The Study of Viral RNA Diversity in Bird Samples Using De Novo Designed Multiplex Genus-Specific Primer Panels

Abstract

Advances in the next generation sequencing (NGS) technologies have significantly increased our ability to detect new viral pathogens and systematically determine the spectrum of viruses prevalent in various biological samples. In addition, this approach has also helped in establishing the associations of viromes with many diseases. However, unlike the metagenomic studies using 16S rRNA for the detection of bacteria, it is impossible to create universal oligonucleotides to target all known and novel viruses, owing to their genomic diversity and variability. On the other hand, sequencing the entire genome is still expensive and has relatively low sensitivity for such applications. The existing approaches for the design of oligonucleotides for targeted enrichment are usually involved in the development of primers for the PCR-based detection of particular viral species or genera, but not for families or higher taxonomic orders. In this study, we have developed a computational pipeline for designing the oligonucleotides capable of covering a significant number of known viruses within various taxonomic orders, as well as their novel variants. We have subsequently designed a genus-specific oligonucleotide panel for targeted enrichment of viral nucleic acids in biological material and demonstrated the possibility of its application for virus detection in bird samples. We have tested our panel using a number of collected samples and have observed superior efficiency in the detection and identification of viral pathogens. Since a reliable, bioinformatics-based analytical method for the rapid identification of the sequences was crucial, an NGS-based data analysis module was developed in this study, and its functionality in the detection of novel viruses and analysis of virome diversity was demonstrated.

Figure 1

Individual primer pairs testing. (1, 8, 18, 19, 28, 33, 36) DNA Ladder (100 bp, 200 bp, 300 bp, 400 bp, 500 bp, 800 bp); (2) ZEBOV, primers ebola_f/r; (3) negative control for primers ebola_f/r; (4) MARV, primers marburg_f/r, (5) negative control for primers marburg_f/r; (6) LASV, primers mammarena_f/r, (7) negative control for primers mammarena_f/r; (9) CCHFV, primers nairo_f/r; (10) PRMV, primers nairo_f/r; (11) negative control for primers nairo_f/r; (12) INKV, primers orthobunya_f/r; (13) negative control for primers orthobunya_f/r; (14) DOBV, primers hanta_f/r; (15) negative control for primers hanta_f/r; (16) KEMV, primers orbi_f1/f2/r; (17) negative control for primers orbi_f1/f2/r; (20) MERS CoV, primers alphacorona_f/r; (21) negative control for primers alphacorona_f/r; (22) MERS (Betacoronavirus), primers betacorona_1_f/r; (23) negative control for primers betacorona_1_f/r; (24) MERS (Betacoronavirus), primers betacorona_2_f/r; (25) negative control for primers betacorona_2_f/r; (26) MERS (Betacoronavirus), primers gammacorona_f/r; (27) negative control for primers gammacorona_f/r; (29) TBEV, primers flavi_f/r; (30) YFV, primers flavi_f/r; (31) JEV, primers flavi_f/r; (32) negative control for primers flavi_f/r; (34) RABV, primers lyssa_f/r; (35) negative control for primers lyssa_f/r.

Figure 2

Reamplification of the multiplex PCR product; ß-C1: Betacoronavirus-specific primers; Colti: coltivirus-specific primers; Orthoreo: Orthoreovirus-specific primers; Sead.: Seadornavirus-specific primers; Orbi: orbivirus-specific primers.

Figure 3

A schematic picture of the bioinformatics pipeline developed for the analysis of the NGS data in this study. Specific third-party tools that were employed are shown in parentheses.

Figure 4

A graphical representation of the percentage of the detected viral reads (grey) with respect to the total number of sequencing reads obtained for the samples listed in Table 3.