Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction

Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.DM was supported by the Portuguese Foundationfor Science and Technologythrough a post-doc fellowship (ref: SFRH/BPD/111519/ 2015). This study was supported by the PortugueseFoundationfor Science and Technology (FCT) under the scope of the strategic fundingof UID/BIO/04469/2013 unitand COMPETE2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145FEDER-000004) fundedby EuropeanRegional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte. This project has received fundingfrom the European Union’s Horizon 2020 research and innovation programme under grant agreementNo 686070. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We applied GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering

The interdependence between environment and metabolism in microbes and their ecosystems

Microbes are ubiquitous in virtually all habitats on Earth and affect human life in multiple ways, from the health-balancing role of the human microbiome, to the involvement of microbial communities in the global nitrogen and carbon cycles. The capacity of microbes to survive and grow in diverse environments relates directly to their ability to utilize available resources, be they from other microbes or from the environment itself. Hence, understanding how the environment shapes the metabolic functionality of individual microbes and complex communities constitutes an important area of research. In the first part of my thesis work, I explored how environmental nutrient composition and intracellular transcriptional regulation data can be integrated to provide insight into the temporal metabolic behavior of a bacterium through the use of genome-scale stoichiometric modeling approaches (Flux Balance Analysis). Thus I developed the method of Temporal Expression-based Analysis of Metabolism (TEAM), and applied it to Shewanella oneidensis, a bacterium studied for its important bioenergy and bioremediation applications. I found that TEAM improves on previous models' predictions of S. oneidensis metabolic fluxes, and recovers the overflow metabolism that has been seen experimentally. This study demonstrated the value of incorporating environmental context and transcriptional data for the prediction of time-dependent metabolic behavior. In the second part of my work, I extended the exploration of microbial metabolism from single species to complex communities in order to understand the robustness of metabolic functions. Specifically, I implemented novel mathematical analyses of metagenomic sequencing data to ask how functional stability of microbial communities could ensue despite large taxonomic variability. Upon representing in matrix form the metabolic capabilities of all genera found in 202 available metabolic ecosystem datasets, I compared the different communities with each other and with various randomized analogues. My results reveal new connections between the abundance of an organism in the community and the functions that it encodes. Furthermore, I found that genus abundances govern the metabolic robustness of a community more than the distribution of genetically encoded functions among the community members, suggesting that communities rely largely on ecological interactions to regulate their overall functionality

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

Efficient enzyme coupling algorithms identify functional pathways in genome-scale metabolic models

Flux coupling identifies sets of reactions whose fluxes are “coupled" or correlated in genome-scale models. By identified sets of coupled reactions, modelers can 1.) reduce the dimensionality of genome-scale models, 2.) identify reactions that must be modulated together during metabolic engineering, and 3.) identify sets of important enzymes using high-throughput data. We present three computational tools to improve the efficiency, applicability, and biological interpretability of flux coupling analysis. The first algorithm (cachedFCF) uses information from intermediate solutions to decrease the runtime of standard flux coupling methods by 10-100 fold. Importantly, cached-FCF makes no assumptions regarding the structure of the underlying model, allowing efficient flux coupling analysis of models with non-convex constraints. We next developed a mathematical framework (FALCON) that incorporates enzyme activity as continuous variables in genome-scale models. Using data from gene expression and fitness assays, we verified that enzyme sets calculated directly from FALCON models are more functionally coherent than sets of enzymes collected from coupled reaction sets. Finally, we present a method (delete-and-couple) for expanding enzyme sets to allow redundancies and branches in the associated metabolic pathways. The expanded enzyme sets align with known biological pathways and retain functional coherence. The expanded enzyme sets allow pathway-level analyses of genome-scale metabolic models. Together, our algorithms extend flux coupling techniques to enzymatic networks and models with transcriptional regulation and other non-convex constraints. By expanding the efficiency and flexibility of flux coupling, we believe this technique will find new applications in metabolic engineering, microbial pathogenesis, and other fields that leverage network modeling

DCcov: Repositioning of Drugs and Drug Combinations for SARS-CoV-2 Infected Lung through Constraint-Based Modelling

The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no effective antiviral drug except treatments for symptomatic therapy. Flux balance analysis is an efficient method to analyze metabolic networks. It allows optimizing for a metabolic function and thus e.g., predicting the growth rate of a specific cell or the production rate of a metabolite of interest. Here flux balance analysis was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the replication of the SARS-CoV-2 virus within the host tissue. Making use of expression data sets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then host-specific essential genes and gene-pairs were determined through in-silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, as well as ferroptosis, sphingolipid metabolism, cysteine metabolism, and fat digestion. By in-silico screening of FDA approved drugs on the putative disease-specific essential genes and gene-pairs, 45 drugs and 99 drug combinations were predicted as promising candidates for COVID-19 focused drug repositioning (https://github.com/sysbiolux/DCcov). Among the 45 drug candidates, six antiviral drugs were found and seven drugs that are being tested in clinical trials against COVID-19. Other drugs like gemcitabine, rosuvastatin and acetylcysteine, and drug combinations like azathioprine-pemetrexed might offer new chances for treating COVID-19

A pipeline for the reconstruction and evaluation of context-specific human metabolic models at a large-scale

Constraint-based (CB) metabolic models provide a mathematical framework and scaffold for in silico cell metabolism analysis and manipulation. In the past decade, significant efforts have been done to model human metabolism, enabled by the increased availability of multi-omics datasets and curated genome-scale reconstructions, as well as the development of several algorithms for context-specific model (CSM) reconstruction. Although CSM reconstruction has revealed insights on the deregulated metabolism of several pathologies, the process of reconstructing representative models of human tissues still lacks benchmarks and appropriate integrated software frameworks, since many tools required for this process are still disperse across various software platforms, some of which are proprietary.In this work, we address this challenge by assembling a scalable CSM reconstruction pipeline capable of integrating transcriptomics data in CB models. We combined omics preprocessing methods inspired by previous efforts with in-house implementations of existing CSM algorithms and new model refinement and validation routines, all implemented in the Troppo Python-based open-source framework. The pipeline was validated with multi-omics datasets from the Cancer Cell Line Encyclopedia (CCLE), also including reference fluxomics measurements for the MCF7 cell line.We reconstructed over 6000 models based on the Human-GEM template model for 733 cell lines featured in the CCLE, using MCF7 models as reference to find the best parameter combinations. These reference models outperform earlier studies using the same template by comparing gene essentiality and fluxomics experiments. We also analysed the heterogeneity of breast cancer cell lines, identifying key changes in metabolism related to cancer aggressiveness. Despite the many challenges in CB modelling, we demonstrate using our pipeline that combining transcriptomics data in metabolic models can be used to investigate key metabolic shifts. Significant limitations were found on these models ability for reliable quantitative flux prediction, thus motivating further work in genome-wide phenotype prediction.Author summary Genome-scale models of human metabolism are promising tools capable of contextualising large omics datasets within a framework that enables analysis and manipulation of metabolic phenotypes. Despite various successes in applying these methods to provide mechanistic hypotheses for deregulated metabolism in disease, there is no standardized workflow to extract these models using existing methods and the tools required to do so are mostly implemented using proprietary software.We have assembled a generic pipeline to extract and validate context-specific metabolic models using multi-omics datasets and implemented it using the troppo framework. We first validate our pipeline using MCF7 cell line models and assess their ability to predict lethal gene knockouts as well as flux activity using multi-omics data. We also demonstrate how this approach can be generalized for large-scale transcriptomics datasets and used to generate insights on the metabolic heterogeneity of cancer and relevant features for other data mining approaches. The pipeline is available as part of an open-source framework that is generic for a variety of applications.Competing Interest StatementThe authors have declared no competing interest.The authors thank the PhD scholarships co-funded by national funds and the European Social Fund through the Portuguese Foundation for Science and Technology (FCT), with references: SFRH/BD/118657/2016 (V.V.), SFRH/BD/133248/2017 (J.F.). This study was also supported by the FCT under the scope of the strategic funding of UIDB/04469/2020 unit.info:eu-repo/semantics/publishedVersio

Adaptability of metabolic networks in evolution and disease

There are 114.101 small molecule metabolites currently annotated in the Human Metabolome Database, which are highly connected amongst each other, with a few metabolites exhibiting an estimated number of more than 103 connections. Redundancy and plasticity are essential features of metabolic networks enabling cells to respond to fluctuating environments, presence of toxic molecules, or genetic perturbations like mutations. These system-level properties are inevitably linked to all aspects of biological systems ensuring cell viability by enabling processes like adaption and differentiation. To this end, the ability to interrogate molecular changes at omics level has opened new opportunities to study the cell at its different layers from the epigenome and transcriptome to its proteome and metabolome. In this thesis, I tackled the question how redundancy and plasticity shape adaptation in metabolic networks in evolutionary and disease contexts. I utilize a multi-omics approach to study comprehensively the metabolic state of a cell and its regulation at the transcriptional and proteomic level. One of the challenges with multi-omics approaches is the integration and interpretation of multi-layered data sets. To approach this challenge, I use genome scale metabolic models as a knowledge-based scaffold to overlay omics data and thereby to enable biological interpretation beyond statistical correlation. This integrative methodology has been applied to two different projects, namely the evolutionary adaptation towards a nutrient source in yeast and the metabolic adaptations following disease progression. For the latter, I also curated a current human genome-scale metabolic model and made it more suitable for flux predictions. In the yeast case study, I investigate the metabolic network adaptations enabling yeast to grow on an alternative carbon source – glycerol. I could show that network redundancy is one of the key features of fast adaptation of the yeast metabolic network to the new nutrient environment. Genomics, transcriptomics, proteomics, metabolomics and metabolic modeling together revealed a shift of the organism’s redox-balance under glycerol consumption as a driving force of adaption, which can be linked to the causal mutation in the enzyme Kgd1. On the other hand, the limitations of metabolic network adaptation also became apparent since all evolved and adapted strains exhibited metabolic trade-offs in other environmental conditions than the adaptation niche. Either an impaired diauxic shift (as in the case of the glycerol mutant) or an increased sensitivity towards osmotic stress (caused by mutations in the HOG pathway) was coupled with efficient use of glycerol. In the second project, the molecular phenotype of regressed breast cancer cells was studied to identify what differentiates these cells from healthy breast tissue and to characterize the potential source of tumor recurrence. Using a breast cancer mouse model with inducible oncogenes, transcriptomics together with an extensive set of different types of metabolomics (targeted and untargeted metabolomics, lipidomics and fluxomics) could show that regressed cancer cells, albeit their apparently normal morphology, possess a highly altered molecular phenotype with an oncogenic memory. While in cancer redundancy and plasticity enable the adaptation towards a proliferative state, in regressed cells, on the contrary, prolonged oncogenic signaling leads to a loss of metabolic network regulation and the entering of an irreversible metabolic state. This state appears to be insensitive to adaptation mechanisms as transcripts and metabolites reciprocally enhance each other to maintain the tumor-like metabolic phenotype. In conclusion, this work demonstrates how genome scale metabolic models can help identifying functional mechanisms from complex and multi-layered omics data. Appropriate genome scale metabolic models combined with metabolite measurements have proven particularly useful in this context. The comprehensive understanding of all integrated aspects of a cell’s physiology is a challenging endeavor and the results of this thesis might stimulate further research towards this goal

Model-guided development of an evolutionarily stable yeast chassis.

First-principle metabolic modelling holds potential for designing microbial chassis that are resilient against phenotype reversal due to adaptive mutations. Yet, the theory of model-based chassis design has rarely been put to rigorous experimental test. Here, we report the development of Saccharomyces cerevisiae chassis strains for dicarboxylic acid production using genome-scale metabolic modelling. The chassis strains, albeit geared for higher flux towards succinate, fumarate and malate, do not appreciably secrete these metabolites. As predicted by the model, introducing product-specific TCA cycle disruptions resulted in the secretion of the corresponding acid. Adaptive laboratory evolution further improved production of succinate and fumarate, demonstrating the evolutionary robustness of the engineered cells. In the case of malate, multi-omics analysis revealed a flux bypass at peroxisomal malate dehydrogenase that was missing in the yeast metabolic model. In all three cases, flux balance analysis integrating transcriptomics, proteomics and metabolomics data confirmed the flux re-routing predicted by the model. Taken together, our modelling and experimental results have implications for the computer-aided design of microbial cell factories

Computing abundance constraints in Saccharomyces cerevisiae’s metabolism

The unicellular eukaryotic organism Saccharomyces cerevisiae (budding yeast) is routinely used for production of high-value chemical compounds in the biotechnology industry. To improve production yields, it is fundamental to understand cellular metabolism, i.e. all biochemical reactions that occur inside the cell. In the past 20 years, genome-scale metabolic models (GEMs) have risen as computational tools for simulating all possible metabolic phenotypes that the cell can attain, while respecting constraints such as mass balances and reaction reversibilities. However, the number of metabolic states bound to only those constraints is infinite; therefore, it becomes necessary to include additional condition-specific constraints. Moreover, we would like these constraints to reflect physical limitations inside the cell, avoiding arbitrary ad-hoc bounds.In this thesis, approaches for including abundance constraints (i.e. constraints based on absolute abundances of different biomolecules) are evaluated in a GEM of S. cerevisiae. First, the GEM approach and how it has been used in S. cerevisiae is reviewed, identifying key areas for development. Afterwards, the concepts of sustainable model development and multi-layer experimental data generation are presented as foundation stones for constructing integrative analysis. Regarding the first concept, a systematic way of recording changes in a GEM using a version-controlled system is introduced, allowing reproducibility and open collaboration from the community. Regarding the second concept, a multi-omics dataset of yeast grown under different temperature, osmotic and ethanol stresses is presented and used throughout the thesis for studying metabolism.The major part of this work focuses on the integration into GEMs of abundance data of two types of bio-molecules: lipids and enzymes. First, a method for integrating lipid requirements in an unbiased way (SLIMEr) is presented and implemented for yeast, to show that lipid metabolism can be re-arranged without spending high amounts of energy. Secondly, a method for adding so-called “enzyme constraints” into a GEM (GECKO) is developed. These enzyme constraints limit reaction rates by the absolute abundance of enzymes, and prove to be crucial for explaining yeast physiology and computing enzyme usage in metabolism. Thirdly, the quantification technique used for estimating enzyme abundances is analyzed in terms of accuracy and precision, and further improved by varying the normalization and scaling steps. Finally, GECKO is used on the stress dataset to create enzyme-constrained models of yeast representing each stress condition. This allows comparing the distribution of enzyme usage within and between conditions, highlighting enzymes that play an important role in the metabolic response to stress