Dowell, J. A. | Search bioRxiv

bioRxiv2026

Strategy Sets the Scene: Genetic architecture of linalool resistance in Botrytis cinerea

Botrytis cinerea is a necrotrophic fungal pathogen that infects thousands of plant species. During infection, these diverse plant hosts produce different specialized metabolites that can inhibit pathogen growth and shape pathogen fitness. However, the genetic architecture of pathogen resistance toward individual host defense metabolites remains poorly understood. To address this question, we exposed 83 B. cinerea isolates to the metabolite linalool and quantified metabolic and structural responses. Exposure revealed extensive phenotypic diversity across isolates. Population structure analysis using genome-wide SNP data showed little genetic subdivision despite diverse hosts and geographic location. Genome-wide association identified 98 genes of interest associated with membrane transport and stress response regulation. Genetic associations were stronger for morphological traits than for metabolic traits, suggesting that hyphal architecture may have a complex genetic architecture contributing to linalool resistance. Together, these results establish natural variation in linalool response and provide candidate loci for understanding how generalist pathogens respond to host-derived chemical defenses. Article SummaryTo understand how a generalist pathogen responds to host defenses, we asked how Botrytis cinerea responds to linalool, a widespread monoterpene involved in plant defense. We exposed 83 B. cinerea isolates to 1000 {micro}M of linalool for 72 hours and quantified metabolic traits (growth curves and growth dynamics over time) and morphological traits (hyphal network features). Using GWA, we linked phenotypic variation to genetic variants. Results indicate substantial natural variation in linalool resistance and distinct genetic architectures across trait classes: metabolic responses are driven by a relatively small number of loci with larger effects, whereas structural/morphological responses appear more polygenic.

genomics↗

bioRxiv2025

Untargeted Metabolomics of Plant Samples using HPLC-DAD and Gaussian Mixture Models

Abstract/SummaryO_ST_ABSPremiseC_ST_ABSPlants produce millions of different chemical compounds, contributing greatly to their physiology and evolutionary trajectories. Most untargeted metabolomic methods are inaccessible, either due to upfront instrument costs or intensive technical training. More accessible methods using diode array detectors often only utilize a few wavelengths, preventing high-throughput observation of total metabolic diversity. MethodsLeaves from the genera Betula, Magnolia, Rosa, and Viburnum were collected, dried and ground, extracted, and analyzed by HPLC-DAD. Chromatographic data was then processed in a curated R pipeline, and resulting resolved peaks were clustered by absorbance spectra using Gaussian Finite Mixture Models (GMMs). To assess clustering, GMM was compared to a more traditional linear discriminant analysis (LDA) method, with clusters identified through literature searches. ResultsSignificant associations between the abundances of chemical classes and whole-metabolome alpha and beta diversity indices were recovered. In general, GMMs performed better than other classification methods like LDA, especially between classes that share common features like non-flavonoid phenolics and flavonoids. DiscussionWe show that our method can easily extract relevant class-level diversity of metabolite profiles among closely related species, genotypes, and ecotypes. Regardless of underlying research question, our method extends the usage of DAD beyond restricted targeted analyses and increases the accessibility of untargeted metabolomics.

biochemistry↗