Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN

Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells. The complexity of genome-scale metabolic networks (GEMs) hinders their application in specific physiological contexts. Here, the authors introduce a framework to reduce thermodynamically curated GEMs to the subnetworks of interest and demonstrate its application by deriving leukemia-specific models.

Systematic reduction of genome-scale models for the study of metabolic phenotypes of human cells

Meriç Ataman, Vassily Hatzimanikatis, Maria Masid Barcon

In the last years, the analysis of cellular metabolism has sparked new interest in systems biology and metabolic modelling. In particular, modelling the phenotypes of healthy and diseased cells will help to understand the main metabolic characteristics of disease development and progression. It will also be key to design more effective therapies. The reconstruction of genome-scale models (GEMs) enables the computation of phenotypic traits based on the genetic composition of a target organism. To overcome the well-known challenges when working with large networks, we generate systematically reduced models around specific subsystems. Within this framework, we curate the GEMs to include the thermodynamic properties of the network metabolites and reactions. Furthermore, we consider the composition and utilization of the extracellular-medium metabolites and the synthesis of the biomass precursor metabolites. The reduced models can be used for a broad range of applications extending from omics data integration to kinetic models. We demonstrate here that the study of reduced human GEMs can provide insight and a systematic framework to compare different versions of GEMs (Recon 2 versus Recon 3) as well as different cellular physiologies, such as diseased versus healthy phenotypes.

2018

Modeling metabolic and signaling pathways in cancer cells

Maria Masid Barcon

Cancer is a leading cause of death in the world, and the mechanisms that underlie this disease are still not completely understood. As cancer develops and progresses, cells undergo a diversity of mutations that sustain their rapid proliferation and the evasion of the immune system. Cancer cells alter their configuration and organization, exhibiting abnormal phenotypes and changes in functionality. The complexity of cancer lies in their heterogeneity and variability among patients, which challenges the current therapies and drug targets. In the last decades, ten hallmarks of cancer cells have been recognized, including alterations in metabolism and the signaling pathways. The sequencing of the human genome and the advances in omics data processing allowed to generate metabolic and signaling networks for human cells at a genome-scale, enlightening the detailed biochemistry and signal transduction processes occurring in human cells, and enabling to study human metabolism and signaling pathways at a systems level. However, the complexity of these networks hinders a consistent and concise physiological representation. In the field of systems biology, mathematical models and computational methods are derived to describe cellular processes based on experimental data and the biological networks. Furthermore, these models have proven to be valuable in understanding the genotype-phenotype relationship of cells and to formulate new hypotheses to guide experimental design. In this thesis, we present modeling approaches and computational methods to investigate the metabolic and signaling alterations in cancer cells and overcome the challenges arising from biological networks of such size and complexity. Firstly, we curated the thermodynamic properties for all the compounds and reactions in the human metabolic genome-scale models (GEMs) Recon 2 and Recon 3D to guarantee the consistency of the predictions with the bioenergetics of the cell. Moreover, we developed a workflow (redHUMAN) for reconstructing reduced-size models that focus on parts of metabolism relevant to a specific physiology, and we introduce a novel method to account for the cellular interactions with the extracellular medium. Using redHUMAN, we reduced the human GEMs around pathways that are altered in cancer physiology. Secondly, we applied a set of computational methods to integrate omics data into the reduced version of Recon 3D to build metabolic models for breast, colon, and ovarian cancers. These models were used to study how different cancer cells use the metabolic pathways to function and survive and how the underlying genetic deregulations affect the metabolic tasks. Thirdly, we developed a method (CONSIGN) to contextualize signaling networks to a specific type of cell under particular conditions, maximizing the consistency with experimental data. We used this method to generate a breast cancer-specific signaling network for the transcription factor MYC. Finally, we created an integrated model of signaling and metabolic models by accounting for the regulation of metabolic genes by transcription factors. We analyzed the interactions of the MYC signaling network in the breast cancer metabolic model. The work in this thesis demonstrates the potential of metabolic and signaling models to identify and infer the genetic origins and the microenvironment effects in the transformed phenotype of cancer cells, marking a step forward towards the study of drug targets and biomarkers.

EPFL2020

Mathematical Modeling of Brain Energy Metabolism, Measured with PET and MRS in Rodents

Bernard Lanz

Positron emission tomography (PET) and nuclear magnetic resonance spectroscopy (MRS) are two biomedical measurement techniques developed in the end of the XXth century, which drastically improved the amount of accessible information available in vivo. PET became popular through the most widely used tracer, fluorodeoxyglucose (FDG), which enables the measurement of the local glucose utilization and is nowadays routinely applied in clinical practice. Nuclear magnetic resonance is used in a large array of applications such as in analytical chemistry or chemical structure determination and is essentially known for its versatile medical imaging capabilities, grouped under the name of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS). In vivo MRS measures concentrations of metabolites by interacting with various nuclei such as 1H, 13C or 15N, with the major asset that MRS enables the identification of the chemical position at which the detected nuclei is located in the measured molecule. When coupled with labeled substrate infusions, dynamic MRS gives the opportunity to probe specific biochemical reactions directly in vivo, which opens the way to a wide range of metabolic studies. However, the correct interpretation of the elaborate dynamic labeling data acquired either with MRS or PET tracer experiments requires the application of adapted metabolic models to derive quantitative metabolic rates characterizing the biochemical processes under study. The work of this thesis involves both PET and MRS studies of brain energy metabolism in rodents and focuses on the development of adapted metabolic models to derive reliable metabolic fluxes characterizing various brain metabolic processes, such as glucose consumption, glial and neuronal oxidative metabolism and neurotransmission. FDG-PET enables the measurement of the cerebral metabolic rate of glucose (CMRGlc) using a three-compartment metabolic model for FDG transport across the blood-brain barrier and FDG phosphorylation to FDG-6P. Although this method is well established, a main drawback of CMRGlc measurement with FDG-PET is the necessity to characterize the available FDG concentration in the plasma over the experiment duration, the so-called arterial input function. This constraint is a strong limitation for preclinical studies in rodents, due to their low blood volume. We developed therefore a new method to extract the FDG input function directly from the PET images in rats and mice, using the time-activity curve of a voxel located in the inferior vena cava. The method was validated by comparison with a dedicated external blood counter and manual blood sampling. Using this method, a CMRGlc of ~0.22 μmol/g/min was determined in the mouse cortex. Glial oxidative metabolism can be specifically assessed using glial specific substrates, in particular [1-11C] acetate. However, no existing metabolic model described the 11C tissue activity curve in terms of neuroglial energy metabolism. In order to extract quantitative metabolic fluxes characterizing the glial TCA cycle rate and glutamate/glutamine cycling, we adapted the neuroglial two-compartment modeling approach previously used in 13C MRS cerebral metabolic studies to interpret positron emission data following 11C-acetate injection in the rat. The precision and accuracy of the estimated metabolic parameters was tested and a composite glial metabolic flux Vgtg=0.136 ± 0.042 μmol/g/min and a neurotransmission flux Vnt=0.170 ± 0.103 μmol/g/min were obtained. This approach enabled a direct comparison of the metabolic fluxes measured using 11C PET with the values determined with 13C MRS. In the field of 13C MRS, we took advantage of the higher sensitivity and spectral resolution available at high magnetic field (14.1T) to measure glial and neuronal oxidative metabolism, glutamatergic neurotransmission and pyruvate carboxylation in more details and with higher precision using both [1,6-13C2] glucose and [2-13C] acetate infusions in rats. In the acetate study, the glial specific uptake of acetate enabled a quantification of glial oxidative metabolism with unrivalled precision. We could estimate for the first time separately the transmitochondrial flux Vx in the glial and neuronal compartment, a biochemical pathway which has been subject of strong controversy. The glial and neuronal Vx were determined with high precision, as calculated through Monte Carlo simulation, and their value was on the same order of magnitude than the respective glial and neuronal TCA cycle flux. The model was extended to estimate the fraction of glutamate located in the glial compartment. A glial glutamate concentration of 0.6 ± 0.1 μmol/g was found. The values of the different metabolic fluxes characterizing the neuroglial system (Vtcag=0.27, Vxg=0.17, VPC=0.087, VNT=0.15, Vtcan=0.37 and Vxn=0.46 μmol/g/min) were in very good agreement with the values found using [1,6-13C2] glucose infusion. In addition, the effect of the MRS temporal resolution, experiment duration and signal- to-noise ratio on the precision of the derived metabolic fluxes was analyzed. Previous simulation studies showed that two-compartment modeling of neuroglial metabolism using 13C glucose infusion could lead to unreliable parameter estimations. However, using polarization transfer 13C MRS at 14.1T, the carbon positions C4, C3 and C2 of glutamate and glutamine as well as the positions C3 and C2 of aspartate could be measured with high SNR. The C2 positions were used in two-compartment modeling for the first time, enabling a precise measurement of the apparent glutamatergic neurotransmission and glial metabolism, including pyruvate carboxylation, as confirmed by Monte Carlo simulation. The glial dilution at the level of acetyl-CoA, which is related to the metabolism of other substrates than glucose, could also be characterized. Brain metabolism under hyperammonemic conditions was probed with 15N MRS under labeled ammonia infusion and modeled using a modified neuroglial two- compartment model describing the dynamics of [5-15N] glutamine, [2-15N] glutamate and [2-15N] glutamine. Due to detoxification processes, the total glutamine concentration, measured with 1H MRS, was steadily increasing. This non-steady- state metabolic condition resulting from the hyperammonemic stress was taken into account in the model, resulting in the determination of the apparent neurotransmission (0.26 ± 0.030 μmol/g/min), the glutamate dehydrogenase (0.029 ± 0.002 μmol/g/min) and net glutamine accumulation (0.033 ± 0.001 μmol/g/min). Finally, compartmental modeling was applied to 13C labeling studies in the awake rat, fed during 5, 24 or 48 h with a [1-13C]-labeled glucose solution, to determine brain glycogen and NAA turnover times (τGlyc=5.3 ± 3.2 h and τNAA=15.6 ± 6.5 h), using 13C spectra of brain extracts. A group of rats followed a mild brain activation protocol over 5 hours, which resulted in a decreased glycogen turnover time (τGlyc =2.9 ± 1.2 h).