Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat Commun. 2014;5:3083. [PMID: 24419221 DOI: 10.1038/ncomms4083]

Personal model-assisted identification of NAD+ and glutathione metabolism as intervention target in NAFLD

To elucidate the molecular mechanisms underlying non‐alcoholic fatty liver disease (NAFLD), we recruited 86 subjects with varying degrees of hepatic steatosis (HS). We obtained experimental data on lipoprotein fluxes and used these individual measurements as personalized constraints of a hepatocyte genome‐scale metabolic model to investigate metabolic differences in liver, taking into account its interactions with other tissues. Our systems level analysis predicted an altered demand for NAD⁺ and glutathione (GSH) in subjects with high HS. Our analysis and metabolomic measurements showed that plasma levels of glycine, serine, and associated metabolites are negatively correlated with HS, suggesting that these GSH metabolism precursors might be limiting. Quantification of the hepatic expression levels of the associated enzymes further pointed to altered de novo GSH synthesis. To assess the effect of GSH and NAD⁺ repletion on the development of NAFLD, we added precursors for GSH and NAD⁺ biosynthesis to the Western diet and demonstrated that supplementation prevents HS in mice. In a proof‐of‐concept human study, we found improved liver function and decreased HS after supplementation with serine (a precursor to glycine) and hereby propose a strategy for NAFLD treatment.

Analysing Algorithms and Data Sources for the Tissue-Specific Reconstruction of Liver Healthy and Cancer Cells

Genome-Scale Metabolic Models (GSMMs), mathematical representations of the cell metabolism in different organisms including humans, are resourceful tools to simulate metabolic phenotypes and understand associated diseases, such as obesity, diabetes and cancer. In the last years, different algorithms have been developed to generate tissue-specific metabolic models that simulate different phenotypes for distinct cell types. Hepatocyte cells are one of the main sites of metabolic conversions, mainly due to their diverse physiological functions. Most of the liver's tissue is formed by hepatocytes, being one of the largest and most important organs regarding its biological functions. Hepatocellular carcinoma is, also, one of the most important human cancers with high mortality rates. In this study, we will analyze four different algorithms (MBA, mCADRE, tINIT and FASTCORE) for tissue-specific model reconstruction, based on a template model and two types of data sources: transcriptomics and proteomics. These methods will be applied to the reconstruction of metabolic models for hepatocyte cells and HepG2 cancer cell line. The models will be analyzed and compared under different perspectives, emphasizing their functional analysis considering a set of metabolic liver tasks. The results show that there is no "ideal" algorithm. However, with the current analysis, we were able to retrieve knowledge about the metabolism of the liver.

A Systematic Evaluation of Methods for Tailoring Genome-Scale Metabolic Models

Genome-scale models of metabolism can illuminate the molecular basis of cell phenotypes. Since some enzymes are only active in specific cell types, several algorithms use omics data to construct cell-line- and tissue-specific metabolic models from genome-scale models. However, these methods are often not rigorously benchmarked, and it is unclear how algorithm and parameter selection (e.g., gene expression thresholds, metabolic constraints) affects model content and predictive accuracy. To investigate this, we built hundreds of models of four different cancer cell lines using six algorithms, four gene expression thresholds, and three sets of metabolic constraints. Model content varied substantially across different parameter sets, but the algorithms generally increased accuracy in gene essentiality predictions. However, model extraction method choice had the largest impact on model accuracy. We further highlight how assumptions during model development influence model prediction accuracy. These insights will guide further development of context-specific models, thus more accurately resolving genotype-phenotype relationships.

Reconciled rat and human metabolic networks for comparative toxicogenomics and biomarker predictions

The laboratory rat has been used as a surrogate to study human biology for more than a century. Here we present the first genome-scale network reconstruction of Rattus norvegicus metabolism, iRno, and a significantly improved reconstruction of human metabolism, iHsa. These curated models comprehensively capture metabolic features known to distinguish rats from humans including vitamin C and bile acid synthesis pathways. After reconciling network differences between iRno and iHsa, we integrate toxicogenomics data from rat and human hepatocytes, to generate biomarker predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature-based evidence delineating metabolite biomarkers unique to humans. Our results provide mechanistic insights into species-specific metabolism and facilitate the selection of biomarkers consistent with rat and human biology. These models can serve as powerful computational platforms for contextualizing experimental data and making functional predictions for clinical and basic science applications.

The rat is a widely-used model for human biology, but we must be aware of metabolic differences. Here, the authors reconstruct the genome-scale metabolic network of the rat, and after reconciling it with an improved human metabolic model, demonstrate the power of the models to integrate toxicogenomics data, providing species-specific biomarker predictions in response to a panel of drugs.

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.

Sericin ameliorated dysmorphic mitochondria in high-cholesterol diet/streptozotocin rat by antioxidative property

Sericin has been implicated in lower cholesterolemic effect due to its properties with several mechanisms. Mitochondria are one of the most important targets to be affected in high blood cholesterol and glucose conditions. The protective role of sericin on mitochondria remains doubtful. To examine this role, electron microscopic, histopathologic, immunohistochemical, and biochemical studies were performed in a high-cholesterol diet/streptozotocin rat model. The results demonstrated that sericin reduced blood cholesterol without hypoglycemic effect. Sericin alleviated dysmorphic mitochondria in heart and liver but not in kidney and also decreased peculiar endoplasmic reticulum in the exocrine pancreas. In addition, sericin decreased hepatic steatosis and preserved zymogen granule referable to the decline of reactive oxygen species production in hepatic mitochondrial extraction and down-regulation of malondialdehyde expression in the liver and exocrine pancreas however irrelevant to lipase activity. This study suggests that sericin has antioxidative property to reduce blood cholesterol by means of diminishing fat deposit in hepatocyte and improves mitochondria and endoplasmic reticulum integrities. [Box: see text].

Genome scale metabolic modeling of cancer

Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome. Cancer specific models of metabolism have also been generated by reducing the number of reactions in the generic model based on high throughput expression data, e.g. transcriptomics and proteomics. Targets for drugs and bio markers for diagnostics have been identified using these models. They have also been used as scaffolds for analysis of high throughput data to allow mechanistic interpretation of changes in expression. Finally, GEMs allow quantitative flux predictions using flux balance analysis (FBA). Here we critically review the requirements for successful FBA simulations of cancer cells and discuss the symmetry between the methods used for modeling of microbial and cancer metabolism. GEMs have great potential for translational research on cancer and will therefore become of increasing importance in the future.

Towards improved genome-scale metabolic network reconstructions: unification, transcript specificity and beyond

Genome-scale metabolic network reconstructions provide a basis for the investigation of the metabolic properties of an organism. There are reconstructions available for multiple organisms, from prokaryotes to higher organisms and methods for the analysis of a reconstruction. One example is the use of flux balance analysis to improve the yields of a target chemical, which has been applied successfully. However, comparison of results between existing reconstructions and models presents a challenge because of the heterogeneity of the available reconstructions, for example, of standards for presenting gene-protein-reaction associations, nomenclature of metabolites and reactions or selection of protonation states. The lack of comparability for gene identifiers or model-specific reactions without annotated evidence often leads to the creation of a new model from scratch, as data cannot be properly matched otherwise. In this contribution, we propose to improve the predictive power of metabolic models by switching from gene-protein-reaction associations to transcript-isoform-reaction associations, thus taking advantage of the improvement of precision in gene expression measurements. To achieve this precision, we discuss available databases that can be used to retrieve this type of information and point at issues that can arise from their neglect. Further, we stress issues that arise from non-standardized building pipelines, like inconsistencies in protonation states. In addition, problems arising from the use of non-specific cofactors, e.g. artificial futile cycles, are discussed, and finally efforts of the metabolic modelling community to unify model reconstructions are highlighted.

Branched Chain Amino Acids Cause Liver Injury in Obese/Diabetic Mice by Promoting Adipocyte Lipolysis and Inhibiting Hepatic Autophagy

The Western meat-rich diet is both high in protein and fat. Although the hazardous effect of a high fat diet (HFD) upon liver structure and function is well recognized, whether the co-presence of high protein intake contributes to, or protects against, HF-induced hepatic injury remains unclear. Increased intake of branched chain amino acids (BCAA, essential amino acids compromising 20% of total protein intake) reduces body weight. However, elevated circulating BCAA is associated with non-alcoholic fatty liver disease and injury. The mechanisms responsible for this quandary remain unknown; the role of BCAA in HF-induced liver injury is unclear. Utilizing HFD or HFD + BCAA models, we demonstrated BCAA supplementation attenuated HFD-induced weight gain, decreased fat mass, activated mammalian target of rapamycin (mTOR), inhibited hepatic lipogenic enzymes, and reduced hepatic triglyceride content. However, BCAA caused significant hepatic damage in HFD mice, evidenced by exacerbated hepatic oxidative stress, increased hepatic apoptosis, and elevated circulation hepatic enzymes. Compared to solely HFD-fed animals, plasma levels of free fatty acids (FFA) in the HFD + BCAA group are significantly further increased, due largely to AMPKα2-mediated adipocyte lipolysis. Lipolysis inhibition normalized plasma FFA levels, and improved insulin sensitivity. Surprisingly, blocking lipolysis failed to abolish BCAA-induced liver injury. Mechanistically, hepatic mTOR activation by BCAA inhibited lipid-induced hepatic autophagy, increased hepatic apoptosis, blocked hepatic FFA/triglyceride conversion, and increased hepatocyte susceptibility to FFA-mediated lipotoxicity. These data demonstrated that BCAA reduces HFD-induced body weight, at the expense of abnormal lipolysis and hyperlipidemia, causing hepatic lipotoxicity. Furthermore, BCAA directly exacerbate hepatic lipotoxicity by reducing lipogenesis and inhibiting autophagy in the hepatocyte.

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BCAA cause hepatic injury via complex mechanisms involving both adipocytes and hepatic cells.

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In the adipocyte, BCAA activate AMPKα2 and stimulate lipolysis, increasing plasma free fatty acids (FFA), which in turn results in hepatic FFA accumulation.

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In the liver, BCAA activate mTOR and inhibit FFA to TG conversion and autophagy, intensifying FFA lipotoxicity.

High fat diet (HFD) induces systemic BCAA catabolic defects. Under HFD conditions, increased BCAA consumption further increases circulating BCAA abundance. BCAA-enhanced adipocyte lipolysis induces hyperlipidemia through activating AMPKα2. Elevated circulating FFA results in insulin resistance and hepatic lipotoxicity. Moreover, BCAA activate hepatic mTOR, inhibit lipogenesis and autophagy, therefore increasing hepatic susceptibility to FFA-mediated lipotoxicity. As BCAA are abundant in protein, our results call for caution regarding the ingestion of high protein diets in obesity and diabetic individuals, unless their BCAA metabolic pathways are determined normal.

Noninvasive Detection of Nonalcoholic Steatohepatitis Using Clinical Markers and Circulating Levels of Lipids and Metabolites

BACKGROUND & AIMS

Use of targeted mass spectrometry (MS)-based methods is increasing in clinical chemistry laboratories. We investigate whether MS-based profiling of plasma improves noninvasive risk estimates of nonalcoholic steatohepatitis (NASH) compared with routinely available clinical parameters and patatin-like phospholipase domain-containing protein 3 (PNPLA3) genotype at rs738409.

METHODS

We used MS-based analytic platforms to measure levels of lipids and metabolites in blood samples from 318 subjects who underwent a liver biopsy because of suspected NASH. The subjects were divided randomly into estimation (n = 223) and validation (n = 95) groups to build and validate the model. Gibbs sampling and stepwise logistic regression, which fulfilled the Bayesian information criterion, were used for variable selection and modeling.

RESULTS

Features of the metabolic syndrome and the variant in PNPLA3 encoding I148M were significantly more common among subjects with than without NASH. We developed a model to identify subjects with NASH based on clinical data and PNPLA3 genotype (NASH Clin Score), which included aspartate aminotransferase (AST), fasting insulin, and PNPLA3 genotype. This model identified subjects with NASH with an area under the receiver operating characteristic of 0.778 (95% confidence interval, 0.709-0.846). We then used backward stepwise logistic regression analyses of variables from the NASH Clin Score and MS-based factors associated with NASH to develop the NASH ClinLipMet Score. This included glutamate, isoleucine, glycine, lysophosphatidylcholine 16:0, phosphoethanolamine 40:6, AST, and fasting insulin, along with PNPLA3 genotype. It identified patients with NASH with an area under the receiver operating characteristic of 0.866 (95% confidence interval, 0.820-0.913). The NASH ClinLipMet score identified patients with NASH with significantly higher accuracy than the NASH Clin Score or MS-based profiling alone.

CONCLUSIONS

A score based on MS (glutamate, isoleucine, glycine, lysophosphatidylcholine 16:0, phosphoethanolamine 40:6) and knowledge of AST, fasting insulin, and PNPLA3 genotype is significantly better than a score based on clinical or metabolic profiles alone in determining the risk of NASH.

Integrative transcriptomic analysis of NAFLD animal model reveals dysregulated genes and pathways in metabolism

Dysregulation of metabolism in hepatocytes leads to hepatic diseases such as hepatitis and non-alcoholic fatty liver disease (NAFLD). NAFLD represents a spectrum of liver diseases ranging from simple steatosis to nonalcoholic steatohepatitis (NASH). NASH is likely to progress to cirrhosis, liver failure and hepatocellular carcinoma, which lead to poor long-term prognosis. However, the exact mechanism of development of NAFLD is not well elucidated. In order to better understand the pathogenesis of NAFLD, we have performed an integrative analysis to livers from NAFLD rat models in a global view of the transcriptome. By systemic and integrative analyses, we have found that transport, angiogenesis and cell adhesion were upregulated in response to high fat diet feeding, which may cause a large amount of free fatty acid transport, hepatic fibrosis and hepatocytes injury. GO tree analysis has shown that angiogenesis was upregulated. GO term in response to high fat diet which may cause fibrosis. The pathway interaction network has indicated that upregulated "valine, leucine, and isoleucine metabolism" may decrease the serum concentration of branched-chain amino acid (BCAA). The enhanced degradation of BCAA in NAFLD animal models may lead to inhibition of the regeneration of hepatocytes, reducing the production of albumin, attenuating the inhibition of liver cancer and decreasing immunity. Overall, high fat diet upregulated a variety of metabolism which have converged at TCA cycle. High fatty has pushed the hepatic mitochondria to a "busy state". Comprehensively, genes participated in dysregulated biological process and metabolisms may be served as indicators for evaluation of NAFLD progression and therapeutic targets.

Tracing insights into human metabolism using chemical engineering approaches

Metabolism coordinates the conversion of available nutrients toward energy, biosynthetic intermediates, and signaling molecules to mediate virtually all biological functions. Dysregulation of metabolic pathways contributes to many diseases, so a detailed understanding of human metabolism has significant therapeutic implications. Over the last decade major technological advances in the areas of analytical chemistry, computational estimation of intracellular fluxes, and biological engineering have improved our ability to observe and engineer metabolic pathways. These approaches are reminiscent of the design, operation, and control of industrial chemical plants. Immune cells have emerged as an intriguing system in which metabolism influences diverse biological functions. Application of metabolic flux analysis and related approaches to macrophages and T cells offers great therapeutic opportunities to biochemical engineers.

Integrative analysis of human omics data using biomolecular networks

High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their interpretation becomes increasingly cryptic and nontrivial as they continue to expand in size and complexity. Systems biology networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information from the data. Two of the most prevalent networks that have been used for such integrative analyses of omics data are genome-scale metabolic models (GEMs) and protein-protein interaction (PPI) networks, both of which have demonstrated success among many different omics and sample types. This integrative approach seeks to unite 'top-down' omics data with 'bottom-up' biological networks in a synergistic fashion that draws on the strengths of both strategies. As the volume and resolution of high-throughput omics data continue to grow, integrative network-based analyses are expected to play an increasingly important role in their interpretation.

MetaboTools: A Comprehensive Toolbox for Analysis of Genome-Scale Metabolic Models

Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration, and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community.

Integrated Network Analysis Reveals an Association between Plasma Mannose Levels and Insulin Resistance

To investigate the biological processes that are altered in obese subjects, we generated cell-specific integrated networks (INs) by merging genome-scale metabolic, transcriptional regulatory and protein-protein interaction networks. We performed genome-wide transcriptomics analysis to determine the global gene expression changes in the liver and three adipose tissues from obese subjects undergoing bariatric surgery and integrated these data into the cell-specific INs. We found dysregulations in mannose metabolism in obese subjects and validated our predictions by detecting mannose levels in the plasma of the lean and obese subjects. We observed significant correlations between plasma mannose levels, BMI, and insulin resistance (IR). We also measured plasma mannose levels of the subjects in two additional different cohorts and observed that an increased plasma mannose level was associated with IR and insulin secretion. We finally identified mannose as one of the best plasma metabolites in explaining the variance in obesity-independent IR.

Molecular signatures of ovarian diseases: Insights from network medicine perspective

Dysfunctions and disorders in the ovary lead to a host of diseases including ovarian cancer, ovarian endometriosis, and polycystic ovarian syndrome (PCOS). Understanding the molecular mechanisms behind ovarian diseases is a great challenge. In the present study, we performed a meta-analysis of transcriptome data for ovarian cancer, ovarian endometriosis, and PCOS, and integrated the information gained from statistical analysis with genome-scale biological networks (protein-protein interaction, transcriptional regulatory, and metabolic). Comparative and integrative analyses yielded reporter biomolecules (genes, proteins, metabolites, transcription factors, and micro-RNAs), and unique or common signatures at protein, metabolism, and transcription regulation levels, which might be beneficial to uncovering the underlying biological mechanisms behind the diseases. These signatures were mostly associated with formation or initiation of cancer development, and pointed out the potential tendency of PCOS and endometriosis to tumorigenesis. Molecules and pathways related to MAPK signaling, cell cycle, and apoptosis were the mutual determinants in the pathogenesis of all three diseases. To our knowledge, this is the first report that screens these diseases from a network medicine perspective. This study provides signatures which could be considered as potential therapeutic targets and/or as medical prognostic biomarkers in further experimental and clinical studies. Abbreviations DAVID: Database for Annotation, Visualization and Integrated Discovery; DEGs: differentially expressed genes; GEO: Gene Expression Omnibus; KEGG: Kyoto Encyclopedia of Genes and Genomes; LIMMA: Linear Models for Microarray Data; MBRole: Metabolite Biological Role; miRNA: micro-RNA; PCOS: polycystic ovarian syndrome; PPI: protein-protein interaction; RMA: Robust Multi-Array Average; TF: transcription factor.

Tissue-Specific Molecular Biomarker Signatures of Type 2 Diabetes: An Integrative Analysis of Transcriptomics and Protein-Protein Interaction Data

Type 2 diabetes mellitus is a major global public health burden. A complex metabolic disease, type 2 diabetes affects multiple different tissues, demanding a "systems medicine" approach to biomarker and novel diagnostic discovery, not to mention data integration across omics-es. In the present study, transcriptomics data from different tissues including beta-cells, pancreatic islets, arterial tissue, peripheral blood mononuclear cells, liver, and skeletal muscle of 228 samples were integrated with protein-protein interaction data and genome scale metabolic models to unravel the molecular and tissue-specific biomarker signatures of type 2 diabetes mellitus. Classifying differentially expressed genes, reconstruction and topological analysis of active protein-protein interaction subnetworks indicated that genomic reprogramming depends on the type of tissue, whereas there are common signatures at different levels. Among all tissue and cell types, Mannosidase Alpha Class 1A Member 2 was the common signature at genome level, and activation-ppara reaction, which stimulates a nuclear receptor protein, was found out as the mutual reporter at metabolic level. Moreover, miR-335 and miR-16-5p came into prominence in regulation of transcription at different tissues. On the other hand, distinct signatures were observed for different tissues at the metabolome level. Various coenzyme-A derivatives were significantly enriched metabolites in pancreatic islets, whereas skeletal muscle was enriched for cholesterol, malate, L-carnitine, and several amino acids. Results have showed utmost importance concerning relations between T2D and cancer, blood coagulation, neurodegenerative diseases, and specific metabolic and signaling pathways.

Dysregulated signaling hubs of liver lipid metabolism reveal hepatocellular carcinoma pathogenesis

Hepatocellular carcinoma (HCC) has a high mortality rate and early detection of HCC is crucial for the application of effective treatment strategies. HCC is typically caused by either viral hepatitis infection or by fatty liver disease. To diagnose and treat HCC it is necessary to elucidate the underlying molecular mechanisms. As a major cause for development of HCC is fatty liver disease, we here investigated anomalies in regulation of lipid metabolism in the liver. We applied a tailored network-based approach to identify signaling hubs associated with regulation of this part of metabolism. Using transcriptomics data of HCC patients, we identified significant dysregulated expressions of lipid-regulated genes, across many different lipid metabolic pathways. Our findings, however, show that viral hepatitis causes HCC by a distinct mechanism, less likely involving lipid anomalies. Based on our analysis we suggest signaling hub genes governing overall catabolic or anabolic pathways, as novel drug targets for treatment of HCC that involves lipid anomalies.

A Genome-Scale Database and Reconstruction of Caenorhabditis elegans Metabolism

We present a genome-scale model of Caenorhabditis elegans metabolism along with the public database ElegCyc (http://elegcyc.bioinf.uni-jena.de:1100), which represents a reference for metabolic pathways in the worm and allows for the visualization as well as analysis of omics datasets. Our model reflects the metabolic peculiarities of C. elegans that make it distinct from other higher eukaryotes and mammals, including mice and humans. We experimentally verify one of these peculiarities by showing that the lifespan-extending effect of L-tryptophan supplementation is dose dependent (hormetic). Finally, we show the utility of our model for analyzing omics datasets through predicting changes in amino acid concentrations after genetic perturbations and analyzing metabolic changes during normal aging as well as during two distinct, reactive oxygen species (ROS)-related lifespan-extending treatments. Our analyses reveal a notable similarity in metabolic adaptation between distinct lifespan-extending interventions and point to key pathways affecting lifespan in nematodes.

Reconstruction of genome-scale human metabolic models using omics data

The impact of genome-scale human metabolic models on human systems biology and medical sciences is becoming greater, thanks to increasing volumes of model building platforms and publicly available omics data. The genome-scale human metabolic models started with Recon 1 in 2007, and have since been used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic models. Integration of tissue/cell type-specific omics data with the generic human metabolic models is the key step, and we discuss omics data and their integration methods to achieve this task. The initial version of the tissue/cell type-specific human metabolic models can further be computationally refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model.

HepatoDyn: A Dynamic Model of Hepatocyte Metabolism That Integrates 13C Isotopomer Data

The liver performs many essential metabolic functions, which can be studied using computational models of hepatocytes. Here we present HepatoDyn, a highly detailed dynamic model of hepatocyte metabolism. HepatoDyn includes a large metabolic network, highly detailed kinetic laws, and is capable of dynamically simulating the redox and energy metabolism of hepatocytes. Furthermore, the model was coupled to the module for isotopic label propagation of the software package IsoDyn, allowing HepatoDyn to integrate data derived from 13C based experiments. As an example of dynamical simulations applied to hepatocytes, we studied the effects of high fructose concentrations on hepatocyte metabolism by integrating data from experiments in which rat hepatocytes were incubated with 20 mM glucose supplemented with either 3 mM or 20 mM fructose. These experiments showed that glycogen accumulation was significantly lower in hepatocytes incubated with medium supplemented with 20 mM fructose than in hepatocytes incubated with medium supplemented with 3 mM fructose. Through the integration of extracellular fluxes and 13C enrichment measurements, HepatoDyn predicted that this phenomenon can be attributed to a depletion of cytosolic ATP and phosphate induced by high fructose concentrations in the medium.

Transcriptomics resources of human tissues and organs

Quantifying the differential expression of genes in various human organs, tissues, and cell types is vital to understand human physiology and disease. Recently, several large‐scale transcriptomics studies have analyzed the expression of protein‐coding genes across tissues. These datasets provide a framework for defining the molecular constituents of the human body as well as for generating comprehensive lists of proteins expressed across tissues or in a tissue‐restricted manner. Here, we review publicly available human transcriptome resources and discuss body‐wide data from independent genome‐wide transcriptome analyses of different tissues. Gene expression measurements from these independent datasets, generated using samples from fresh frozen surgical specimens and postmortem tissues, are consistent. Overall, the different genome‐wide analyses support a distribution in which many proteins are found in all tissues and relatively few in a tissue‐restricted manner. Moreover, we discuss the applications of publicly available omics data for building genome‐scale metabolic models, used for analyzing cell and tissue functions both in physiological and in disease contexts.

Assessing Cardiac Metabolism: A Scientific Statement From the American Heart Association

In a complex system of interrelated reactions, the heart converts chemical energy to mechanical energy. Energy transfer is achieved through coordinated activation of enzymes, ion channels, and contractile elements, as well as structural and membrane proteins. The heart's needs for energy are difficult to overestimate. At a time when the cardiovascular research community is discovering a plethora of new molecular methods to assess cardiac metabolism, the methods remain scattered in the literature. The present statement on "Assessing Cardiac Metabolism" seeks to provide a collective and curated resource on methods and models used to investigate established and emerging aspects of cardiac metabolism. Some of those methods are refinements of classic biochemical tools, whereas most others are recent additions from the powerful tools of molecular biology. The aim of this statement is to be useful to many and to do justice to a dynamic field of great complexity.

Reconstruction of Tissue-Specific Metabolic Networks Using CORDA

Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation.

A kidney-specific genome-scale metabolic network model for analyzing focal segmental glomerulosclerosis

Focal Segmental Glomerulosclerosis (FSGS) is a type of nephrotic syndrome which accounts for 20 and 40 % of such cases in children and adults, respectively. The high prevalence of FSGS makes it the most common primary glomerular disorder causing end-stage renal disease. Although the pathogenesis of this disorder has been widely investigated, the exact mechanism underlying this disease is still to be discovered. Current therapies seek to stop the progression of FSGS and often fail to cure the patients since progression to end-stage renal failure is usually inevitable. In the present work, we use a kidney-specific metabolic network model to study FSGS. The model was obtained by merging two previously published kidney-specific metabolic network models. The validity of the new model was checked by comparing the inactivating reaction genes identified in silico to the list of kidney disease implicated genes. To model the disease state, we used a complete list of FSGS metabolic biomarkers extracted from transcriptome and proteome profiling of patients as well as genetic deficiencies known to cause FSGS. We observed that some specific pathways including chondroitin sulfate degradation, eicosanoid metabolism, keratan sulfate biosynthesis, vitamin B6 metabolism, and amino acid metabolism tend to show variations in FSGS model compared to healthy kidney. Furthermore, we computationally searched for the potential drug targets that can revert the diseased metabolic state to the healthy state. Interestingly, only one drug target, N-acetylgalactosaminidase, was found whose inhibition could alter cellular metabolism towards healthy state.

Constraint Based Modeling Going Multicellular

Constraint based modeling has seen applications in many microorganisms. For example, there are now established methods to determine potential genetic modifications and external interventions to increase the efficiency of microbial strains in chemical production pipelines. In addition, multiple models of multicellular organisms have been created including plants and humans. While initially the focus here was on modeling individual cell types of the multicellular organism, this focus recently started to switch. Models of microbial communities, as well as multi-tissue models of higher organisms have been constructed. These models thereby can include different parts of a plant, like root, stem, or different tissue types in the same organ. Such models can elucidate details of the interplay between symbiotic organisms, as well as the concerted efforts of multiple tissues and can be applied to analyse the effects of drugs or mutations on a more systemic level. In this review we give an overview of the recent development of multi-tissue models using constraint based techniques and the methods employed when investigating these models. We further highlight advances in combining constraint based models with dynamic and regulatory information and give an overview of these types of hybrid or multi-level approaches.

Genome-scale study reveals reduced metabolic adaptability in patients with non-alcoholic fatty liver disease

Non-alcoholic fatty liver disease (NAFLD) is a major risk factor leading to chronic liver disease and type 2 diabetes. Here we chart liver metabolic activity and functionality in NAFLD by integrating global transcriptomic data, from human liver biopsies, and metabolic flux data, measured across the human splanchnic vascular bed, within a genome-scale model of human metabolism. We show that an increased amount of liver fat induces mitochondrial metabolism, lipolysis, glyceroneogenesis and a switch from lactate to glycerol as substrate for gluconeogenesis, indicating an intricate balance of exacerbated opposite metabolic processes in glycemic regulation. These changes were associated with reduced metabolic adaptability on a network level in the sense that liver fat accumulation puts increasing demands on the liver to adaptively regulate metabolic responses to maintain basic liver functions. We propose that failure to meet excessive metabolic challenges coupled with reduced metabolic adaptability may lead to a vicious pathogenic cycle leading to the co-morbidities of NAFLD.

Personalized Cardiovascular Disease Prediction and Treatment-A Review of Existing Strategies and Novel Systems Medicine Tools

Cardiovascular disease (CVD) continues to constitute the leading cause of death globally. CVD risk stratification is an essential tool to sort through heterogeneous populations and identify individuals at risk of developing CVD. However, applications of current risk scores have recently been shown to result in considerable misclassification of high-risk subjects. In addition, despite long standing beneficial effects in secondary prevention, current CVD medications have in a primary prevention setting shown modest benefit in terms of increasing life expectancy. A systems biology approach to CVD risk stratification may be employed for improving risk-estimating algorithms through addition of high-throughput derived omics biomarkers. In addition, modeling of personalized benefit-of-treatment may help in guiding choice of intervention. In the area of medicine, realizing that CVD involves perturbations of large complex biological networks, future directions in drug development may involve moving away from a reductionist approach toward a system level approach. Here, we review current CVD risk scores and explore how novel algorithms could help to improve the identification of risk and maximize personalized treatment benefit. We also discuss possible future directions in the development of effective treatment strategies for CVD through the use of genome-scale metabolic models (GEMs) as well as other biological network-based approaches.

Benchmarking Procedures for High-Throughput Context Specific Reconstruction Algorithms

Recent progress in high-throughput data acquisition has shifted the focus from data generation to processing and understanding of how to integrate collected information. Context specific reconstruction based on generic genome scale models like ReconX or HMR has the potential to become a diagnostic and treatment tool tailored to the analysis of specific individuals. The respective computational algorithms require a high level of predictive power, robustness and sensitivity. Although multiple context specific reconstruction algorithms were published in the last 10 years, only a fraction of them is suitable for model building based on human high-throughput data. Beside other reasons, this might be due to problems arising from the limitation to only one metabolic target function or arbitrary thresholding. This review describes and analyses common validation methods used for testing model building algorithms. Two major methods can be distinguished: consistency testing and comparison based testing. The first is concerned with robustness against noise, e.g., missing data due to the impossibility to distinguish between the signal and the background of non-specific binding of probes in a microarray experiment, and whether distinct sets of input expressed genes corresponding to i.e., different tissues yield distinct models. The latter covers methods comparing sets of functionalities, comparison with existing networks or additional databases. We test those methods on several available algorithms and deduce properties of these algorithms that can be compared with future developments. The set of tests performed, can therefore serve as a benchmarking procedure for future algorithms.

Applications of Genome-Scale Metabolic Models in Biotechnology and Systems Medicine

Genome-scale metabolic models (GEMs) have become a popular tool for systems biology, and they have been used in many fields such as industrial biotechnology and systems medicine. Since more and more studies are being conducted using GEMs, they have recently received considerable attention. In this review, we introduce the basic concept of GEMs and provide an overview of their applications in biotechnology, systems medicine, and some other fields. In addition, we describe the general principle of the applications and analyses built on GEMs. The purpose of this review is to introduce the application of GEMs in biological analysis and to promote its wider use by biologists.

Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine

Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. Constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge base to studies that take advantage of the model's topology and, most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data.An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine.

Mathematical models of cancer metabolism

Metabolism is essential for life, and its alteration is implicated in multiple human diseases. The transformation from a normal to a cancerous cell requires metabolic changes to fuel the high metabolic demands of cancer cells, including but not limited to cell proliferation and cell migration. In recent years, there have been a number of new discoveries connecting known aberrations in oncogenic and tumour suppressor pathways with metabolic alterations required to sustain cell proliferation and migration. However, an understanding of the selective advantage of these metabolic alterations is still lacking. Here, we review the literature on mathematical models of metabolism, with an emphasis on their contribution to the identification of the selective advantage of metabolic phenotypes that seem otherwise wasteful or accidental. We will show how the molecular hallmarks of cancer can be related to cell proliferation and tissue remodelling, the two major physiological requirements for the development of a multicellular structure. We will cover different areas such as genome-wide gene expression analysis, flux balance models, kinetic models, reaction diffusion models and models of the tumour microenvironment. We will also highlight current challenges and how their resolution will help to achieve a better understanding of cancer metabolism and the metabolic vulnerabilities of cancers.

Cancer Metabolism: A Modeling Perspective

Tumor cells alter their metabolism to maintain unregulated cellular proliferation and survival, but this transformation leaves them reliant on constant supply of nutrients and energy. In addition to the widely studied dysregulated glucose metabolism to fuel tumor cell growth, accumulating evidences suggest that utilization of amino acids and lipids contributes significantly to cancer cell metabolism. Also recent progresses in our understanding of carcinogenesis have revealed that cancer is a complex disease and cannot be understood through simple investigation of genetic mutations of cancerous cells. Cancer cells present in complex tumor tissues communicate with the surrounding microenvironment and develop traits which promote their growth, survival, and metastasis. Decoding the full scope and targeting dysregulated metabolic pathways that support neoplastic transformations and their preservation requires both the advancement of experimental technologies for more comprehensive measurement of omics as well as the advancement of robust computational methods for accurate analysis of the generated data. Here, we review cancer-associated reprogramming of metabolism and highlight the capability of genome-scale metabolic modeling approaches in perceiving a system-level perspective of cancer metabolism and in detecting novel selective drug targets.

Forward Individualized Medicine from Personal Genomes to Interactomes

When considering the variation in the genome, transcriptome, proteome and metabolome, and their interaction with the environment, every individual can be rightfully considered as a unique biological entity. Individualized medicine promises to take this uniqueness into account to optimize disease treatment and thereby improve health benefits for every patient. The success of individualized medicine relies on a precise understanding of the genotype-phenotype relationship. Although omics technologies advance rapidly, there are several challenges that need to be overcome: Next generation sequencing can efficiently decipher genomic sequences, epigenetic changes, and transcriptomic variation in patients, but it does not automatically indicate how or whether the identified variation will cause pathological changes. This is likely due to the inability to account for (1) the consequences of gene-gene and gene-environment interactions, and (2) (post)transcriptional as well as (post)translational processes that eventually determine the concentration of key metabolites. The technologies to accurately measure changes in these latter layers are still under development, and such measurements in humans are also mainly restricted to blood and circulating cells. Despite these challenges, it is already possible to track dynamic changes in the human interactome in healthy and diseased states by using the integration of multi-omics data. In this review, we evaluate the potential value of current major bioinformatics and systems biology-based approaches, including genome wide association studies, epigenetics, gene regulatory and protein-protein interaction networks, and genome-scale metabolic modeling. Moreover, we address the question whether integrative analysis of personal multi-omics data will help understanding of personal genotype-phenotype relationships.

Kinetic Studies to Elucidate Impaired Metabolism of Triglyceride-rich Lipoproteins in Humans

To develop novel strategies for prevention and treatment of dyslipidemia, it is essential to understand the pathophysiology of dyslipoproteinemia in humans. Lipoprotein metabolism is a complex system in which abnormal concentrations of various lipoprotein particles can result from alterations in their rates of production, conversion, and/or catabolism. Traditional methods that measure plasma lipoprotein concentrations only provide static estimates of lipoprotein metabolism and hence limited mechanistic information. By contrast, the use of tracers labeled with stable isotopes and mathematical modeling, provides us with a powerful tool for probing lipid and lipoprotein kinetics in vivo and furthering our understanding of the pathogenesis of dyslipoproteinemia.

Stratification of Hepatocellular Carcinoma Patients Based on Acetate Utilization

Hepatocellular carcinoma (HCC) is a deadly form of liver cancer that is increasingly prevalent. We analyzed global gene expression profiling of 361 HCC tumors and 49 adjacent noncancerous liver samples by means of combinatorial network-based analysis. We investigated the correlation between transcriptome and proteome of HCC and reconstructed a functional genome-scale metabolic model (GEM) for HCC. We identified fundamental metabolic processes required for cell proliferation using the network centric view provided by the GEM. Our analysis revealed tight regulation of fatty acid biosynthesis (FAB) and highly significant deregulation of fatty acid oxidation in HCC. We predicted mitochondrial acetate as an emerging substrate for FAB through upregulation of mitochondrial acetyl-CoA synthetase (ACSS1) in HCC. We analyzed heterogeneous expression of ACSS1 and ACSS2 between HCC patients stratified by high and low ACSS1 and ACSS2 expression and revealed that ACSS1 is associated with tumor growth and malignancy under hypoxic conditions in human HCC.

In search for symmetries in the metabolism of cancer

Even though aerobic glycolysis, or the Warburg effect, is arguably the most common trait of metabolic reprogramming in cancer, it is unobserved in certain tumor types. Systems biology advocates a global view on metabolism to dissect which traits are consistently reprogrammed in cancer, and hence likely to constitute an obligate step for the evolution of cancer cells. We refer to such traits as symmetric. Here, we review early systems biology studies that attempted to reveal symmetric traits in the metabolic reprogramming of cancer, discuss the symmetry of reprogramming of nucleotide metabolism, and outline the current limitations that, if unlocked, could elucidate whether symmetries in cancer metabolism may be claimed.

Integrated metabolic modelling reveals cell-type specific epigenetic control points of the macrophage metabolic network

BACKGROUND

The reconstruction of context-specific metabolic models from easily and reliably measurable features such as transcriptomics data will be increasingly important in research and medicine. Current reconstruction methods suffer from high computational effort and arbitrary threshold setting. Moreover, understanding the underlying epigenetic regulation might allow the identification of putative intervention points within metabolic networks. Genes under high regulatory load from multiple enhancers or super-enhancers are known key genes for disease and cell identity. However, their role in regulation of metabolism and their placement within the metabolic networks has not been studied.

METHODS

Here we present FASTCORMICS, a fast and robust workflow for the creation of high-quality metabolic models from transcriptomics data. FASTCORMICS is devoid of arbitrary parameter settings and due to its low computational demand allows cross-validation assays. Applying FASTCORMICS, we have generated models for 63 primary human cell types from microarray data, revealing significant differences in their metabolic networks.

RESULTS

To understand the cell type-specific regulation of the alternative metabolic pathways we built multiple models during differentiation of primary human monocytes to macrophages and performed ChIP-Seq experiments for histone H3 K27 acetylation (H3K27ac) to map the active enhancers in macrophages. Focusing on the metabolic genes under high regulatory load from multiple enhancers or super-enhancers, we found these genes to show the most cell type-restricted and abundant expression profiles within their respective pathways. Importantly, the high regulatory load genes are associated to reactions enriched for transport reactions and other pathway entry points, suggesting that they are critical regulatory control points for cell type-specific metabolism.

CONCLUSIONS

By integrating metabolic modelling and epigenomic analysis we have identified high regulatory load as a common feature of metabolic genes at pathway entry points such as transporters within the macrophage metabolic network. Analysis of these control points through further integration of metabolic and gene regulatory networks in various contexts could be beneficial in multiple fields from identification of disease intervention strategies to cellular reprogramming.

The gut microbiota modulates host amino acid and glutathione metabolism in mice

The gut microbiota has been proposed as an environmental factor that promotes the progression of metabolic diseases. Here, we investigated how the gut microbiota modulates the global metabolic differences in duodenum, jejunum, ileum, colon, liver, and two white adipose tissue depots obtained from conventionally raised (CONV-R) and germ-free (GF) mice using gene expression data and tissue-specific genome-scale metabolic models (GEMs). We created a generic mouse metabolic reaction (MMR) GEM, reconstructed 28 tissue-specific GEMs based on proteomics data, and manually curated GEMs for small intestine, colon, liver, and adipose tissues. We used these functional models to determine the global metabolic differences between CONV-R and GF mice. Based on gene expression data, we found that the gut microbiota affects the host amino acid (AA) metabolism, which leads to modifications in glutathione metabolism. To validate our predictions, we measured the level of AAs and N-acetylated AAs in the hepatic portal vein of CONV-R and GF mice. Finally, we simulated the metabolic differences between the small intestine of the CONV-R and GF mice accounting for the content of the diet and relative gene expression differences. Our analyses revealed that the gut microbiota influences host amino acid and glutathione metabolism in mice.

Extensive weight loss reveals distinct gene expression changes in human subcutaneous and visceral adipose tissue

Weight loss has been shown to significantly improve Adipose tissue (AT) function, however changes in AT gene expression profiles particularly in visceral AT (VAT) have not been systematically studied. Here, we tested the hypothesis that extensive weight loss in response to bariatric surgery (BS) causes AT gene expression changes, which may affect energy and lipid metabolism, inflammation and secretory function of AT. We assessed gene expression changes by whole genome expression chips in AT samples obtained from six morbidly obese individuals, who underwent a two step BS strategy with sleeve gastrectomy as initial and a Roux-en-Y gastric bypass as second step surgery after 12 ± 2 months. Global gene expression differences in VAT and subcutaneous (S)AT were analyzed through the use of genome-scale metabolic model (GEM) for adipocytes. Significantly altered gene expressions were PCR-validated in 16 individuals, which also underwent a two-step surgery intervention. We found increased expression of cell death-inducing DFFA-like effector a (CIDEA), involved in formation of lipid droplets in both fat depots in response to significant weight loss. We observed that expression of the genes associated with metabolic reactions involved in NAD+, glutathione and branched chain amino acid metabolism are significantly increased in AT depots after surgery-induced weight loss.

Mondo-Mlx Mediates Organismal Sugar Sensing through the Gli-Similar Transcription Factor Sugarbabe

The ChREBP/Mondo-Mlx transcription factors are activated by sugars and are essential for sugar tolerance. They promote the conversion of sugars to lipids, but beyond this, their physiological roles are insufficiently understood. Here, we demonstrate that in an organism-wide setting in Drosophila, Mondo-Mlx controls the majority of sugar-regulated genes involved in nutrient digestion and transport as well as carbohydrate, amino acid, and lipid metabolism. Furthermore, human orthologs of the Mondo-Mlx targets display enrichment among gene variants associated with high circulating triglycerides. In addition to direct regulation of metabolic genes, Mondo-Mlx maintains metabolic homeostasis through downstream effectors, including the Activin ligand Dawdle and the Gli-similar transcription factor Sugarbabe. Sugarbabe controls a subset of Mondo-Mlx-dependent processes, including de novo lipogenesis and fatty acid desaturation. In sum, Mondo-Mlx is a master regulator of other sugar-responsive pathways essential for adaptation to a high-sugar diet.

Reporter pathway analysis from transcriptome data: Metabolite-centric versus Reaction-centric approach

A systems-based investigation of the effect of perturbations on metabolic machinery is crucial to elucidate the mechanism behind perturbations. One way to investigate the perturbation-induced changes within the cell metabolism is to focus on pathway-level effects. In this study, three different perturbation types (genetic, environmental and disease-based) are analyzed to compute a list of reporter pathways, metabolic pathways which are significantly affected from a perturbation. The most common omics data type, transcriptome, is used as an input to the bioinformatic analysis. The pathways are scored by two alternative approaches: by averaging the changes in the expression levels of the genes controlling the associated reactions (reaction-centric), and by averaging the changes in the associated metabolites which were scored based on the associated genes (metabolite-centric). The analysis reveals the superiority of the novel metabolite-centric approach over the commonly used reaction-centric approach since it is based on metabolites which better represent the cross-talk among different pathways, enabling a more global and realistic cataloguing of network-wide perturbation effects.

Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome

The human gut microbiome is known to be associated with various human disorders, but a major challenge is to go beyond association studies and elucidate causalities. Mathematical modeling of the human gut microbiome at a genome scale is a useful tool to decipher microbe-microbe, diet-microbe and microbe-host interactions. Here, we describe the CASINO (Community And Systems-level INteractive Optimization) toolbox, a comprehensive computational platform for analysis of microbial communities through metabolic modeling. We first validated the toolbox by simulating and testing the performance of single bacteria and whole communities in vitro. Focusing on metabolic interactions between the diet, gut microbiota, and host metabolism, we demonstrated the predictive power of the toolbox in a diet-intervention study of 45 obese and overweight individuals and validated our predictions by fecal and blood metabolomics data. Thus, modeling could quantitatively describe altered fecal and serum amino acid levels in response to diet intervention.