Latest RNA and DNA nanopore sequencing allows for rapid avian influenza profiling
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Avian influenza virus (AIV) currently causes a panzootic with extensive mortality in wild birds, poultry, and wild mammals, thus posing a major threat to global health and underscoring the need for efficient monitoring of its distribution and evolution. Here, we utilized a well-defined AIV strain to systematically investigate AIV characterization through rapid, portable nanopore sequencing by (i) benchmarking the performance of fully portable RNA extraction and viral detection; (ii) comparing the latest DNA and RNA nanopore sequencing approaches for in-depth AIV profiling; and (iii) evaluating the performance of various computational pipelines for viral consensus sequence creation and phylogenetic analysis. Our results show that the latest RNA-specific nanopores can accurately genomically profile AIV from native RNA while additionally detecting RNA epigenetic modifications. We further identified an optimal laboratory and bioinformatic pipeline for reconstructing viral consensus genomes from nanopore sequencing data at various rarefaction thresholds, which we validated by application to real-world environmental samples for AIV monitoring in livestock.
Author Summary
We tested portable, rapid, and easy-to-use technology to obtain more information about the potentially zoonotic RNA virus avian influenza virus, or AIV. AIV has spread globally via the migratory paths of wild birds, and endangers domestic birds, mammals, and human populations given past evidence of infections of different animal species. We here used novel genomic technology that is based on nanopores to explore the genomes of the virus; we established optimized ways of creating the viral genome by comparing different laboratory and computational approaches and the performance of nanopores that either sequence the viral RNA directly or the converted DNA. We then applied the optimized protocol to dust samples which were collected from a duck farm in France during an AIV outbreak. We showed that we were able to use the resulting data to reconstruct the relationship between the virus responsible for the outbreak and previously detected AIV. Altogether, we showed how novel easy-to-use genomic technology can support the surveillance of potentially zoonotic pathogens by accurately recreating the viral genomes to better understand evolution and transmission of these pathogens.
