Speaker
Description
Lemna minor of the Lemnaceae (aka duckweeds) family is a superior model for investigating plant-microbe interactions. We designed a project, which aims to quantify the effects of a synthetic bacterial community across the largest genotype × environmental stress matrix (6,480 combinations) examined in a plant–microbe study to date. Multi-omics (metabolome, transcriptome, ionome, and phenome) studies will be applied to understand how microbes affect stress resilience and growth performance of 30 genome-resolved L. minor genotypes. In this report, we present a novel metabolomics workflow for deep metabolome annotation and interpretation in L. minor. The workflow integrates complementary LC–MS data processing, statistical prioritization of biologically relevant features, multi-layer metabolite annotation, confidence-level labeling, and pathway-based interpretation. Metabolite annotation combines MS/MS spectral library matching, fragmentation-based annotation, and spectral similarity or metabolic relationship-based approaches. These results are harmonized into an automated confidence-ranking system spanning confirmed structures to unknown features. As a case study, the workflow was applied to two L. minor genotypes with contrasting growth performance. Our analysis detected thousands of metabolic features, including a subset of high-confidence annotations. Initial analyses identified broad metabolic differences between the two model genotypes involving amino acid and nitrogen, phenylpropanoid/flavonoid, carbon and energy, lipid, and pigment-associated metabolism. Although the biological interpretation is ongoing, we can conclude that the novel workflow offers a promising approach to automate deep, evidence-based metabolome annotation, as well as providing a flexible foundation for future research into duckweed metabolomics and the plant-microbiome relationship.
Keywords
metabolomics; multi-stress, metabolite annotation
References
No
| Corresponding author email | rollet@ipk-gatersleben.de |
|---|---|
| Scientific Session | Cell Biology, Physiology, Metabolisms |
