Exploiting long read sequencing to detect azole fungicide resistance mutations in Pyrenophora teres using unique molecular identifiers
Article
Zulak, Katherine G., Farfan-Caceres, Lina, Knight, Noel L. and Lopez-Ruiz, Francisco J.. 2024. "Exploiting long read sequencing to detect azole fungicide resistance mutations in Pyrenophora teres using unique molecular identifiers." Scientific Reports. 14 (1). https://doi.org/10.1038/s41598-024-56801-z
| Article Title | Exploiting long read sequencing to detect azole fungicide resistance mutations in Pyrenophora teres using unique molecular identifiers |
|---|---|
| ERA Journal ID | 201487 |
| Article Category | Article |
| Authors | Zulak, Katherine G., Farfan-Caceres, Lina, Knight, Noel L. and Lopez-Ruiz, Francisco J. |
| Journal Title | Scientific Reports |
| Journal Citation | 14 (1) |
| Article Number | 6285 |
| Number of Pages | 13 |
| Year | 2024 |
| Publisher | Nature Publishing Group |
| Place of Publication | United Kingdom |
| ISSN | 2045-2322 |
| Digital Object Identifier (DOI) | https://doi.org/10.1038/s41598-024-56801-z |
| Web Address (URL) | https://www.nature.com/articles/s41598-024-56801-z |
| Abstract | Resistance to fungicides is a global challenge as target proteins under selection can evolve rapidly, reducing fungicide efficacy. To manage resistance, detection technologies must be fast and flexible enough to cope with a rapidly increasing number of mutations. The most important agricultural fungicides are azoles that target the ergosterol biosynthetic enzyme sterol 14α-demethylase (CYP51). Mutations associated with azole resistance in the Cyp51 promoter and coding sequence can co-occur in the same allele at different positions and codons, increasing the complexity of resistance detection. Resistance mutations arise rapidly and cannot be detected using traditional amplification-based methods if they are not known. To capture the complexity of azole resistance in two net blotch pathogens of barley we used the Oxford Nanopore MinION to sequence the promoter and coding sequence of Cyp51A. This approach detected all currently known mutations from biologically complex samples increasing the simplicity of resistance detection as multiple alleles can be profiled in a single assay. With the mobility and decreasing cost of long read sequencing, we demonstrate this approach is broadly applicable for characterizing resistance within known agrochemical target sites. |
| Contains Sensitive Content | Does not contain sensitive content |
| ANZSRC Field of Research 2020 | 300409. Crop and pasture protection (incl. pests, diseases and weeds) |
| Byline Affiliations | Curtin University |
| Centre for Crop Health |
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