Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.

A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

Vassily Hatzimanikatis, Maria Masid Barcon, Ljubisa Miskovic, Mohammad Omid Oftadeh, Pierre Guy Rémy Salvy

Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the most accurate models of biological systems include Expression and Thermodynamics FLux (ETFL), which efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To adapt this model for Saccharomyces cerevisiae, we developed yETFL, in which we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the ability of yETFL to predict maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the presented formulation can be extended to a wide range of eukaryotic organisms to the benefit of academic and industrial research. Formulating metabolic networks mathematically can help researchers study metabolic diseases and optimize the production of industrially important molecules. Here, the authors propose a framework that allows to model eukaryotic metabolism considering gene expression and thermodynamic constraints.

NATURE PORTFOLIO2021

How uncertainty in kinetic parameters affects metabolic control analysis of optimally grown E.coli

Meriç Ataman, Georgios Fengos, Tuure Eelis Hameri, Vassily Hatzimanikatis, Ljubisa Miskovic

Large-scale kinetic models of metabolism are essential for understanding and predicting the behavior of cellular systems when subject to perturbations. Despite the advances in experimental measurement technologies, the numerous parameters that are required to build kinetic models remain scarce and involve uncertainty. Even after incorporating the partially available experimental data, models still have many degrees of freedom. Due to this parametric uncertainty, some reactions are able to operate in forward and reverse directions. An operational configuration consists of reactions that operate in a unique direction. This flexibility results in the existence of alternative operational configurations representing the same physiology, with very distinct regulatory properties. In this study, we focus on one operational configuration and we investigate how the underlying uncertainty in the kinetic parameters affects the robustness of the model predictions and regulatory capabilities. To study this question, we used a large-scale non-linear kinetic model built using integrated fluxomics and metabolomics data describing the physiology for aerobically grown E. coli. Because of the under-determined nature of the system, there are multiple kinetic parameters that can render the model feasible within the selected operational configuration. To account for the variability of the kinetic parameters within the designated operational configuration, we selected a reference vector of fluxes and concentrations close to their nominal values. Then, we used the ORACLE (optimization and risk analysis of complex living entities) framework to build populations of kinetic models that are consistent with the given physiology, while satisfying the stoichiometric and thermodynamic constraints. From these, we built non-linear models to test how the system’s response to perturbations changed with respect to the chosen kinetic parameters. This allowed us to quantify the effect of the uncertainty in the kinetic parameters on the robustness of the regulatory properties of the system.

2016

Detection of environmental glucocorticoids and investigation of their effects using the zebrafish embryo as a model

Anita Orsolya Hidasi

Synthetic glucocorticoids (GCs) are widely used anti-inflammatory drugs. They mimic cortisol, the natural stress hormone in humans and in fish. Cortisol binds and activates the glucocorticoid receptor (GR), which regulates genes governing development, immune response, osmoregulation, and behavior. Therefore, the presence of GCs in the aquatic environment may cause endocrine disruption through impairing these essential physiological processes in fish. The aim of this thesis was to assess the effects of environmentally relevant concentrations of GCs in teleost fish, focusing on the inflammatory response and molecular mechanisms of GC action on different levels, from mRNA to proteins to metabolites. To confirm the environmental presence of GCs, GC activity was determined along a Swiss freshwater stream impacted by a wastewater treatment plant, using effect-directed analysis. The effluent samples showed the highest potency to activate the GR, as determined by the GR-CALUX® assay. The chemical analysis found clobetasol propionate (CP), a highly potent GC, to be responsible for 63% of the total GR activity in the sample. CP was selected as a model chemical to study GC effects in fish. Embryos of zebrafish (Danio rerio) aged until 5 days post fertilization (dpf) were used as a model organism; working with embryonic stages is a refinement of animal experiments. We demonstrated that CP is readily taken up by the embryos. Bacterial lipopolysaccharides (LPS) induce one of the main inflammatory cascades. As observed in LPS challenge assays, the inflammatory response of the embryos was significantly reduced after exposure to ¿0.1 nM CP. We showed that mRNA expression of several LPS mechanism- and GC action-related genes was regulated by CP. Among them, Annexin a1b (anxa1b) was significantly down-regulated after exposure to ¿0.05 nM CP. Sensitive regulation of anxa1b by the non-steroidal anti-inflammatory drug diclofenac (DCF) suggested that this gene product is a potential biomarker of steroidal and non-steroidal immunosuppressive effects. An LC-MS/MS-based targeted proteomics technique was developed to measure proteins, as it is these molecules that actually carry out cellular functions. 12 out of 40 GC action-related protein targets were detectable in control embryo digests and were subsequently monitored after CP, DCF and grab water sample exposures. We found that the muscle protein Myhz2 was regulated only after DCF exposure, while I¿B¿, an anti-inflammatory protein, was regulated only by CP, in agreement with mRNA level data. Global and targeted metabolomics analyses were performed in zebrafish embryos exposed to CP from 4 to 5 dpf, in collaboration with M. Lamoree (VU Amsterdam). Several of the detected compounds showed significant response to CP: lysine, nicotinamide, choline, hypoxanthine, tyrosine and tryptophan. To conclude, this thesis demonstrates that GCs are present in the aquatic environment and may suppress the inflammatory response of fish at environmentally relevant concentrations.