Unraveling the web of viroinformatics: computational tools and databases in virus research. J Virol. 2015;89:1489-1501. [PMID: 25428870 DOI: 10.1128/jvi.02027-14]

Technological advancements in viral vector designing and optimization for therapeutic applications

Viral vector engineering is critical to the advancement of several sectors of biotechnology, gene therapy, and vaccine development. These vectors were produced from viruses, were employed to deliver therapeutic genes or to alter biological processes. The potential for viral vectors to improve the precision, safety, and efficiency of therapeutic interventions has boosted their demand. The dynamic interplay between technological advancements and computational tools in establishing the landscape of viral vector engineering and vector optimization for therapeutic reasons is discussed in this chapter. It also emphasizes the importance of in silico techniques in maximizing vector potential for therapeutics and many phases of viral vector engineering, from genomic analysis to computer modelling and advancements to improve precise gene delivery. High-throughput screening propels the expedited process of vector selection, and computational techniques to analyze complex omics data to further enhance vector capabilities have been discussed. As in silico models reveal insights into off-target effects and integration sites, vector safety (biodistribution and toxicity) remains a crucial part and bridges the gap between preclinical and clinical investigations. Despite the limitations, this chapter depicts a future in which technology and computing merge to catapult viral vector therapy into an era of boundless possibilities.

Computational resources and chemoinformatics for translational health research

The integration of computational resources and chemoinformatics has revolutionized translational health research. It has offered a powerful set of tools for accelerating drug discovery. This chapter overviews the computational resources and chemoinformatics methods used in translational health research. The resources and methods can be used to analyze large datasets, identify potential drug candidates, predict drug-target interactions, and optimize treatment regimens. These resources have the potential to transform the drug discovery process and foster personalized medicine research. We discuss insights into their various applications in translational health and emphasize the need for addressing challenges, promoting collaboration, and advancing the field to fully realize the potential of these tools in transforming healthcare.

Bioinformatic Approaches for Comparative Analysis of Viruses

The field of viral genomic studies has experienced an unprecedented increase in data volume. New strains of known viruses are constantly being added to the GenBank database and so are completely new species with little or no resemblance to our databases of sequences. In addition to this, metagenomic techniques have the potential to further increase the number and rate of sequenced genomes. Besides, it is important to consider that viruses have a set of unique features that often break down molecular biology dogmas, e.g., the flux of information from RNA to DNA in retroviruses and the use of RNA molecules as genomes. As a result, extracting meaningful information from viral genomes remains a challenge and standard methods for comparing the unknown and our databases of characterized sequences may need adaptations. Thus, several bioinformatic approaches and tools have been created to address the challenge of analyzing viral data. This chapter offers descriptions and protocols of some of the most important bioinformatic techniques for comparative analysis of viruses. The authors also provide comments and discussion on how viruses' unique features can affect standard analyses and how to overcome some of the major sources of problems. Protocols and topics emphasize online tools (which are more accessible to users) and give the real experience of what most bioinformaticians do in day-by-day work with command-line pipelines. The topics discussed include (1) clustering related genomes, (2) whole genome multiple sequence alignments for small RNA viruses, (3) protein alignment for marker genes and species affiliation, (4) variant calling and annotation, and (5) virome analyses and pathogen identification.

Investigating Variability in Viral Presence and Abundance across Soybean Seed Development Stages Using Transcriptome Analysis

Plant transcriptomes offer a valuable resource for studying viral communities (viromes). In this study, we explore how plant transcriptome data can be applied to virome research. We analyzed 40 soybean transcriptomes across different growth stages and identified six viruses: broad bean wilt virus 2 (BBWV2), brassica yellow virus (BrYV), beet western yellow virus (BWYV), cucumber mosaic virus (CMV), milk vetch dwarf virus (MDV), and soybean mosaic virus (SMV). SMV was the predominant virus in both Glycine max (GM) and Glycine soja (GS) cultivars. Our analysis confirmed its abundance in both, while BBWV2 and CMV were more prevalent in GS than GM. The viral proportions varied across developmental stages, peaking in open flowers. Comparing viral abundance measured by viral reads and fragments per kilobase of transcript per million (FPKM) values revealed insights. SMV showed similar FPKM values in GM and GS, but BBWV2 and CMV displayed higher FPKM proportions in GS. Notably, the differences in viral abundance between GM and GS were generally insignificant based on the FPKM values across developmental stages, except for the apical bud stage in four GM cultivars. We also detected MDV, a multi-segmented virus, in two GM samples, with variable proportions of its segments. In conclusion, our study demonstrates the potential of plant transcriptomes for virome research, highlighting their strengths and limitations.

Navigating the Landscape: A Comprehensive Review of Current Virus Databases

Viruses are abundant and diverse entities that have important roles in public health, ecology, and agriculture. The identification and surveillance of viruses rely on an understanding of their genome organization, sequences, and replication strategy. Despite technological advancements in sequencing methods, our current understanding of virus diversity remains incomplete, highlighting the need to explore undiscovered viruses. Virus databases play a crucial role in providing access to sequences, annotations and other metadata, and analysis tools for studying viruses. However, there has not been a comprehensive review of virus databases in the last five years. This study aimed to fill this gap by identifying 24 active virus databases and included an extensive evaluation of their content, functionality and compliance with the FAIR principles. In this study, we thoroughly assessed the search capabilities of five database catalogs, which serve as comprehensive repositories housing a diverse array of databases and offering essential metadata. Moreover, we conducted a comprehensive review of different types of errors, encompassing taxonomy, names, missing information, sequences, sequence orientation, and chimeric sequences, with the intention of empowering users to effectively tackle these challenges. We expect this review to aid users in selecting suitable virus databases and other resources, and to help databases in error management and improve their adherence to the FAIR principles. The databases listed here represent the current knowledge of viruses and will help aid users find databases of interest based on content, functionality, and scope. The use of virus databases is integral to gaining new insights into the biology, evolution, and transmission of viruses, and developing new strategies to manage virus outbreaks and preserve global health.

Differences between Omicron SARS-CoV-2 RBD and other variants in their ability to interact with cell receptors and monoclonal antibodies

SARS-CoV-2 remains a health threat with the continuous emergence of new variants. This work aims to expand the knowledge about the SARS-CoV-2 receptor-binding domain (RBD) interactions with cell receptors and monoclonal antibodies (mAbs). By using constant-pH Monte Carlo simulations, the free energy of interactions between the RBD from different variants and several partners (Angiotensin-Converting Enzyme-2 (ACE2) polymorphisms and various mAbs) were predicted. Computed RBD-ACE2-binding affinities were higher for two ACE2 polymorphisms (rs142984500 and rs4646116) typically found in Europeans which indicates a genetic susceptibility. This is amplified for Omicron (BA.1) and its sublineages BA.2 and BA.3. The antibody landscape was computationally investigated with the largest set of mAbs so far in the literature. From the 32 studied binders, groups of mAbs were identified from weak to strong binding affinities (e.g. S2K146). These mAbs with strong binding capacity and especially their combination are amenable to experimentation and clinical trials because of their high predicted binding affinities and possible neutralization potential for current known virus mutations and a universal coronavirus.Communicated by Ramaswamy H. Sarma.

Towards an optimal monoclonal antibody with higher binding affinity to the receptor-binding domain of SARS-CoV-2 spike proteins from different variants

A highly efficient and robust multiple scales in silico protocol, consisting of atomistic Molecular Dynamics (MD), coarse-grain (CG) MD, and constant-pH CG Monte Carlo (MC), has been developed and used to study the binding affinities of selected antigen-binding fragments of the monoclonal antibody (mAbs) CR3022 and several of its here optimized versions against 11 SARS-CoV-2 variants including the wild type. Totally 235,000 mAbs structures were initially generated using the RosettaAntibodyDesign software, resulting in top 10 scored CR3022-like-RBD complexes with critical mutations and compared to the native one, all having the potential to block virus-host cell interaction. Of these 10 finalists, two candidates were further identified in the CG simulations to be the best against all SARS-CoV-2 variants. Surprisingly, all 10 candidates and the native CR3022 exhibited a higher affinity for the Omicron variant despite its highest number of mutations. The multiscale protocol gives us a powerful rational tool to design efficient mAbs. The electrostatic interactions play a crucial role and appear to be controlling the affinity and complex building. Studied mAbs carrying a more negative total net charge show a higher affinity. Structural determinants could be identified in atomistic simulations and their roles are discussed in detail to further hint at a strategy for designing the best RBD binder. Although the SARS-CoV-2 was specifically targeted in this work, our approach is generally suitable for many diseases and viral and bacterial pathogens, leukemia, cancer, multiple sclerosis, rheumatoid, arthritis, lupus, and more.

Evolving ribonucleocapsid assembly/packaging signals in the genomes of the human and animal coronaviruses: targeting, transmission and evolution

A world-wide COVID-19 pandemic intensified strongly the studies of molecular mechanisms related to the coronaviruses. The origin of coronaviruses and the risks of human-to-human, animal-to-human and human-to-animal transmission of coronaviral infections can be understood only on a broader evolutionary level by detailed comparative studies. In this paper, we studied ribonucleocapsid assembly-packaging signals (RNAPS) in the genomes of all seven known pathogenic human coronaviruses, SARS-CoV, SARS-CoV-2, MERS-CoV, HCoV-OC43, HCoV-HKU1, HCoV-229E and HCoV-NL63 and compared them with RNAPS in the genomes of the related animal coronaviruses including SARS-Bat-CoV, MERS-Camel-CoV, MHV, Bat-CoV MOP1, TGEV and one of camel alphacoronaviruses. RNAPS in the genomes of coronaviruses were evolved due to weakly specific interactions between genomic RNA and N proteins in helical nucleocapsids. Combining transitional genome mapping and Jaccard correlation coefficients allows us to perform the analysis directly in terms of underlying motifs distributed over the genome. In all coronaviruses, RNAPS were distributed quasi-periodically over the genome with the period about 54 nt biased to 57 nt and to 51 nt for the genomes longer and shorter than that of SARS-CoV, respectively. The comparison with the experimentally verified packaging signals for MERS-CoV, MHV and TGEV proved that the distribution of particular motifs is strongly correlated with the packaging signals. We also found that many motifs were highly conserved in both characters and positioning on the genomes throughout the lineages that make them promising therapeutic targets. The mechanisms of encapsidation can affect the recombination and co-infection as well.Communicated by Ramaswamy H. Sarma.

Next Generation Sequencing Approaches to Characterize the Respiratory Tract Virome

The COVID-19 pandemic and heightened perception of the risk of emerging viral infections have boosted the efforts to better understand the virome or complete repertoire of viruses in health and disease, with a focus on infectious respiratory diseases. Next-generation sequencing (NGS) is widely used to study microorganisms, allowing the elucidation of bacteria and viruses inhabiting different body systems and identifying new pathogens. However, NGS studies suffer from a lack of standardization, in particular, due to various methodological approaches and no single format for processing the results. Here, we review the main methodological approaches and key stages for studies of the human virome, with an emphasis on virome changes during acute respiratory viral infection, with applications for clinical diagnostics and epidemiologic analyses.

The Landscape of Virus-Host Protein–Protein Interaction Databases

Knowledge of virus-host interactomes has advanced exponentially in the last decade by the use of high-throughput screening technologies to obtain a more comprehensive landscape of virus-host protein–protein interactions. In this article, we present a systematic review of the available virus-host protein–protein interaction database resources. The resources covered in this review are both generic virus-host protein–protein interaction databases and databases of protein–protein interactions for a specific virus or for those viruses that infect a particular host. The databases are reviewed on the basis of the specificity for a particular virus or host, the number of virus-host protein–protein interactions included, and the functionality in terms of browse, search, visualization, and download. Further, we also analyze the overlap of the databases, that is, the number of virus-host protein–protein interactions shared by the various databases, as well as the structure of the virus-host protein–protein interaction network, across viruses and hosts.

The International Virus Bioinformatics Meeting 2022

The International Virus Bioinformatics Meeting 2022 took place online, on 23-25 March 2022, and has attracted about 380 participants from all over the world. The goal of the meeting was to provide a meaningful and interactive scientific environment to promote discussion and collaboration and to inspire and suggest new research directions and questions. The participants created a highly interactive scientific environment even without physical face-to-face interactions. This meeting is a focal point to gain an insight into the state-of-the-art of the virus bioinformatics research landscape and to interact with researchers in the forefront as well as aspiring young scientists. The meeting featured eight invited and 18 contributed talks in eight sessions on three days, as well as 52 posters, which were presented during three virtual poster sessions. The main topics were: SARS-CoV-2, viral emergence and surveillance, virus-host interactions, viral sequence analysis, virus identification and annotation, phages, and viral diversity. This report summarizes the main research findings and highlights presented at the meeting.

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.

In Silico Approaches for the Prediction and Analysis of Antiviral Peptides: A Review

In light of the growing resistance toward current antiviral drugs, efforts to discover novel and effective antiviral therapeutic agents remain a pressing scientific effort. Antiviral peptides (AVPs) represent promising therapeutic agents due to their extraordinary advantages in terms of potency, efficacy and pharmacokinetic properties. The growing volume of newly discovered peptide sequences in the post-genomic era requires computational approaches for timely and accurate identification of AVPs. Machine learning (ML) methods such as random forest and support vector machine represent robust learning algorithms that are instrumental in successful peptide-based drug discovery. Therefore, this review summarizes the current state-of-the-art application of ML methods for identifying AVPs directly from the sequence information. We compare the efficiency of these methods in terms of the underlying characteristics of the dataset used along with feature encoding methods, ML algorithms, cross-validation methods and prediction performance. Finally, guidelines for the development of robust AVP models are also discussed. It is anticipated that this review will serve as a useful guide for the design and development of robust AVP and related therapeutic peptide predictors in the future.

Up State of the SARS-COV-2 Spike Homotrimer Favors an Increased Virulence for New Variants

The COVID-19 pandemic has spread worldwide. However, as soon as the first vaccines-the only scientifically verified and efficient therapeutic option thus far-were released, mutations combined into variants of SARS-CoV-2 that are more transmissible and virulent emerged, raising doubts about their efficiency. This study aims to explain possible molecular mechanisms responsible for the increased transmissibility and the increased rate of hospitalizations related to the new variants. A combination of theoretical methods was employed. Constant-pH Monte Carlo simulations were carried out to quantify the stability of several spike trimeric structures at different conformational states and the free energy of interactions between the receptor-binding domain (RBD) and angiotensin-converting enzyme II (ACE2) for the most worrying variants. Electrostatic epitopes were mapped using the PROCEEDpKa method. These analyses showed that the increased virulence is more likely to be due to the improved stability to the S trimer in the opened state, in which the virus can interact with the cellular receptor, ACE2, rather than due to alterations in the complexation RBD-ACE2, since the difference observed in the free energy values was small (although more attractive in general). Conversely, the South African/Beta variant (B.1.351), compared with the SARS-CoV-2 wild type (wt), is much more stable in the opened state with one or two RBDs in the up position than in the closed state with three RBDs in the down position favoring the infection. Such results contribute to understanding the natural history of disease and indicate possible strategies for developing new therapeutic molecules and adjusting the vaccine doses for higher B-cell antibody production.

Automated download and clean-up of family-specific databases for kmer-based virus identification

SUMMARY

Here, we present an automated pipeline for Download Of NCBI Entries (DONE) and continuous updating of a local sequence database based on user-specified queries. The database can be created with either protein or nucleotide sequences containing all entries or complete genomes only. The pipeline can automatically clean the database by removing entries with matches to a database of user-specified sequence contaminants. The default contamination entries include sequences from the UniVec database of plasmids, marker genes and sequencing adapters from NCBI, an E.coli genome, rRNA sequences, vectors and satellite sequences. Furthermore, duplicates are removed and the database is automatically screened for sequences from green fluorescent protein, luciferase and antibiotic resistance genes that might be present in some GenBank viral entries, and could lead to false positives in virus identification. For utilizing the database, we present a useful opportunity for dealing with possible human contamination. We show the applicability of DONE by downloading a virus database comprising 37 virus families. We observed an average increase of 16 776 new entries downloaded per month for the 37 families. In addition, we demonstrate the utility of a custom database compared to a standard reference database for classifying both simulated and real sequence data.

AVAILABILITYAND IMPLEMENTATION

The DONE pipeline for downloading and cleaning is deposited in a publicly available repository (https://bitbucket.org/genomicepidemiology/done/src/master/).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A review on viral data sources and search systems for perspective mitigation of COVID-19

With the outbreak of the COVID-19 disease, the research community is producing unprecedented efforts dedicated to better understand and mitigate the effects of the pandemic. In this context, we review the data integration efforts required for accessing and searching genome sequences and metadata of SARS-CoV2, the virus responsible for the COVID-19 disease, which have been deposited into the most important repositories of viral sequences. Organizations that were already present in the virus domain are now dedicating special interest to the emergence of COVID-19 pandemics, by emphasizing specific SARS-CoV2 data and services. At the same time, novel organizations and resources were born in this critical period to serve specifically the purposes of COVID-19 mitigation while setting the research ground for contrasting possible future pandemics. Accessibility and integration of viral sequence data, possibly in conjunction with the human host genotype and clinical data, are paramount to better understand the COVID-19 disease and mitigate its effects. Few examples of host-pathogen integrated datasets exist so far, but we expect them to grow together with the knowledge of COVID-19 disease; once such datasets will be available, useful integrative surveillance mechanisms can be put in place by observing how common variants distribute in time and space, relating them to the phenotypic impact evidenced in the literature.

Biases in Viral Metagenomics-Based Detection, Cataloguing and Quantification of Bacteriophage Genomes in Human Faeces, a Review

The human gut is colonised by a vast array of microbes that include bacteria, viruses, fungi, and archaea. While interest in these microbial entities has largely focused on the bacterial constituents, recently the viral component has attracted more attention. Metagenomic advances, compared to classical isolation procedures, have greatly enhanced our understanding of the composition, diversity, and function of viruses in the human microbiome (virome). We highlight that viral extraction methodologies are crucial in terms of identifying and characterising communities of viruses infecting eukaryotes and bacteria. Different viral extraction protocols, including those used in some of the most significant human virome publications to date, have introduced biases affecting their a overall conclusions. It is important that protocol variations should be clearly highlighted across studies, with the ultimate goal of identifying and acknowledging biases associated with different protocols and, perhaps, the generation of an unbiased and standardised method for examining this portion of the human microbiome.

Next-generation computational tools and resources for coronavirus research: From detection to vaccine discovery

The COVID-19 pandemic has affected 215 countries and territories around the world with 60,187,347 coronavirus cases and 17,125,719 currently infected patients confirmed as of the November 25, 2020. Currently, many countries are working on developing new vaccines and therapeutic drugs for this novel virus strain, and a few of them are in different phases of clinical trials. The advancement in high-throughput sequence technologies, along with the application of bioinformatics, offers invaluable knowledge on genomic characterization and molecular pathogenesis of coronaviruses. Recent multi-disciplinary studies using bioinformatics methods like sequence-similarity, phylogenomic, and computational structural biology have provided an in-depth understanding of the molecular and biochemical basis of infection, atomic-level recognition of the viral-host receptor interaction, functional annotation of important viral proteins, and evolutionary divergence across different strains. Additionally, various modern immunoinformatic approaches are also being used to target the most promiscuous antigenic epitopes from the SARS-CoV-2 proteome for accelerating the vaccine development process. In this review, we summarize various important computational tools and databases available for systematic sequence-structural study on coronaviruses. The features of these public resources have been comprehensively discussed, which may help experimental biologists with predictive insights useful for ongoing research efforts to find therapeutics against the infectious COVID-19 disease.

Virxicon: A Lexicon Of Viral Sequences

Motivation

Viruses are the most abundant biological entities and constitute a large reservoir of genetic diversity. In recent years, knowledge about them has increased significantly as a result of dynamic development in life sciences and rapid technological progress. This knowledge is scattered across various data repositories, making a comprehensive analysis of viral data difficult.

Results

In response to the need for gathering a comprehensive knowledge of viruses and viral sequences, we developed Virxicon, a lexicon of all experimentally acquired sequences for RNA and DNA viruses. The ability to quickly obtain data for entire viral groups, searching sequences by levels of taxonomic hierarchy—according to the Baltimore classification and ICTV taxonomy—and tracking the distribution of viral data and its growth over time are unique features of our database compared to the other tools.

Availabilityand implementation

Virxicon is a publicly available resource, updated weekly. It has an intuitive web interface and can be freely accessed at http://virxicon.cs.put.poznan.pl/.

On the interactions of the receptor-binding domain of SARS-CoV-1 and SARS-CoV-2 spike proteins with monoclonal antibodies and the receptor ACE2

A new betacoronavirus named SARS-CoV-2 has emerged as a new threat to global health and economy. A promising target for both diagnosis and therapeutics treatments of the new disease named COVID-19 is the coronavirus (CoV) spike (S) glycoprotein. By constant-pH Monte Carlo simulations and the PROCEEDpKa method, we have mapped the electrostatic epitopes for four monoclonal antibodies and the angiotensin-converting enzyme 2 (ACE2) on both SARS-CoV-1 and the new SARS-CoV-2 S receptor binding domain (RBD) proteins. We also calculated free energy of interactions and shown that the S RBD proteins from both SARS viruses binds to ACE2 with similar affinities. However, the affinity between the S RBD protein from the new SARS-CoV-2 and ACE2 is higher than for any studied antibody previously found complexed with SARS-CoV-1. Based on physical chemical analysis and free energies estimates, we can shed some light on the involved molecular recognition processes, their clinical aspects, the implications for drug developments, and suggest structural modifications on the CR3022 antibody that would improve its binding affinities for SARS-CoV-2 and contribute to address the ongoing international health crisis.

Integration rates of human papilloma virus genome in a molecular survey on cervical specimens among Iranian patients

The human papilloma virus (HPV) as a major causative agent of different cancers is under investigation globally. In this study, we aim to investigate HPV infection in different cytological and pathological stages by different molecular methods, and then the viral genome integration of HPV-16 and -18 is determined by a specific real-time PCR method. The study included women who underwent liquid-based cytology. HPV PCR was conducted by MY09/11 universal primers, HPV genotyping was performed by INNO-LiPA HPV genotyping assay, and the viral genome status was defined by two real-time PCR assays. The statistics were calculated by SPSS v.22 software. In 1668 women included in the study with mean age±std. deviation of 35.6±0.7, HPV was detected in 632 (38%) participants. Following genotyping analyses, 16 HPV types and 713 strains were detected. HPV-16 and HPV-18 from high-risk types and HPV-6 and HPV-11 from low-risk types were the dominant types. We found HPV-16 strains in mixed form (58.8%), and of the HPV-18 strains, the episomal form was prevalent (92.9%). The statistics revealed significant presence of HPV-6 and within normal limits cases; HPV-16 and atypical squamous cells of undetermined significance; HPV-33 as well as HPV-39 and low-grade squamous intraepithelial lesion; HPV-6 and atypical squamous cells of undetermined significance; and HPV-35 as well as HPV-56 and squamous cell carcinoma. Our study showed high prevalence of HPV in low-grade cervical lesions, although it is associated with higher grades. The HPV molecular testing extra to cytology is recommended. HPV-16 and HPV-18 have different programs in genome integration in infected cells.

Rapid alignment-free phylogenetic identification of metagenomic sequences

MOTIVATION

Taxonomic classification is at the core of environmental DNA analysis. When a phylogenetic tree can be built as a prior hypothesis to such classification, phylogenetic placement (PP) provides the most informative type of classification because each query sequence is assigned to its putative origin in the tree. This is useful whenever precision is sought (e.g. in diagnostics). However, likelihood-based PP algorithms struggle to scale with the ever-increasing throughput of DNA sequencing.

RESULTS

We have developed RAPPAS (Rapid Alignment-free Phylogenetic Placement via Ancestral Sequences) which uses an alignment-free approach, removing the hurdle of query sequence alignment as a preliminary step to PP. Our approach relies on the precomputation of a database of k-mers that may be present with non-negligible probability in relatives of the reference sequences. The placement is performed by inspecting the stored phylogenetic origins of the k-mers in the query, and their probabilities. The database can be reused for the analysis of several different metagenomes. Experiments show that the first implementation of RAPPAS is already faster than competing likelihood-based PP algorithms, while keeping similar accuracy for short reads. RAPPAS scales PP for the era of routine metagenomic diagnostics.

AVAILABILITY AND IMPLEMENTATION

Program and sources freely available for download at https://github.com/blinard-BIOINFO/RAPPAS.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Meta-iAVP: A Sequence-Based Meta-Predictor for Improving the Prediction of Antiviral Peptides Using Effective Feature Representation

In spite of the large-scale production and widespread distribution of vaccines and antiviral drugs, viruses remain a prominent human disease. Recently, the discovery of antiviral peptides (AVPs) has become an influential antiviral agent due to their extraordinary advantages. With the avalanche of newly-found peptide sequences in the post-genomic era, there is a great demand to develop a sequence-based predictor for timely identifying AVPs as this information is very useful for both basic research and drug development. In this study, we propose a novel sequence-based meta-predictor with an effective feature representation, called Meta-iAVP, for the accurate prediction of AVPs from given peptide sequences. Herein, the effective feature representation was extracted from a set of prediction scores derived from various machine learning algorithms and types of features. To the best of our knowledge, the model proposed herein represents the first meta-based approach for the prediction of AVPs. An overall accuracy and Matthews correlation coefficient of 95.20% and 0.90, respectively, was achieved from the independent test set on an objective benchmark dataset. Comparative analysis suggested that Meta-iAVP was superior to that of existing methods and therefore represents a useful tool for AVP prediction. Finally, in an effort to facilitate high-throughput prediction of AVPs, the model was deployed as the Meta-iAVP web server and is made freely available online at http://codes.bio/meta-iavp/ where users can submit query peptide sequences for determining the likelihood of whether or not these peptides are AVPs.

The Prevalence of HPV Genotypes in Iranian Population: An Update

Background & Objective:

Human papillomavirus (HPV) is the main cause of genital warts and some anogenital cancers in male and female subjects which is commonly transmitted by sexual contacts. The objective of this cross-sectional study was to examine the prevalence of HPV genotypes in 10,266 Iranian male and female population, according to their age.

Methods:

Samples were collected from the penile and anal sites of male subjects and the vagina and cervix of female subjects in a time period between 2011 and 2016. HPV DNA was detected in PCR using the MY09 and MY11 primers, and the INNO-LiPA assay was applied for HPV genotyping. To investigate the relevance of HPV infection and age, the samples were classified into 4 age groups (13-29, 30-44, 45-59, and 60-74).

Results:

Totally, the most common low risk HPV genotypes detected in the studied male and female subjects were HPV-6 (77.7% and 43.3%) and HPV-11 (13.7% and 11.4%), and more frequent high risk HPV genotypes were HPV-16 (5.5% and 16.6%) and HPV-52 (3.2% and 9.6%), respectively. High burden of the HPV infection was observed at ranges of 30 and 44 years (51.8%) with a peak at ranges between 30 and 32 years. No considerable statistically significant correlation was found between HPV infection and age (P=1).

Conclusion:

This study gave an epidemiological overview of circulating HPV genotypes in Iranian population to develop future vaccination policies, though the findings of prevalent HPV genotypes in female subjects were inconsistent with the previous studies reported in Iran.

TAR-VIR: a pipeline for TARgeted VIRal strain reconstruction from metagenomic data

Background

Strain-level RNA virus characterization is essential for developing prevention and treatment strategies. Viral metagenomic data, which can contain sequences of both known and novel viruses, provide new opportunities for characterizing RNA viruses. Although there are a number of pipelines for analyzing viruses in metagenomic data, they have different limitations. First, viruses that lack closely related reference genomes cannot be detected with high sensitivity. Second, strain-level analysis is usually missing.

Results

In this study, we developed a hybrid pipeline named TAR-VIR that reconstructs viral strains without relying on complete or high-quality reference genomes. It is optimized for identifying RNA viruses from metagenomic data by combining an effective read classification method and our in-house strain-level de novo assembly tool. TAR-VIR was tested on both simulated and real viral metagenomic data sets. The results demonstrated that TAR-VIR competes favorably with other tested tools.

Conclusion

TAR-VIR can be used standalone for viral strain reconstruction from metagenomic data. Or, its read recruiting stage can be used with other de novo assembly tools for superior viral functional and taxonomic analyses. The source code and the documentation of TAR-VIR are available at https://github.com/chjiao/TAR-VIR.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2878-2) contains supplementary material, which is available to authorized users.

Drug repurposing for new, efficient, broad spectrum antivirals

Emerging viruses are a major threat to human health. Recent outbreaks have emphasized the urgent need for new antiviral treatments. For several pathogenic viruses, considerable efforts have focused on vaccine development. However, during epidemics infected individuals need to be treated urgently. High-throughput screening of clinically tested compounds provides a rapid means to identify undiscovered, antiviral functions for well-characterized therapeutics. Repurposed drugs can bypass part of the early cost and time needed for validation and authorization. In this review we describe recent efforts to find broad spectrum antivirals through drug repurposing. We have chosen several candidates and propose strategies to understand their mechanism of action and to determine how resistance to antivirals develops in infected cells.

Vgas: A Viral Genome Annotation System

The in-depth study of viral genomes is of great help in many aspects, especially in the treatment of human diseases caused by viral infections. With the rapid accumulation of viral sequencing data, improved, or alternative gene-finding systems have become necessary to process and mine these data. In this article, we present Vgas, a system combining an ab initio method and a similarity-based method to automatically find viral genes and perform gene function annotation. Vgas was compared with existing programs, such as Prodigal, GeneMarkS, and Glimmer. Through testing 5,705 virus genomes downloaded from RefSeq, Vgas demonstrated its superiority with the highest average precision and recall (both indexes were 1% higher or more than the other programs); particularly for small virus genomes (≤ 10 kb), it showed significantly improved performance (precision was 6% higher, and recall was 2% higher). Moreover, Vgas presents an annotation module to provide functional information for predicted genes based on BLASTp alignment. This characteristic may be specifically useful in some cases. When combining Vgas with GeneMarkS and Prodigal, better prediction results could be obtained than with each of the three individual programs, suggesting that collaborative prediction using several different software programs is an alternative for gene prediction. Vgas is freely available at http://cefg.uestc.cn/vgas/ or http://121.48.162.133/vgas/. We hope that Vgas could be an alternative virus gene finder to annotate new genomes or reannotate existing genome.

Severe Dengue Prognosis Using Human Genome Data and Machine Learning

Dengue has become one of the most important worldwide arthropod-borne diseases. Dengue phenotypes are based on laboratorial and clinical exams, which are known to be inaccurate.

OBJECTIVE

We present a machine learning approach for the prediction of dengue fever severity based solely on human genome data.

METHODS

One hundred and two Brazilian dengue patients and controls were genotyped for 322 innate immunity single nucleotide polymorphisms (SNPs). Our model uses a support vector machine algorithm to find the optimal loci classification subset and then an artificial neural network (ANN) is used to classify patients into dengue fever or severe dengue.

RESULTS

The ANN trained on 13 key immune SNPs selected under dominant or recessive models produced median values of accuracy greater than 86%, and sensitivity and specificity over 98% and 51%, respectively.

CONCLUSION

The proposed classification method, using only genome markers, can be used to identify individuals at high risk for developing the severe dengue phenotype even in uninfected conditions.

SIGNIFICANCE

Our results suggest that the genetic context is a key element in phenotype definition in dengue. The methodology proposed here is extendable to other Mendelian based and genetically influenced diseases.

Critical issues in application of molecular methods to environmental virology

Waterborne diseases have significant public health and socioeconomic implications worldwide. Many viral pathogens are commonly associated with water-related diseases, namely enteric viruses. Also, novel recently discovered human-associated viruses have been shown to be a causative agent of gastroenteritis or other clinical symptoms. A wide range of analytical methods is available for virus detection in environmental water samples. Viral isolation is historically carried out via propagation on permissive cell lines; however, some enteric viruses are difficult or not able to propagate on existing cell lines. Real-time polymerase chain reaction (qPCR) screening of viral nucleic acid is routinely used to investigate virus contamination in water due to the high sensitivity and specificity. Additionally, the introduction of metagenomic approaches into environmental virology has facilitated the discovery of viruses that cannot be grown in cell culture. This review (i) highlights the applications of molecular techniques in environmental virology such as PCR and its modifications to overcome the critical issues associated with the inability to discriminate between infectious viruses and nonviable viruses, (ii) outlines the strengths and weaknesses of Nucleic Acid Sequence Based Amplification (NASBA) and microarray, (iii) discusses the role of digital PCR as an emerging water quality monitoring assay and its advantages over qPCR, (iv) addresses the viral metagenomics in terms of detecting emerging viral pathogens and diversity in aquatic environment. Indeed, there are many challenges for selecting methods to detect classic and emerging viruses in environmental samples. While the existing techniques have revealed the importance and diversity of viruses in the water environment, further developments are necessary to enable more rapid and accurate methodologies for viral water quality monitoring and regulation.

Overview of Virus Metagenomic Classification Methods and Their Biological Applications

Metagenomics poses opportunities for clinical and public health virology applications by offering a way to assess complete taxonomic composition of a clinical sample in an unbiased way. However, the techniques required are complicated and analysis standards have yet to develop. This, together with the wealth of different tools and workflows that have been proposed, poses a barrier for new users. We evaluated 49 published computational classification workflows for virus metagenomics in a literature review. To this end, we described the methods of existing workflows by breaking them up into five general steps and assessed their ease-of-use and validation experiments. Performance scores of previous benchmarks were summarized and correlations between methods and performance were investigated. We indicate the potential suitability of the different workflows for (1) time-constrained diagnostics, (2) surveillance and outbreak source tracing, (3) detection of remote homologies (discovery), and (4) biodiversity studies. We provide two decision trees for virologists to help select a workflow for medical or biodiversity studies, as well as directions for future developments in clinical viral metagenomics.

HIV Vaccination: A Roadmap among Advancements and Concerns

Since the identification of the Human Immunodeficiency Virus type 1 (HIV-1) as the etiologic agent of AIDS (Acquired Immunodeficiency Syndrome), many efforts have been made to stop the AIDS pandemic. A major success of medical research has been the development of the highly active antiretroviral therapy and its availability to an increasing number of people worldwide, with a considerable effect on survival. However, a safe and effective vaccine able to prevent and eradicate the HIV pandemic is still lacking. Clinical trials and preclinical proof-of-concept studies in nonhuman primate (NHP) models have provided insights into potential correlates of protection against the HIV-1 infection, which include broadly neutralizing antibodies (bnAbs), non-neutralizing antibodies targeting the variable loops 1 and 2 (V1V2) regions of the HIV-1 envelope (Env), polyfunctional antibody, and Env-specific T-cell responses. In this review, we provide a brief overview of different HIV-1 vaccine approaches and discuss the current understanding of the cellular and humoral correlates of HIV-1 immunity.

A Reference Viral Database (RVDB) To Enhance Bioinformatics Analysis of High-Throughput Sequencing for Novel Virus Detection

To facilitate bioinformatics analysis of high-throughput sequencing (HTS) data for the detection of both known and novel viruses, we have developed a new reference viral database (RVDB) that provides a broad representation of different virus species from eukaryotes by including all viral, virus-like, and virus-related sequences (excluding bacteriophages), regardless of their size. In particular, RVDB contains endogenous nonretroviral elements, endogenous retroviruses, and retrotransposons. Sequences were clustered to reduce redundancy while retaining high viral sequence diversity. A particularly useful feature of RVDB is the reduction of cellular sequences, which can enhance the run efficiency of large transcriptomic and genomic data analysis and increase the specificity of virus detection.

Detection of distantly related viruses by high-throughput sequencing (HTS) is bioinformatically challenging because of the lack of a public database containing all viral sequences, without abundant nonviral sequences, which can extend runtime and obscure viral hits. Our reference viral database (RVDB) includes all viral, virus-related, and virus-like nucleotide sequences (excluding bacterial viruses), regardless of length, and with overall reduced cellular sequences. Semantic selection criteria (SEM-I) were used to select viral sequences from GenBank, resulting in a first-generation viral database (VDB). This database was manually and computationally reviewed, resulting in refined, semantic selection criteria (SEM-R), which were applied to a new download of updated GenBank sequences to create a second-generation VDB. Viral entries in the latter were clustered at 98% by CD-HIT-EST to reduce redundancy while retaining high viral sequence diversity. The viral identity of the clustered representative sequences (creps) was confirmed by BLAST searches in NCBI databases and HMMER searches in PFAM and DFAM databases. The resulting RVDB contained a broad representation of viral families, sequence diversity, and a reduced cellular content; it includes full-length and partial sequences and endogenous nonretroviral elements, endogenous retroviruses, and retrotransposons. Testing of RVDBv10.2, with an in-house HTS transcriptomic data set indicated a significantly faster run for virus detection than interrogating the entirety of the NCBI nonredundant nucleotide database, which contains all viral sequences but also nonviral sequences. RVDB is publically available for facilitating HTS analysis, particularly for novel virus detection. It is meant to be updated on a regular basis to include new viral sequences added to GenBank.

IMPORTANCE To facilitate bioinformatics analysis of high-throughput sequencing (HTS) data for the detection of both known and novel viruses, we have developed a new reference viral database (RVDB) that provides a broad representation of different virus species from eukaryotes by including all viral, virus-like, and virus-related sequences (excluding bacteriophages), regardless of their size. In particular, RVDB contains endogenous nonretroviral elements, endogenous retroviruses, and retrotransposons. Sequences were clustered to reduce redundancy while retaining high viral sequence diversity. A particularly useful feature of RVDB is the reduction of cellular sequences, which can enhance the run efficiency of large transcriptomic and genomic data analysis and increase the specificity of virus detection.

Bioinformatic Approaches for Comparative Analysis of Viruses

The field of viral genomic studies has experienced an unprecedented increase in data volume. New strains of known viruses are constantly being added to the GenBank database and so are completely new species with little or no resemblance to our databases of sequences. In addition to this, metagenomic techniques have the potential to further increase the number and rate of sequenced genomes. Besides, it is important to consider that viruses have a set of unique features that often break down molecular biology dogmas, e.g., the flux of information from RNA to DNA in retroviruses and the use of RNA molecules as genomes. As a result, extracting meaningful information from viral genomes remains a challenge and standard methods for comparing the unknown and our databases of characterized sequences may need to be modified. Thus, several bioinformatic approaches and tools have been created to address the challenge of analyzing viral data. In this chapter, we offer descriptions and protocols of some of the most important bioinformatic techniques for comparative analysis of viruses. We also provide comments and discussion on how viruses' unique features can affect standard analyses and how to overcome some of the major sources of problems. Topics include: (1) Clustering of related genomes, (2) Whole genome multiple sequence alignments for small RNA viruses, (3) Protein alignments for marker genes, (4) Analyses based on ortholog groups, and (5) Taxonomic identification and comparisons of viruses from environmental datasets.

Assessing Species Diversity Using Metavirome Data: Methods and Challenges

Assessing biodiversity is an important step in the study of microbial ecology associated with a given environment. Multiple indices have been used to quantify species diversity, which is a key biodiversity measure. Measuring species diversity of viruses in different environments remains a challenge relative to measuring the diversity of other microbial communities. Metagenomics has played an important role in elucidating viral diversity by conducting metavirome studies; however, metavirome data are of high complexity requiring robust data preprocessing and analysis methods. In this review, existing bioinformatics methods for measuring species diversity using metavirome data are categorised broadly as either sequence similarity-dependent methods or sequence similarity-independent methods. The former includes a comparison of DNA fragments or assemblies generated in the experiment against reference databases for quantifying species diversity, whereas estimates from the latter are independent of the knowledge of existing sequence data. Current methods and tools are discussed in detail, including their applications and limitations. Drawbacks of the state-of-the-art method are demonstrated through results from a simulation. In addition, alternative approaches are proposed to overcome the challenges in estimating species diversity measures using metavirome data.

ChimericSeq: An open-source, user-friendly interface for analyzing NGS data to identify and characterize viral-host chimeric sequences

Identification of viral integration sites has been important in understanding the pathogenesis and progression of diseases associated with particular viral infections. The advent of next-generation sequencing (NGS) has enabled researchers to understand the impact that viral integration has on the host, such as tumorigenesis. Current computational methods to analyze NGS data of virus-host junction sites have been limited in terms of their accessibility to a broad user base. In this study, we developed a software application (named ChimericSeq), that is the first program of its kind to offer a graphical user interface, compatibility with both Windows and Mac operating systems, and optimized for effectively identifying and annotating virus-host chimeric reads within NGS data. In addition, ChimericSeq’s pipeline implements custom filtering to remove artifacts and detect reads with quantitative analytical reporting to provide functional significance to discovered integration sites. The improved accessibility of ChimericSeq through a GUI interface in both Windows and Mac has potential to expand NGS analytical support to a broader spectrum of the scientific community.

drVM: a new tool for efficient genome assembly of known eukaryotic viruses from metagenomes

Background: Virus discovery using high-throughput next-generation sequencing has become more commonplace. However, although analysis of deep next-generation sequencing data allows us to identity potential pathogens, the entire analytical procedure requires competency in the bioinformatics domain, which includes implementing proper software packages and preparing prerequisite databases. Simple and user-friendly bioinformatics pipelines are urgently required to obtain complete viral genome sequences from metagenomic data.

Results: This manuscript presents a pipeline, drVM (detect and reconstruct known viral genomes from metagenomes), for rapid viral read identification, genus-level read partition, read normalization, de novo assembly, sequence annotation, and coverage profiling. The first two procedures and sequence annotation rely on known viral genomes as a reference database. drVM was validated via the analysis of over 300 sequencing runs generated by Illumina and Ion Torrent platforms to provide complete viral genome assemblies for a variety of virus types including DNA viruses, RNA viruses, and retroviruses. drVM is available for free download at: https://sourceforge.net/projects/sb2nhri/files/drVM/ and is also assembled as a Docker container, an Amazon machine image, and a virtual machine to facilitate seamless deployment.

Conclusions: drVM was compared with other viral detection tools to demonstrate its merits in terms of viral genome completeness and reduced computation time. This substantiates the platform's potential to produce prompt and accurate viral genome sequences from clinical samples.

Metagenomic Approaches to Assess Bacteriophages in Various Environmental Niches

Bacteriophages are ubiquitous and numerous parasites of bacteria and play a critical evolutionary role in virtually every ecosystem, yet our understanding of the extent of the diversity and role of phages remains inadequate for many ecological niches, particularly in cases in which the host is unculturable. During the past 15 years, the emergence of the field of viral metagenomics has drastically enhanced our ability to analyse the so-called viral ‘dark matter’ of the biosphere. Here, we review the evolution of viral metagenomic methodologies, as well as providing an overview of some of the most significant applications and findings in this field of research.

Analysis of Variability in HIV-1 Subtype A Strains in Russia Suggests a Combination of Deep Sequencing and Multitarget RNA Interference for Silencing of the Virus

Any method for silencing the activity of the HIV-1 retrovirus should tackle the extremely high variability of HIV-1 sequences and mutational escape. We studied sequence variability in the vicinity of selected RNA interference (RNAi) targets from isolates of HIV-1 subtype A in Russia, and we propose that using artificial RNAi is a potential alternative to traditional antiretroviral therapy. We prove that using multiple RNAi targets overcomes the variability in HIV-1 isolates. The optimal number of targets critically depends on the conservation of the target sequences. The total number of targets that are conserved with a probability of 0.7-0.8 should exceed at least 2. Combining deep sequencing and multitarget RNAi may provide an efficient approach to cure HIV/AIDS.

Statoviruses, A novel taxon of RNA viruses present in the gastrointestinal tracts of diverse mammals

Next-generation sequencing has expanded our understanding of the viral populations that constitute the mammalian virome. We describe a novel taxon of viruses named Statoviruses, for Stool associated Tombus-like viruses, present in multiple metagenomic datasets. These viruses define a novel clade that is phylogenetically related to the RNA virus families Tombusviridae and Flaviviridae. Five distinct statovirus types were identified in human, macaque, mouse, and cow gastrointestinal tract samples. The prototype genome, statovirus A, was frequently identified in macaque stool samples from multiple geographically distinct cohorts. Another genome, statovirus C1, was discovered in a stool sample from a human child with fever, cough, and rash. Further experimental data will clarify whether these viruses are infectious to mammals or if they originate from another source present in the mammalian gastrointestinal tract.

The Promise of Proteomics in the Study of Oncogenic Viruses

Oncogenic viruses are responsible for about 15% human cancers. This article explores the promise and challenges of viral proteomics in the study of the oncogenic human DNA viruses, HPV, McPyV, EBV and KSHV. These viruses have coevolved with their hosts and cause persistent infections. Each virus encodes oncoproteins that manipulate key cellular pathways to promote viral replication and evade the host immune response. Viral proteomics can identify cellular pathways perturbed by viral infection, identify cellular proteins that are crucial for viral persistence and oncogenesis, and identify important diagnostic and therapeutic targets.

Named entity linking of geospatial and host metadata in GenBank for advancing biomedical research

DATABASE URL

: https://zodo.asu.edu/zoophydb/.

viruSITE-integrated database for viral genomics

Viruses are the most abundant biological entities and the reservoir of most of the genetic diversity in the Earth's biosphere. Viral genomes are very diverse, generally short in length and compared to other organisms carry only few genes. viruSITE is a novel database which brings together high-value information compiled from various resources. viruSITE covers the whole universe of viruses and focuses on viral genomes, genes and proteins. The database contains information on virus taxonomy, host range, genome features, sequential relatedness as well as the properties and functions of viral genes and proteins. All entries in the database are linked to numerous information resources. The above-mentioned features make viruSITE a comprehensive knowledge hub in the field of viral genomics.

The web interface of the database was designed so as to offer an easy-to-navigate, intuitive and user-friendly environment. It provides sophisticated text searching and a taxonomy-based browsing system. viruSITE also allows for an alternative approach based on sequence search. A proprietary genome browser generates a graphical representation of viral genomes. In addition to retrieving and visualising data, users can perform comparative genomics analyses using a variety of tools.

Database URL: http://www.virusite.org/

Systems Perspective of Morbillivirus Replication

Systems biology refers to system-wide changes in biological components such as RNA/DNA (genomics), protein (proteomics) and lipids (lipidomics). In this review, we provide comprehensive information about morbillivirus replication. Besides discussing the role of individual viral/host proteins in virus replication, we also discuss how systems-level analyses could improve our understanding of morbillivirus replication, host-pathogen interaction, immune response and disease resistance. Finally, we discuss how viroinformatics is likely to provide important insights for understanding genome-genome, genome-protein and protein-protein interactions.

Big Data Offers Novel Insights for Oncolytic Virus Immunotherapy

Large-scale assays, such as microarrays, next-generation sequencing and various “omics” technologies, have explored multiple aspects of the immune response following virus infection, often from a public health perspective. Yet a lack of similar data exists for monitoring immune engagement during oncolytic virus immunotherapy (OVIT) in the cancer setting. Tracking immune signatures at the tumour site can create a snapshot or longitudinally analyse immune cell activation, infiltration and functionality within global populations or individual cells. Mapping immune changes over the course of oncolytic biotherapy—from initial infection to tumour stabilisation/regression through to long-term cure or escape/relapse—has the potential to generate important therapeutic insights around virus-host interactions. Further, correlating such immune signatures with specific tumour outcomes has significant value for guiding the development of novel oncolytic virus immunotherapy strategies. Here, we provide insights for OVIT from large-scale analyses of immune populations in the infection, vaccination and immunotherapy setting. We analyse several approaches to manipulating immune engagement during OVIT. We further explore immunocentric changes in the tumour tissue following immunotherapy, and compile several immune signatures of therapeutic success. Ultimately, we highlight clinically relevant large-scale approaches with the potential to strengthen future oncolytic strategies to optimally engage the immune system.

Variability in Humoral Immunity to Measles Vaccine: New Developments

Despite the existence of an effective measles vaccine, resurgence in measles cases in the USA and across Europe has occurred, including in individuals vaccinated with two doses of the vaccine. Host genetic factors result in inter-individual variation in measles vaccine-induced antibodies, and play a role in vaccine failure. Studies have identified HLA (human leukocyte antigen) and non-HLA genetic influences that individually or jointly contribute to the observed variability in the humoral response to vaccination among healthy individuals. In this exciting era, new high-dimensional approaches and techniques including vaccinomics, systems biology, GWAS, epitope prediction and sophisticated bioinformatics/statistical algorithms provide powerful tools to investigate immune response mechanisms to the measles vaccine. These might predict, on an individual basis, outcomes of acquired immunity post measles vaccination.

High-throughput approaches to unravel hepatitis C virus-host interactions

Hepatitis C virus (HCV) remains a major global health burden, with more than 130 million individuals chronically infected and at risk for the development of hepatocellular carcinoma (HCC). The recent clinical licensing of direct-acting antivirals enables viral cure. However, limited access to therapy and treatment failure in patient subgroups warrants a continuing effort to develop complementary antiviral strategies. Furthermore, once fibrosis is established, curing HCV infection does not eliminate the risk for HCC. High-throughput approaches and screens have enabled the investigation of virus-host interactions on a genome-wide scale. Gain- and loss-of-function screens have identified essential host-dependency factors in the HCV viral life cycle, such as host cell entry factors or regulatory factors for viral replication and assembly. Network analyses of systems-scale data sets provided a comprehensive view of the cellular state following HCV infection, thus improving our understanding of the virus-induced responses of the target cell. Interactome, metabolomics and gene expression studies identified dysregulated cellular processes potentially contributing to HCV pathogenesis and HCC. Drug screens using chemical libraries led to the discovery of novel antivirals. Here, we review the contribution of high-throughput approaches for the investigation of virus-host interactions, viral pathogenesis and drug discovery.