Project description
Yeast: metabolic adaptation to new environments
Changes in surrounding or environmental conditions bring changes within cells through alterations in gene expression and protein levels. When it comes to microorganisms, such changes may affect the vast majority of genes; yet the underlying mechanism of this plasticity remains largely unknown. The EU-funded MARY project aims to investigate the molecular pathways that enable environmental adaptation. Researchers will study yeast isolates that grow in different environmental niches, and with the help of metabolic models, they will decipher the gene expression changes that go hand in hand with adaptation. Apart from providing fundamental knowledge into the mechanism of cell adaptation, results will also have a profound impact on biotechnology, offering new opportunities for the manipulation of yeast.
Objective
Microbes have evolved to make the best use of their environmental resources while retaining sufficient adaptive capacity to cope with changing conditions. However, even simple changes in the environment require major cellular rearrangements, that manifest in the transcriptome, proteome and metabolome of the cell. For instance, a change in the availability of just four non-essential nutrients affects expression of 2/3rds of all yeast genes. In essence, we do not understand the key molecular mechanisms that govern such huge plasticity, which buffering mechanisms prevent the collapse of the cellular system, and how an optimal resource allocation is achieved to allow cells to thrive in so many environments. In this project, we will analyse a large collection of yeast wild isolates grown in conditions that resemble different ecological niches. We will identify the key molecular pathways that enable environmental adaption. Thanks to unique high-throughput capacities of the host laboratory, I will be able to record proteomes, metabolomes and growth properties of the wild yeast strains. I’ll link these molecular datasets to fitness and metabolism. My background being in big data analysis and network modelling, I will use genome-scale metabolic models and machine-learning to characterize how changes in protein resource allocation impact the distribution of metabolic flux in such context, and define, what are the regulatory mechanisms influencing metabolism at its different levels. Eventually, the knowledge gained will enable me to build a predictive model to understand the drivers of adaptations to ecological niche. By deepening our understanding of the mechanisms behind metabolic niche adaptation, this project will not only answer fundamental questions about the inner working of the cell but will also increase our ability to engineer yeast for biotechnological applications and to understand the problematic resilience of fungal pathogens to therapeutic interventions.
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Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
OX1 2JD Oxford
United Kingdom