Model Of Human Metabolism Research Articles

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

Constraint-based (CB) metabolic models provide a mathematical framework and scaffold for in silico cell metabolism analysis and manipulation. In the past decade, significant efforts have been done to model human metabolism, enabled by the increased availability of multi-omics datasets and curated genome-scale reconstructions, as well as the development of several algorithms for context-specific model (CSM) reconstruction. Although CSM reconstruction has revealed insights on the deregulated metabolism of several pathologies, the process of reconstructing representative models of human tissues still lacks benchmarks and appropriate integrated software frameworks, since many tools required for this process are still disperse across various software platforms, some of which are proprietary. In this work, we address this challenge by assembling a scalable CSM reconstruction pipeline capable of integrating transcriptomics data in CB models. We combined omics preprocessing methods inspired by previous efforts with in-house implementations of existing CSM algorithms and new model refinement and validation routines, all implemented in the Troppo Python-based open-source framework. The pipeline was validated with multi-omics datasets from the Cancer Cell Line Encyclopedia (CCLE), also including reference fluxomics measurements for the MCF7 cell line. We reconstructed over 6000 models based on the Human-GEM template model for 733 cell lines featured in the CCLE, using MCF7 models as reference to find the best parameter combinations. These reference models outperform earlier studies using the same template by comparing gene essentiality and fluxomics experiments. We also analysed the heterogeneity of breast cancer cell lines, identifying key changes in metabolism related to cancer aggressiveness. Despite the many challenges in CB modelling, we demonstrate using our pipeline that combining transcriptomics data in metabolic models can be used to investigate key metabolic shifts. Significant limitations were found on these models ability for reliable quantitative flux prediction, thus motivating further work in genome-wide phenotype prediction.

Multi-Omics Integrative Analysis Coupled to Control Theory and Computational Simulation of a Genome-Scale metabolic Model Reveal Controlling Biological Switches in Human Astrocytes Under Palmitic Acid-Induced Lipotoxicity

Astrocytes play an important role in various processes in the brain, including pathological conditions such as neurodegenerative diseases. Recent studies have shown that the increase in saturated fatty acids such as palmitic acid (PA) triggers pro-inflammatory pathways in the brain. The use of synthetic neurosteroids such as tibolone has demonstrated neuro-protective mechanisms. However, broad studies, with a systemic point of view on the neurodegenerative role of PA and the neuro-protective mechanisms of tibolone are lacking. In this study, we performed the integration of multi-omic data (transcriptome and proteome) into a human astrocyte genomic scale metabolic model to study the astrocytic response during palmitate treatment. We evaluated metabolic fluxes in three scenarios (healthy, induced inflammation by PA, and tibolone treatment under PA inflammation). We also applied a control theory approach to identify those reactions that exert more control in the astrocytic system. Our results suggest that PA generates a modulation of central and secondary metabolism, showing a switch in energy source use through inhibition of folate cycle and fatty acid β-oxidation and upregulation of ketone bodies formation. We found 25 metabolic switches under PA-mediated cellular regulation, 9 of which were critical only in the inflammatory scenario but not in the protective tibolone one. Within these reactions, inhibitory, total, and directional coupling profiles were key findings, playing a fundamental role in the (de)regulation in metabolic pathways that may increase neurotoxicity and represent potential treatment targets. Finally, the overall framework of our approach facilitates the understanding of complex metabolic regulation, and it can be used for in silico exploration of the mechanisms of astrocytic cell regulation, directing a more complex future experimental work in neurodegenerative diseases.

Ethanol Inhibits the Metabolism of the Multiple Sclerosis Drug Dimethyl Fumarate to its Active Metabolite and Decreases Brain Exposure

HypothesisDimethyl fumarate is primarily metabolized to the monomethyl fumarate active metabolite by carboxylesterase 1 and is subject to inhibition by ethanol.MethodsA pharmacokinetic study was conducted to determine the effect of ethanol on dimethyl fumarate disposition in ES1e mice, a strain that is a better model of human carboxylesterase drug metabolism than typical rodent strains. The disposition of dimethyl fumarate and monomethyl fumarate, the purported active metabolite, was determined in a control group (n=30) receiving 100 mg/kg of dimethyl fumarate via oral gavage versus an ethanol group (n=30) receiving 3 g/kg of ethanol by oral gavage 10 minutes prior to dosing with 100 mg/kg of dimethyl fumarate. The area‐under‐the‐curve (AUC) and maximum plasma concentration (Cmax) were determined in plasma and brain tissue from the drug and metabolite concentrations determined by LC‐ mass spectrometry. The AUC and Cmax were compared between the control and ethanol group.ResultsLike dimethyl fumarate disposition in humans, only monomethyl fumarate concentrations were quantifiable in the plasma in the control group. In the ethanol group, dimethyl fumarate was quantifiable in both the plasma and brain tissue. Control Ethanol DMF MMF DMF MMF AUCPLASMA(ng/ml*min) ND 6072 4629 3948 CmaxPLASMA(ng/ml) ND 210 64 65 AUCBRAIN (ng/ml*min) ND 980 20 787 DMF=dimethyl fumarate; MMF=monomethyl fumarate (active metabolite); ND=not detectedThe terminal elimination half‐life of monomethyl fumarate in the control and ethanol groups was 14.4 and 44.3 minutes, respectively.ConclusionsEthanol inhibits the hydrolysis of dimethyl fumarate to active monomethyl fumarate by carboxylesterase 1 and has a drastic effect on the disposition of dimethyl fumarate and monomethyl fumarate. The therapeutic effect of dimethyl fumarate is likely to be significantly altered by the consumption of ethanol or other drug that inhibits carboxylesterase 1 potentially altering the beneficial effect of chronic dimethyl fumarate therapy on Relapsing‐Remitting Multiple Sclerosis.

Comparative Transcriptome Analysis Provides Insight into Spatio-Temporal Expression Characteristics and Genetic Regulatory Network in Postnatal Developing Subcutaneous and Visceral Fat of Bama Pig.

The depot differences between Subcutaneous Fat (SAF) and Visceral Fat (VAF) are critical for human well-being and disease processes in regard to energy metabolism and endocrine function. Miniature pigs (Sus scrofa) are ideal biomedical models for human energy metabolism and obesity due to the similarity of their lipid metabolism with that of humans. However, the regulation of differences in fat deposition and development remains unclear. In this study, the development of SAF and VAF was characterized and compared in Bama pig during postnatal development (infancy, puberty and adulthood), using RNA sequencing techniques (RNA-Seq). The transcriptome of SAF and VAF was profiled and isolated from 1-, 3- and 6 months-old pigs and identified 23,636 expressed genes, of which 1,165 genes were differentially expressed between the depots and/or developmental stages. Upregulated genes in SAF showed significant function and pathway enrichment in the central nervous system development, lipid metabolism, oxidation-reduction process and cell adhesion, whereas genes involved in the immune system, actin cytoskeleton organization, male gonad development and the hippo signaling pathway were preferentially expressed in VAF. Miner analysis of short time-series expression demonstrated that differentiation in gene expression patterns between the two depots corresponded to their distinct responses in sexual development, hormone signaling pathways, lipid metabolism and the hippo signaling pathway. Transcriptome analysis of SAF and VAF suggested that the depot differences in adipose tissue are not only related to lipid metabolism and endocrine function, but are closely associated with sexual development and organ size regulation.

Reduced Growth, Altered Gut Microbiome and Metabolite Profile, and Increased Chronic Kidney Disease Risk in Young Pigs Consuming a Diet Containing Highly Resistant Protein

High-heat processed foods contain proteins that are partially resistant to enzymatic digestion and pass through to the colon. The fermentation of resistant proteins by gut microbes produces products that may contribute to chronic disease risk. This pilot study examined the effects of a resistant protein diet on growth, fecal microbiome, protein fermentation metabolites, and the biomarkers of health status in pigs as a model of human digestion and metabolism. Weanling pigs were fed with standard or resistant protein diets for 4 weeks. The resistant protein, approximately half as digestible as the standard protein, was designed to enter the colon for microbial fermentation. Fecal and blood samples were collected to assess the microbiome and circulating metabolites and biomarkers. The resistant protein diet group consumed less feed and grew to ~50% of the body mass of the standard diet group. The diets had unique effects on the fecal microbiome, as demonstrated by clustering in the principal coordinate analysis. There were 121 taxa that were significantly different between groups (adjusted-p < 0.05). Compared with control, plasma tri-methylamine-N-oxide, homocysteine, neopterin, and tyrosine were increased and plasma acetic acid was lowered following the resistant protein diet (all p < 0.05). Compared with control, estimated glomerular filtration rate (p < 0.01) and liver function marker aspartate aminotransferase (p < 0.05) were also lower following the resistant protein diet. A resistant protein diet shifted the composition of the fecal microbiome. The microbial fermentation of resistant protein affected the levels of circulating metabolites and the biomarkers of health status toward a profile indicative of increased inflammation and the risk of chronic kidney disease.

Control of Cholesterol Metabolism Using a Systems Approach.

Simple SummaryCholesterol is the main sterol in mammals that is essential for healthy cell functionining. It plays a key role in metabolic regulation and signaling, it is a precursor molecule of bile acids, oxysterols, and all steroid hormones. It also contributes to the structural makeup of the membranes. Its homeostasis is tightly controlled since it can harm the body if it is allowed to reach abnormal blood concentrations. One of the diseases associated with elevated cholesterol levels being the major cause of morbidities and mortalities worldwide, is atherosclerosis. In this study, we have developed a model of the cholesterol metabolism taking into account local inflammation and oxidative stress. The aim was to investigate the impact of the interplay of those processes and cholesterol metabolism disturbances on the atherosclerosis development and progression. We have also analyzed the effect of combining different classes of drugs targeting selected components of cholesterol metabolism.Cholesterol is an essential component of mammalian cells and is involved in many fundamental physiological processes; hence, its homeostasis in the body is tightly controlled, and any disturbance has serious consequences. Disruption of the cellular metabolism of cholesterol, accompanied by inflammation and oxidative stress, promotes the formation of atherosclerotic plaques and, consequently, is one of the leading causes of death in the Western world. Therefore, new drugs to regulate disturbed cholesterol metabolism are used and developed, which help to control cholesterol homeostasis but still do not entirely cure atherosclerosis. In this study, a Petri net-based model of human cholesterol metabolism affected by a local inflammation and oxidative stress, has been created and analyzed. The use of knockout of selected pathways allowed us to observe and study the effect of various combinations of commonly used drugs on atherosclerosis. The analysis results led to the conclusion that combination therapy, targeting multiple pathways, may be a fundamental concept in the development of more effective strategies for the treatment and prevention of atherosclerosis.

Whole-body metabolic modelling predicts isoleucine dependency of SARS-CoV-2 replication

We aimed at investigating host-virus co-metabolism during SARS-CoV-2 infection. Therefore, we extended comprehensive sex-specific, whole-body organ resolved models of human metabolism with the necessary reactions to replicate SARS-CoV-2 in the lung as well as selected peripheral organs. Using this comprehensive host-virus model, we obtained the following key results: 1. The predicted maximal possible virus shedding rate was limited by isoleucine availability. 2. The supported initial viral load depended on the increase in CD4+ T-cells, consistent with the literature. 3. During viral infection, the whole-body metabolism changed including the blood metabolome, which agreed well with metabolomic studies from COVID-19 patients and healthy controls. 4. The virus shedding rate could be reduced by either inhibition of the guanylate kinase 1 or availability of amino acids, e.g., in the diet. 5. The virus variants differed in their maximal possible virus shedding rates, which could be inversely linked to isoleucine occurrences in the sequences. Taken together, this study presents the metabolic crosstalk between host and virus and emphasises the role of amino acid metabolism during SARS-CoV-2 infection, in particular of isoleucine. As such, it provides an example of how computational modelling can complement more canonical approaches to gain insight into host-virus crosstalk and to identify potential therapeutic strategies.

Patient-Specific Genome-Scale Metabolic Models for Individualized Predictions of Liver Disease

The prevalence of liver disease is steadily increasing, coupled with the limited availability of therapeutic treatments. Recent literature points to metabolic reprogramming as a key feature of liver failure. Hence, we sought to uncover the metabolic pathways and mechanisms associated with liver disease and acute liver failure. We generated patient-specific genome scale metabolic models by integrating RNA-seq data from patient liver samples with a generalized human metabolic model. Flux balance analysis simulations showed a distinct separation of non-alcohol associated and alcohol-associated disease states. Our analysis suggests that the alcohol associated liver has an increased flux through nucleotide and glycerophospholipid metabolic pathways. By contrast, non-alcohol associated liver has an increased flux through fatty acid oxidation, the carnitine shuttle, and bile acid recycling pathways. Importantly, there was significant variation of metabolic fluxes between patients within the same clinical category of disease stage, pointing to the necessity and opportunity for personalized medicine in treating liver disease. We conclude that the metabolic reprogramming occurring in alcohol-associated liver disease is likely distinct from the adaptations in non-alcohol associated liver disease, potentially requiring alternative therapeutic approaches.

Host metabolic reprogramming in response to SARS-CoV-2 infection: A systems biology approach

Understanding the pathogenesis of SARS-CoV-2 is essential for developing effective treatment strategies. Viruses hijack the host metabolism to redirect the resources for their replication and survival. The influence of SARS-CoV-2 on host metabolism is yet to be fully understood. In this study, we analyzed the transcriptomic data obtained from different human respiratory cell lines and patient samples (nasopharyngeal swab, peripheral blood mononuclear cells, lung biopsy, bronchoalveolar lavage fluid) to understand metabolic alterations in response to SARS-CoV-2 infection. We explored the expression pattern of metabolic genes in the comprehensive genome-scale network model of human metabolism, Recon3D, to extract key metabolic genes, pathways, and reporter metabolites under each SARS-CoV-2-infected condition. A SARS-CoV-2 core metabolic interactome was constructed for network-based drug repurposing. Our analysis revealed the host-dependent dysregulation of glycolysis, mitochondrial metabolism, amino acid metabolism, nucleotide metabolism, glutathione metabolism, polyamine synthesis, and lipid metabolism. We observed different pro- and antiviral metabolic changes and generated hypotheses on how the host metabolism can be targeted for reducing viral titers and immunomodulation. These findings warrant further exploration with more samples and in vitro studies to test predictions.

Exploring the Metabolic Heterogeneity of Cancers: A Benchmark Study of Context-Specific Models.

Metabolic heterogeneity is a hallmark of cancer and can distinguish a normal phenotype from a cancer phenotype. In the systems biology domain, context-specific models facilitate extracting physiologically relevant information from high-quality data. Here, to utilize the heterogeneity of metabolic patterns to discover biomarkers of all cancers, we benchmarked thousands of context-specific models using well-established algorithms for the integration of omics data into the generic human metabolic model Recon3D. By analyzing the active reactions capable of carrying flux and their magnitude through flux balance analysis, we proved that the metabolic pattern of each cancer is unique and could act as a cancer metabolic fingerprint. Subsequently, we searched for proper feature selection methods to cluster the flux states characterizing each cancer. We employed PCA-based dimensionality reduction and a random forest learning algorithm to reveal reactions containing the most relevant information in order to effectively identify the most influential fluxes. Conclusively, we discovered different pathways that are probably the main sources for metabolic heterogeneity in cancers. We designed the GEMbench website to interactively present the data, methods, and analysis results.

Simultaneous Integration of Gene Expression and Nutrient Availability for Studying the Metabolism of Hepatocellular Carcinoma Cell Lines.

How cancer cells utilize nutrients to support their growth and proliferation in complex nutritional systems is still an open question. However, it is certainly determined by both genetics and an environmental-specific context. The interactions between them lead to profound metabolic specialization, such as consuming glucose and glutamine and producing lactate at prodigious rates. To investigate whether and how glucose and glutamine availability impact metabolic specialization, we integrated computational modeling on the genome-scale metabolic reconstruction with an experimental study on cell lines. We used the most comprehensive human metabolic network model to date, Recon3D, to build cell line-specific models. RNA-Seq data was used to specify the activity of genes in each cell line and the uptake rates were quantitatively constrained according to nutrient availability. To integrated both constraints we applied a novel method, named Gene Expression and Nutrients Simultaneous Integration (GENSI), that translates the relative importance of gene expression and nutrient availability data into the metabolic fluxes based on an observed experimental feature(s). We applied GENSI to study hepatocellular carcinoma addiction to glucose/glutamine. We were able to identify that proliferation, and lactate production is associated with the presence of glucose but does not necessarily increase with its concentration when the latter exceeds the physiological concentration. There was no such association with glutamine. We show that the integration of gene expression and nutrient availability data into genome-wide models improves the prediction of metabolic phenotypes.

Rapid Identification of New Biomarkers for the Classification of GM1 Type 2 Gangliosidosis Using an Unbiased 1H NMR-Linked Metabolomics Strategy.

Biomarkers currently available for the diagnosis, prognosis, and therapeutic monitoring of GM1 gangliosidosis type 2 (GM1T2) disease are mainly limited to those discovered in targeted proteomic-based studies. In order to identify and establish new, predominantly low-molecular-mass biomarkers for this disorder, we employed an untargeted, multi-analyte approach involving high-resolution 1H NMR analysis coupled to a range of multivariate analysis and computational intelligence technique (CIT) strategies to explore biomolecular distinctions between blood plasma samples collected from GM1T2 and healthy control (HC) participants (n = 10 and 28, respectively). The relationship of these differences to metabolic mechanisms underlying the pathogenesis of GM1T2 disorder was also investigated. 1H NMR-linked metabolomics analyses revealed significant GM1T2-mediated dysregulations in ≥13 blood plasma metabolites (corrected p < 0.04), and these included significant upregulations in 7 amino acids, and downregulations in lipoprotein-associated triacylglycerols and alanine. Indeed, results acquired demonstrated a profound distinctiveness between the GM1T2 and HC profiles. Additionally, employment of a genome-scale network model of human metabolism provided evidence that perturbations to propanoate, ethanol, amino-sugar, aspartate, seleno-amino acid, glutathione and alanine metabolism, fatty acid biosynthesis, and most especially branched-chain amino acid degradation (p = 10−12−10−5) were the most important topologically-highlighted dysregulated pathways contributing towards GM1T2 disease pathology. Quantitative metabolite set enrichment analysis revealed that pathological locations associated with these dysfunctions were in the order fibroblasts > Golgi apparatus > mitochondria > spleen ≈ skeletal muscle ≈ muscle in general. In conclusion, results acquired demonstrated marked metabolic imbalances and alterations to energy demand, which are consistent with GM1T2 disease pathogenesis mechanisms.

Progress on genome-scale metabolic models integrated with multi-omics data

The genome-scale metabolic network model (GEM) is a mathematical framework based on gene-protein-reaction associations combined with stoichiometric balance and is capable of facilitating the computation and prediction of multiscale phenotypes by optimizing the objective function of interest. It has been increasingly used as an important tool for understanding cellular metabolism and characterizing cell phenotypes. In cells, metabolism is tightly controlled by intricate regulatory mechanisms at the different system levels and is strictly regulated to ensure the dynamic adaptation of biochemical reaction fluxes for maintaining cell homeostasis to ultimately achieve optimal metabolic fitness. Advances in high-throughput screening and analysis technologies have generated massive amounts of genome sequences, along with transcriptomic, proteomic and metabolomic data, providing quantitative regulatory information to gain insights into cellular metabolism; however, integrating the available omics data into constraint-based metabolic models and quantitatively profiling genotype-phenotype relationships remains an outstanding challenge for computational biology. Here, we describe the recent developments in introducing macromolecular expression into GEMs and generating metabolic expression (ME) models, which increase the complexity and predictive capability of computational frameworks. Various algorithms employ different approaches to combine additional layers of omics data to limit the cone of allowable flux distributions in the metabolic model. In this review, we categorize all methods by five different grouping criteria and evaluate their practical perspectives. The first category of methods utilizes a threshold to distinguish active and inactive states of the corresponding reactions based on the gene expression measurement data. The second uses omics data to build cell- and tissue-specific models of human metabolism by removing unexpressed reactions from the global human metabolic network. The third category of methods involves modifying reaction bounds on the basis of mRNA and protein abundance, in which the width of the “flux cone” is adjusted via the maximum possible flux in the upper bound of the FBA optimization problem dependent on gene and protein expression levels. The imposition of constraints further defines the associated solution space of the model to improve the prediction accuracy. The fourth model incorporates transcriptional regulation networks (TRNs), which describe the phenomenological interactions between different biomolecules in response to genetic and environmental perturbation, into GEMs and avoids the obstacles of information formulation to achieve comprehensive knowledge regarding the metabolic and regulatory events occurring inside the cell. The last category integrates time-series transcriptomics data with flux-based bilevel optimization to comprehend the interplay between metabolism and regulation in time-dependent processes. We compare the advantages and limitations of different categories and explore the application areas of integrated models in analyzing metabolic characteristics, interpreting phenotypic states and the consequences of environmental and genetic perturbations while discovering potential drug targets and screening anti-metabolic drugs for cancer treatment. Finally, we also highlight the future perspectives and challenges for GEM-based reconstruction with omics data integration.

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.

Differential effects on human cytochromes P450 by CRISPR/Cas9-induced genetic knockout of cytochrome P450 reductase and cytochrome b5 in HepaRG cells

HepaRG cells are increasingly accepted as model for human drug metabolism and other hepatic functions. We used lentiviral transduction of undifferentiated HepaRG cells to deliver Cas9 and two alternative sgRNAs targeted at NADPH:cytochrome P450 oxidoreductase (POR), the obligate electron donor for microsomal cytochromes P450 (CYP). Cas9-expressing HepaRGVC (vector control) cells were phenotypically similar to wild type HepaRG cells and could be differentiated into hepatocyte-like cells by DMSO. Genetic POR-knockout resulted in phenotypic POR knockdown of up to 90% at mRNA, protein, and activity levels. LC–MS/MS measurement of seven CYP-activities showed differential effects of POR-knockdown with CYP2C8 being least and CYP2C9 being most affected. Further studies on cytochrome b5 (CYB5), an alternative NADH-dependent electron donor indicated particularly strong support of CYP2C8-dependent amodiaquine N-deethylation by CYB5 and this was confirmed by genetic CYB5 single- and POR/CYB5 double-knockout. POR-knockdown also affected CYP expression on mRNA and protein level, with CYP1A2 being induced severalfold, while CYP2C9 was strongly downregulated. In summary our results show that POR/NADPH- and CYB5/NADH-electron transport systems influence human drug metabolizing CYPs differentially and differently than mouse Cyps. Our Cas9-expressing HepaRGVC cells should be suitable to study the influence of diverse genes on drug metabolism and other hepatic functions.

Stimulation of cholesterol biosynthesis in mitochondrial complex I-deficiency lowers reductive stress and improves motor function and survival in mice

The majority of cellular energy is produced by the mitochondrial oxidative phosphorylation (OXPHOS) system. Failure of the first OXPHOS enzyme complex, NADH:ubiquinone oxidoreductase or complex I (CI), is associated with multiple signs and symptoms presenting at variable ages of onset. There is no approved drug treatment yet to slow or reverse the progression of CI-deficient disorders. Here, we present a comprehensive human metabolic network model of genetically characterized CI-deficient patient-derived fibroblasts. Model calculations predicted that increased cholesterol production, export, and utilization can counterbalance the surplus of reducing equivalents in patient-derived fibroblasts, as these pathways consume considerable amounts of NAD(P)H. We show that fibrates attenuated increased NAD(P)H levels and improved CI-deficient fibroblast growth by stimulating the production of cholesterol via enhancement of its cellular efflux. In CI-deficient (Ndufs4−/−) mice, fibrate treatment resulted in prolonged survival and improved motor function, which was accompanied by an increased cholesterol efflux from peritoneal macrophages. Our results shine a new light on the use of compensatory biological pathways in mitochondrial dysfunction, which may lead to novel therapeutic interventions for mitochondrial diseases for which currently no cure exists.

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.

Systematic metabolic analysis of potential target, therapeutic drug, diagnostic method and animal model applicability in three neurodegenerative diseases.

Considerable evidence suggests that metabolic abnormalities are associated with neurodegenerative diseases. This study aimed to conduct a systematic metabolic analysis of Alzheimer’s disease (AD), Parkinson’s disease (PD) and Huntington’s disease (HD). Human and mouse model microarray datasets were downloaded from the Gene Expression Omnibus database. The metabolic genes and pathways were collected from the Recon 3D human metabolic model. Drug and target information was obtained from the DrugBank database. This study identified ATP1A1, ATP6V1G2, GOT1, HPRT1, MAP2K1, PCMT1 and PLK2 as key metabolic genes that were downregulated in AD, PD and HD. We screened 57 drugs that target these genes, such as digoxin, ouabain and diazoxide. This study constructed multigene diagnostic models for AD, PD and HD by using metabolic gene expression profiles in blood, all models showed high accuracy (AUC > 0.8) both in the experimental and validation sets. Furthermore, analysis of animal models showed that there was almost no consistency among the metabolic changes between mouse models and human diseases. This study systematically revealed the metabolic damage among AD, PD, and HD and uncovered the differences between animal models and human diseases. This information may be helpful for understanding the metabolic mechanisms and drug development for neurodegenerative diseases.

A biomathematical model of human erythropoiesis and iron metabolism

Anaemia therapy or perisurgical support of erythropoiesis often require both, EPO and iron medication. However, excessive iron medication can result in iron overload and it is challenging to control haemoglobin levels in a desired range. To support this task, we develop a biomathematical model to simulate EPO- and iron medication in humans. We combine our previously established model of human erythropoiesis including comprehensive pharmacokinetic models of EPO applications with a newly developed model of iron metabolism including iron supplementation. Equations were derived by translating known biological mechanisms into ordinary differential equations. Qualitative model behaviour is studied in detail considering a variety of interventions such as bleeding, iron malnutrition and medication. The model can explain time courses of erythrocytes, reticulocytes, haemoglobin, haematocrit, red blood cells, EPO, serum iron, ferritin, transferrin saturation, and transferrin under a variety of scenarios including EPO and iron application into healthy volunteers or chemotherapy patients. Unknown model parameters were determined by fitting the predictions of the model to time series data from literature. We demonstrate how the model can be used to make predictions of untested therapy options such as cytotoxic chemotherapy supported by iron and EPO. Following our ultimate goal of establishing a model of anaemia treatment in chronic kidney disease, we aim at translating our model to this pathological condition in the near future.

Personalized whole‐body models integrate metabolism, physiology, and the gut microbiome

Comprehensive molecular‐level models of human metabolism have been generated on a cellular level. However, models of whole‐body metabolism have not been established as they require new methodological approaches to integrate molecular and physiological data. We developed a new metabolic network reconstruction approach that used organ‐specific information from literature and omics data to generate two sex‐specific whole‐body metabolic (WBM) reconstructions. These reconstructions capture the metabolism of 26 organs and six blood cell types. Each WBM reconstruction represents whole‐body organ‐resolved metabolism with over 80,000 biochemical reactions in an anatomically and physiologically consistent manner. We parameterized the WBM reconstructions with physiological, dietary, and metabolomic data. The resulting WBM models could recapitulate known inter‐organ metabolic cycles and energy use. We also illustrate that the WBM models can predict known biomarkers of inherited metabolic diseases in different biofluids. Predictions of basal metabolic rates, by WBM models personalized with physiological data, outperformed current phenomenological models. Finally, integrating microbiome data allowed the exploration of host–microbiome co‐metabolism. Overall, the WBM reconstructions, and their derived computational models, represent an important step toward virtual physiological humans.