Identification and quantification of defective virus genomes in high throughput sequencing data using DVG-profiler, a novel post-sequence alignment processing algorithm

Abstract

Most viruses are known to spontaneously generate defective viral genomes (DVG) due to errors during replication. These DVGs are subgenomic and contain deletions that render them unable to complete a full replication cycle in the absence of a co-infecting, non-defective helper virus. DVGs, especially of the copyback type, frequently observed with paramyxoviruses, have been recognized to be important triggers of the antiviral innate immune response. DVGs have therefore gained interest for their potential to alter the attenuation and immunogenicity of vaccines. To investigate this potential, accurate identification and quantification of DVGs is essential. Conventional methods, such as RT-PCR, are labor intensive and will only detect primer sequence-specific species. High throughput sequencing (HTS) is much better suited for this undertaking. Here, we present an HTS-based algorithm called DVG-profiler to identify and quantify all DVG sequences in an HTS data set generated from a virus preparation. DVG-profiler identifies DVG breakpoints relative to a reference genome and reports the directionality of each segment from within the same read. The specificity and sensitivity of the algorithm was assessed using both in silico data sets as well as HTS data obtained from parainfluenza virus 5, Sendai virus and mumps virus preparations. HTS data from the latter were also compared with conventional RT-PCR data and with data obtained using an alternative algorithm. The data presented here demonstrate the high specificity, sensitivity, and robustness of DVG-profiler. This algorithm was implemented within an open source cloud-based computing environment for analyzing HTS data. DVG-profiler might prove valuable not only in basic virus research but also in monitoring live attenuated vaccines for DVG content and to assure vaccine lot to lot consistency.

Population Variability Generated during Rescue Process and Passaging of Recombinant Mumps Viruses

“…In our previous research regarding mumps 5 cb DIGs that included three different mumps strains (Urabe, Jeryl Lynn 5 and 9218/Zg98), the longest mumps 5 cb DIGs detected were 1344 nucleotides long [50]. Bosma et al [51] analyzed 5 cb DIGs in populations of recombinant mumps viruses based on 88-1961 strain. The longest mumps 5 cb DIGs described by Bosma et al [51] were 2878 nucleotides long, which is, again, shorter than our amplicons.…”

“…Bosma et al [51] analyzed 5 cb DIGs in populations of recombinant mumps viruses based on 88-1961 strain. The longest mumps 5 cb DIGs described by Bosma et al [51] were 2878 nucleotides long, which is, again, shorter than our amplicons.…”

The Omicron Variant Breaks the Evolutionary Lineage of Sars-Cov2 Variants

“…The fact that does not seem to happen means that viruses that mutate much faster than the human lifespan do not possess a capacity of infinite adaptation. As the collective immunity spreads, they cannot continuously generate new viable and efficient variants issued from mutation and recombination of themselves and end up generating defective genomes Bosma (2019), Rezelj (2021), Nayak (1989). To survive this programmed decline, they need to re-assort with a viable "helper" virus, a situation not necessarily fulfilled.…”

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

“…Defective interfering viral particles were first described 60 yr ago in influenza virus stocks propagated at a high multiplicity of infection in embryonated hen eggs (von Magnus 1954). Since then interfering DVGs have been detected both during in vitro and in vivo infections with various influenza types and subtypes (Baum et al 2010;Saira et al 2013;Vasilijevic et al 2017;Sheng et al 2018;Alnaji et al 2019;Bosma et al 2019). In mice there is evidence that DVGs can limit influenza virus-induced pathology and trigger antiviral immunity (Tapia et al 2013;Dimmock and Easton 2015), potentially through a preferential recognition by the cytosolic sensor RIG-I (Baum et al 2010).…”

“…The advent of next-generation sequencing (NGS) has enabled to characterize the diversity of viral DVGs at an unprecedented scale. Several bioinformatics tools, such as ViReMa (Routh and Johnson 2014;Alnaji et al 2019), DItector (Beauclair et al 2018), DVG-profiler (Bosma et al 2019), or VODKA (Sun et al 2019) were designed in order to identify the short Illumina reads that contain DVG deletion breakpoints and are discarded by usual alignment al-gorithms. Two critical issues are (i) how to differentiate true deletion DVGs from artifactual deletions produced during RNA sample processing for RNA-seq, and (ii) how to accurately quantify the relative frequency of each DVG species with respect to the full-length genome.…”