Metabolic Adaptation and Resilience in the face of a changing environment

Project Information

MARY

Grant agreement ID: 101026830

DOI

10.3030/101026830

Project closed

EC signature date 10 December 2021

Start date 22 August 2022

End date 11 December 2024

Funded under

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Total cost

€ 212 933,76

EU contribution

€ 212 933,76

Coordinated by

THE CHANCELLOR, MASTERS AND SCHOLARS OF THE UNIVERSITY OF OXFORD
United Kingdom

Periodic Reporting for period 1 - MARY (Metabolic Adaptation and Resilience in the face of a changing environment)

Reporting period: 2022-08-22 to 2024-08-21

Summary of the context and overall objectives of the project

The MARY project set out to explore the mechanisms of metabolic resilience and adaptation in yeast, focusing on how cells respond to diverse environmental and genetic challenges. Metabolic resilience, the ability of an organism to maintain and reorganize its metabolism in the face of stress or change, is fundamental to the survival and functionality of living systems. Understanding these processes has significant implications for both science and industry, as it sheds light on cellular homeostasis, evolutionary biology, and the potential to engineer microbial systems for biotechnological applications.
The study of yeast (Saccharomyces cerevisiae), a model organism with well-characterized genetics and broad industrial utility, provided an ideal framework for this research. Yeast is critical to industries such as bioethanol production, pharmaceuticals, and food fermentation, and optimizing its performance under variable conditions could lead to significant economic and environmental benefits. Moreover, investigating how yeast adapts to genetic diversity and environmental fluctuations has the potential to illuminate general principles of cellular adaptation that apply to more complex organisms, including humans.
The overarching objectives of the MARY project were to:
1. Investigate the relationship between metabolic resource allocation, protein dynamics, and environmental niches, using genetically diverse yeast strains to identify how these factors contribute to metabolic resilience.
2. Explore the regulatory mechanisms underlying metabolic adaptation, focusing on how genetic and environmental inputs drive changes at the proteomic and metabolic levels.
3. Develop new approaches and datasets that contribute to the predictive modeling of strain fitness and metabolic behavior, leveraging multi-omics integration and advanced computational methods.
These objectives address critical scientific and societal needs by providing insights into the adaptability of living systems and offering tools to optimize microbial performance in industrial processes. By bridging gaps in our understanding of metabolic resilience, the project contributes to advancing biotechnology, improving sustainable production methods, and enhancing our knowledge of cellular adaptation.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

One of the key accomplishments of the action was the development and application of a high-throughput proteomics pipeline, enabling the detailed analysis of datasets of genetically diverse yeast strains. This pipeline facilitated the creation of a comprehensive dataset encompassing over 1,500 proteins measured across 796 natural yeast isolates. This dataset was leveraged to study how yeast adapts to chromosomal imbalances and demonstrated proteome-level dosage compensation in natural aneuploids. This results, published in Nature, made a significant contribution to understanding protein regulation in natural isolates and highlighted the importance of considering genetic diversity to uncover regulatory mechanisms.
Another major focus was the investigation of metabolic adaptation through the lens of amino acid supplementation. Although still under analysis, this work revealed nutrient-driven proteomic reprogramming mediated by the environmental stress response and redox balance. These findings highlight the dynamic regulatory mechanisms that enable metabolic flexibility in yeast.
The project also collaborated to generate a multi-omics dataset integrating proteomic and transcriptomic data from 942 natural yeast isolates. This work, published in PNAS, provided critical insights into the genetic control of gene expression variation and emphasized the distinct regulatory mechanisms operating at the transcriptomic and proteomic levels. The datasets produced during this collaboration, as well as others generated thanks to the analytical pipeline from this project, are available in public repositories such as PRIDE, ensuring accessibility for further research.
Dissemination of the results was highly effective, with multiple publications in high-profile journals (Nature, PNAS), preprints in bioRxiv with further work under preparation and presentations at international conferences. These efforts have not only advanced the field of yeast biology but also established resources and methodologies for future research in metabolic resilience and industrial biotechnology. Although the project MARY has ended, key research lines are continued.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

The MARY project advanced the state of the art by generating novel insights into metabolic adaptation mechanisms in yeast. Key contributions include the discovery of proteome-level dosage compensation mechanisms in natural aneuploids and the identification of nutrient-driven proteomic reprogramming linked to environmental stress responses. These findings challenge existing paradigms of metabolic regulation and open new avenues for understanding cellular and metabolic adaptation.
Beyond the scientific insights, the high-throughput proteomics pipeline developed during the project represents a significant technological advancement. By enabling the efficient processing and analysis of large-scale proteomic datasets, this methodology facilitates the exploration of genetic and environmental factors shaping metabolic systems. The pipeline's adaptability has also supported other collaborative studies, broadening its impact.
The datasets produced during the project—ranging from multi-omics integrations to comprehensive proteomic profiles—offer valuable resources for the scientific community. These datasets provide the foundation for future studies, including predictive modeling of strain fitness and the exploration of metabolic adaptation in diverse environments. The findings also hold promise for biotechnological applications, such as optimizing yeast strains for industrial fermentation processes and bio-production.
In terms of socio-economic and societal impact, the project's results have the potential to enhance industrial biotechnology by improving strain performance in diverse conditions, leading to more sustainable and efficient production processes.

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