Deep sequencing of High Plains wheat mosaic virus from sweet corn to guide seed health testing reveals multiple variants for all eight genome segments and two major isolate types
Wilson, J. R.; Ohlson, E. W.; Willie, K. J.; Khatri, N.; du Toit, L. J.
Show abstract
High Plains wheat mosaic virus (HPWMoV) is a wheat and maize-infecting virus of phytosanitary concern due to its potential for seed transmission. Recent phytosanitary restrictions have required sweet corn seed lots to test negative for HPWMoV prior to import into certain countries. To inform the design of more sensitive and broad-spectrum diagnostic primers for seed health testing and phytosanitary certification, we performed deep sequencing of HPWMoV-positive tissue collected from fields in two major sweet corn seed production regions in the Pacific Northwest, the Columbia Basin and Treasure Valley. Virus-like particle enrichment prior to Illumina sequencing facilitated near complete genome coverage (>95%) for the 21 HPWMoV isolates sequenced. De novo assembly of the eight viral genome segments revealed high levels of diversity for each segment, with at least two variants identified for each RNA and three variants for RNA3, RNA6, and RNA8. Within each sample, only one variant per RNA segment was usually present, with the notable exception of RNA3, sorting each isolate into what we designated type A and type B isolates. All but one previously sequenced HPWMoV isolate can be sorted into these two types. Two samples contained at least two variants for every RNA, totaling 17 genome segments, potentially representing a co-infection of type A and type B isolates. Despite this variability, we successfully designed two primer and probe sets for reverse transcription-quantitative polymerase chain reactions (RT-qPCR) that detected all 20 isolates tested in a duplex diagnostic assay, making the assay suitable for seed health testing for HPWMoV.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The α-tubulin of Laodelphax striatellus facilitates the passage of rice stripe virus (RSV) and enhances horizontal transmission 95%
- A CRISPR-Cas9 screen reveals a role for WD repeat-containing protein 81 (WDR81) in the entry of late penetrating viruses 93%
- Identification and characterization of novel bat coronaviruses in Spain 93%
Similar papers in this journal
- Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel 92%
- Individual bat viromes reveal the co-infection, spillover and emergence risk of potential zoonotic viruses 92%
- Polerovirus N-terminal readthrough domain structures reveal novel molecular strategies for mitigating virus transmission by aphids 92%
Similar papers in this journal
- Ever-increasing viral diversity associated with the red imported fire ant Solenopsis invicta (Formicidae: Hymenoptera) 94%
- Development of an RT-RPA assay for La Crosse virus detection provides insights into age-dependent neuroinvasion in mice 93%
- Nucleotide sequence analysis reveals the presence of PVY-Tam isolates affecting tamarillo in Colombia 93%
Similar papers in this journal
- Segment 2 from influenza A(H1N1)pdm09 viruses confers temperature sensitive HA yield on candidate vaccine virus growth in eggs that is complemented by PB2 701D 93%
- Murine norovirus virulence factor 1 (VF1) protein contributes to viral fitness during persistent infection 92%
- Population diversity of cassava mosaic begomoviruses increases over the course of serial vegetative propagation 92%
Similar papers in this journal
- A Respiratory Syncytial Virus trailer sequence modulates viral replication and copy-back defective viral genome generation and propagation kinetics 94%
- Application of the CPER reverse genetics system for genetic engineering of rabies virus 94%
- Limited effect of short- to mid-term storage conditions on an Australian farmland soil RNA virome 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.