Human Genome-scale Metabolic Models Research Articles

Multiomics analysis reveals metabolic subtypes and identifies diacylglycerol kinase α (DGKA) as a potential therapeutic target for intrahepatic cholangiocarcinoma.

Intrahepatic cholangiocarcinoma (iCCA) is a highly heterogeneous and lethal hepatobiliary tumor with few therapeutic strategies. The metabolic reprogramming of tumor cells plays an essential role in the development of tumors, while the metabolic molecular classification of iCCA is largely unknown. Here, we performed an integrated multiomics analysis and metabolic classification to depict differences in metabolic characteristics of iCCA patients, hoping to provide a novel perspective to understand and treat iCCA. We performed integrated multiomics analysis in 116 iCCA samples, including whole-exome sequencing, bulk RNA-sequencing and proteome analysis. Based on the non-negative matrix factorization method and the protein abundance of metabolic genes in human genome-scale metabolic models, the metabolic subtype of iCCA was determined. Survival and prognostic gene analyses were used to compare overall survival (OS) differences between metabolic subtypes. Cell proliferation analysis, 5-ethynyl-2'-deoxyuridine (EdU) assay, colony formation assay, RNA-sequencing and Western blotting were performed to investigate the molecular mechanisms of diacylglycerol kinase α (DGKA) in iCCA cells. Three metabolic subtypes (S1-S3) with subtype-specific biomarkers of iCCA were identified. These metabolic subtypes presented with distinct prognoses, metabolic features, immune microenvironments, and genetic alterations. The S2 subtype with the worst survival showed the activation of some special metabolic processes, immune-suppressed microenvironment and Kirsten rat sarcoma viral oncogene homolog (KRAS)/AT-rich interactive domain 1A (ARID1A) mutations. Among the S2 subtype-specific upregulated proteins, DGKA was further identified as a potential drug target for iCCA, which promoted cell proliferation by enhancing phosphatidic acid (PA) metabolism and activating mitogen-activated protein kinase (MAPK) signaling. Via multiomics analyses, we identified three metabolic subtypes of iCCA, revealing that the S2 subtype exhibited the poorest survival outcomes. We further identified DGKA as a potential target for the S2 subtype.

Integrative computational modeling to unravel novel potential biomarkers in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a major health problem around the world. The management of this disease is complicated by the lack of noninvasive diagnostic tools and the few treatment options available. Better clinical outcomes can be achieved if HCC is detected early, but unfortunately, clinical signs appear when the disease is in its late stages. We aim to identify novel genes that can be targeted for the diagnosis and therapy of HCC. We performed a meta-analysis of transcriptomics data to identify differentially expressed genes and applied network analysis to identify hub genes. Fatty acid metabolism, complement and coagulation cascade, chemical carcinogenesis and retinol metabolism were identified as key pathways in HCC. Furthermore, we integrated transcriptomics data into a reference human genome-scale metabolic model to identify key reactions and subsystems relevant in HCC. We conclude that fatty acid activation, purine metabolism, vitamin D, and E metabolism are key processes in the development of HCC and therefore need to be further explored for the development of new therapies. We provide the first evidence that GABRP, HBG1 and DAK (TKFC) genes are important in HCC in humans and warrant further studies.

The human microbial exposome: expanding the Exposome-Explorer database with gut microbial metabolites

Metabolites produced by the gut microbiota play an important role in the cross-talk with the human host. Many microbial metabolites are biologically active and can pass the gut barrier and make it into the systemic circulation, where they form the gut microbial exposome, i.e. the totality of gut microbial metabolites in body fluids or tissues of the host. A major difficulty faced when studying the microbial exposome and its role in health and diseases is to differentiate metabolites solely or partially derived from microbial metabolism from those produced by the host or coming from the diet. Our objective was to collect data from the scientific literature and build a database on gut microbial metabolites and on evidence of their microbial origin. Three types of evidence on the microbial origin of the gut microbial exposome were defined: (1) metabolites are produced in vitro by human faecal bacteria; (2) metabolites show reduced concentrations in humans or experimental animals upon treatment with antibiotics; (3) metabolites show reduced concentrations in germ-free animals when compared with conventional animals. Data was manually collected from peer-reviewed publications and inserted in the Exposome-Explorer database. Furthermore, to explore the chemical space of the microbial exposome and predict metabolites uniquely formed by the microbiota, genome-scale metabolic models (GSMMs) of gut bacterial strains and humans were compared. A total of 1848 records on one or more types of evidence on the gut microbial origin of 457 metabolites was collected in Exposome-Explorer. Data on their known precursors and concentrations in human blood, urine and faeces was also collected. About 66% of the predicted gut microbial metabolites (n = 1543) were found to be unique microbial metabolites not found in the human GSMM, neither in the list of 457 metabolites curated in Exposome-Explorer, and can be targets for new experimental studies. This new data on the gut microbial exposome, freely available in Exposome-Explorer (http://exposome-explorer.iarc.fr/), will help researchers to identify poorly studied microbial metabolites to be considered in future studies on the gut microbiota, and study their functionalities and role in health and diseases.

Computational Approaches to Assess Abnormal Metabolism in Alzheimer's Disease Using Transcriptomics.

Transcriptome-integrated human genome-scale metabolic models (GEMs) have been used widely to assess alterations in metabolism in response to disease. Transcriptome integration leads to identification of metabolic reactions that are differentially inactivated in the tissue of interest. Among the methods available for mapping transcriptome data on GEMs, we focus here on an Integrative Metabolic Analysis Tool (iMAT), which we have recently applied to the analysis of Alzheimer's disease (AD). We provide a detailed protocol for applying iMAT to create models of personalized metabolic networks, which can be further processed to identify reactions associated with abnormal metabolism.

Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases.

Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.

Genome-scale Metabolic Model Guided Subtyping Lung Cancer towards Personalized Diagnosis

Mathematical modeling of biological systems are useful in (i) gaining better understanding about the physiological processes in an organism, (ii) simulating alternative scenarios, (iii) finding targets for improved performance within metabolic engineering context (iv) performing several functional analyses, e.g. identify drug targets (v) process scheduling within the context of industrial biotechnology etc. Increasing the predictive capability of these models is of common interest within systems biology studies which allows identification of more effective and personalized treatment strategies for complex metabolic diseases such as cancer by investigation of disease metabolism and providing correct subtyping and staging. By transforming gene-level information to flux/metabolite level information, current disease state can be analyzed and diagnosis of cancer subtype can be performed using a less invasive methods.In this study, subtyping and staging of lung cancer, that is one of the main causes of cancer related deaths, was performed by integrating publicly available RNAseq data of normal, lung adenoma and adenocarcinomas and lung squamous cell neoplasms to human genome scale metabolic model and classification of obtained flux distributions using linear support vector machine (SVM) classifications. Differential flux analysis and pathway enrichment methods showed that model adequately represented tumour metabolism. SVM classification accuracies were calculated as more than 99% for normal and cancer cells and 94% for adenomas and adenocarcinomas and squamous cell neoplasms, indicating high predictive capability of flux distributions.

M2R: a Python add-on to cobrapy for modifying human genome-scale metabolic reconstruction using the gut microbiota models.

MotivationThe gut microbiota is the human body’s largest population of microorganisms that interact with human intestinal cells. They use ingested nutrients for fundamental biological processes and have important impacts on human physiology, immunity and metabolome in the gastrointestinal tract.ResultsHere, we present M2R, a Python add-on to cobrapy that allows incorporating information about the gut microbiota metabolism models to human genome-scale metabolic models (GEMs) like RECON3D. The idea behind the software is to modify the lower bounds of the exchange reactions in the model using aggregated in- and out-fluxes from selected microbes. M2R enables users to quickly and easily modify the pool of the metabolites that enter and leave the GEM, which is particularly important for those looking into an analysis of the metabolic interaction between the gut microbiota and human cells and its dysregulation.Availability and implementationM2R is freely available under an MIT License at https://github.com/e-weglarz-tomczak/m2r.Supplementary informationSupplementary data are available at Bioinformatics online.

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.

Network-based metabolic characterization of renal cell carcinoma

An emerging hallmark of cancer is metabolic reprogramming, which presents opportunities for cancer diagnosis and treatment based on metabolism. We performed a comprehensive metabolic network analysis of major renal cell carcinoma (RCC) subtypes including clear cell, papillary and chromophobe by integrating transcriptomic data with the human genome-scale metabolic model to understand the coordination of metabolic pathways in cancer cells. We identified metabolic alterations of each subtype with respect to tumor-adjacent normal samples and compared them to understand the differences between subtypes. We found that genes of amino acid metabolism and redox homeostasis are significantly altered in RCC subtypes. Chromophobe showed metabolic divergence compared to other subtypes with upregulation of genes involved in glutamine anaplerosis and aspartate biosynthesis. A difference in transcriptional regulation involving HIF1A is observed between subtypes. We identified E2F1 and FOXM1 as other major transcriptional activators of metabolic genes in RCC. Further, the co-expression pattern of metabolic genes in each patient showed the variations in metabolism within RCC subtypes. We also found that co-expression modules of each subtype have tumor stage-specific behavior, which may have clinical implications.

Unlocking Human Brain Metabolism by Genome-Scale and Multiomics Metabolic Models: Relevance for Neurology Research, Health, and Disease.

Neurology research and clinical practice are transforming toward postgenomics integrative biology. One such example is the study of human brain metabolism that is highly sophisticated due to reactions occurring in and between the astrocytes and neurons. Because of the inherent difficulty of performing experimental studies in human brain, metabolic network modeling has grown in importance to decipher the contribution of brain metabolite kinetics to human health and disease. Multiomics system science-driven metabolic models, using genome-scale and transcriptomics Big Data, offer the promise of new insights on metabolic networks in human brain. Added to this, the availability of omics technologies in both developed and developing world, neurology research, and clinical practice ought to be repositioned with a view to systems medicine. In this expert analysis, we present a critical and in-depth overview of the basic tenets of human brain metabolism, together with the most recent metabolic modeling strategies and computational studies of brain in health and neurological diseases. Human genome-scale metabolic models developed in a both global and brain-specific manner and multiomics synthesis of knowledge are highlighted in particular. We conclude by underscoring the value of multiomics modeling for metabolic diseases and computational investigations of the brain networks, with a view to unlocking the pathophysiology of Alzheimer's disease, Parkinson's disease, migraine, stroke, epilepsy, and multiple sclerosis, among other neurological disorders of importance for global health.

Framework and resource for more than 11,000 gene-transcript-protein-reaction associations in human metabolism

Alternative splicing plays important roles in generating different transcripts from one gene, and consequently various protein isoforms. However, there has been no systematic approach that facilitates characterizing functional roles of protein isoforms in the context of the entire human metabolism. Here, we present a systematic framework for the generation of gene-transcript-protein-reaction associations (GeTPRA) in the human metabolism. The framework in this study generated 11,415 GeTPRA corresponding to 1,106 metabolic genes for both principal and nonprincipal transcripts (PTs and NPTs) of metabolic genes. The framework further evaluates GeTPRA, using a human genome-scale metabolic model (GEM) that is biochemically consistent and transcript-level data compatible, and subsequently updates the human GEM. A generic human GEM, Recon 2M.1, was developed for this purpose, and subsequently updated to Recon 2M.2 through the framework. Both PTs and NPTs of metabolic genes were considered in the framework based on prior analyses of 446 personal RNA-Seq data and 1,784 personal GEMs reconstructed using Recon 2M.1. The framework and the GeTPRA will contribute to better understanding human metabolism at the systems level and enable further medical applications.

Predicting growth of the healthy infant using a genome scale metabolic model

An estimated 165 million children globally have stunted growth, and extensive growth data are available. Genome scale metabolic models allow the simulation of molecular flux over each metabolic enzyme, and are well adapted to analyze biological systems. We used a human genome scale metabolic model to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant’s biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body composition. The model corroborates the finding that essential amino and fatty acids do not limit growth, but that energy is the main growth limiting factor. Disruptions to the supply and demand of energy markedly affected the predicted growth, indicating that elevated energy expenditure may be detrimental. The model was used to simulate the metabolic effect of mineral deficiencies, and showed the greatest growth reduction for deficiencies in copper, iron, and magnesium ions which affect energy production through oxidative phosphorylation. The model and simulation method were integrated to a platform and shared with the research community. The growth model constitutes another step towards the complete representation of human metabolism, and may further help improve the understanding of the mechanisms underlying stunting.

Abstract B17: In-depth proteomics unveils fatty acid oxidation role in controlling vascular permeability

Abstract Endothelial cells (ECs) play a key role in maintaining vascular functionality. Alterations in vessel stability and permeability are hallmarks of tumor angiogenesis and may affect the efficacy of anti-cancer therapy. Modulating EC metabolism has emerged as a promising strategy to target angiogenesis in pathological conditions, but so far, little is known about the role of EC metabolism in the regulation of vessel stability and permeability. In this work we took advantage of a simplified in-vitro model of angiogenesis, where HUVECs (human umbilical vein endothelial cells) form a vascular-like network on a tridimensional matrix, to elucidate the role of EC metabolism during this process. By integrating high resolution proteomic data with a human genome scale metabolic model we built up the first predictive model of metabolic fluxes occurring in ECs forming a complex network and identified increased fluxes for reactions involving fatty acid oxidation (FAO) enzymes when ECs are assembled into a fully formed network. Upon inhibition of CPT1, the FAO rate-limiting enzyme, we disrupted the network and reduced cellular ATP levels and oxygen consumption, which were restored by replenishing the tricarboxylic acid cycle (TCAc). Remarkably, phosphoproteomic changes measured upon CPT1A inhibition evoked those triggered by thrombin, a potent inducer of EC permeability through calcium signaling. Indeed, acute CPT1A inhibition increased EC permeability in-vitro and leakage of fully formed blood vessel in-vivo, which were restored by replenishing the TCAc or inhibiting calcium influx. FAO emerges as central regulator of blood vessel stability, revealing the possibility of targeting FAO to interfere with tumor vessel permeability. Citation Format: Francesca Patella, Zachary T. Schug, Erez Persi, Lisa J. Neilson, Zahra Erami, Daniele Avanzato, Federica Maione, Juan R. Hernandez-Fernaud, Gillian Mackay, Liang Zheng, Steven Reid, Christian Frezza, Enrico Giraudo, Alessandra Fiorio Pla, Kurt Anderson, Eytan Ruppin, Eyal Gottlieb, Sara Zanivan. In-depth proteomics unveils fatty acid oxidation role in controlling vascular permeability. [abstract]. In: Proceedings of the AACR Special Conference: Tumor Angiogenesis and Vascular Normalization: Bench to Bedside to Biomarkers; Mar 5-8, 2015; Orlando, FL. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl):Abstract nr B17.

Investigation on metabolism of cisplatin resistant ovarian cancer using a genome scale metabolic model and microarray data.

Many cancer cells show significant resistance to drugs that kill drug sensitive cancer cells and non-tumor cells and such resistance might be a consequence of the difference in metabolism. Therefore, studying the metabolism of drug resistant cancer cells and comparison with drug sensitive and normal cell lines is the objective of this research. Metabolism of cisplatin resistant and sensitive A2780 epithelial ovarian cancer cells and normal ovarian epithelium has been studied using a generic human genome-scale metabolic model and transcription data. The results demonstrate that the most different metabolisms belong to resistant and normal models, and the different reactions are involved in various metabolic pathways. However, large portion of distinct reactions are related to extracellular transport for three cell lines. Capability of metabolic models to secrete lactate was investigated to find the origin of Warburg effect. Computational results introduced SLC25A10 gene, which encodes mitochondrial dicarboxylate transporter involved in exchanging of small metabolites across the mitochondrial membrane that may play key role in high growing capacity of sensitive and resistant cancer cells. The metabolic models were also used to find single and combinatorial targets that reduce the cancer cells growth. Effect of proposed target genes on growth and oxidative phosphorylation of normal cells were determined to estimate drug side-effects. The deletion results showed that although the cisplatin did not cause resistant cancer cells death, but it shifts the cancer cells to a more vulnerable metabolism.

Novel insights into obesity and diabetes through genome-scale metabolic modeling

The growing prevalence of metabolic diseases, such as obesity and diabetes, are putting a high strain on global healthcare systems as well as increasing the demand for efficient treatment strategies. More than 360 million people worldwide are suffering from type 2 diabetes (T2D) and, with the current trends, the projection is that 10% of the global adult population will be affected by 2030. In light of the systemic properties of metabolic diseases as well as the interconnected nature of metabolism, it is necessary to begin taking a holistic approach to study these diseases. Human genome-scale metabolic models (GEMs) are topological and mathematical representations of cell metabolism and have proven to be valuable tools in the area of systems biology. Successful applications of GEMs include the process of gaining further biological and mechanistic understanding of diseases, finding potential biomarkers, and identifying new drug targets. This review will focus on the modeling of human metabolism in the field of obesity and diabetes, showing its vast range of applications of clinical importance as well as point out future challenges.

Metabolic Associations of Reduced Proliferation and Oxidative Stress in Advanced Breast Cancer

Aberrant metabolism is a hallmark of cancer, but whole metabolomic flux measurements remain scarce. To bridge this gap, we developed a novel metabolic phenotypic analysis (MPA) method that infers metabolic phenotypes based on the integration of transcriptomics or proteomics data within a human genome-scale metabolic model. MPA was applied to conduct the first genome-scale study of breast cancer metabolism based on the gene expression of a large cohort of clinical samples. The modeling correctly predicted cell lines' growth rates, tumor lipid levels, and amino acid biomarkers, outperforming extant metabolic modeling methods. Experimental validation was obtained in vitro. The analysis revealed a subtype-independent "go or grow" dichotomy in breast cancer, where proliferation rates decrease as tumors evolve metastatic capability. MPA also identified a stoichiometric tradeoff that links the observed reduction in proliferation rates to the growing need to detoxify reactive oxygen species. Finally, a fundamental stoichiometric tradeoff between serine and glutamine metabolism was found, presenting a novel hallmark of estrogen receptor (ER)(+) versus ER(-) tumor metabolism. Together, our findings greatly extend insights into core metabolic aberrations and their impact in breast cancer.