A systems biology understanding of protein constraints in the metabolism of budding yeasts
Doctoral thesis, 2023
Fermentation technologies, such as bread making and production of alcoholic beverages, have been crucial for development of humanity throughout history. Saccharomyces cerevisiae provides a natural platform for this, due to its capability to transform sugars into ethanol. This, and other yeasts, are now used for production of pharmaceuticals, including insulin and artemisinic acid, flavors, fragrances, nutraceuticals, and fuel precursors. In this thesis, different systems biology methods were developed to study interactions between metabolism, enzymatic capabilities, and regulation of gene expression in budding yeasts.
In paper I, a study of three different yeast species (S. cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus), exposed to multiple conditions, was carried out to understand their adaptation to environmental stress. Paper II revises the use of genome-scale metabolic models (GEMs) for the study and directed engineering of diverse yeast species. Additionally, 45 GEMs for different yeasts were collected, analyzed, and tested. In paper III, GECKO 2.0, a toolbox for integration of enzymatic constraints and proteomics data into GEMs, was developed and used for reconstruction of enzyme-constrained models (ecGEMs) for three yeast species and model organisms. Proteomics data and ecGEMs were used to further characterize the impact of environmental stress over metabolism of budding yeasts.
On paper IV, gene engineering targets for increased accumulation of heme in S. cerevisiae cells were predicted with an ecGEM. Predictions were experimentally validated, yielding a 70-fold increase in intracellular heme. The prediction method was systematized and applied to the production of 102 chemicals in S. cerevisiae (Paper V). Results highlighted general principles for systems metabolic engineering and enabled understanding of the role of protein limitations in bio-based chemical production. Paper VI presents a hybrid model integrating an enzyme-constrained metabolic network, coupled to a gene regulatory model of nutrient-sensing mechanisms in S. cerevisiae. This model improves prediction of protein expression patterns while providing a rational connection between metabolism and the use of nutrients from the environment.
This thesis demonstrates that integration of multiple systems biology approaches is valuable for understanding the connection of cell physiology at different levels, and provides tools for directed engineering of cells for the benefit of society.
In paper I, a study of three different yeast species (S. cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus), exposed to multiple conditions, was carried out to understand their adaptation to environmental stress. Paper II revises the use of genome-scale metabolic models (GEMs) for the study and directed engineering of diverse yeast species. Additionally, 45 GEMs for different yeasts were collected, analyzed, and tested. In paper III, GECKO 2.0, a toolbox for integration of enzymatic constraints and proteomics data into GEMs, was developed and used for reconstruction of enzyme-constrained models (ecGEMs) for three yeast species and model organisms. Proteomics data and ecGEMs were used to further characterize the impact of environmental stress over metabolism of budding yeasts.
On paper IV, gene engineering targets for increased accumulation of heme in S. cerevisiae cells were predicted with an ecGEM. Predictions were experimentally validated, yielding a 70-fold increase in intracellular heme. The prediction method was systematized and applied to the production of 102 chemicals in S. cerevisiae (Paper V). Results highlighted general principles for systems metabolic engineering and enabled understanding of the role of protein limitations in bio-based chemical production. Paper VI presents a hybrid model integrating an enzyme-constrained metabolic network, coupled to a gene regulatory model of nutrient-sensing mechanisms in S. cerevisiae. This model improves prediction of protein expression patterns while providing a rational connection between metabolism and the use of nutrients from the environment.
This thesis demonstrates that integration of multiple systems biology approaches is valuable for understanding the connection of cell physiology at different levels, and provides tools for directed engineering of cells for the benefit of society.
gene regulation
omics analysis
enzyme capacity
metabolism
systems biology
stress adaptation
genome-scale modeling
metabolic engineering
10:an Lecture hall, Kemi building
Opponent: Vassilly Hatzimanikatis, Ecole Polytechnique Federale de Lausanne
Author
Iván Domenzain Del Castillo Cerecer
Chalmers, Life Sciences, Systems and Synthetic Biology
Stress-induced expression is enriched for evolutionarily young genes in diverse budding yeasts
Nature Communications,;Vol. 11(2020)
Journal article
Evaluating accessibility, usability and interoperability of genome-scale metabolic models for diverse yeasts species
FEMS Yeast Research,;Vol. 21(2021)
Review article
Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0
Nature Communications,;Vol. 13(2022)
Journal article
Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae
Proceedings of the National Academy of Sciences of the United States of America,;Vol. 119(2022)
Journal article
Computational biology predicts metabolic engineering targets for increased production of 102 valuable chemicals in yeast. Domenzain, I., Lu, Y., Shi, J., Lu, H. and Nielsen, J.
A novel yeast hybrid modeling framework integrating Boolean and enzyme-constrained networks enables exploration of the interplay between signaling and metabolism
PLoS Computational Biology,;Vol. 17(2021)
Journal article
Archeologists have revealed traces of intentional fermentation of grains which can be dated back to 10,000 – 12,000 years ago, coinciding or even preceding the estimated origins of agriculture and sedentary societies. This suggests that fermentation technologies, such as bread making and production of alcoholic beverages, have been a crucial activity in human history, since its earliest days.
Saccharomyces cerevisiae, a unicellular organism also known as baker’s yeast, has provided a natural platform to drive fermentation processes, mainly due to its capacity to ferment sugars into ethanol. The transformation of nutrients, such as sugars, inside cells generates energy and the necessary precursors that the cells need for survival and growth. This process is known as metabolism, and is composed of thousands of chemical reactions. These reactions require enzymes and proteins, encoded by the genes of cells.
Throughout history humans have learned to use the metabolic potential of Saccharomyces cerevisiae and other yeasts to obtain chemical products that can be beneficial for society. This has resulted in development of applications for production of widely used pharmaceuticals, including insulin and artemisinic acid, flavors, fragrances, cosmetics, and fuel precursors.
In this thesis I use different quantitative approaches in systems biology to understand how different yeast species have evolved proteins that enable them to adapt to diverse environmental conditions. Furthermore, mathematical models and software resources were developed to aid to understand how the differences between enzymes affect the function of the cell. These models were used to predict how yeast cells can be engineered to increase their production of desired products, offering a successful example on an increased production of heme inside of S. cerevisiae cells. Heme is an important precursor of the protein that carries oxygen in human blood.
Finally, this thesis provides the community of biological scientists with models, software tools and methods for gaining understanding of the role of enzymes in living systems, and how they can be used for directed engineering purposes, such as the production of chemicals and pharmaceuticals; but also, for gaining basic knowledge on how cells function and interact with their environment, which has the potential to contribute to our understanding of human disease.
Saccharomyces cerevisiae, a unicellular organism also known as baker’s yeast, has provided a natural platform to drive fermentation processes, mainly due to its capacity to ferment sugars into ethanol. The transformation of nutrients, such as sugars, inside cells generates energy and the necessary precursors that the cells need for survival and growth. This process is known as metabolism, and is composed of thousands of chemical reactions. These reactions require enzymes and proteins, encoded by the genes of cells.
Throughout history humans have learned to use the metabolic potential of Saccharomyces cerevisiae and other yeasts to obtain chemical products that can be beneficial for society. This has resulted in development of applications for production of widely used pharmaceuticals, including insulin and artemisinic acid, flavors, fragrances, cosmetics, and fuel precursors.
In this thesis I use different quantitative approaches in systems biology to understand how different yeast species have evolved proteins that enable them to adapt to diverse environmental conditions. Furthermore, mathematical models and software resources were developed to aid to understand how the differences between enzymes affect the function of the cell. These models were used to predict how yeast cells can be engineered to increase their production of desired products, offering a successful example on an increased production of heme inside of S. cerevisiae cells. Heme is an important precursor of the protein that carries oxygen in human blood.
Finally, this thesis provides the community of biological scientists with models, software tools and methods for gaining understanding of the role of enzymes in living systems, and how they can be used for directed engineering purposes, such as the production of chemicals and pharmaceuticals; but also, for gaining basic knowledge on how cells function and interact with their environment, which has the potential to contribute to our understanding of human disease.
Model-Based Construction And Optimisation Of Versatile Chassis Yeast Strains For Production Of Valuable Lipid And Aromatic Compounds (CHASSY)
European Commission (EC) (EC/H2020/720824), 2016-12-01 -- 2020-11-30.
Subject Categories
Cell Biology
Biochemistry and Molecular Biology
Microbiology
Bioinformatics and Systems Biology
Areas of Advance
Life Science Engineering (2010-2018)
ISBN
978-91-7905-911-8
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5377
Publisher
Chalmers
10:an Lecture hall, Kemi building
Opponent: Vassilly Hatzimanikatis, Ecole Polytechnique Federale de Lausanne