Human metabolic atlas: an online resource for human metabolism

Human tissue-specific genome-scale metabolic models (GEMs) provide comprehensive understanding of human metabolism, which is of great value to the biomedical research community. To make this kind of data easily accessible to the public, we have designed and deployed the human metabolic atlas (HMA) website (http://www.metabolicatlas.org). This online resource provides comprehensive information about human metabolism, including the results of metabolic network analyses. We hope that it can also serve as an information exchange interface for human metabolism knowledge within the research community. The HMA consists of three major components: Repository, Hreed (Human REaction Entities Database) and Atlas. Repository is a collection of GEMs for specific human cell types and human-related microorganisms in SBML (System Biology Markup Language) format. The current release consists of several types of GEMs: a generic human GEM, 82 GEMs for normal cell types, 16 GEMs for different cancer cell types, 2 curated GEMs and 5 GEMs for human gut bacteria. Hreed contains detailed information about biochemical reactions. A web interface for Hreed facilitates an access to the Hreed reaction data, which can be easily retrieved by using specific keywords or names of related genes, proteins, compounds and cross-references. Atlas web interface can be used for visualization of the GEMs collection overlaid on KEGG metabolic pathway maps with a zoom/pan user interface. The HMA is a unique tool for studying human metabolism, ranging in scope from an individual cell, to a specific organ, to the overall human body. This resource is freely available under a Creative Commons Attribution-NonCommercial 4.0 International License

Biochemical characterization of human gluconokinase and the proposed metabolic impact of gluconic acid as determined by constraint based metabolic network analysis.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files. This article is open access.The metabolism of gluconate is well characterized in prokaryotes where it is known to be degraded following phosphorylation by gluconokinase. Less is known of gluconate metabolism in humans. Human gluconokinase activity was recently identified proposing questions about the metabolic role of gluconate in humans. Here we report the recombinant expression, purification and biochemical characterization of isoform I of human gluconokinase alongside substrate specificity and kinetic assays of the enzyme catalyzed reaction. The enzyme, shown to be a dimer, had ATP dependent phosphorylation activity and strict specificity towards gluconate out of 122 substrates tested. In order to evaluate the metabolic impact of gluconate in humans we modeled gluconate metabolism using steady state metabolic network analysis. The results indicate that significant metabolic flux changes in anabolic pathways linked to the hexose monophosphate shunt (HMS) are induced through a small increase in gluconate concentration. We argue that the enzyme takes part in a context specific carbon flux route into the HMS that, in humans, remains incompletely explored. Apart from the biochemical description of human gluconokinase, the results highlight that little is known of the mechanism of gluconate metabolism in humans despite its widespread use in medicine and consumer products.info:eu-repo/grantAgreement/EC/FP7/23281

A community-driven global reconstruction of human metabolism

Multiple models of human metabolism have been reconstructed, but each represents only a subset of our knowledge. Here we describe Recon 2, a community-driven, consensus 'metabolic reconstruction', which is the most comprehensive representation of human metabolism that is applicable to computational modeling. Compared with its predecessors, the reconstruction has improved topological and functional features, including ~2× more reactions and ~1.7× more unique metabolites. Using Recon 2 we predicted changes in metabolite biomarkers for 49 inborn errors of metabolism with 77% accuracy when compared to experimental data. Mapping metabolomic data and drug information onto Recon 2 demonstrates its potential for integrating and analyzing diverse data types. Using protein expression data, we automatically generated a compendium of 65 cell type–specific models, providing a basis for manual curation or investigation of cell-specific metabolic properties. Recon 2 will facilitate many future biomedical studies and is freely available at http://humanmetabolism.org/

Using Genome-scale Models to Predict Biological Capabilities

Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started

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.Analytical BioScience

Cofactors revisited - Predicting the impact of flavoprotein-related diseases on a genome scale

Flavin adenine dinucleotide (FAD) and its precursor flavin mononucleotide (FMN) are redox cofactors that are required for the activity of more than hundred human enzymes. Mutations in the genes encoding these proteins cause severe phenotypes, including a lack of energy supply and accumulation of toxic intermediates. Ideally, patients should be diagnosed before they show symptoms so that treatment and/or preventive care can start immediately. This can be achieved by standardized newborn screening tests. However, many of the flavin-related diseases lack appropriate biomarker profiles. Genome-scale metabolic models can aid in biomarker research by predicting altered profiles of potential biomarkers. Unfortunately, current models, including the most recent human metabolic reconstructions Recon and HMR, typically treat enzyme-bound flavins incorrectly as free metabolites. This in turn leads to artificial degrees of freedom in pathways that are strictly coupled. Here, we present a reconstruction of human metabolism with a curated and extended flavoproteome. To illustrate the functional consequences, we show that simulations with the curated model - unlike simulations with earlier Recon versions - correctly predict the metabolic impact of multiple-acyl-CoA-dehydrogenase deficiency as well as of systemic flavin-depletion. Moreover, simulations with the new model allowed us to identify a larger number of biomarkers in flavoproteome-related diseases, without loss of accuracy. We conclude that adequate inclusion of cofactors in constraint-based modelling contributes to higher precision in computational predictions.FWN – Publicaties zonder aanstelling Universiteit Leide

Current Status and Future Prospects of Genome-Scale Metabolic Modeling to Optimize the Use of Mesenchymal Stem Cells in Regenerative Medicine.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadMesenchymal stem cells are a promising source for externally grown tissue replacements and patient-specific immunomodulatory treatments. This promise has not yet been fulfilled in part due to production scaling issues and the need to maintain the correct phenotype after re-implantation. One aspect of extracorporeal growth that may be manipulated to optimize cell growth and differentiation is metabolism. The metabolism of MSCs changes during and in response to differentiation and immunomodulatory changes. MSC metabolism may be linked to functional differences but how this occurs and influences MSC function remains unclear. Understanding how MSC metabolism relates to cell function is however important as metabolite availability and environmental circumstances in the body may affect the success of implantation. Genome-scale constraint based metabolic modeling can be used as a tool to fill gaps in knowledge of MSC metabolism, acting as a framework to integrate and understand various data types (e.g., genomic, transcriptomic and metabolomic). These approaches have long been used to optimize the growth and productivity of bacterial production systems and are being increasingly used to provide insights into human health research. Production of tissue for implantation using MSCs requires both optimized production of cell mass and the understanding of the patient and phenotype specific metabolic situation. This review considers the current knowledge of MSC metabolism and how it may be optimized along with the current and future uses of genome scale constraint based metabolic modeling to further this aim.Icelandic Research Fund Institute for Systems Biology's Translational Research Fellows Progra

An integrative systems biology study to understand immune aging in people living with HIV

Antiretroviral therapy (ART) reduces viral replication, restores T helper cells and improves the survival of people living with HIV (PWH), transforming a life-threatening disease into a manageable chronic infection. Nevertheless, PWH under ART shows aging-related diseases such as bone abnormalities, non-HIV-associated cancers, and cardiovascular and neurocognitive diseases. The complex immune metabolic dysregulation leading to these comorbidities is called immune aging. The main question raised by my thesis was, what are the complex mechanisms responsible for immune aging in HIV? Using advanced system biology and machine learning tools, I used multi-omics-based patient stratification to identify biologic perturbations associated with immune aging in PWH. First, we investigated PWH with Metabolic Syndrome (MetS), a relatively common agingrelated disease in HIV-1. In paper I, we identified the dysregulation of glutamate metabolism in PWH with MetS using plasma metabolomics and measure of cell transporters by flow cytrometry. Then, we investigated the mechanisms of differing PWH on long-term successful ART from HIV-negative controls (HC). In paper II, we identified the dysregulation of amino acids and, more specifically, glutaminolysis (i.e., lysis of glutamine to glutamate) in PWH compared to HC using metabolomics in two independent cohorts to avoid the potential cohort biases. We identified five neurosteroids to be lower in PWH and potentially create neurological impairments in PWH. The glutaminolysis inhibition in chronically infected HIV-1 promonocytic (U1) cells induced apoptosis and latency reversal which could clear HIV reservoirs. The first two papers universally clarified our knowledge about dysregulated metabolic traits following a prolonged ART in PWH. However, we observed heterogeneity among the clinically defined PWH. Therefore, we focused more on the multi-omics data-driven approaches to stratify the at-risk group who were either dysregulated metabolically atrisk PWH (paper III) or immunometabolic at-risk group (paper IV) and clarified the biological aging process by measuring transcriptomics age (paper V). In paper III, we found three groups of PWH based on multi-omics integration of lipidomics, metabolomics, and microbiome. The severe at-risk metabolic complications showed increased weight-related comorbidities and di- and triglycerides compared to the other clusters. At-risk and HC-like groups displayed similar metabolic profiles but were different from HC. An increase in Prevotella was linked to the overrepresentation of men having sex with men (MSM) in the at-risk group. The microbiome-associated metabolites (MAM) appeared dysregulated in all HIV groups compared to controls. We improved this clustering by adding transcriptomics and proteomics data for a refined immunometabolic at-risk-related clustering in PWH. In paper IV, immune-driven HC-like and at-risk groups were clustered based on metabolomics, transcriptomics, and proteomics. Several biomarkers from central carbon metabolism (CCM) and senescence-associated proteins were linked to the at-risk phenotype based on random forest, structural causal modeling, and co-expression networks. Senescent protein changes were associated with a deficiency in macrophage function based on single-cell data, cell profiling, flow cytometry, and proteomics from macrophage data and in vitro validation. We also developed personalized and group-level genome-scale metabolic models (GSMM) and confirmed the implication of metabolites from CCM and polyamides in at-risk phenotypes. Finally, we investigated the accelerated aging process (AAP) in PWH. In paper V, we calculated the biological age of PWH using transcriptomics data and grouped patients into aging groups; The decelerated aging process (DAP) group was linked with higher age, European origin, and a higher proportion of tenofovir disoproxil fumarate /alafenamide (TDF/TAF). AAP had a downregulation of metabolic pathways and an upregulation of inflammatory pathways. In conclusion, my thesis identifies underlying mechanisms of immune aging using system biology tools in three independent cohorts of PWH for mechanistic studies and to improve their care and achieve healthy aging

Transcriptional regulatory dynamics drive coordinated metabolic and neural response to social challenge in mice

Agonistic encounters are powerful effectors of future behavior, and the ability to learn from this type of social challenge is an essential adaptive trait. We recently identified a conserved transcriptional program defining the response to social challenge across animal species, highly enriched in transcription factor (TF), energy metabolism, and developmental signaling genes. To understand the trajectory of this program and to uncover the most important regulatory influences controlling this response, we integrated gene expression data with the chromatin landscape in the hypothalamus, frontal cortex, and amygdala of socially challenged mice over time. The expression data revealed a complex spatiotemporal patterning of events starting with neural signaling molecules in the frontal cortex and ending in the modulation of developmental factors in the amygdala and hypothalamus, underpinned by a systems-wide shift in expression of energy metabolism-related genes. The transcriptional signals were correlated with significant shifts in chromatin accessibility and a network of challenge-associated TFs. Among these, the conserved metabolic and developmental regulator ESRRA was highlighted for an especially early and important regulatory role. Cell-type deconvolution analysis attributed the differential metabolic and developmental signals in this social context primarily to oligodendrocytes and neurons, respectively, and we show that ESRRA is expressed in both cell types. Localizing ESRRA binding sites in cortical chromatin, we show that this nuclear receptor binds both differentially expressed energy-related and neurodevelopmental TF genes. These data link metabolic and neurodevelopmental signali ng to social challenge, and identify key regulatory drivers of this process with unprecedented tissue and temporal resolution