Modeling yeast metabolism in plant phyllosphere
Modeling yeast metabolism in plant phyllosphereMicrobial Ecology
Suitable for MSc students for a minimum of 6 months
Available from January 2024
Background & aim
The phyllosphere (the aerial parts of plants), harbors a rich microbial life, including bacteria, fungi, viruses, and yeasts. The phyllosphere is a harsh and dynamic environment where yeasts are exposed to several different (a)biotic stresses, including low nutrient availability, ultraviolet radiation, temperature/humidity oscillations, as well as toxic compounds. Among yeasts, Aureobasidium, Vishniacozyma and Sporobolomyces genera are highly abundant on leaves of different plant species, including wheat. These genera are also the most abundant in our ‘Phyllosphere Yeast Repository’ - an extensive collection of yeast isolates, genomes and metabolomes. To date, the mechanisms employed by these yeasts to thrive in the phyllosphere have remained a mystery. In this project, our primary focus will be on delving into the physiology of these less-researched yeast strains residing in the phyllosphere. Our methodology will entail enhancing an in-house computational pipeline, which we will then apply to construct a comprehensive genome-scale metabolic model (GEM) for these yeast strains. We will evaluate the resulting GEM by simulating their growth to gain a deeper understanding of the adaptive strategies employed by these yeasts in the phyllosphere.
- Improve computational tools to support the reconstruction of genome-scale metabolic models.
- Reconstruct the first genome-scale metabolic model of an important phyllosphere-related yeast.
- Predict carbon utilization by simulating the growth of phyllosphere yeasts based on genome-scale metabolic modeling.
Approaches & techniques
This project centers on the development of a Genome-Scale Metabolic Model (GEM) for yeast strains capable of thriving in the phyllosphere. We will utilize recently assembled genomes, combining both long and short sequencing reads, annotated from various tools and databases. The resulting draft model will undergo further refinement through manual curation and gap-filling, resulting in the final version.
As part of this internship, you will be involved in enhancing a pipeline designed to semi-automate the creation of taxon-specific draft metabolic networks, which will serve as templates for Metadraft (Olivier B. 2019), a tool for manual construction of GEMs. This will entail a combination of taxonomic-specific UniProt searches, integration of the Rhea database, and automated draft network construction, alongside the creation of a user-friendly tool or code for broader accessibility and use by others. With the improved pipeline, we will evaluate the resulting metabolic model by comparing it to novel growth data across diverse conditions and carbon utilization scenarios, including BIOLOG plates. We will conduct simulations through flux balance analysis using COBRA or alternative libraries, primarily implemented in Python. For visualizing and analyzing the associated reaction networks, we will employ tools such as Escher and Cytoscape.
Throughout this internship, you will have the opportunity to enhance your skills in bioinformatics, metabolic modeling, and plant-microbe interactions. You will actively participate in our group meetings and journal club discussions. To apply, please submit a motivation letter and your CV.
Location & contact:
Department of Bioinformatics, Wageningen University, Wageningen, The Netherlands - firstname.lastname@example.org (Chrats Melkonian)
Department of Microbial Ecology, Netherlands Institute of Ecology, Wageningen, The Netherlands - email@example.com (Viviane Cordovez)
Gouka, L.; Vogels, C.; Hansen, L.H.; Raaijmakers, J.M.; Cordovez, V. (2022a). Genetic, phenotypic and metabolic diversity of yeasts from wheat flag leaves. Frontiers in Plant Science 13:908628.
Gouka, L.; Raaijmakers, J.M.; Cordovez, V. (2022b). Ecology and functional potential of phyllosphere yeasts. Trends in Plant Science S1360-1385(22)00159-5.
Brett G. Olivier. (2019, October 8). SystemsBioinformatics/cbmpy-metadraft: Metadraft. Zenodo. http://doi.org/10.5281/zenodo.2398336.