses between accessions that contain the virus with accessions that do not contain it. This work suggests that PHPAV1 can replicate in genomic backgrounds different from those that actually contain the virus and opens the door for future studies on virus-host coevolution.

GenBank

GenBank^® (https://www.ncbi.nlm.nih.gov/genbank/) is a comprehensive, public database that contains 15.3 trillion base pairs from over 2.5 billion nucleotide sequences for 504 000 formally described species. Recent updates include resources for data from the SARS-CoV-2 virus, including a SARS-CoV-2 landing page, NCBI Datasets, NCBI Virus and the Submission Portal. We also discuss upcoming changes to GI identifiers, a new data management interface for BioProject, and advice for providing contextual metadata in submissions.

Autism-related dietary preferences mediate autism-gut microbiome associations

There is increasing interest in the potential contribution of the gut microbiome to autism spectrum disorder (ASD). However, previous studies have been underpowered and have not been designed to address potential confounding factors in a comprehensive way. We performed a large autism stool metagenomics study (n = 247) based on participants from the Australian Autism Biobank and the Queensland Twin Adolescent Brain project. We found negligible direct associations between ASD diagnosis and the gut microbiome. Instead, our data support a model whereby ASD-related restricted interests are associated with less-diverse diet, and in turn reduced microbial taxonomic diversity and looser stool consistency. In contrast to ASD diagnosis, our dataset was well powered to detect microbiome associations with traits such as age, dietary intake, and stool consistency. Overall, microbiome differences in ASD may reflect dietary preferences that relate to diagnostic features, and we caution against claims that the microbiome has a driving role in ASD.

Larger, more connected societies of ants have a higher prevalence of viruses

The benefits of cooperative living for foraging, nesting, defence and buffering environmental challenges lead animals with the most highly social lifestyles to dominate many ecosystems. However, living in larger, more highly connected groups should also increase the risks of pathogen exposure and transmission. While over long timescales selective responses could buffer the impacts of potential higher pathogen prevalence, similar processes are unlikely over short timescales. The red fire ant Solenopsis invicta is ideal for measuring the effects of group size on pathogen prevalence because two types of society coexist in this species: smaller single-nest single-queen colonies that are highly aggressive to their neighbours and larger multiple-queen colonies that exchange resources with neighbouring nests. We compare the presence of viruses between these two colony types using metagenomic sequence classification of RNA-sequencing reads. We find that queens from multiple-queen colonies have 8.3-times higher viral load and 1.5-times higher viral diversity than queens from single-queen colonies. This finding characterizes a rarely considered cost of transitions to more highly social living. Furthermore, our results show that highly social invertebrates can harbour many viruses.

Computational methods for inferring location and genealogy of overlapping genes in virus genomes: approaches and applications

Viruses may evolve to increase the amount of encoded genetic information by means of overlapping genes, which utilize several reading frames. Such overlapping genes may be especially impactful for genomes of small size, often serving a source of novel accessory proteins, some of which play a crucial role in viral pathogenicity or in promoting the systemic spread of virus. Diverse genome-based metrics were proposed to facilitate recognition of overlapping genes that otherwise may be overlooked during genome annotation. They can detect the atypical codon bias associated with the overlap (e.g. a statistically significant reduction in variability at synonymous sites) or other sequence-composition features peculiar to overlapping genes. In this review, I compare nine computational methods, discuss their strengths and limitations, and survey how they were applied to detect candidate overlapping genes in the genome of SARS-CoV-2, the etiological agent of COVID-19 pandemic.

Down in the pond: Isolation and characterization of a new Serratia marcescens strain (LVF3) from the surface water near frog's lettuce (Groenlandia densa)

Serratia marcescens is a species that belongs to the family of Yersiniaceae. This family comprises taxa representing opportunistic human- and phytopathogens but also plant growth-promoting rhizobacteria (PGPR). This study describes a novel Gram-negative strain (LVF3R) of the species Serratia marcescens. The strain was characterized genomically, morphologically, and physiologically. In addition, the potential of the isolate to act as a host strain to assess the diversity of Serratia associated phages in environmental samples was explored. Average nucleotide identity analysis revealed that LVF3R belongs to the species Serratia marcescens. In silico analysis and ProphageSeq data resulted in the identification of one prophage, which is capable of viral particle formation. Electron microscopy showed cells of a rod-shaped, flagellated morphotype. The cells revealed a length and width of 1-1.6 μm and 0.8 μm, respectively. LVF3R showed optimal growth at 30 C and in the presence of up to 2% (w/v) NaCl. It exhibited resistances to ampicillin, erythromycin, oxacillin, oxytetracycline, rifampicin, tetracycline, and vancomycin. Genome data indicate that strain S. marcescens LVF3R is a potential PGPR strain. It harbors genes coding for indole acetic acid (IAA) biosynthesis, siderophore production, plant polymer degradation enzymes, acetoin synthesis, flagellar proteins, type IV secretion system, chemotaxis, phosphorous solubilization, and biofilm formation.

Transformation of Seed Non-Transmissible Hop Viroids in Nicotiana benthamiana Causes Distortions in Male Gametophyte Development

Viroids are small, non-coding, parasitic RNAs that promote developmental distortions in sensitive plants. We analyzed pollen of Nicotiana benthamiana after infection and/or ectopic transformation with cDNAs of citrus bark cracking viroid (CBCVd), apple fruit crinkle viroid (AFCVd) and potato spindle tuber viroid (PSTVd) variant AS1. These viroids were seed non-transmissible in N. benthamiana. All viroids propagated to high levels in immature anthers similar to leaves, while their levels were drastically reduced by approximately 3.6 × 10³, 800 and 59 times in mature pollen of CBCVd, AFCVd and PSTVd infected N. benthamiana, respectively, in comparison to leaves. These results suggest similar elimination processes during male gametophyte development as in the Nicotiana tabacum we presented in our previous study. Mature pollen of N. benthamiana showed no apparent defects in infected plants although all three viroids induced strong pathological symptoms on leaves. While Nicotiana species have naturally bicellular mature pollen, we noted a rare occurrence of mature pollen with three nuclei in CBCVd-infected N. benthamiana. Changes in the expression of ribosomal marker proteins in AFCVd-infected pollen were detected, suggesting some changes in pollen metabolism. N. benthamiana transformed with 35S-driven viroid cDNAs showed strong symptoms including defects in pollen development. A large number of aborted pollen (34% and 62%) and a slight increase of young pollen grains (8% and 15%) were found in mature pollen of AFCVd and CBCVd transformants, respectively, in comparison to control plants (3.9% aborted pollen and 0.3% young pollen). Moreover, pollen grains with malformed nuclei or trinuclear pollen were found in CBCVd-transformed plants. Our results suggest that "forcing" overexpression of seed non-transmissible viroid led to strong pollen pathogenesis. Viroid adaptation to pollen metabolism can be assumed as an important factor for viroid transmissibility through pollen and seeds.

DNA Viral Diversity, Abundance, and Functional Potential Vary across Grassland Soils with a Range of Historical Moisture Regimes

Soil viruses are abundant, but the influence of the environment and climate on soil viruses remains poorly understood. Here, we addressed this gap by comparing the diversity, abundance, lifestyle, and metabolic potential of DNA viruses in three grassland soils with historical differences in average annual precipitation, low in eastern Washington (WA), high in Iowa (IA), and intermediate in Kansas (KS). Bioinformatics analyses were applied to identify a total of 2,631 viral contigs, including 14 complete viral genomes from three deep metagenomes (1 terabase [Tb] each) that were sequenced from bulk soil DNA. An additional three replicate metagenomes (∼0.5 Tb each) were obtained from each location for statistical comparisons. Identified viruses were primarily bacteriophages targeting dominant bacterial taxa. Both viral and host diversity were higher in soil with lower precipitation. Viral abundance was also significantly higher in the arid WA location than in IA and KS. More lysogenic markers and fewer clustered regularly interspaced short palindromic repeats (CRISPR) spacer hits were found in WA, reflecting more lysogeny in historically drier soil. More putative auxiliary metabolic genes (AMGs) were also detected in WA than in the historically wetter locations. The AMGs occurring in 18 pathways could potentially contribute to carbon metabolism and energy acquisition in their hosts. Structural equation modeling (SEM) suggested that historical precipitation influenced viral life cycle and selection of AMGs. The observed and predicted relationships between soil viruses and various biotic and abiotic variables have value for predicting viral responses to environmental change.

Correcting the Estimation of Viral Taxa Distributions in Next-Generation Sequencing Data after Applying Artificial Neural Networks

Estimating the taxonomic composition of viral sequences in a biological samples processed by next-generation sequencing is an important step in comparative metagenomics. Mapping sequencing reads against a database of known viral reference genomes, however, fails to classify reads from novel viruses whose reference sequences are not yet available in public databases. Instead of a mapping approach, and in order to classify sequencing reads at least to a taxonomic level, the performance of artificial neural networks and other machine learning models was studied. Taxonomic and genomic data from the NCBI database were used to sample labelled sequencing reads as training data. The fitted neural network was applied to classify unlabelled reads of simulated and real-world test sets. Additional auxiliary test sets of labelled reads were used to estimate the conditional class probabilities, and to correct the prior estimation of the taxonomic distribution in the actual test set. Among the taxonomic levels, the biological order of viruses provided the most comprehensive data base to generate training data. The prediction accuracy of the artificial neural network to classify test reads to their viral order was considerably higher than that of a random classification. Posterior estimation of taxa frequencies could correct the primary classification results.

OncoDB: an interactive online database for analysis of gene expression and viral infection in cancer

Large-scale multi-omics datasets, most prominently from the TCGA consortium, have been made available to the public for systematic characterization of human cancers. However, to date, there is a lack of corresponding online resources to utilize these valuable data to study gene expression dysregulation and viral infection, two major causes for cancer development and progression. To address these unmet needs, we established OncoDB, an online database resource to explore abnormal patterns in gene expression as well as viral infection that are correlated to clinical features in cancer. Specifically, OncoDB integrated RNA-seq, DNA methylation, and related clinical data from over 10 000 cancer patients in the TCGA study as well as from normal tissues in the GTEx study. Another unique aspect of OncoDB is its focus on oncoviruses. By mining TCGA RNA-seq data, we have identified six major oncoviruses across cancer types and further correlated viral infection to changes in host gene expression and clinical outcomes. All the analysis results are integratively presented in OncoDB with a flexible web interface to search for data related to RNA expression, DNA methylation, viral infection, and clinical features of the cancer patients. OncoDB is freely accessible at http://oncodb.org.

Metatranscriptomics to characterize respiratory virome, microbiome, and host response directly from clinical samples

We developed a metatranscriptomics method that can simultaneously capture the respiratory virome, microbiome, and host response directly from low biomass samples. Using nasal swab samples, we capture RNA virome with sufficient sequencing depth required to assemble complete genomes. We find a surprisingly high frequency of respiratory syncytial virus (RSV) and coronavirus (CoV) in healthy children, and a high frequency of RSV-A and RSV-B co-detections in children with symptomatic RSV. In addition, we have identified commensal and pathogenic bacteria and fungi at the species level. Functional analysis revealed that H. influenzae was highly active in symptomatic RSV subjects. The host nasal transcriptome reveled upregulation of the innate immune system, anti-viral response and inflammasome pathway, and downregulation of fatty acid pathways in children with symptomatic RSV. Overall, we demonstrate that our method is broadly applicable to infer the transcriptome landscape of an infected system, surveil respiratory infections, and to sequence RNA viruses directly from clinical samples.

Virtual screening and in vitro validation identifies the first reported inhibitors of Salmonella enterica HPPK

HPPK, which directly precedes DHPS in the folate biosynthetic pathway, is a promising but chronically under-exploited anti-microbial target. Here we report the identification of new S. enterica HPPK inhibitors, offering potential for new resistance circumventing S. enterica therapies as well as avenues for diversifying the current HPPK inhibitor space.

Development of Novel PCR Assays for Improved Detection of Enterovirus D68

Enterovirus D68 (EV-D68) causes a range of clinical manifestations, including asthma-like illness, severe respiratory disease, and acute flaccid myelitis. EV-D68 has caused worldwide outbreaks since 2014 and is now recognized as a reemerging infection in many countries. EV-D68-specific PCR assays are widely used for the diagnosis of EV-D68 infection; however, assay sensitivity is a concern because of genetic changes in recently circulated EV-D68. To address this, we summarized EV-D68 sequences from previously reported world outbreaks from 2014 through 2020 on GenBank, and found several mutations at the primer and probe binding sites of the existing EV-D68-specific PCR assays. Subsequently, we designed two novel assays corresponding to the recently reported EV-D68 sequences: an EV-D68-specific real-time and seminested PCR. In an analysis of 22 EV-D68 confirmed cases during a recent EV-D68 outbreak in Japan, the new real-time PCR had higher sensitivity than the existing assay (100% versus 45%, P < 0.01) and a lower median C_T value (27.8 versus 32.8, P = 0.005). Sensitivity was higher for the new nonnested PCR (91%) than for the existing seminested PCR assay (50%, P < 0.01). The specificity of the new real-time PCR was 100% using samples from non-EV-D68-infected cases (n = 135). In conclusion, our novel assays had higher sensitivity than the existing assay and might lead to more accurate diagnosis of recently circulating EV-D68. To prepare for future EV-D68 outbreaks, EV-D68-specific assays must be continuously monitored and updated.

Diversity and distribution of viruses inhabiting the deepest ocean on Earth

As the most abundant biological entities on the planet, viruses significantly influence the overall functioning of marine ecosystems. The abundance, distribution, and biodiversity of viral communities in the upper ocean have been relatively well studied, but our understanding of viruses in the hadal biosphere remains poor. Here, we established the oceanic trench viral genome dataset (OTVGD) by analysing 19 microbial metagenomes derived from seawater and sediment samples of the Mariana, Yap, and Kermadec Trenches. The trench viral communities harbored remarkably high novelty, and they were predicted to infect ecologically important microbial clades, including Thaumarchaeota and Oleibacter. Significant inter-trench and intra-trench exchange of viral communities was proposed. Moreover, viral communities in different habitats (seawater/sediment and depth-stratified ocean zones) exhibited distinct niche-dependent distribution patterns and genomic properties. Notably, microbes and viruses in the hadopelagic seawater seemed to preferably adopt lysogenic lifestyles compared to those in the upper ocean. Furthermore, niche-specific auxiliary metabolic genes were identified in the hadal viral genomes, and a novel viral D-amino acid oxidase was functionally and phylogenetically characterized, suggesting the contribution of these genes in the utilization of refractory organic matter. Together, these findings highlight the genomic novelty, dynamic movement, and environment-driven diversification of viral communities in oceanic trenches, and suggest that viruses may influence the hadal ecosystem by reprogramming the metabolism of their hosts and modulating the community of keystone microbes.

Sequence Compression Benchmark (SCB) database-A comprehensive evaluation of reference-free compressors for FASTA-formatted sequences

Background

Nearly all molecular sequence databases currently use gzip for data compression. Ongoing rapid accumulation of stored data calls for a more efficient compression tool. Although numerous compressors exist, both specialized and general-purpose, choosing one of them was difficult because no comprehensive analysis of their comparative advantages for sequence compression was available.

Findings

We systematically benchmarked 430 settings of 48 compressors (including 29 specialized sequence compressors and 19 general-purpose compressors) on representative FASTA-formatted datasets of DNA, RNA, and protein sequences. Each compressor was evaluated on 17 performance measures, including compression strength, as well as time and memory required for compression and decompression. We used 27 test datasets including individual genomes of various sizes, DNA and RNA datasets, and standard protein datasets. We summarized the results as the Sequence Compression Benchmark database (SCB database, http://kirr.dyndns.org/sequence-compression-benchmark/), which allows custom visualizations to be built for selected subsets of benchmark results.

Conclusion

We found that modern compressors offer a large improvement in compactness and speed compared to gzip. Our benchmark allows compressors and their settings to be compared using a variety of performance measures, offering the opportunity to select the optimal compressor on the basis of the data type and usage scenario specific to a particular application.

Nanotechnology based solutions to combat zoonotic viruses with special attention to SARS, MERS, and COVID 19: Detection, protection and medication

Zoonotic viruses originate from birds or animal sources and responsible for disease transmission from animals to people through zoonotic spill over and presents a significant global health concern due to lack of rapid diagnostics and therapeutics. The Corona viruses (CoV) were known to be transmitted in mammals. Early this year, SARS-CoV-2, a novel strain of corona virus, was identified as the causative pathogen of an outbreak of viral pneumonia in Wuhan, China. The disease later named corona virus disease 2019 (COVID-19), subsequently spread across the globe rapidly. Nano-particles and viruses are comparable in size, which serves to be a major advantage of using nano-material in clinical strategy to combat viruses. Nanotechnology provides novel solutions against zoonotic viruses by providing cheap and efficient detection methods, novel, and new effective rapid diagnostics and therapeutics. The prospective of nanotechnology in COVID 19 is exceptionally high due to their small size, large surface-to-volume ratio, susceptibility to modification, intrinsic viricidal activity. The nano-based strategies address the COVID 19 by extending their role in i) designing nano-materials for drug/vaccine delivery, ii) developing nano-based diagnostic approaches like nano-sensors iii) novel nano-based personal protection equipment to be used in prevention strategies.This review aims to bring attention to the significant contribution of nanotechnology to mitigate against zoonotic viral pandemics by prevention, faster diagnosis and medication point of view.

VGEA: an RNA viral assembly toolkit

Next generation sequencing (NGS)-based studies have vastly increased our understanding of viral diversity. Viral sequence data obtained from NGS experiments are a rich source of information, these data can be used to study their epidemiology, evolution, transmission patterns, and can also inform drug and vaccine design. Viral genomes, however, represent a great challenge to bioinformatics due to their high mutation rate and forming quasispecies in the same infected host, bringing about the need to implement advanced bioinformatics tools to assemble consensus genomes well-representative of the viral population circulating in individual patients. Many tools have been developed to preprocess sequencing reads, carry-out de novo or reference-assisted assembly of viral genomes and assess the quality of the genomes obtained. Most of these tools however exist as standalone workflows and usually require huge computational resources. Here we present (Viral Genomes Easily Analyzed), a Snakemake workflow for analyzing RNA viral genomes. VGEA enables users to map sequencing reads to the human genome to remove human contaminants, split bam files into forward and reverse reads, carry out de novo assembly of forward and reverse reads to generate contigs, pre-process reads for quality and contamination, map reads to a reference tailored to the sample using corrected contigs supplemented by the user's choice of reference sequences and evaluate/compare genome assemblies. We designed a project with the aim of creating a flexible, easy-to-use and all-in-one pipeline from existing/stand-alone bioinformatics tools for viral genome analysis that can be deployed on a personal computer. VGEA was built on the Snakemake workflow management system and utilizes existing tools for each step: fastp (Chen et al., 2018) for read trimming and read-level quality control, BWA (Li & Durbin, 2009) for mapping sequencing reads to the human reference genome, SAMtools (Li et al., 2009) for extracting unmapped reads and also for splitting bam files into fastq files, IVA (Hunt et al., 2015) for de novo assembly to generate contigs, shiver (Wymant et al., 2018) to pre-process reads for quality and contamination, then map to a reference tailored to the sample using corrected contigs supplemented with the user's choice of existing reference sequences, SeqKit (Shen et al., 2016) for cleaning shiver assembly for QUAST, QUAST (Gurevich et al., 2013) to evaluate/assess the quality of genome assemblies and MultiQC (Ewels et al., 2016) for aggregation of the results from fastp, BWA and QUAST. Our pipeline was successfully tested and validated with SARS-CoV-2 (n = 20), HIV-1 (n = 20) and Lassa Virus (n = 20) datasets all of which have been made publicly available. VGEA is freely available on GitHub at: https://github.com/pauloluniyi/VGEA under the GNU General Public License.

STAT: a fast, scalable, MinHash-based k-mer tool to assess Sequence Read Archive next-generation sequence submissions

Sequence Read Archive submissions to the National Center for Biotechnology Information often lack useful metadata, which limits the utility of these submissions. We describe the Sequence Taxonomic Analysis Tool (STAT), a scalable k-mer-based tool for fast assessment of taxonomic diversity intrinsic to submissions, independent of metadata. We show that our MinHash-based k-mer tool is accurate and scalable, offering reliable criteria for efficient selection of data for further analysis by the scientific community, at once validating submissions while also augmenting sample metadata with reliable, searchable, taxonomic terms.

High-throughput identification of viral termini and packaging mechanisms in virome datasets using PhageTermVirome

Viruses that infect bacteria (phages) are increasingly recognized for their importance in diverse ecosystems but identifying and annotating them in large-scale sequence datasets is still challenging. Although efficient scalable virus identification tools are emerging, defining the exact ends (termini) of phage genomes is still particularly difficult. The proper identification of termini is crucial, as it helps in characterizing the packaging mechanism of bacteriophages and provides information on various aspects of phage biology. Here, we introduce PhageTermVirome (PTV) as a tool for the easy and rapid high-throughput determination of phage termini and packaging mechanisms using modern large-scale metagenomics datasets. We successfully tested the PTV algorithm on a mock virome dataset and then used it on two real virome datasets to achieve the rapid identification of more than 100 phage termini and packaging mechanisms, with just a few hours of computing time. Because PTV allows the identification of free fully formed viral particles (by recognition of termini present only in encapsidated DNA), it can also complement other virus identification softwares to predict the true viral origin of contigs in viral metagenomics datasets. PTV is a novel and unique tool for high-throughput characterization of phage genomes, including phage termini identification and characterization of genome packaging mechanisms. This software should help researchers better visualize, map and study the virosphere. PTV is freely available for downloading and installation at https://gitlab.pasteur.fr/vlegrand/ptv.

Whole-genome-based phylogenomic analysis of the Belgian 2016-2017 influenza A(H3N2) outbreak season allows improved surveillance

Seasonal influenza epidemics are associated with high mortality and morbidity in the human population. Influenza surveillance is critical for providing information to national influenza programmes and for making vaccine composition predictions. Vaccination prevents viral infections, but rapid influenza evolution results in emerging mutants that differ antigenically from vaccine strains. Current influenza surveillance relies on Sanger sequencing of the haemagglutinin (HA) gene. Its classification according to World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC) guidelines is based on combining certain genotypic amino acid mutations and phylogenetic analysis. Next-generation sequencing technologies enable a shift to whole-genome sequencing (WGS) for influenza surveillance, but this requires laboratory workflow adaptations and advanced bioinformatics workflows. In this study, 253 influenza A(H3N2) positive clinical specimens from the 2016–2017 Belgian season underwent WGS using the Illumina MiSeq system. HA-based classification according to WHO/ECDC guidelines did not allow classification of all samples. A new approach, considering the whole genome, was investigated based on using powerful phylogenomic tools including beast and Nextstrain, which substantially improved phylogenetic classification. Moreover, Bayesian inference via beast facilitated reassortment detection by both manual inspection and computational methods, detecting intra-subtype reassortants at an estimated rate of 15 %. Real-time analysis (i.e. as an outbreak is ongoing) via Nextstrain allowed positioning of the Belgian isolates into the globally circulating context. Finally, integration of patient data with phylogenetic groups and reassortment status allowed detection of several associations that would have been missed when solely considering HA, such as hospitalized patients being more likely to be infected with A(H3N2) reassortants, and the possibility to link several phylogenetic groups to disease severity indicators could be relevant for epidemiological monitoring. Our study demonstrates that WGS offers multiple advantages for influenza monitoring in (inter)national influenza surveillance, and proposes an improved methodology. This allows leveraging all information contained in influenza genomes, and allows for more accurate genetic characterization and reassortment detection.

Predicting hosts based on early SARS-CoV-2 samples and analyzing the 2020 pandemic

The SARS-CoV-2 pandemic has raised concerns in the identification of the hosts of the virus since the early stages of the outbreak. To address this problem, we proposed a deep learning method, DeepHoF, based on extracting viral genomic features automatically, to predict the host likelihood scores on five host types, including plant, germ, invertebrate, non-human vertebrate and human, for novel viruses. DeepHoF made up for the lack of an accurate tool, reaching a satisfactory AUC of 0.975 in the five-classification, and could make a reliable prediction for the novel viruses without close neighbors in phylogeny. Additionally, to fill the gap in the efficient inference of host species for SARS-CoV-2 using existing tools, we conducted a deep analysis on the host likelihood profile calculated by DeepHoF. Using the isolates sequenced in the earliest stage of the COVID-19 pandemic, we inferred that minks, bats, dogs and cats were potential hosts of SARS-CoV-2, while minks might be one of the most noteworthy hosts. Several genes of SARS-CoV-2 demonstrated their significance in determining the host range. Furthermore, a large-scale genome analysis, based on DeepHoF's computation for the later pandemic in 2020, disclosed the uniformity of host range among SARS-CoV-2 samples and the strong association of SARS-CoV-2 between humans and minks.