Reconstruction of a catalogue of genome-scale metabolic models with enzymatic constraints using GECKO 2.0

Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes

A systems biology understanding of protein constraints in the metabolism of budding yeasts

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

Constraint-based modeling of yeast metabolism and protein secretion

Yeasts are extensively exploited as cell factories for producing alcoholic beverages, biofuels, bio-pharmaceutical proteins, and other value-added chemicals. To improve the performance of yeast cell factories, it is necessary to understand their metabolism. Genome-scale metabolic models (GEMs) have been widely used to study cellular metabolism systematically. However, GEMs for yeast species have not been equally developed. GEMs for the well-studied yeasts such as Saccharomyces cerevisiae have been updated several times, while most of the other yeast species have no available GEM. Additionally, classical GEMs only account for the metabolic reactions, which limits their usage to study complex phenotypes that are not controlled by metabolism alone. Thus, other biological processes can be integrated with GEMs to fulfill diverse research purposes. \ua0In this thesis, the GEM for S. cerevisiae was updated to the latest version Yeast8, which serves as the basic model for the remaining work of the thesis including two dimensions: 1) Yeast8 was used as a template for generating GEMs of other yeast species/strains, and 2) Yeast8 was expanded to account for more biological processes. Regarding the first dimension, strain-specific GEMs for 1,011 S. cerevisiae isolates from diverse origins and species-specific GEMs for 343 yeast/fungi species were generated. These GEMs enabled explore the phenotypic diversity of the single species from diverse ecological and geographical origins, and evolution tempo among diverse yeast species. Regarding the second dimension, other biological processes were formulated within Yeast8. Firstly, Yeast8 was expanded to account for enzymatic constraints, resulting in enzyme-constrained GEMs (ecGEMs). Secondly, Yeast8 was expanded to the model CofactorYeast by accounting for enzyme cofactors such as metal ions, which was used to simulate the interaction between metal ions and metabolism, and the cellular responses to metal ion limitation. Lastly, Yeast8 was expanded to include the protein synthesis and secretion processes, named as pcSecYeast. pcSecYeast was used to simulate the competition of the recombinant protein with the native secretory-pathway-processed proteins. Besides that, pcSecYeast enabled the identification of overexpression targets for improving recombinant protein production. \ua0When developing these complex models, issues were identified among which the lack of enzyme turnover rates, i.e., kcatvalues, needs to be solved. Accordingly, a machine learning method for kcat prediction and automated incorporation into GEMs were developed, facilitating the generation of functional ecGEMs in a large scale

Global Functional Atlas of \u3cem\u3eEscherichia coli\u3c/em\u3e Encompassing Previously Uncharacterized Proteins

One-third of the 4,225 protein-coding genes of Escherichia coli K-12 remain functionally unannotated (orphans). Many map to distant clades such as Archaea, suggesting involvement in basic prokaryotic traits, whereas others appear restricted to E. coli, including pathogenic strains. To elucidate the orphans’ biological roles, we performed an extensive proteomic survey using affinity-tagged E. coli strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome consisting of 5,993 putative physical interactions and 74,776 putative functional associations, most of which are novel. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multiprotein complexes and functional modules together with annotated gene products, whereas a machine-learning strategy based on network integration implicated the orphans in specific biological processes. We provide additional experimental evidence supporting orphan participation in protein synthesis, amino acid metabolism, biofilm formation, motility, and assembly of the bacterial cell envelope. This resource provides a “systems-wide” functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins

Joint Genetic Analysis of Gene Expression Data with Inferred Cellular Phenotypes

Even within a defined cell type, the expression level of a gene differs in individual samples. The effects of genotype, measured factors such as environmental conditions, and their interactions have been explored in recent studies. Methods have also been developed to identify unmeasured intermediate factors that coherently influence transcript levels of multiple genes. Here, we show how to bring these two approaches together and analyse genetic effects in the context of inferred determinants of gene expression. We use a sparse factor analysis model to infer hidden factors, which we treat as intermediate cellular phenotypes that in turn affect gene expression in a yeast dataset. We find that the inferred phenotypes are associated with locus genotypes and environmental conditions and can explain genetic associations to genes in trans. For the first time, we consider and find interactions between genotype and intermediate phenotypes inferred from gene expression levels, complementing and extending established results

Deep learning-based k(cat) prediction enables improved enzyme-constrained model reconstruction

Enzyme turnover numbers (k(cat)) are key to understanding cellular metabolism, proteome allocation and physiological diversity, but experimentally measured k(cat) data are sparse and noisy. Here we provide a deep learning approach (DLKcat) for high-throughput k(cat) prediction for metabolic enzymes from any organism merely from substrate structures and protein sequences. DLKcat can capture k(cat) changes for mutated enzymes and identify amino acid residues with a strong impact on k(cat) values. We applied this approach to predict genome-scale k(cat) values for more than 300 yeast species. Additionally, we designed a Bayesian pipeline to parameterize enzyme-constrained genome-scale metabolic models from predicted k(cat) values. The resulting models outperformed the corresponding original enzyme-constrained genome-scale metabolic models from previous pipelines in predicting phenotypes and proteomes, and enabled us to explain phenotypic differences. DLKcat and the enzyme-constrained genome-scale metabolic model construction pipeline are valuable tools to uncover global trends of enzyme kinetics and physiological diversity, and to further elucidate cellular metabolism on a large scale

Messenger RNA Destabilization by -1 Programmed Ribosomal Frameshifting

Although first discovered in viruses, previous studies have identified programmed -1 ribosomal frameshifting (-1 PRF) signals in eukaryotic genomic sequences, and suggested a role in mRNA stability. This work improves and extends the computational methods used to search for potential -1 PRF signals. It continues to examine four yeast -1 PRF signals and show that they promote significant mRNA destabilization through the nonsense mediated (NMD) and no-go (NGD) decay pathways. Yeast EST2 mRNA is highly unstable and contains up to five -1 PRF signals. Ablation of the -1 PRF signals or of NMD stabilizes this mRNA. These same computational methods identified an operational programmed -1 ribosomal frameshift (-1 PRF) signal in the human mRNA encoding CCR5. A -1 PRF event on the CCR5 mRNA directs translating ribosomes to a premature termination codon, destabilizing it through the nonsense-mediated mRNA decay (NMD) pathway. CCR5-mediated -1 PRF is stimulated by at least two miRNAs, one of which is shown to directly interact with the CCR5 -1 PRF signal. Structural analyses reveal a complex and dynamic mRNA structure in the -1 PRF signal, suggesting structural plasticity as the underlying biophysical basis for regulation of -1 PRF

Histone variants in archaea - An undiscovered country.

Exchanging core histones in the nucleosome for paralogous variants can have important functional ramifications. Many of these variants, and their physiological roles, have been characterized in exquisite detail in model eukaryotes, including humans. In comparison, our knowledge of histone biology in archaea remains rudimentary. This is true in particular for our knowledge of histone variants. Many archaea encode several histone genes that differ in sequence, but do these paralogs make distinct, adaptive contributions to genome organization and regulation in a manner comparable to eukaryotes? Below, we review what we know about histone variants in archaea at the level of structure, regulation, and evolution. In all areas, our knowledge pales when compared to the wealth of insight that has been gathered for eukaryotes. Recent findings, however, provide tantalizing glimpses into a rich and largely undiscovered country that is at times familiar and eukaryote-like and at times strange and uniquely archaeal. We sketch a preliminary roadmap for further exploration of this country; an undertaking that may ultimately shed light not only on chromatin biology in archaea but also on the origin of histone-based chromatin in eukaryotes