Speaker
Description
In Arabidopsis thaliana, the functions of only about 30% of genes have been experimentally characterized, partly because many gene functions are difficult to discern under laboratory conditions and might only be relevant under natural environments. Here, we conducted a comprehensive ecological study of Arabidopsis thaliana across natural habitats, integrating detailed phenotypic assessments with extensive transcriptomic analyses. Intensive phenotyping of over 2,500 naturally occurring plants across two distinct, environmentally contrasting habitats and multiple seasons (2021–2024) generated a dataset exceeding 35,000 data points for 9 quantitative traits. By sequencing over 1,000 transcriptomes from these plants, we linked environmental conditions directly to shifts in gene expression and regulatory networks. Leveraging advanced machine learning techniques, we integrated transcriptomic and phenotypic data to predict and experimentally validate novel gene functions. Using petiole length as a proof of concept, a trait highly sensitive to temperature and light cues, our computational predictions accurately identified both known and previously unknown genes influencing this trait, as confirmed by mutant analysis. Extending this approach to all measured traits, we validated the ecological importance of known genes under natural conditions and predicted hundreds of previously unknown gene functions. This integrative ecological genomics approach provides unprecedented insights into gene functions and regulations, offering a powerful framework for discovering critical genes involved in plant development and environmental responsiveness in natural ecosystems.
| Status Group | Doctoral Researcher |
|---|---|
| Poster Presentation Option | Yes, I’m willing to present as a poster. |
