bioRxivMicrobiologyPreprint
Genome-wide association mapping reveals genes underlying population-level metabolome diversity in a fungal crop pathogen
N. K. Singh, S. M. Tralamazza, L. N. Abraham, G. Glauser, D. Croll
Abstract
BackgroundFungi produce a wide range of specialized metabolites (SMs) involved in biotic interactions. Pathways for the production of SMs are often encoded in clusters of tightly arranged genes identified as biosynthetic gene clusters. Such gene clusters can undergo horizontal gene transfers between species and rapid evolutionary change within species. The acquisition, rearrangement and deletion of gene clusters can generate significant metabolome diversity. However, the genetic basis underlying variation in SM production remains poorly understood. ResultsHere, we analyzed metabolite production of a large population of the fungal pathogen of wheat, Zymoseptoria tritici. The pathogen causes major yield losses and shows variation in gene clusters. We performed untargeted ultra-high performance liquid chromatography-high resolution mass spectrometry to profile the metabolite diversity among 102 isolates of the same species. We found substantial variation in the abundance of the detected metabolites among isolates. Integrating whole-genome sequencing data, we performed metabolite genome-wide association mapping to identify loci underlying variation in metabolite production (i.e. metabolite-GWAS). We found that significantly associated SNP reside mostly in coding and gene regulatory regions. Associated genes encode mainly transport and catalytic activities. The metabolite-GWAS identified also a polymorphism in the 3UTR region of a virulence gene related to metabolite production and showing expression variation. ConclusionsTaken together, our study provides a significant resource to unravel polymorphism underlying metabolome diversity within a species. Integrating metabolome screens should be feasible for a range of different plant pathogens and help prioritize molecular studies.
- organism: fungi (other)
- assay: metabolomics
- research focus: GWAS / association mapping
- approach: statistical model
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