VODKA2: a fast and accurate method to detect non-standard viral genomes from large RNA-seq data sets.

During viral replication, viruses carrying an RNA genome produce non-standard viral genomes (nsVGs), including copy-back viral genomes (cbVGs) and deletion viral genomes (delVGs), that play a crucial role in regulating viral replication and pathogenesis. Because of their critical roles in determining the outcome of RNA virus infections, the study of nsVGs has flourished in recent years, exposing a need for bioinformatic tools that can accurately identify them within next-generation sequencing data obtained from infected samples. Here, we present our data analysis pipeline, Viral Opensource DVG Key Algorithm 2 (VODKA2), that is optimized to run on a parallel computing environment for fast and accurate detection of nsVGs from large data sets.

VODKA2: A fast and accurate method to detect non-standard viral genomes from large RNA-seq datasets.

During viral replication, viruses carrying an RNA genome produce non-standard viral genomes (nsVGs), including copy-back viral genomes (cbVGs) and deletion viral genomes (delVGs), that play a crucial role in regulating viral replication and pathogenesis. Because of their critical roles in determining the outcome of RNA virus infections, the study of nsVGs has flourished in recent years exposing a need for bioinformatic tools that can accurately identify them within Next-Generation Sequencing data obtained from infected samples. Here, we present our data analysis pipeline, Viral Opensource DVG Key Algorithm2 (VODKA2), that is optimized to run on a High Performance Computing (HPC) environment for fast and accurate detection of nsVGs from large data sets.

Accumulation of copy-back viral genomes during respiratory syncytial virus infection is preceded by diversification of the copy-back viral genome population followed by selection.

RNA viruses generate nonstandard viral genomes during their replication, including viral genomes of the copy-back (cbVGs) type that cannot replicate in the absence of a standard virus. cbVGs play a crucial role in shaping virus infection outcomes due to their ability to interfere with virus replication and induce strong immune responses. However, despite their critical role during infection, the principles that drive the selection and evolution of cbVGs within a virus population are poorly understood. As cbVGs are dependent on the virus replication machinery to be generated and replicated, we hypothesized that host factors that affect virus replication exert selective pressure on cbVGs and drive their evolution within a virus population. To test this hypothesis, we used respiratory syncytial virus (RSV) as a model and took an experimental evolution approach by serially passaging RSV in immune-competent human lung adenocarcinoma A549 control and immune-deficient A549 Signal transducer and activator of transcription 1 (STAT1) KO cells, which allow higher levels of virus replication. As predicted, we observed that virus populations accumulated higher amounts of cbVGs in the more permissive A549 STAT1 KO cells over time; however, unexpectedly, the predominant cbVG species after passages in the two conditions were different. While A549 STAT1 KO cells accumulated relatively short cbVGs, A549 control cells mainly contained cbVGs of much longer predicted size, which have not been described previously. These long cbVGs were predominant at first in both cell lines in vitro and the predominant ones observed in samples from RSV-infected patients. Although sustained high replication levels are associated with cbVG generation and accumulation, our data show that sustained high levels of virus replication are critical for cbVG population diversification, a process that precedes the generation of shorter cbVGs that selectively accumulate over time. Taken together, we show that selection and evolution of cbVGs within a virus population are shaped by how resistant or permissive a host is to RSV.

Detection of respiratory syncytial virus defective genomes in nasal secretions is associated with distinct clinical outcomes.

Respiratory syncytial virus (RSV) causes respiratory illness in children, immunosuppressed individuals and the elderly. However, the viral factors influencing the clinical outcome of RSV infections remain poorly defined. Defective viral genomes (DVGs) can suppress virus replication by competing for viral proteins and by stimulating antiviral immunity. We studied the association between detection of DVGs of the copy-back type and disease severity in three RSV A-confirmed cohorts. In hospitalized children, detection of DVGs in respiratory samples at or around the time of admission associated strongly with more severe disease, higher viral load and a stronger pro-inflammatory response. Interestingly, in experimentally infected adults, the presence of DVGs in respiratory secretions differentially associated with RSV disease severity depending on when DVGs were detected. Detection of DVGs early after infection associated with low viral loads and mild disease, whereas detection of DVGs late after infection, especially if DVGs were present for prolonged periods, associated with high viral loads and severe disease. Taken together, we demonstrate that the kinetics of DVG accumulation and duration could predict clinical outcome of RSV A infection in humans, and thus could be used as a prognostic tool to identify patients at risk of worse clinical disease.

RNA-seq accuracy and reproducibility for the mapping and quantification of influenza defective viral genomes.

Like most RNA viruses, influenza viruses generate defective viral genomes (DVGs) with large internal deletions during replication. There is accumulating evidence supporting a biological relevance of such DVGs. However, further understanding of the molecular mechanisms that underlie the production and biological activity of DVGs is conditioned upon the sensitivity and accuracy of detection methods, that is, next-generation sequencing (NGS) technologies and related bioinformatics algorithms. Although many algorithms were developed, their sensitivity and reproducibility were mostly assessed on simulated data. Here, we introduce DG-seq, a time-efficient pipeline for DVG detection and quantification, and a set of biological controls to assess the performance of not only our bioinformatics algorithm but also the upstream NGS steps. Using these tools, we provide the first rigorous comparison of the two commonly used sample processing methods for RNA-seq, with or without a PCR preamplification step. Our data show that preamplification confers a limited advantage in terms of sensitivity and introduces size- but also sequence-dependent biases in DVG quantification, thereby providing a strong rationale to favor preamplification-free methods. We further examine the features of DVGs produced by wild-type and transcription-defective (PA-K635A or PA-R638A) influenza viruses, and show an increased diversity and frequency of DVGs produced by the PA mutants compared to the wild-type virus. Finally, we demonstrate a significant enrichment in DVGs showing direct, A/T-rich sequence repeats at the deletion breakpoint sites. Our findings provide novel insights into the mechanisms of influenza virus DVG production.

Gut Microbial Diversity Is Reduced in Smokers with Crohn's Disease.

BACKGROUND: Smoking has a negative impact on Crohn's disease (CD), but the mechanisms underlying this association are unclear. We compared the gut microbiota composition of smoking with nonsmoking patients with CD using a metagenomic approach. METHODS: Stool samples and clinical data were collected from current smokers and nonsmokers with CD from France and the Netherlands, matched for country, gender, age, disease activity, and body mass index. Fecal DNA was sequenced on an Illumina HiSeq 2500. On average, 40 million paired-end reads were generated per sample. Gene richness and the Shannon index were computed to assess microbial diversity. Wilcoxon's signed-rank tests for paired samples were performed to detect differences between the 2 groups. RESULTS: In total, 21 smoking and 21 nonsmoking patients with CD were included. Compared with nonsmoking patients, gut microbial gene richness (P = 0.01), genus diversity (P < 0.01), and species diversity (P = 0.01) were decreased in smoking patients. This was accompanied by a reduced relative abundance of the genera Collinsella (P = 0.02), Enterorhabdus (P = 0.02), and Gordonibacter (P = 0.02) in smokers. No statistically significant differences at the species level were observed, although smokers had lower proportions of Faecalibacterium prausnitzii (P = 0.10). CONCLUSIONS: Gut microbial diversity is reduced in smokers with CD compared with nonsmokers with CD. The microbial profile differs between these groups at the genus level. Future studies should evaluate whether intestinal microbes mediate the adverse effects of smoking in CD.