bioRxivMicrobiology
Real-time, MinION-based, amplicon sequencing for lineage typing of infectious bronchitis virus from upper respiratory samples
S. L. Butt, E. C. Erwood, J. Zhang, H. S. Sellers, K. Young, K. K. Lahmers, J. B. Stanton
Abstract
Infectious bronchitis (IB) causes significant economic losses in the global poultry industry. Control of infectious bronchitis is hindered by the genetic diversity of the causative agent, infectious bronchitis virus (IBV), which has led to the emergence of several serotypes that lack complete serologic cross-protection. While serotyping by definition requires immunologic characterization, genotyping is an efficient means to identify IBVs detected in samples. Sanger sequencing of the S1 subunit of the spike gene is currently used to genotype IBV; however, the universal S1 PCR was created to work from cultured IBV and it is inefficient at detecting mixed isolates. This paper describes a MinION-based AmpSeq method that genetically typed IBV from clinical samples, including samples with multiple isolates. Total RNA was extracted from fifteen tracheal scrapings and choanal cleft swab samples, randomly reverse transcribed, and PCR amplified using modified S1-targeted primers. Amplicons were barcoded to allow for pooling of samples, processed per manufacturers instructions into a 1D MinION sequencing library, and sequenced on the MinION. The AmpSeq method detected IBV in 13 of 14 IBV-positive samples. AmpSeq accurately detected and genotyped both IBV lineages in three of five samples containing two IBV lineages. Additionally, one sample contained three IBV lineages, and AmpSeq accurately detected two of the three. Strain identification, including detection of different strains from the same lineage, was also possible with this AmpSeq method. The results demonstrate the feasibility of using MinION-based AmpSeq for rapid and accurate identification and lineage typing of IBV from oral swab samples.
- assay: bulk RNA-seq
- research focus: host-pathogen & immune response
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
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