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Martyshina AV, Sirotkina AG, Gosteva IV. Temporal multiscale modeling of biochemical regulatory networks: Calcium-regulated hepatocyte lipid and glucose metabolism. Biosystems 2024; 240:105227. [PMID: 38718915 DOI: 10.1016/j.biosystems.2024.105227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/29/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024]
Abstract
Hepatocyte lipid and glucose metabolism is regulated not only by major hormones like insulin and glucagon but also by many other factors, including calcium ions. Recently, mitochondria-associated membrane (MAM) dysfunction combined with incorrect IP3-receptor regulation has been shown to result in abnormal calcium signaling in hepatocytes. This dysfunction could further lead to hepatic metabolism pathology. However, the exact contribution of MAM dysfunction, incorrect IP3-receptor regulation and insulin resistance to the calcium-insulin-glucagon interplay is not understood yet. In this work, we analyze the role of abnormal calcium signaling and insulin dysfunction in hepatocytes by proposing a model of hepatocyte metabolic regulatory network with a detailed focus on the model construction details besides the biological aspect. In this work, we analyze the role of abnormal calcium signaling and insulin dysfunction in hepatocytes by proposing a model of hepatocyte metabolic regulatory network. We focus on the model construction details, model validation, and predictions. We describe the dynamic regulation of signaling processes by sigmoid Hill function. In particular, we study the effect of both the Hill function slope and the distance between Hill function extremes on metabolic processes in hepatocytes as a model of nonspecific insulin dysfunction. We also address the significant time difference between characteristic time of glucose hepatic processing and a typical calcium oscillation period in hepatocytes. Our modeling results show that calcium signaling dysfunction results in an abnormal increase in postprandial glucose levels, an abnormal glucose decrease in fasting, and a decreased amount of stored glycogen. An insulin dysfunction of glucose phosphorylation, glucose dephosphorylation, and glycogen breakdown also cause a noticeable effect. We also get some insight into the so-called hepatic insulin resistance paradox, confirming the hypothesis regarding indirect insulin action on hepatocytes via dysfunctional adipocyte lipolysis.
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Affiliation(s)
- Arina V Martyshina
- Sarov Physics and Technology Institute, National Research Nuclear University MEPhI, Sarov, Russian Federation
| | - Anna G Sirotkina
- Sarov Physics and Technology Institute, National Research Nuclear University MEPhI, Sarov, Russian Federation
| | - Irina V Gosteva
- Sarov Physics and Technology Institute, National Research Nuclear University MEPhI, Sarov, Russian Federation.
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2
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Gopalakrishnan S, Johnson W, Valderrama-Gomez MA, Icten E, Tat J, Ingram M, Fung Shek C, Chan PK, Schlegel F, Rolandi P, Kontoravdi C, Lewis NE. COSMIC-dFBA: A novel multi-scale hybrid framework for bioprocess modeling. Metab Eng 2024; 82:183-192. [PMID: 38387677 DOI: 10.1016/j.ymben.2024.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 02/01/2024] [Accepted: 02/19/2024] [Indexed: 02/24/2024]
Abstract
Metabolism governs cell performance in biomanufacturing, as it fuels growth and productivity. However, even in well-controlled culture systems, metabolism is dynamic, with shifting objectives and resources, thus limiting the predictive capability of mechanistic models for process design and optimization. Here, we present Cellular Objectives and State Modulation In bioreaCtors (COSMIC)-dFBA, a hybrid multi-scale modeling paradigm that accurately predicts cell density, antibody titer, and bioreactor metabolite concentration profiles. Using machine-learning, COSMIC-dFBA decomposes the instantaneous metabolite uptake and secretion rates in a bioreactor into weighted contributions from each cell state (growth or antibody-producing state) and integrates these with a genome-scale metabolic model. A major strength of COSMIC-dFBA is that it can be parameterized with only metabolite concentrations from spent media, although constraining the metabolic model with other omics data can further improve its capabilities. Using COSMIC-dFBA, we can predict the final cell density and antibody titer to within 10% of the measured data, and compared to a standard dFBA model, we found the framework showed a 90% and 72% improvement in cell density and antibody titer prediction, respectively. Thus, we demonstrate our hybrid modeling framework effectively captures cellular metabolism and expands the applicability of dFBA to model the dynamic conditions in a bioreactor.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, UK
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego, USA; Department of Bioengineering, University of California San Diego, USA.
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3
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Kulyashov MA, Kolmykov SK, Khlebodarova TM, Akberdin IR. State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs. Microorganisms 2023; 11:2987. [PMID: 38138131 PMCID: PMC10745598 DOI: 10.3390/microorganisms11122987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 12/09/2023] [Accepted: 12/13/2023] [Indexed: 12/24/2023] Open
Abstract
Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. Herein, we provide an overview of various computational strategies implemented for methanotrophic systems. We highlight functional capabilities as well as limitations of the most popular web resources for the reconstruction, modification and optimization of the genome-scale metabolic models for methane-utilizing bacteria.
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Affiliation(s)
- Mikhail A. Kulyashov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
| | - Semyon K. Kolmykov
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
| | - Tamara M. Khlebodarova
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Systems Biology, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
- Kurchatov Genomics Center, Institute of Cytology and Genetics SB RAS, 630090 Novosibirsk, Russia
| | - Ilya R. Akberdin
- Department of Computational Biology, Scientific Center for Information Technologies and Artificial Intelligence, Sirius University of Science and Technology, 354340 Sochi, Russia; (M.A.K.); (S.K.K.); (T.M.K.)
- Department of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia
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4
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Huang Y, Mohanty V, Dede M, Tsai K, Daher M, Li L, Rezvani K, Chen K. Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux. Nat Commun 2023; 14:4883. [PMID: 37573313 PMCID: PMC10423258 DOI: 10.1038/s41467-023-40457-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 07/26/2023] [Indexed: 08/14/2023] Open
Abstract
Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux's capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.
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Affiliation(s)
- Yuefan Huang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
- Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, 77030, USA
| | - Vakul Mohanty
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Merve Dede
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Kyle Tsai
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - May Daher
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Li Li
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Katayoun Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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5
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Sen P, Orešič M. Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 2023; 13:855. [PMID: 37512562 PMCID: PMC10383060 DOI: 10.3390/metabo13070855] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
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6
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Molversmyr H, Øyås O, Rotnes F, Vik JO. Extracting functionally accurate context-specific models of Atlantic salmon metabolism. NPJ Syst Biol Appl 2023; 9:19. [PMID: 37244928 DOI: 10.1038/s41540-023-00280-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 05/05/2023] [Indexed: 05/29/2023] Open
Abstract
Constraint-based models (CBMs) are used to study metabolic network structure and function in organisms ranging from microbes to multicellular eukaryotes. Published CBMs are usually generic rather than context-specific, meaning that they do not capture differences in reaction activities, which, in turn, determine metabolic capabilities, between cell types, tissues, environments, or other conditions. Only a subset of a CBM's metabolic reactions and capabilities are likely to be active in any given context, and several methods have therefore been developed to extract context-specific models from generic CBMs through integration of omics data. We tested the ability of six model extraction methods (MEMs) to create functionally accurate context-specific models of Atlantic salmon using a generic CBM (SALARECON) and liver transcriptomics data from contexts differing in water salinity (life stage) and dietary lipids. Three MEMs (iMAT, INIT, and GIMME) outperformed the others in terms of functional accuracy, which we defined as the extracted models' ability to perform context-specific metabolic tasks inferred directly from the data, and one MEM (GIMME) was faster than the others. Context-specific versions of SALARECON consistently outperformed the generic version, showing that context-specific modeling better captures salmon metabolism. Thus, we demonstrate that results from human studies also hold for a non-mammalian animal and major livestock species.
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Affiliation(s)
- Håvard Molversmyr
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Ove Øyås
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Filip Rotnes
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway
| | - Jon Olav Vik
- Faculty of Chemistry, Biotechnology and Food Science, Norwegian University of Life Sciences, Ås, Norway.
- Faculty of Biosciences, Norwegian University of Life Sciences, Ås, Norway.
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7
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Jalili M, Scharm M, Wolkenhauer O, Salehzadeh-Yazdi A. Metabolic function-based normalization improves transcriptome data-driven reduction of genome-scale metabolic models. NPJ Syst Biol Appl 2023; 9:15. [PMID: 37210409 DOI: 10.1038/s41540-023-00281-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Accepted: 05/10/2023] [Indexed: 05/22/2023] Open
Abstract
Genome-scale metabolic models (GEMs) are extensively used to simulate cell metabolism and predict cell phenotypes. GEMs can also be tailored to generate context-specific GEMs, using omics data integration approaches. To date, many integration approaches have been developed, however, each with specific pros and cons; and none of these algorithms systematically outperforms the others. The key to successful implementation of such integration algorithms lies in the optimal selection of parameters, and thresholding is a crucial component in this process. To improve the predictive accuracy of context-specific models, we introduce a new integration framework that improves the ranking of related genes and homogenizes the expression values of those gene sets using single-sample Gene Set Enrichment Analysis (ssGSEA). In this study, we coupled ssGSEA with GIMME and validated the advantages of the proposed framework to predict the ethanol formation of yeast grown in the glucose-limited chemostats, and to simulate metabolic behaviors of yeast growth in four different carbon sources. This framework enhances the predictive accuracy of GIMME which we demonstrate for predicting the yeast physiology in nutrient-limited cultures.
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Affiliation(s)
- Mahdi Jalili
- Hematology, Oncology and SCT Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Olaf Wolkenhauer
- Department of Systems Biology and Bioinformatics, University of Rostock, Rostock, Germany
- Stellenbosch University, Stellenbosch Institute for Advanced Study (STIAS), Wallenberg Research Centre, Stellenbosch, South Africa
- Leibniz Institute for Food Systems Biology at the Technical University Munich, Freising, Germany
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8
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Jamshidi N, Nigam KB, Nigam SK. Loss of the Kidney Urate Transporter, Urat1, Leads to Disrupted Redox Homeostasis in Mice. Antioxidants (Basel) 2023; 12:antiox12030780. [PMID: 36979028 PMCID: PMC10045411 DOI: 10.3390/antiox12030780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 02/28/2023] [Accepted: 03/15/2023] [Indexed: 03/29/2023] Open
Abstract
High uric acid is associated with gout, hypertension, metabolic syndrome, cardiovascular disease, and kidney disease. URAT1 (SLC22A12), originally discovered in mice as Rst, is generally considered a very selective uric acid transporter compared to other closely-related kidney uric acid transporters such as OAT1 (SLC22A6, NKT) and OAT3 (SLC22A8). While the role of URAT1 in regulating human uric acid is well-established, in recent studies the gene has been linked to redox regulation in flies as well as progression of renal cell carcinoma. We have now identified over twenty metabolites in the Urat1 knockout that are generally distinct from metabolites accumulating in the Oat1 and Oat3 knockout mice, with distinct molecular properties as revealed by chemoinformatics and machine learning analysis. These metabolites are involved in seemingly disparate aspects of cellular metabolism, including pyrimidine, fatty acid, and amino acid metabolism. However, through integrative systems metabolic analysis of the transcriptomic and metabolomic data using a human metabolic reconstruction to build metabolic genome-scale models (GEMs), the cellular response to loss of Urat1/Rst revealed compensatory processes related to reactive oxygen species handling and maintaining redox state balances via Vitamin C metabolism and cofactor charging reactions. These observations are consistent with the increasingly appreciated role of the antioxidant properties of uric acid. Collectively, the results highlight the role of Urat1/Rst as a transporter strongly tied to maintaining redox homeostasis, with implications for metabolic side effects from drugs that block its function.
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Affiliation(s)
- Neema Jamshidi
- Department of Radiological Sciences, University of California, Los Angeles, CA 90095, USA
- Institute of Engineering in Medicine, University of California, San Diego, La Jolla, CA 92093, USA;
- Correspondence:
| | - Kabir B. Nigam
- Department of Psychiatry, Brigham and Women’s Hospital, Boston, MA 02130, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02130, USA
| | - Sanjay K. Nigam
- Institute of Engineering in Medicine, University of California, San Diego, La Jolla, CA 92093, USA;
- Departments of Pediatrics and Medicine (Nephrology), University of California, San Diego, La Jolla, CA 92093, USA
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9
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Clasen F, Nunes PM, Bidkhori G, Bah N, Boeing S, Shoaie S, Anastasiou D. Systematic diet composition swap in a mouse genome-scale metabolic model reveals determinants of obesogenic diet metabolism in liver cancer. iScience 2023; 26:106040. [PMID: 36844450 PMCID: PMC9947310 DOI: 10.1016/j.isci.2023.106040] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 09/08/2022] [Accepted: 01/20/2023] [Indexed: 01/26/2023] Open
Abstract
Dietary nutrient availability and gene expression, together, influence tissue metabolic activity. Here, we explore whether altering dietary nutrient composition in the context of mouse liver cancer suffices to overcome chronic gene expression changes that arise from tumorigenesis and western-style diet (WD). We construct a mouse genome-scale metabolic model and estimate metabolic fluxes in liver tumors and non-tumoral tissue after computationally varying the composition of input diet. This approach, called Systematic Diet Composition Swap (SyDiCoS), revealed that, compared to a control diet, WD increases production of glycerol and succinate irrespective of specific tissue gene expression patterns. Conversely, differences in fatty acid utilization pathways between tumor and non-tumor liver are amplified with WD by both dietary carbohydrates and lipids together. Our data suggest that combined dietary component modifications may be required to normalize the distinctive metabolic patterns that underlie selective targeting of tumor metabolism.
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Affiliation(s)
- Frederick Clasen
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Patrícia M. Nunes
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Gholamreza Bidkhori
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Nourdine Bah
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Stefan Boeing
- Bioinformatics and Biostatistics Science Technology Platform, Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
| | - Saeed Shoaie
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral and Craniofacial Sciences, King’s College London, London SE1 9RT, UK
- Science for Life Laboratory (SciLifeLab), KTH - Royal Institute of Technology, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
| | - Dimitrios Anastasiou
- Cancer Metabolism Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK
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10
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Context-Specific Genome-Scale Metabolic Modelling and Its Application to the Analysis of COVID-19 Metabolic Signatures. Metabolites 2023; 13:metabo13010126. [PMID: 36677051 PMCID: PMC9866716 DOI: 10.3390/metabo13010126] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/27/2022] [Accepted: 01/10/2023] [Indexed: 01/19/2023] Open
Abstract
Genome-scale metabolic models (GEMs) have found numerous applications in different domains, ranging from biotechnology to systems medicine. Herein, we overview the most popular algorithms for the automated reconstruction of context-specific GEMs using high-throughput experimental data. Moreover, we describe different datasets applied in the process, and protocols that can be used to further automate the model reconstruction and validation. Finally, we describe recent COVID-19 applications of context-specific GEMs, focusing on the analysis of metabolic implications, identification of biomarkers and potential drug targets.
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11
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Cheng CT, Lai JM, Chang PMH, Hong YR, Huang CYF, Wang FS. Identifying essential genes in genome-scale metabolic models of consensus molecular subtypes of colorectal cancer. PLoS One 2023; 18:e0286032. [PMID: 37205704 DOI: 10.1371/journal.pone.0286032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/06/2023] [Indexed: 05/21/2023] Open
Abstract
Identifying essential targets in the genome-scale metabolic networks of cancer cells is a time-consuming process. The present study proposed a fuzzy hierarchical optimization framework for identifying essential genes, metabolites and reactions. On the basis of four objectives, the present study developed a framework for identifying essential targets that lead to cancer cell death and evaluating metabolic flux perturbations in normal cells that have been caused by cancer treatment. Through fuzzy set theory, a multiobjective optimization problem was converted into a trilevel maximizing decision-making (MDM) problem. We applied nested hybrid differential evolution to solve the trilevel MDM problem to identify essential targets in genome-scale metabolic models for five consensus molecular subtypes (CMSs) of colorectal cancer. We used various media to identify essential targets for each CMS and discovered that most targets affected all five CMSs and that some genes were CMS-specific. We obtained experimental data on the lethality of cancer cell lines from the DepMap database to validate the identified essential genes. The results reveal that most of the identified essential genes were compatible with the colorectal cancer cell lines obtained from DepMap and that these genes, with the exception of EBP, LSS, and SLC7A6, could generate a high level of cell death when knocked out. The identified essential genes were mostly involved in cholesterol biosynthesis, nucleotide metabolisms, and the glycerophospholipid biosynthetic pathway. The genes involved in the cholesterol biosynthetic pathway were also revealed to be determinable, if a cholesterol uptake reaction was not induced when the cells were in the culture medium. However, the genes involved in the cholesterol biosynthetic pathway became non-essential if such a reaction was induced. Furthermore, the essential gene CRLS1 was revealed as a medium-independent target for all CMSs.
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Affiliation(s)
- Chao-Ting Cheng
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Jin-Mei Lai
- Department of Life Science, College of Science and Engineering, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Peter Mu-Hsin Chang
- Department of Oncology, Taipei Veterans General Hospital, Taipei, Taiwan
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Yi-Ren Hong
- Department of Biochemistry and Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung City, Taiwan
| | - Chi-Ying F Huang
- Institute of Biopharmaceutical Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Biotechnology and Laboratory Science in Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
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12
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Cuperlovic-Culf M, Nguyen-Tran T, Bennett SAL. Machine Learning and Hybrid Methods for Metabolic Pathway Modeling. Methods Mol Biol 2023; 2553:417-439. [PMID: 36227553 DOI: 10.1007/978-1-0716-2617-7_18] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Computational cell metabolism models seek to provide metabolic explanations of cell behavior under different conditions or following genetic alterations, help in the optimization of in vitro cell growth environments, or predict cellular behavior in vivo and in vitro. In the extremes, mechanistic models can include highly detailed descriptions of a small number of metabolic reactions or an approximate representation of an entire metabolic network. To date, all mechanistic models have required details of individual metabolic reactions, either kinetic parameters or metabolic flux, as well as information about extracellular and intracellular metabolite concentrations. Despite the extensive efforts and the increasing availability of high-quality data, required in vivo data are not available for the majority of known metabolic reactions; thus, mechanistic models are based primarily on ex vivo kinetic measurements and limited flux information. Machine learning approaches provide an alternative for derivation of functional dependencies from existing data. The increasing availability of metabolomic and lipidomic data, with growing feature coverage as well as sample set size, is expected to provide new data options needed for derivation of machine learning models of cell metabolic processes. Moreover, machine learning analysis of longitudinal data can lead to predictive models of cell behaviors over time. Conversely, machine learning models trained on steady-state data can provide descriptive models for the comparison of metabolic states in different environments or disease conditions. Additionally, inclusion of metabolic network knowledge in these analyses can further help in the development of models with limited data.This chapter will explore the application of machine learning to the modeling of cell metabolism. We first provide a theoretical explanation of several machine learning and hybrid mechanistic machine learning methods currently being explored to model metabolism. Next, we introduce several avenues for improving these models with machine learning. Finally, we provide protocols for specific examples of the utilization of machine learning in the development of predictive cell metabolism models using metabolomic data. We describe data preprocessing, approaches for training of machine learning models for both descriptive and predictive models, and the utilization of these models in synthetic and systems biology. Detailed protocols provide a list of software tools and libraries used for these applications, step-by-step modeling protocols, troubleshooting, as well as an overview of existing limitations to these approaches.
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Affiliation(s)
- Miroslava Cuperlovic-Culf
- Digital Technologies Research Centre, National Research Council of Canada, Ottawa, ON, Canada.
- Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, ON, Canada.
| | - Thao Nguyen-Tran
- Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, ON, Canada
- Neural Regeneration Laboratory, Ottawa Institute of Systems Biology, Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
- Department of Chemistry and Biomolecular Sciences, Centre for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, Canada
| | - Steffany A L Bennett
- Department of Biochemistry, Microbiology, and Immunology, University of Ottawa, Ottawa, ON, Canada
- Neural Regeneration Laboratory, Ottawa Institute of Systems Biology, Brain and Mind Research Institute, University of Ottawa, Ottawa, ON, Canada
- Department of Chemistry and Biomolecular Sciences, Centre for Catalysis Research and Innovation, University of Ottawa, Ottawa, ON, Canada
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13
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Gopalakrishnan S, Joshi CJ, Valderrama-Gómez MÁ, Icten E, Rolandi P, Johnson W, Kontoravdi C, Lewis NE. Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data. Metab Eng 2023; 75:181-191. [PMID: 36566974 PMCID: PMC10258867 DOI: 10.1016/j.ymben.2022.12.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/01/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.
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Affiliation(s)
| | - Chintan J Joshi
- Department of Pediatrics, University of California San Diego, United States
| | | | - Elcin Icten
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Pablo Rolandi
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - William Johnson
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, UK
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego, United States; Department of Bioengineering, University of California San Diego, United States.
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14
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Jamialahmadi O, Salehabadi E, Hashemi-Najafabadi S, Motamedian E, Bagheri F, Mancina RM, Romeo S. Cellular Genome-Scale Metabolic Modeling Identifies New Potential Drug Targets Against Hepatocellular Carcinoma. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:671-682. [PMID: 36508280 DOI: 10.1089/omi.2022.0122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Genome-scale metabolic modeling (GEM) is one of the key approaches to unpack cancer metabolism and for discovery of new drug targets. In this study, we report the Transcriptional Regulated Flux Balance Analysis-CORE (TRFBA-), an algorithm for GEM using key growth-correlated reactions using hepatocellular carcinoma (HCC), an important global health burden, as a case study. We generated a HepG2 cell-specific GEM by integrating this cell line transcriptomic data with a generic human metabolic model to forecast potential drug targets for HCC. A total of 108 essential genes for growth were predicted by the TRFBA-CORE. These genes were enriched for metabolic pathways involved in cholesterol, sterol, and steroid biosynthesis. Furthermore, we silenced a predicted essential gene, 11-beta dehydrogenase hydroxysteroid type 2 (HSD11B2), in HepG2 cells resulting in a reduction in cell viability. To further identify novel potential drug targets in HCC, we examined the effect of nine drugs targeting the essential genes, and observed that most drugs inhibited the growth of HepG2 cells. Some of these drugs in this model performed better than Sorafenib, the first-line therapeutic against HCC. A HepG2 cell-specific GEM highlights sterol metabolism to be essential for cell growth. HSD11B2 downregulation results in lower cell growth. Most of the compounds, selected by drug repurposing approach, show a significant inhibitory effect on cell growth in a wide range of concentrations. These findings offer new molecular leads for drug discovery for hepatic cancer while also illustrating the importance of GEM and drug repurposing in cancer therapeutics innovation.
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Affiliation(s)
- Oveis Jamialahmadi
- Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Department of Biotechnology and Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Ehsan Salehabadi
- Department of Biotechnology and Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Sameereh Hashemi-Najafabadi
- Department of Biomedical Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Ehsan Motamedian
- Department of Biotechnology and Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Fatemeh Bagheri
- Department of Biotechnology and Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Rosellina Margherita Mancina
- Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden
| | - Stefano Romeo
- Wallenberg Laboratory, Department of Molecular and Clinical Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.,Clinical Nutrition Unit, Department of Medical and Surgical Sciences, University Magna Graecia, Catanzaro, Italy.,Cardiology Department, Sahlgrenska University Hospital, Gothenburg, Sweden
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15
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Jamshidi N, Nigam SK. Drug transporters OAT1 and OAT3 have specific effects on multiple organs and gut microbiome as revealed by contextualized metabolic network reconstructions. Sci Rep 2022; 12:18308. [PMID: 36316339 PMCID: PMC9622871 DOI: 10.1038/s41598-022-21091-w] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 09/22/2022] [Indexed: 11/07/2022] Open
Abstract
In vitro and in vivo studies have established the organic anion transporters OAT1 (SLC22A6, NKT) and OAT3 (SLC22A8) among the main multi-specific "drug" transporters. They also transport numerous endogenous metabolites, raising the possibility of drug-metabolite interactions (DMI). To help understand the role of these drug transporters on metabolism across scales ranging from organ systems to organelles, a formal multi-scale analysis was performed. Metabolic network reconstructions of the omics-alterations resulting from Oat1 and Oat3 gene knockouts revealed links between the microbiome and human metabolism including reactions involving small organic molecules such as dihydroxyacetone, alanine, xanthine, and p-cresol-key metabolites in independent pathways. Interestingly, pairwise organ-organ interactions were also disrupted in the two Oat knockouts, with altered liver, intestine, microbiome, and skin-related metabolism. Compared to older models focused on the "one transporter-one organ" concept, these more sophisticated reconstructions, combined with integration of a multi-microbial model and more comprehensive metabolomics data for the two transporters, provide a considerably more complex picture of how renal "drug" transporters regulate metabolism across the organelle (e.g. endoplasmic reticulum, Golgi, peroxisome), cellular, organ, inter-organ, and inter-organismal scales. The results suggest that drugs interacting with OAT1 and OAT3 can have far reaching consequences on metabolism in organs (e.g. skin) beyond the kidney. Consistent with the Remote Sensing and Signaling Theory (RSST), the analysis demonstrates how transporter-dependent metabolic signals mediate organ crosstalk (e.g., gut-liver-kidney) and inter-organismal communication (e.g., gut microbiome-host).
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Affiliation(s)
- Neema Jamshidi
- grid.19006.3e0000 0000 9632 6718Department of Radiological Sciences, University of California, Los Angeles, Los Angeles, CA USA ,grid.266100.30000 0001 2107 4242Institute of Engineering in Medicine, University of California, San Diego, La Jolla, CA USA
| | - Sanjay K. Nigam
- grid.266100.30000 0001 2107 4242Departments of Pediatrics and Medicine (Nephrology), University of California, San Diego, La Jolla, CA USA
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16
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Aminian-Dehkordi J, Valiei A, Mofrad MRK. Emerging computational paradigms to address the complex role of gut microbial metabolism in cardiovascular diseases. Front Cardiovasc Med 2022; 9:987104. [PMID: 36299869 PMCID: PMC9589059 DOI: 10.3389/fcvm.2022.987104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Accepted: 09/20/2022] [Indexed: 11/13/2022] Open
Abstract
The human gut microbiota and its associated perturbations are implicated in a variety of cardiovascular diseases (CVDs). There is evidence that the structure and metabolic composition of the gut microbiome and some of its metabolites have mechanistic associations with several CVDs. Nevertheless, there is a need to unravel metabolic behavior and underlying mechanisms of microbiome-host interactions. This need is even more highlighted when considering that microbiome-secreted metabolites contributing to CVDs are the subject of intensive research to develop new prevention and therapeutic techniques. In addition to the application of high-throughput data used in microbiome-related studies, advanced computational tools enable us to integrate omics into different mathematical models, including constraint-based models, dynamic models, agent-based models, and machine learning tools, to build a holistic picture of metabolic pathological mechanisms. In this article, we aim to review and introduce state-of-the-art mathematical models and computational approaches addressing the link between the microbiome and CVDs.
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Affiliation(s)
| | | | - Mohammad R. K. Mofrad
- Department of Bioengineering and Mechanical Engineering, University of California, Berkeley, Berkeley, CA, United States
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17
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Sen P, Govaere O, Sinioja T, McGlinchey A, Geng D, Ratziu V, Bugianesi E, Schattenberg JM, Vidal-Puig A, Allison M, Cockell S, Daly AK, Hyötyläinen T, Anstee QM, Orešič M. Quantitative modeling of human liver reveals dysregulation of glycosphingolipid pathways in nonalcoholic fatty liver disease. iScience 2022; 25:104949. [PMID: 36065182 PMCID: PMC9440293 DOI: 10.1016/j.isci.2022.104949] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 01/21/2022] [Accepted: 08/11/2022] [Indexed: 11/09/2022] Open
Abstract
Nonalcoholic fatty liver disease (NAFLD) is an increasingly prevalent disease that is associated with multiple metabolic disturbances, yet the metabolic pathways underlying its progression are poorly understood. Here, we studied metabolic pathways of the human liver across the full histological spectrum of NAFLD. We analyzed whole liver tissue transcriptomics and serum metabolomics data obtained from a large, prospectively enrolled cohort of 206 histologically characterized patients derived from the European NAFLD Registry and developed genome-scale metabolic models (GEMs) of human hepatocytes at different stages of NAFLD. We identified several metabolic signatures in the liver and blood of these patients, specifically highlighting the alteration of vitamins (A, E) and glycosphingolipids, and their link with complex glycosaminoglycans in advanced fibrosis. Furthermore, we derived GEMs and identified metabolic signatures of three common NAFLD-associated gene variants (PNPLA3, TM6SF2, and HSD17B13). The study demonstrates dysregulated liver metabolic pathways which may contribute to the progression of NAFLD.
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Affiliation(s)
- Partho Sen
- School of Medical Sciences, Örebro University, 70281 Örebro, Sweden
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
| | - Olivier Govaere
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Tim Sinioja
- Department of Chemistry, Örebro University, 70281 Örebro, Sweden
| | - Aidan McGlinchey
- School of Medical Sciences, Örebro University, 70281 Örebro, Sweden
| | - Dawei Geng
- Department of Chemistry, Örebro University, 70281 Örebro, Sweden
| | - Vlad Ratziu
- Assistance Publique-Hôpitaux de Paris, hôpital Beaujon, University Paris-Diderot, Paris, France
| | - Elisabetta Bugianesi
- Department of Medical Sciences, Division of Gastro-Hepatology, A.O. Città della Salute e della Scienza di Torino, University of Turin, Turin, Italy
| | - Jörn M. Schattenberg
- Metabolic Liver Research Programm, Department of Medicine, University Hospital Mainz, Mainz, Germany
| | - Antonio Vidal-Puig
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK
| | - Michael Allison
- Liver Unit, Department of Medicine, Cambridge Biomedical Research Centre, Cambridge University NHS Foundation Trust, UK
| | - Simon Cockell
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Ann K. Daly
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | | | - Quentin M. Anstee
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Newcastle NIHR Biomedical Research Center, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
| | - Matej Orešič
- School of Medical Sciences, Örebro University, 70281 Örebro, Sweden
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, 20520 Turku, Finland
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18
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Ng RH, Lee JW, Baloni P, Diener C, Heath JR, Su Y. Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer. Front Oncol 2022; 12:914594. [PMID: 35875150 PMCID: PMC9303011 DOI: 10.3389/fonc.2022.914594] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.
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Affiliation(s)
- Rachel H. Ng
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Jihoon W. Lee
- Medical Scientist Training Program, University of Washington, Seattle, WA, United States
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | | | | | - James R. Heath
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
- *Correspondence: James R. Heath, ; Yapeng Su,
| | - Yapeng Su
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
- Herbold Computational Biology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
- *Correspondence: James R. Heath, ; Yapeng Su,
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19
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Li Z, Ji BW, Dixit PD, Tchourine K, Lien EC, Hosios AM, Abbott KL, Rutter JC, Westermark AM, Gorodetsky EF, Sullivan LB, Vander Heiden MG, Vitkup D. Cancer cells depend on environmental lipids for proliferation when electron acceptors are limited. Nat Metab 2022; 4:711-723. [PMID: 35739397 DOI: 10.1038/s42255-022-00588-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 05/17/2022] [Indexed: 01/31/2023]
Abstract
Production of oxidized biomass, which requires regeneration of the cofactor NAD+, can be a proliferation bottleneck that is influenced by environmental conditions. However, a comprehensive quantitative understanding of metabolic processes that may be affected by NAD+ deficiency is currently missing. Here, we show that de novo lipid biosynthesis can impose a substantial NAD+ consumption cost in proliferating cancer cells. When electron acceptors are limited, environmental lipids become crucial for proliferation because NAD+ is required to generate precursors for fatty acid biosynthesis. We find that both oxidative and even net reductive pathways for lipogenic citrate synthesis are gated by reactions that depend on NAD+ availability. We also show that access to acetate can relieve lipid auxotrophy by bypassing the NAD+ consuming reactions. Gene expression analysis demonstrates that lipid biosynthesis strongly anti-correlates with expression of hypoxia markers across tumor types. Overall, our results define a requirement for oxidative metabolism to support biosynthetic reactions and provide a mechanistic explanation for cancer cell dependence on lipid uptake in electron acceptor-limited conditions, such as hypoxia.
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Affiliation(s)
- Zhaoqi Li
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brian W Ji
- Department of Systems Biology, Columbia University, New York, NY, USA
- Physician-Scientist Training Pathway, Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Purushottam D Dixit
- Department of Systems Biology, Columbia University, New York, NY, USA
- Department of Physics, University of Florida, Gainesville, FL, USA
- University of Florida Genetics Institute, University of Florida, Gainesville, FL, USA
- Department of Chemical Engineering, University of Florida, Gainesville, FL, USA
- University of Florida Cancer Center, University of Florida, Gainesville, FL, USA
| | | | - Evan C Lien
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Aaron M Hosios
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Keene L Abbott
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Justine C Rutter
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Health Sciences and Technology (HST) and Harvard/MIT MD-PhD Program, Harvard Medical School, Boston, MA, USA
| | - Anna M Westermark
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Elizabeth F Gorodetsky
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lucas B Sullivan
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Matthew G Vander Heiden
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Dennis Vitkup
- Department of Systems Biology, Columbia University, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.
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20
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Grausa K, Mozga I, Pleiko K, Pentjuss A. Integrative Gene Expression and Metabolic Analysis Tool IgemRNA. Biomolecules 2022; 12:biom12040586. [PMID: 35454176 PMCID: PMC9029533 DOI: 10.3390/biom12040586] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 04/11/2022] [Accepted: 04/14/2022] [Indexed: 01/27/2023] Open
Abstract
Genome-scale metabolic modeling is widely used to study the impact of metabolism on the phenotype of different organisms. While substrate modeling reflects the potential distribution of carbon and other chemical elements within the model, the additional use of omics data, e.g., transcriptome, has implications when researching the genotype–phenotype responses to environmental changes. Several algorithms for transcriptome analysis using genome-scale metabolic modeling have been proposed. Still, they are restricted to specific objectives and conditions and lack flexibility, have software compatibility issues, and require advanced user skills. We classified previously published algorithms, summarized transcriptome pre-processing, integration, and analysis methods, and implemented them in the newly developed transcriptome analysis tool IgemRNA, which (1) has a user-friendly graphical interface, (2) tackles compatibility issues by combining previous data input and pre-processing algorithms in MATLAB, and (3) introduces novel algorithms for the automatic comparison of different transcriptome datasets with or without Cobra Toolbox 3.0 optimization algorithms. We used publicly available transcriptome datasets from Saccharomyces cerevisiae BY4741 and H4-S47D strains for validation. We found that IgemRNA provides a means for transcriptome and environmental data validation on biochemical network topology since the biomass function varies for different phenotypes. Our tool can detect problematic reaction constraints.
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Affiliation(s)
- Kristina Grausa
- Department of Computer Systems, Latvia University of Life Sciences and Technologies, Liela Street 2, LV-3001 Jelgava, Latvia; (K.G.); (I.M.)
| | - Ivars Mozga
- Department of Computer Systems, Latvia University of Life Sciences and Technologies, Liela Street 2, LV-3001 Jelgava, Latvia; (K.G.); (I.M.)
| | - Karlis Pleiko
- Laboratory of Precision and Nanomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, 50411 Tartu, Estonia;
- Faculty of Medicine, University of Latvia, LV-1586 Riga, Latvia
| | - Agris Pentjuss
- Institute of Microbiology and Biotechnology, University of Latvia, Jelgavas Street 1, LV-1004 Riga, Latvia
- Correspondence:
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21
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Li X, Yilmaz LS, Walhout AJ. Compartmentalization of metabolism between cell types in multicellular organisms: a computational perspective. CURRENT OPINION IN SYSTEMS BIOLOGY 2022; 29:100407. [PMID: 35224313 PMCID: PMC8865431 DOI: 10.1016/j.coisb.2021.100407] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
In multicellular organisms, metabolism is compartmentalized at many levels, including tissues and organs, different cell types, and subcellular compartments. Compartmentalization creates a coordinated homeostatic system where each compartment contributes to the production of energy and biomolecules the organism needs to carrying out specific metabolic tasks. Experimentally studying metabolic compartmentalization and metabolic interactions between cells and tissues in multicellular organisms is challenging at a systems level. However, recent progress in computational modeling provides an alternative approach to this problem. Here we discuss how integrating metabolic network modeling with omics data offers an opportunity to reveal metabolic states at the level of organs, tissues and, ultimately, individual cells. We review the current status of genome-scale metabolic network models in multicellular organisms, methods to study metabolic compartmentalization in silico, and insights gained from computational analyses. We also discuss outstanding challenges and provide perspectives for the future directions of the field.
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22
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Purohit V, Wagner A, Yosef N, Kuchroo VK. Systems-based approaches to study immunometabolism. Cell Mol Immunol 2022; 19:409-420. [PMID: 35121805 PMCID: PMC8891302 DOI: 10.1038/s41423-021-00783-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 09/17/2021] [Indexed: 02/06/2023] Open
Abstract
Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), reveal new layers of complexity in cellular systems. An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells. The diverse spectra of immune cell phenotypes, sparsity of immune cell numbers in vivo, limitations in the number of metabolites identified, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and high dependence on the local milieu make investigations in immunometabolism challenging, especially at the single-cell level. In this review, we define the systemic nature of immunometabolism, summarize cell- and system-based approaches, and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells. We close the review by discussing the applications and shortcomings of metabolic modeling techniques. With systems-oriented studies of metabolism expected to become a mainstay of immunological research, an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.
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Affiliation(s)
- Vinee Purohit
- Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA
| | - Allon Wagner
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, 94720, USA
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA
| | - Nir Yosef
- Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA, 94720, USA
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA
| | - Vijay K Kuchroo
- Evergrande Center for Immunologic Diseases and Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, 02141, USA.
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23
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Gouda G, Gupta MK, Donde R, Behera L, Vadde R. Metabolic pathway-based target therapy to hepatocellular carcinoma: a computational approach. THERANOSTICS AND PRECISION MEDICINE FOR THE MANAGEMENT OF HEPATOCELLULAR CARCINOMA, VOLUME 2 2022:83-103. [DOI: 10.1016/b978-0-323-98807-0.00003-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/06/2023]
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24
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Cheng CT, Wang TY, Chen PR, Wu WH, Lai JM, Chang PMH, Hong YR, Huang CYF, Wang FS. Computer-Aided Design for Identifying Anticancer Targets in Genome-Scale Metabolic Models of Colon Cancer. BIOLOGY 2021; 10:biology10111115. [PMID: 34827109 PMCID: PMC8614794 DOI: 10.3390/biology10111115] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2021] [Revised: 10/25/2021] [Accepted: 10/26/2021] [Indexed: 01/21/2023]
Abstract
Simple Summary Discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. This study developed a fuzzy optimization framework for identifying anticancer targets. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. The computational results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is, and a two-target combination of 5-FU and folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells. Abstract The efficient discovery of anticancer targets with minimal side effects is a major challenge in drug discovery and development. Early prediction of side effects is key for reducing development costs, increasing drug efficacy, and increasing drug safety. This study developed a fuzzy optimization framework for Identifying AntiCancer Targets (IACT) using constraint-based models. Four objectives were established to evaluate the mortality of treated cancer cells and to minimize side effects causing toxicity-induced tumorigenesis on normal cells and smaller metabolic perturbations. Fuzzy set theory was applied to evaluate potential side effects and investigate the magnitude of metabolic deviations in perturbed cells compared with their normal counterparts. The framework was applied to identify not only gene regulator targets but also metabolite- and reaction-centric targets. A nested hybrid differential evolution algorithm with a hierarchical fitness function was applied to solve multilevel IACT problems. The results show that the combination of a carbon metabolism target and any one-target gene that participates in the sphingolipid, glycerophospholipid, nucleotide, cholesterol biosynthesis, or pentose phosphate pathways is more effective for treatment than one-target inhibition is. A clinical antimetabolite drug 5-fluorouracil (5-FU) has been used to inhibit synthesis of deoxythymidine-5′-triphosphate for treatment of colorectal cancer. The computational results reveal that a two-target combination of 5-FU and a folate supplement can improve cell viability, reduce metabolic deviation, and reduce side effects of normal cells.
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Affiliation(s)
- Chao-Ting Cheng
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
| | - Tsun-Yu Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
| | - Pei-Rong Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
| | - Wu-Hsiung Wu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
| | - Jin-Mei Lai
- Department of Life Science, Fu-Jen Catholic University, New Taipei City 24205, Taiwan;
| | - Peter Mu-Hsin Chang
- Department of Oncology, Taipei Veterans General Hospital, Taipei 11217, Taiwan;
- Faculty of Medicine, National Yang Ming Chiao Tung University, Taipei 11211, Taiwan
| | - Yi-Ren Hong
- Department of Biochemistry, Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung City 80708, Taiwan;
| | - Chi-Ying F. Huang
- Institute of Biopharmaceutical Sciences, National Yang Ming Chiao Tung University, Taipei 11211, Taiwan;
- Department of Biotechnology and Laboratory Science in Medicine, National Yang Ming Chiao Tung University, Taipei 11211, Taiwan
| | - Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 62102, Taiwan; (C.-T.C.); (T.-Y.W.); (P.-R.C.); (W.-H.W.)
- Correspondence: ; Tel.: +886-5-2720411 (ext. 33404)
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25
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Doran S, Arif M, Lam S, Bayraktar A, Turkez H, Uhlen M, Boren J, Mardinoglu A. Multi-omics approaches for revealing the complexity of cardiovascular disease. Brief Bioinform 2021; 22:bbab061. [PMID: 33725119 PMCID: PMC8425417 DOI: 10.1093/bib/bbab061] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 01/20/2021] [Accepted: 02/05/2021] [Indexed: 02/06/2023] Open
Abstract
The development and progression of cardiovascular disease (CVD) can mainly be attributed to the narrowing of blood vessels caused by atherosclerosis and thrombosis, which induces organ damage that will result in end-organ dysfunction characterized by events such as myocardial infarction or stroke. It is also essential to consider other contributory factors to CVD, including cardiac remodelling caused by cardiomyopathies and co-morbidities with other diseases such as chronic kidney disease. Besides, there is a growing amount of evidence linking the gut microbiota to CVD through several metabolic pathways. Hence, it is of utmost importance to decipher the underlying molecular mechanisms associated with these disease states to elucidate the development and progression of CVD. A wide array of systems biology approaches incorporating multi-omics data have emerged as an invaluable tool in establishing alterations in specific cell types and identifying modifications in signalling events that promote disease development. Here, we review recent studies that apply multi-omics approaches to further understand the underlying causes of CVD and provide possible treatment strategies by identifying novel drug targets and biomarkers. We also discuss very recent advances in gut microbiota research with an emphasis on how diet and microbial composition can impact the development of CVD. Finally, we present various biological network analyses and other independent studies that have been employed for providing mechanistic explanation and developing treatment strategies for end-stage CVD, namely myocardial infarction and stroke.
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Affiliation(s)
- Stephen Doran
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Simon Lam
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom
| | - Abdulahad Bayraktar
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum, Turkey
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jan Boren
- Institute of Medicine, Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital Gothenburg, Sweden
| | - Adil Mardinoglu
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, SE1 9RT, United Kingdom
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
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26
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Xu R, Razaghi-Moghadam Z, Nikoloski Z. Maximization of non-idle enzymes improves the coverage of the estimated maximal in vivo enzyme catalytic rates in Escherichia coli. Bioinformatics 2021; 37:3848-3855. [PMID: 34358300 DOI: 10.1093/bioinformatics/btab575] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 07/19/2021] [Accepted: 08/04/2021] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Constraint-based modeling approaches allow the estimation of maximal in vivo enzyme catalytic rates that can serve as proxies for enzyme turnover numbers. Yet, genome-scale flux profiling remains a challenge in deploying these approaches to catalogue proxies for enzyme catalytic rates across organisms. RESULTS Here we formulate a constraint-based approach, termed NIDLE-flux, to estimate fluxes at a genome-scale level by using the principle of efficient usage of expressed enzymes. Using proteomics data from Escherichia coli, we show that the fluxes estimated by NIDLE-flux and the existing approaches are in excellent qualitative agreement (Pearson correlation > 0.9). We also find that the maximal in vivo catalytic rates estimated by NIDLE-flux exhibits a Pearson correlation of 0.74 with in vitro enzyme turnover numbers. However, NIDLE-flux results in a 1.4-fold increase in the size of the estimated maximal in vivo catalytic rates in comparison to the contenders. Integration of the maximum in vivo catalytic rates with publically available proteomics and metabolomics data provide a better match to fluxes estimated by NIDLE-flux. Therefore, NIDLE-flux facilitates more effective usage of proteomics data to estimate proxies for kcatomes. AVAILABILITY https://github.com/Rudan-X/NIDLE-flux-code. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rudan Xu
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, Potsdam, 14476, Germany
| | - Zahra Razaghi-Moghadam
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, Potsdam, 14476, Germany.,Systems Biology and Mathematical Modelling Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam, 14476, Germany
| | - Zoran Nikoloski
- Bioinformatics, Institute of Biochemistry and Biology, University of Potsdam, Karl-Liebknecht-Str. 24-25, Potsdam, 14476, Germany.,Systems Biology and Mathematical Modelling Group, Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, Potsdam, 14476, Germany
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27
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Moyer D, Pacheco AR, Bernstein DB, Segrè D. Stoichiometric Modeling of Artificial String Chemistries Reveals Constraints on Metabolic Network Structure. J Mol Evol 2021; 89:472-483. [PMID: 34230992 PMCID: PMC8318951 DOI: 10.1007/s00239-021-10018-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 06/12/2021] [Indexed: 11/15/2022]
Abstract
Uncovering the general principles that govern the structure of metabolic networks is key to understanding the emergence and evolution of living systems. Artificial chemistries can help illuminate this problem by enabling the exploration of chemical reaction universes that are constrained by general mathematical rules. Here, we focus on artificial chemistries in which strings of characters represent simplified molecules, and string concatenation and splitting represent possible chemical reactions. We developed a novel Python package, ARtificial CHemistry NEtwork Toolbox (ARCHNET), to study string chemistries using tools from the field of stoichiometric constraint-based modeling. In addition to exploring the topological characteristics of different string chemistry networks, we developed a network-pruning algorithm that can generate minimal metabolic networks capable of producing a specified set of biomass precursors from a given assortment of environmental nutrients. We found that the composition of these minimal metabolic networks was influenced more strongly by the metabolites in the biomass reaction than the identities of the environmental nutrients. This finding has important implications for the reconstruction of organismal metabolic networks and could help us better understand the rise and evolution of biochemical organization. More generally, our work provides a bridge between artificial chemistries and stoichiometric modeling, which can help address a broad range of open questions, from the spontaneous emergence of an organized metabolism to the structure of microbial communities.
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Affiliation(s)
- Devlin Moyer
- Bioinformatics Program, Boston University, Boston, MA, 02215, USA
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Alan R Pacheco
- Bioinformatics Program, Boston University, Boston, MA, 02215, USA
- Biological Design Center, Boston University, Boston, MA, 02215, USA
| | - David B Bernstein
- Biological Design Center, Boston University, Boston, MA, 02215, USA
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Daniel Segrè
- Bioinformatics Program, Boston University, Boston, MA, 02215, USA.
- Department of Biology, Boston University, Boston, MA, 02215, USA.
- Biological Design Center, Boston University, Boston, MA, 02215, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.
- Department of Physics, Boston University, Boston, MA, 02215, USA.
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28
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Walakira A, Rozman D, Režen T, Mraz M, Moškon M. Guided extraction of genome-scale metabolic models for the integration and analysis of omics data. Comput Struct Biotechnol J 2021; 19:3521-3530. [PMID: 34194675 PMCID: PMC8225705 DOI: 10.1016/j.csbj.2021.06.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 06/04/2021] [Accepted: 06/04/2021] [Indexed: 02/05/2023] Open
Abstract
Omics data can be integrated into a reference model using various model extraction methods (MEMs) to yield context-specific genome-scale metabolic models (GEMs). How to chose the appropriate MEM, thresholding rule and threshold remains a challenge. We integrated mouse transcriptomic data from a Cyp51 knockout mice diet experiment (GSE58271) using five MEMs (GIMME, iMAT, FASTCORE, INIT an tINIT) in a combination with a recently published mouse GEM iMM1865. Except for INIT and tINIT, the size of extracted models varied with the MEM used (t-test: p-value < 0.001). The Jaccard index of iMAT models ranged from 0.27 to 1.0. Out of the three factors under study in the experiment (diet, gender and genotype), gender explained most of the variability ( > 90%) in PC1 for FASTCORE. In iMAT, each of the three factors explained less than 40% of the variability within PC1, PC2 and PC3. Among all the MEMs, FASTCORE captured the most of the true variability in the data by clustering samples by gender. Our results show that for the efficient use of MEMs in the context of omics data integration and analysis, one should apply various MEMs, thresholding rules, and thresholding values to select the MEM and its configuration that best captures the true variability in the data. This selection can be guided by the methodology as proposed and used in this paper. Moreover, we describe certain approaches that can be used to analyse the results obtained with the selected MEM and to put these results in a biological context.
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Affiliation(s)
- Andrew Walakira
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Damjana Rozman
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Tadeja Režen
- Centre for Functional Genomics and Bio-Chips, Institute for Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Mraz
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
| | - Miha Moškon
- Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia
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29
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Stuani L, Sabatier M, Saland E, Cognet G, Poupin N, Bosc C, Castelli FA, Gales L, Turtoi E, Montersino C, Farge T, Boet E, Broin N, Larrue C, Baran N, Cissé MY, Conti M, Loric S, Kaoma T, Hucteau A, Zavoriti A, Sahal A, Mouchel PL, Gotanègre M, Cassan C, Fernando L, Wang F, Hosseini M, Chu-Van E, Le Cam L, Carroll M, Selak MA, Vey N, Castellano R, Fenaille F, Turtoi A, Cazals G, Bories P, Gibon Y, Nicolay B, Ronseaux S, Marszalek JR, Takahashi K, DiNardo CD, Konopleva M, Pancaldi V, Collette Y, Bellvert F, Jourdan F, Linares LK, Récher C, Portais JC, Sarry JE. Mitochondrial metabolism supports resistance to IDH mutant inhibitors in acute myeloid leukemia. J Exp Med 2021; 218:e20200924. [PMID: 33760042 PMCID: PMC7995203 DOI: 10.1084/jem.20200924] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 11/25/2020] [Accepted: 01/11/2021] [Indexed: 12/17/2022] Open
Abstract
Mutations in IDH induce epigenetic and transcriptional reprogramming, differentiation bias, and susceptibility to mitochondrial inhibitors in cancer cells. Here, we first show that cell lines, PDXs, and patients with acute myeloid leukemia (AML) harboring an IDH mutation displayed an enhanced mitochondrial oxidative metabolism. Along with an increase in TCA cycle intermediates, this AML-specific metabolic behavior mechanistically occurred through the increase in electron transport chain complex I activity, mitochondrial respiration, and methylation-driven CEBPα-induced fatty acid β-oxidation of IDH1 mutant cells. While IDH1 mutant inhibitor reduced 2-HG oncometabolite and CEBPα methylation, it failed to reverse FAO and OxPHOS. These mitochondrial activities were maintained through the inhibition of Akt and enhanced activation of peroxisome proliferator-activated receptor-γ coactivator-1 PGC1α upon IDH1 mutant inhibitor. Accordingly, OxPHOS inhibitors improved anti-AML efficacy of IDH mutant inhibitors in vivo. This work provides a scientific rationale for combinatory mitochondrial-targeted therapies to treat IDH mutant AML patients, especially those unresponsive to or relapsing from IDH mutant inhibitors.
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MESH Headings
- Acute Disease
- Aminopyridines/pharmacology
- Animals
- Cell Line, Tumor
- Doxycycline/pharmacology
- Drug Resistance, Neoplasm/drug effects
- Drug Resistance, Neoplasm/genetics
- Enzyme Inhibitors/pharmacology
- Epigenesis, Genetic/drug effects
- Glycine/analogs & derivatives
- Glycine/pharmacology
- HL-60 Cells
- Humans
- Isocitrate Dehydrogenase/antagonists & inhibitors
- Isocitrate Dehydrogenase/genetics
- Isocitrate Dehydrogenase/metabolism
- Isoenzymes/antagonists & inhibitors
- Isoenzymes/genetics
- Isoenzymes/metabolism
- Leukemia, Myeloid/drug therapy
- Leukemia, Myeloid/genetics
- Leukemia, Myeloid/metabolism
- Mice, Inbred NOD
- Mice, Knockout
- Mice, SCID
- Mitochondria/drug effects
- Mitochondria/genetics
- Mitochondria/metabolism
- Mutation
- Oxadiazoles/pharmacology
- Oxidative Phosphorylation/drug effects
- Piperidines/pharmacology
- Pyridines/pharmacology
- Triazines/pharmacology
- Xenograft Model Antitumor Assays/methods
- Mice
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Affiliation(s)
- Lucille Stuani
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Marie Sabatier
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Estelle Saland
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Guillaume Cognet
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Nathalie Poupin
- UMR1331 Toxalim, Université de Toulouse, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, Ecole Nationale Vétérinaire de Toulouse, INP-Purpan, Université Paul Sabatier, Toulouse, France
| | - Claudie Bosc
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Florence A. Castelli
- CEA/DSV/iBiTec-S/SPI, Laboratoire d’Etude du Métabolisme des Médicaments, MetaboHUB-Paris, Gif-sur-Yvette, France
| | - Lara Gales
- Toulouse Biotechnology Institute, Université de Toulouse, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Institut National des sciences appliquées, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Evgenia Turtoi
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
- Montpellier Alliance for Metabolomics and Metabolism Analysis, Platform for Translational Oncometabolomics, Biocampus, Centre National de la Recherche Scientifique, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Montpellier, France
| | - Camille Montersino
- Aix-Marseille University, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Institut Paoli-Calmettes, Centre de Recherches en Cancérologie de Marseille, Marseille, France
| | - Thomas Farge
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Emeline Boet
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Nicolas Broin
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Clément Larrue
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Natalia Baran
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Madi Y. Cissé
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
| | - Marc Conti
- Institut National de la Santé et de la Recherché Médicale U938, Hôpital St Antoine, Paris, France
- Integracell, Longjumeau, France
| | - Sylvain Loric
- Institut National de la Santé et de la Recherché Médicale U938, Hôpital St Antoine, Paris, France
| | - Tony Kaoma
- Proteome and Genome Research Unit, Department of Oncology, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Alexis Hucteau
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Aliki Zavoriti
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Ambrine Sahal
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Pierre-Luc Mouchel
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
- Service d'Hématologie, Institut Universitaire du Cancer de Toulouse-Oncopole, CHU de Toulouse, Toulouse, France
| | - Mathilde Gotanègre
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Cédric Cassan
- UMR1332 Biologie du Fruit et Pathologie, Plateforme Métabolome Bordeaux, Institut National de la Recherche Agronomique, Université de Bordeaux, Villenave d'Ornon, France
| | - Laurent Fernando
- UMR1331 Toxalim, Université de Toulouse, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, Ecole Nationale Vétérinaire de Toulouse, INP-Purpan, Université Paul Sabatier, Toulouse, France
| | - Feng Wang
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Mohsen Hosseini
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
| | - Emeline Chu-Van
- CEA/DSV/iBiTec-S/SPI, Laboratoire d’Etude du Métabolisme des Médicaments, MetaboHUB-Paris, Gif-sur-Yvette, France
| | - Laurent Le Cam
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
| | - Martin Carroll
- Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Mary A. Selak
- Division of Hematology and Oncology, Department of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Norbert Vey
- Aix-Marseille University, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Institut Paoli-Calmettes, Centre de Recherches en Cancérologie de Marseille, Marseille, France
| | - Rémy Castellano
- Aix-Marseille University, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Institut Paoli-Calmettes, Centre de Recherches en Cancérologie de Marseille, Marseille, France
| | - François Fenaille
- CEA/DSV/iBiTec-S/SPI, Laboratoire d’Etude du Métabolisme des Médicaments, MetaboHUB-Paris, Gif-sur-Yvette, France
| | - Andrei Turtoi
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
| | - Guillaume Cazals
- Laboratoire de Mesures Physiques, Université de Montpellier, Montpellier, France
| | - Pierre Bories
- Réseau Régional de Cancérologie Onco-Occitanie, Toulouse, France
| | - Yves Gibon
- UMR1332 Biologie du Fruit et Pathologie, Plateforme Métabolome Bordeaux, Institut National de la Recherche Agronomique, Université de Bordeaux, Villenave d'Ornon, France
| | | | | | - Joseph R. Marszalek
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Koichi Takahashi
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Courtney D. DiNardo
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Marina Konopleva
- Departments of Leukemia and Genomic Medicine, The University of Texas, MD Anderson Cancer Center, Houston, TX
| | - Véra Pancaldi
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- Barcelona Supercomputing Center, Barcelona, Spain
| | - Yves Collette
- Aix-Marseille University, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Institut Paoli-Calmettes, Centre de Recherches en Cancérologie de Marseille, Marseille, France
| | - Floriant Bellvert
- Toulouse Biotechnology Institute, Université de Toulouse, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Institut National des sciences appliquées, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Fabien Jourdan
- UMR1331 Toxalim, Université de Toulouse, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement, Ecole Nationale Vétérinaire de Toulouse, INP-Purpan, Université Paul Sabatier, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
| | - Laetitia K. Linares
- Institut de Recherche en Cancérologie de Montpellier, Institut National de la Santé et de la Recherché Médicale, Université de Montpellier, Institut Régional du Cancer Montpellier, Montpellier, France
| | - Christian Récher
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
- Service d'Hématologie, Institut Universitaire du Cancer de Toulouse-Oncopole, CHU de Toulouse, Toulouse, France
| | - Jean-Charles Portais
- Toulouse Biotechnology Institute, Université de Toulouse, Centre National de la Recherche Scientifique, Institut National de la Recherche Agronomique, Institut National des sciences appliquées, Toulouse, France
- MetaToul-MetaboHUB, National Infrastructure of Metabolomics and Fluxomics, Toulouse, France
- STROMALab, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale U1031, EFS, INP-ENVT, UPS, Toulouse, France
| | - Jean-Emmanuel Sarry
- Centre de Recherches en Cancérologie de Toulouse, Université de Toulouse, Institut National de la Santé et de la Recherché Médicale, Centre National de la Recherche Scientifique, Toulouse, France
- LabEx Toucan, Toulouse, France
- Equipe Labellisée Ligue Nationale Contre le Cancer 2018, Toulouse, France
- Centre Hospitalier Universitaire de Toulouse, Toulouse, France
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30
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Schuster S, Ewald J, Kaleta C. Modeling the energy metabolism in immune cells. Curr Opin Biotechnol 2021; 68:282-291. [PMID: 33770632 DOI: 10.1016/j.copbio.2021.03.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 02/16/2021] [Accepted: 03/01/2021] [Indexed: 02/08/2023]
Abstract
In this review, we summarize and briefly discuss various approaches to modeling the metabolism in human immune cells, with a focus on energy metabolism. These approaches include metabolic reconstruction, elementary modes, and flux balance analysis, which are often subsumed under constraint-based modeling. Further approaches are evolutionary game theory and kinetic modeling. Many immune cells such as macrophages show the Warburg effect, meaning that glycolysis is upregulated upon activation. We outline a minimal model for explaining that effect using optimization. The effect of a confrontation with pathogen cells on immunometabolism is highlighted. Models describing the differences between M1 and M2 macrophages, ROS production in neutrophils, and tryptophan metabolism are discussed. Obstacles and future prospects are outlined.
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Affiliation(s)
- Stefan Schuster
- Department of Bioinformatics, Matthias Schleiden Institute, Friedrich Schiller University Jena, Ernst-Abbe-Pl. 2, 07743 Jena, Germany.
| | - Jan Ewald
- Department of Bioinformatics, Matthias Schleiden Institute, Friedrich Schiller University Jena, Ernst-Abbe-Pl. 2, 07743 Jena, Germany
| | - Christoph Kaleta
- Medical Systems Biology Group, Institute of Experimental Medicine, Christian-Albrechts-University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany
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31
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Dougherty BV, Rawls KD, Kolling GL, Vinnakota KC, Wallqvist A, Papin JA. Identifying functional metabolic shifts in heart failure with the integration of omics data and a heart-specific, genome-scale model. Cell Rep 2021; 34:108836. [PMID: 33691118 DOI: 10.1016/j.celrep.2021.108836] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 01/07/2021] [Accepted: 02/17/2021] [Indexed: 11/28/2022] Open
Abstract
In diseased states, the heart can shift to use different carbon substrates, measured through changes in uptake of metabolites by imaging methods or blood metabolomics. However, it is not known whether these measured changes are a result of transcriptional changes or external factors. Here, we explore transcriptional changes in late-stage heart failure using publicly available data integrated with a model of heart metabolism. First, we present a heart-specific genome-scale metabolic network reconstruction (GENRE), iCardio. Next, we demonstrate the utility of iCardio in interpreting heart failure gene expression data by identifying tasks inferred from differential expression (TIDEs), which represent metabolic functions associated with changes in gene expression. We identify decreased gene expression for nitric oxide (NO) and N-acetylneuraminic acid (Neu5Ac) synthesis as common metabolic markers of heart failure. The methods presented here for constructing a tissue-specific model and identifying TIDEs can be extended to multiple tissues and diseases of interest.
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Affiliation(s)
- Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA
| | - Kalyan C Vinnakota
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA; The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry & Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.
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32
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Towards the routine use of in silico screenings for drug discovery using metabolic modelling. Biochem Soc Trans 2021; 48:955-969. [PMID: 32369553 PMCID: PMC7329353 DOI: 10.1042/bst20190867] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2020] [Revised: 04/01/2020] [Accepted: 04/06/2020] [Indexed: 12/12/2022]
Abstract
Currently, the development of new effective drugs for cancer therapy is not only hindered by development costs, drug efficacy, and drug safety but also by the rapid occurrence of drug resistance in cancer. Hence, new tools are needed to study the underlying mechanisms in cancer. Here, we discuss the current use of metabolic modelling approaches to identify cancer-specific metabolism and find possible new drug targets and drugs for repurposing. Furthermore, we list valuable resources that are needed for the reconstruction of cancer-specific models by integrating various available datasets with genome-scale metabolic reconstructions using model-building algorithms. We also discuss how new drug targets can be determined by using gene essentiality analysis, an in silico method to predict essential genes in a given condition such as cancer and how synthetic lethality studies could greatly benefit cancer patients by suggesting drug combinations with reduced side effects.
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33
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Rodríguez-Mier P, Poupin N, de Blasio C, Le Cam L, Jourdan F. DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks. PLoS Comput Biol 2021; 17:e1008730. [PMID: 33571201 PMCID: PMC7904180 DOI: 10.1371/journal.pcbi.1008730] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 02/24/2021] [Accepted: 01/21/2021] [Indexed: 11/18/2022] Open
Abstract
The correct identification of metabolic activity in tissues or cells under different conditions can be extremely elusive due to mechanisms such as post-transcriptional modification of enzymes or different rates in protein degradation, making difficult to perform predictions on the basis of gene expression alone. Context-specific metabolic network reconstruction can overcome some of these limitations by leveraging the integration of multi-omics data into genome-scale metabolic networks (GSMN). Using the experimental information, context-specific models are reconstructed by extracting from the generic GSMN the sub-network most consistent with the data, subject to biochemical constraints. One advantage is that these context-specific models have more predictive power since they are tailored to the specific tissue, cell or condition, containing only the reactions predicted to be active in such context. However, an important limitation is that there are usually many different sub-networks that optimally fit the experimental data. This set of optimal networks represent alternative explanations of the possible metabolic state. Ignoring the set of possible solutions reduces the ability to obtain relevant information about the metabolism and may bias the interpretation of the true metabolic states. In this work we formalize the problem of enumerating optimal metabolic networks and we introduce DEXOM, an unified approach for diversity-based enumeration of context-specific metabolic networks. We developed different strategies for this purpose and we performed an exhaustive analysis using simulated and real data. In order to analyze the extent to which these results are biologically meaningful, we used the alternative solutions obtained with the different methods to measure: 1) the improvement of in silico predictions of essential genes in Saccharomyces cerevisiae using ensembles of metabolic network; and 2) the detection of alternative enriched pathways in different human cancer cell lines. We also provide DEXOM as an open-source library compatible with COBRA Toolbox 3.0, available at https://github.com/MetExplore/dexom.
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Affiliation(s)
- Pablo Rodríguez-Mier
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Nathalie Poupin
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
| | - Carlo de Blasio
- IRCM, Institut de Recherche en Cancérologie de Montpellier, INSERM U1194, Université de Montpellier, Institut régional du Cancer de Montpellier, Montpellier, France
- Equipe Labellisée par la Ligue contre le Cancer, Paris, France
| | - Laurent Le Cam
- IRCM, Institut de Recherche en Cancérologie de Montpellier, INSERM U1194, Université de Montpellier, Institut régional du Cancer de Montpellier, Montpellier, France
- Equipe Labellisée par la Ligue contre le Cancer, Paris, France
| | - Fabien Jourdan
- Toxalim (Research Centre in Food Toxicology), Université de Toulouse, INRAE, ENVT, INP-Purpan, UPS, Toulouse, France
- * E-mail:
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34
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Dusad V, Thiel D, Barahona M, Keun HC, Oyarzún DA. Opportunities at the Interface of Network Science and Metabolic Modeling. Front Bioeng Biotechnol 2021; 8:591049. [PMID: 33569373 PMCID: PMC7868444 DOI: 10.3389/fbioe.2020.591049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 12/22/2020] [Indexed: 12/17/2022] Open
Abstract
Metabolism plays a central role in cell physiology because it provides the molecular machinery for growth. At the genome-scale, metabolism is made up of thousands of reactions interacting with one another. Untangling this complexity is key to understand how cells respond to genetic, environmental, or therapeutic perturbations. Here we discuss the roles of two complementary strategies for the analysis of genome-scale metabolic models: Flux Balance Analysis (FBA) and network science. While FBA estimates metabolic flux on the basis of an optimization principle, network approaches reveal emergent properties of the global metabolic connectivity. We highlight how the integration of both approaches promises to deliver insights on the structure and function of metabolic systems with wide-ranging implications in discovery science, precision medicine and industrial biotechnology.
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Affiliation(s)
- Varshit Dusad
- Department of Life Sciences, Imperial College London, London, United Kingdom
| | - Denise Thiel
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Mauricio Barahona
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Hector C Keun
- Department of Surgery and Cancer, Imperial College London, London, United Kingdom.,Department of Metabolism, Digestion and Reproduction, Imperial College London, London, United Kingdom
| | - Diego A Oyarzún
- School of Biological Sciences, University of Edinburgh, Edinburgh, United Kingdom.,School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
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35
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Seaver SMD, Liu F, Zhang Q, Jeffryes J, Faria JP, Edirisinghe JN, Mundy M, Chia N, Noor E, Beber M, Best AA, DeJongh M, Kimbrel JA, D’haeseleer P, McCorkle SR, Bolton JR, Pearson E, Canon S, Wood-Charlson EM, Cottingham RW, Arkin AP, Henry CS. The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res 2021; 49:D575-D588. [PMID: 32986834 PMCID: PMC7778927 DOI: 10.1093/nar/gkaa746] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 08/25/2020] [Accepted: 09/24/2020] [Indexed: 12/31/2022] Open
Abstract
For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical 'Rosetta Stone' to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/biochem and KBase.
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Affiliation(s)
- Samuel M D Seaver
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Filipe Liu
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Qizhi Zhang
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - James Jeffryes
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - José P Faria
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Janaka N Edirisinghe
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Michael Mundy
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Nicholas Chia
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Elad Noor
- Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, CH-8093 Zürich, Switzerland
| | - Moritz E Beber
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
| | - Aaron A Best
- Department of Biology, Hope College, Holland, MI 49423, USA
| | - Matthew DeJongh
- Department of Computer Science, Hope College, Holland, MI 49423, USA
| | - Jeffrey A Kimbrel
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Patrik D’haeseleer
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Sean R McCorkle
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Jay R Bolton
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Erik Pearson
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Shane Canon
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Elisha M Wood-Charlson
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Robert W Cottingham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Adam P Arkin
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Christopher S Henry
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
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36
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Seaver SMD, Liu F, Zhang Q, Jeffryes J, Faria JP, Edirisinghe JN, Mundy M, Chia N, Noor E, Beber ME, Best AA, DeJongh M, Kimbrel JA, D'haeseleer P, McCorkle SR, Bolton JR, Pearson E, Canon S, Wood-Charlson EM, Cottingham RW, Arkin AP, Henry CS. The ModelSEED Biochemistry Database for the integration of metabolic annotations and the reconstruction, comparison and analysis of metabolic models for plants, fungi and microbes. Nucleic Acids Res 2021; 49:D1555. [PMID: 33179751 DOI: 10.1101/2020.03.31.018663] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2023] Open
Abstract
ABSTRACTFor over ten years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions;; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical “Rosetta Stone” to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies, and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org and KBase.
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Affiliation(s)
- Samuel M D Seaver
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Filipe Liu
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Qizhi Zhang
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - James Jeffryes
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - José P Faria
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Janaka N Edirisinghe
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Michael Mundy
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Nicholas Chia
- Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Elad Noor
- Department of Biology, Institute of Molecular Systems Biology, Eidgenössische Technische Hochschule Zürich, CH-8093 Zürich, Switzerland
| | - Moritz E Beber
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, 2800, Denmark
| | - Aaron A Best
- Department of Biology, Hope College, Holland, MI 49423, USA
| | - Matthew DeJongh
- Department of Computer Science, Hope College, Holland, MI 49423, USA
| | - Jeffrey A Kimbrel
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Patrik D'haeseleer
- Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
| | - Sean R McCorkle
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA
| | - Jay R Bolton
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Erik Pearson
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Shane Canon
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Elisha M Wood-Charlson
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Robert W Cottingham
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830, USA
| | - Adam P Arkin
- Environmental Genomics and Systems Biology Division, E.O. Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Christopher S Henry
- Computing, Environment, and Life Sciences Division, Argonne National Laboratory, Lemont, IL 60439, USA
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37
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38
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Suthers PF, Foster CJ, Sarkar D, Wang L, Maranas CD. Recent advances in constraint and machine learning-based metabolic modeling by leveraging stoichiometric balances, thermodynamic feasibility and kinetic law formalisms. Metab Eng 2020; 63:13-33. [PMID: 33310118 DOI: 10.1016/j.ymben.2020.11.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/13/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022]
Abstract
Understanding the governing principles behind organisms' metabolism and growth underpins their effective deployment as bioproduction chassis. A central objective of metabolic modeling is predicting how metabolism and growth are affected by both external environmental factors and internal genotypic perturbations. The fundamental concepts of reaction stoichiometry, thermodynamics, and mass action kinetics have emerged as the foundational principles of many modeling frameworks designed to describe how and why organisms allocate resources towards both growth and bioproduction. This review focuses on the latest algorithmic advancements that have integrated these foundational principles into increasingly sophisticated quantitative frameworks.
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Affiliation(s)
- Patrick F Suthers
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA
| | - Charles J Foster
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Debolina Sarkar
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA; DOE Center for Advanced Bioenergy and Bioproducts Innovation, The Pennsylvania State University, University Park, PA, USA.
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39
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Hanna EM, Zhang X, Eide M, Fallahi S, Furmanek T, Yadetie F, Zielinski DC, Goksøyr A, Jonassen I. ReCodLiver0.9: Overcoming Challenges in Genome-Scale Metabolic Reconstruction of a Non-model Species. Front Mol Biosci 2020; 7:591406. [PMID: 33324679 PMCID: PMC7726423 DOI: 10.3389/fmolb.2020.591406] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 10/22/2020] [Indexed: 12/13/2022] Open
Abstract
The availability of genome sequences, annotations, and knowledge of the biochemistry underlying metabolic transformations has led to the generation of metabolic network reconstructions for a wide range of organisms in bacteria, archaea, and eukaryotes. When modeled using mathematical representations, a reconstruction can simulate underlying genotype-phenotype relationships. Accordingly, genome-scale metabolic models (GEMs) can be used to predict the response of organisms to genetic and environmental variations. A bottom-up reconstruction procedure typically starts by generating a draft model from existing annotation data on a target organism. For model species, this part of the process can be straightforward, due to the abundant organism-specific biochemical data. However, the process becomes complicated for non-model less-annotated species. In this paper, we present a draft liver reconstruction, ReCodLiver0.9, of Atlantic cod (Gadus morhua), a non-model teleost fish, as a practicable guide for cases with comparably few resources. Although the reconstruction is considered a draft version, we show that it already has utility in elucidating metabolic response mechanisms to environmental toxicants by mapping gene expression data of exposure experiments to the resulting model.
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Affiliation(s)
- Eileen Marie Hanna
- Department of Computer Science and Mathematics, Lebanese American University, Byblos, Lebanon
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Xiaokang Zhang
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
| | - Marta Eide
- Department of Biological Sciences, University of Bergen, Bergen, Norway
| | - Shirin Fallahi
- Department of Mathematics, University of Bergen, Bergen, Norway
| | | | - Fekadu Yadetie
- Department of Biological Sciences, University of Bergen, Bergen, Norway
| | - Daniel Craig Zielinski
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States
| | - Anders Goksøyr
- Department of Biological Sciences, University of Bergen, Bergen, Norway
| | - Inge Jonassen
- Computational Biology Unit, Department of Informatics, University of Bergen, Bergen, Norway
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40
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Lee LY, Pandey AK, Maron BA, Loscalzo J. Network medicine in Cardiovascular Research. Cardiovasc Res 2020; 117:2186-2202. [PMID: 33165538 DOI: 10.1093/cvr/cvaa321] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2020] [Revised: 09/08/2020] [Accepted: 10/30/2020] [Indexed: 12/21/2022] Open
Abstract
The ability to generate multi-omics data coupled with deeply characterizing the clinical phenotype of individual patients promises to improve understanding of complex cardiovascular pathobiology. There remains an important disconnection between the magnitude and granularity of these data and our ability to improve phenotype-genotype correlations for complex cardiovascular diseases. This shortcoming may be due to limitations associated with traditional reductionist analytical methods, which tend to emphasize a single molecular event in the pathogenesis of diseases more aptly characterized by crosstalk between overlapping molecular pathways. Network medicine is a rapidly growing discipline that considers diseases as the consequences of perturbed interactions between multiple interconnected biological components. This powerful integrative approach has enabled a number of important discoveries in complex disease mechanisms. In this review, we introduce the basic concepts of network medicine and highlight specific examples by which this approach has accelerated cardiovascular research. We also review how network medicine is well-positioned to promote rational drug design for patients with cardiovascular diseases, with particular emphasis on advancing precision medicine.
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Affiliation(s)
- Laurel Y Lee
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Arvind K Pandey
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
| | - Bradley A Maron
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.,Department of Cardiology, Boston VA Healthcare System, Boston, MA, USA
| | - Joseph Loscalzo
- Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA
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Pannala VR, Estes SK, Rahim M, Trenary I, O’Brien TP, Shiota C, Printz RL, Reifman J, Shiota M, Young JD, Wallqvist A. Toxicant-Induced Metabolic Alterations in Lipid and Amino Acid Pathways Are Predictive of Acute Liver Toxicity in Rats. Int J Mol Sci 2020; 21:ijms21218250. [PMID: 33158035 PMCID: PMC7663358 DOI: 10.3390/ijms21218250] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/30/2020] [Accepted: 11/02/2020] [Indexed: 02/07/2023] Open
Abstract
Liver disease and disorders associated with aberrant hepatocyte metabolism can be initiated via drug and environmental toxicant exposures. In this study, we tested the hypothesis that gene and metabolic profiling can reveal commonalities in liver response to different toxicants and provide the capability to identify early signatures of acute liver toxicity. We used Sprague Dawley rats and three classical hepatotoxicants: acetaminophen (2 g/kg), bromobenzene (0.4 g/kg), and carbon tetrachloride (0.3 g/kg), to identify early perturbations in liver metabolism after a single acute exposure dose. We measured changes in liver genes and plasma metabolites at two time points (5 and 10 h) and used genome-scale metabolic models to identify commonalities in liver responses across the three toxicants. We found strong correlations for gene and metabolic profiles between the toxicants, indicative of similarities in the liver response to toxicity. We identified several injury-specific pathways in lipid and amino acid metabolism that changed similarly across the three toxicants. Our findings suggest that several plasma metabolites in lipid and amino acid metabolism are strongly associated with the progression of liver toxicity, and as such, could be targeted and clinically assessed for their potential as early predictors of acute liver toxicity.
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Affiliation(s)
- Venkat R. Pannala
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA;
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA
- Correspondence: (V.R.P.); (J.D.Y.); (A.W.); Tel.: +1-301-619-1976 (V.R.P.); +1-615-343-4253 (J.D.Y.); +1-301-619-1989 (A.W.); Fax: +301-619-1983 (A.W. & V.R.P.); +615-343-7951 (J.D.Y.)
| | - Shanea K. Estes
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; (S.K.E.); (T.P.O.); (C.S.); (R.L.P.); (M.S.)
| | - Mohsin Rahim
- Department of Chemical and Biomolecular Engineering, Vanderbilt University School of Engineering, Nashville, TN 37232, USA; (M.R.); (I.T.)
| | - Irina Trenary
- Department of Chemical and Biomolecular Engineering, Vanderbilt University School of Engineering, Nashville, TN 37232, USA; (M.R.); (I.T.)
| | - Tracy P. O’Brien
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; (S.K.E.); (T.P.O.); (C.S.); (R.L.P.); (M.S.)
| | - Chiyo Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; (S.K.E.); (T.P.O.); (C.S.); (R.L.P.); (M.S.)
| | - Richard L. Printz
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; (S.K.E.); (T.P.O.); (C.S.); (R.L.P.); (M.S.)
| | - Jaques Reifman
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA;
| | - Masakazu Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; (S.K.E.); (T.P.O.); (C.S.); (R.L.P.); (M.S.)
| | - Jamey D. Young
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; (S.K.E.); (T.P.O.); (C.S.); (R.L.P.); (M.S.)
- Department of Chemical and Biomolecular Engineering, Vanderbilt University School of Engineering, Nashville, TN 37232, USA; (M.R.); (I.T.)
- Correspondence: (V.R.P.); (J.D.Y.); (A.W.); Tel.: +1-301-619-1976 (V.R.P.); +1-615-343-4253 (J.D.Y.); +1-301-619-1989 (A.W.); Fax: +301-619-1983 (A.W. & V.R.P.); +615-343-7951 (J.D.Y.)
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Development Command, Fort Detrick, MD 21702, USA;
- Correspondence: (V.R.P.); (J.D.Y.); (A.W.); Tel.: +1-301-619-1976 (V.R.P.); +1-615-343-4253 (J.D.Y.); +1-301-619-1989 (A.W.); Fax: +301-619-1983 (A.W. & V.R.P.); +615-343-7951 (J.D.Y.)
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Yilmaz LS, Li X, Nanda S, Fox B, Schroeder F, Walhout AJ. Modeling tissue-relevant Caenorhabditis elegans metabolism at network, pathway, reaction, and metabolite levels. Mol Syst Biol 2020; 16:e9649. [PMID: 33022146 PMCID: PMC7537831 DOI: 10.15252/msb.20209649] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 09/10/2020] [Accepted: 09/11/2020] [Indexed: 01/04/2023] Open
Abstract
Metabolism is a highly compartmentalized process that provides building blocks for biomass generation during development, homeostasis, and wound healing, and energy to support cellular and organismal processes. In metazoans, different cells and tissues specialize in different aspects of metabolism. However, studying the compartmentalization of metabolism in different cell types in a whole animal and for a particular stage of life is difficult. Here, we present MEtabolic models Reconciled with Gene Expression (MERGE), a computational pipeline that we used to predict tissue-relevant metabolic function at the network, pathway, reaction, and metabolite levels based on single-cell RNA-sequencing (scRNA-seq) data from the nematode Caenorhabditis elegans. Our analysis recapitulated known tissue functions in C. elegans, captured metabolic properties that are shared with similar tissues in human, and provided predictions for novel metabolic functions. MERGE is versatile and applicable to other systems. We envision this work as a starting point for the development of metabolic network models for individual cells as scRNA-seq continues to provide higher-resolution gene expression data.
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Affiliation(s)
- Lutfu Safak Yilmaz
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Xuhang Li
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Shivani Nanda
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
| | - Bennett Fox
- Boyce Thompson Institute, Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA
| | - Frank Schroeder
- Boyce Thompson Institute, Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY, USA
| | - Albertha Jm Walhout
- Program in Systems Biology, Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA, USA
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Shiratsubaki IS, Fang X, Souza ROO, Palsson BO, Silber AM, Siqueira-Neto JL. Genome-scale metabolic models highlight stage-specific differences in essential metabolic pathways in Trypanosoma cruzi. PLoS Negl Trop Dis 2020; 14:e0008728. [PMID: 33021977 PMCID: PMC7567352 DOI: 10.1371/journal.pntd.0008728] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 10/16/2020] [Accepted: 08/17/2020] [Indexed: 02/07/2023] Open
Abstract
Chagas disease is a neglected tropical disease and a leading cause of heart failure in Latin America caused by a protozoan called Trypanosoma cruzi. This parasite presents a complex multi-stage life cycle. Anti-Chagas drugs currently available are limited to benznidazole and nifurtimox, both with severe side effects. Thus, there is a need for alternative and more efficient drugs. Genome-scale metabolic models (GEMs) can accurately predict metabolic capabilities and aid in drug discovery in metabolic genes. This work developed an extended GEM, hereafter referred to as iIS312, of the published and validated T. cruzi core metabolism model. From iIS312, we then built three stage-specific models through transcriptomics data integration, and showed that epimastigotes present the most active metabolism among the stages (see S1-S4 GEMs). Stage-specific models predicted significant metabolic differences among stages, including variations in flux distribution in core metabolism. Moreover, the gene essentiality predictions suggest potential drug targets, among which some have been previously proven lethal, including glutamate dehydrogenase, glucokinase and hexokinase. To validate the models, we measured the activity of enzymes in the core metabolism of the parasite at different stages, and showed the results were consistent with model predictions. Our results represent a potential step forward towards the improvement of Chagas disease treatment. To our knowledge, these stage-specific models are the first GEMs built for the stages Amastigote and Trypomastigote. This work is also the first to present an in silico GEM comparison among different stages in the T. cruzi life cycle.
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Affiliation(s)
- Isabel S Shiratsubaki
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California, United States of America
- Department of Bioengineering, UC San Diego, La Jolla, California, United States of America
| | - Xin Fang
- Department of Bioengineering, UC San Diego, La Jolla, California, United States of America
| | - Rodolpho O O Souza
- Laboratory of Biochemistry of Tryps - LaBTryps, Department of Parasitology, Institute of Biomedical Science, University of São Paulo, São Paulo, São Paulo, Brazil
| | - Bernhard O Palsson
- Department of Bioengineering, UC San Diego, La Jolla, California, United States of America
- Department of Pediatrics, UC San Diego, La Jolla, California, United States of America
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
| | - Ariel M Silber
- Laboratory of Biochemistry of Tryps - LaBTryps, Department of Parasitology, Institute of Biomedical Science, University of São Paulo, São Paulo, São Paulo, Brazil
| | - Jair L Siqueira-Neto
- Skaggs School of Pharmacy and Pharmaceutical Sciences, UC San Diego, La Jolla, California, United States of America
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Moolamalla STR, Vinod PK. Genome-scale metabolic modelling predicts biomarkers and therapeutic targets for neuropsychiatric disorders. Comput Biol Med 2020; 125:103994. [PMID: 32980779 DOI: 10.1016/j.compbiomed.2020.103994] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 09/06/2020] [Accepted: 09/07/2020] [Indexed: 01/06/2023]
Abstract
Distinguishing neuropsychiatric disorders is challenging due to the overlap in symptoms and genetic risk factors. People suffering from these disorders face personal and professional challenges. Understanding the dysregulation of brain metabolism under disease condition can aid in effective diagnosis and in developing treatment strategies based on the metabolism. In this study, we reconstructed the metabolic network of three major neuropsychiatric disorders, schizophrenia (SCZ), bipolar disorder (BD) and major depressive disorder (MDD) using transcriptomic data and constrained based modelling approach. We integrated brain transcriptomic data from six independent studies with a recent comprehensive genome-scale metabolic model Recon3D. The analysis of the reconstructed network revealed the flux-level alterations in the peroxisome-mitochondria-golgi axis in neuropsychiatric disorders. We also extracted reporter metabolites and pathways that distinguish these three neuropsychiatric disorders. We found differences with respect to fatty acid oxidation, aromatic and branched chain amino acid metabolism, bile acid synthesis, glycosaminoglycans synthesis and modifications, and phospholipid metabolism. Further, we predicted network perturbations that transform the disease metabolic state to a healthy metabolic state for each disorder. These analyses provide local and global views of the metabolic changes in SCZ, BD and MDD, which may have clinical implications.
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Affiliation(s)
- S T R Moolamalla
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - P K Vinod
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
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45
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A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation. Biochem Soc Trans 2020; 48:1889-1903. [DOI: 10.1042/bst20190840] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/16/2020] [Accepted: 08/21/2020] [Indexed: 02/07/2023]
Abstract
The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.
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Dahal S, Yurkovich JT, Xu H, Palsson BO, Yang L. Synthesizing Systems Biology Knowledge from Omics Using Genome-Scale Models. Proteomics 2020; 20:e1900282. [PMID: 32579720 PMCID: PMC7501203 DOI: 10.1002/pmic.201900282] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Revised: 06/13/2020] [Indexed: 12/18/2022]
Abstract
Omic technologies have enabled the complete readout of the molecular state of a cell at different biological scales. In principle, the combination of multiple omic data types can provide an integrated view of the entire biological system. This integration requires appropriate models in a systems biology approach. Here, genome-scale models (GEMs) are focused upon as one computational systems biology approach for interpreting and integrating multi-omic data. GEMs convert the reactions (related to metabolism, transcription, and translation) that occur in an organism to a mathematical formulation that can be modeled using optimization principles. A variety of genome-scale modeling methods used to interpret multiple omic data types, including genomics, transcriptomics, proteomics, metabolomics, and meta-omics are reviewed. The ability to interpret omics in the context of biological systems has yielded important findings for human health, environmental biotechnology, bioenergy, and metabolic engineering. The authors find that concurrent with advancements in omic technologies, genome-scale modeling methods are also expanding to enable better interpretation of omic data. Therefore, continued synthesis of valuable knowledge, through the integration of omic data with GEMs, are expected.
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Affiliation(s)
- Sanjeev Dahal
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
| | | | - Hao Xu
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA
- Department of Pediatrics, University of California San Diego, La Jolla, CA, USA
| | - Laurence Yang
- Department of Chemical Engineering, Queen’s University, Kingston, Canada
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47
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Clarke R, Kraikivski P, Jones BC, Sevigny CM, Sengupta S, Wang Y. A systems biology approach to discovering pathway signaling dysregulation in metastasis. Cancer Metastasis Rev 2020; 39:903-918. [PMID: 32776157 PMCID: PMC7487029 DOI: 10.1007/s10555-020-09921-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Accepted: 07/13/2020] [Indexed: 02/07/2023]
Abstract
Total metastatic burden is the primary cause of death for many cancer patients. While the process of metastasis has been studied widely, much remains to be understood. Moreover, few agents have been developed that specifically target the major steps of the metastatic cascade. Many individual genes and pathways have been implicated in metastasis but a holistic view of how these interact and cooperate to regulate and execute the process remains somewhat rudimentary. It is unclear whether all of the signaling features that regulate and execute metastasis are yet fully understood. Novel features of a complex system such as metastasis can often be discovered by taking a systems-based approach. We introduce the concepts of systems modeling and define some of the central challenges facing the application of a multidisciplinary systems-based approach to understanding metastasis and finding actionable targets therein. These challenges include appreciating the unique properties of the high-dimensional omics data often used for modeling, limitations in knowledge of the system (metastasis), tumor heterogeneity and sampling bias, and some of the issues key to understanding critical features of molecular signaling in the context of metastasis. We also provide a brief introduction to integrative modeling that focuses on both the nodes and edges of molecular signaling networks. Finally, we offer some observations on future directions as they relate to developing a systems-based model of the metastatic cascade.
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Affiliation(s)
- Robert Clarke
- Department of Oncology, Georgetown University Medical Center, 3970 Reservoir Rd NW, Washington, DC, 20057, USA.
- Hormel Institute and Department of Biochemistry, Molecular Biology and Biophysics, University of Minnesota, Austin, MN, 55912, USA.
| | - Pavel Kraikivski
- Academy of Integrated Science, Division of Systems Biology, Virginia Polytechnic and State University, Blacksburg, VA, 24061, USA
| | - Brandon C Jones
- Department of Oncology, Georgetown University Medical Center, 3970 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Catherine M Sevigny
- Department of Oncology, Georgetown University Medical Center, 3970 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Surojeet Sengupta
- Department of Oncology, Georgetown University Medical Center, 3970 Reservoir Rd NW, Washington, DC, 20057, USA
| | - Yue Wang
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, VA, 22203, USA
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48
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Mechanism-based identification of plasma metabolites associated with liver toxicity. Toxicology 2020; 441:152493. [DOI: 10.1016/j.tox.2020.152493] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/01/2020] [Accepted: 05/08/2020] [Indexed: 12/25/2022]
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49
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Frioux C, Singh D, Korcsmaros T, Hildebrand F. From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes. Comput Struct Biotechnol J 2020; 18:1722-1734. [PMID: 32670511 PMCID: PMC7347713 DOI: 10.1016/j.csbj.2020.06.028] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2020] [Revised: 06/16/2020] [Accepted: 06/17/2020] [Indexed: 12/12/2022] Open
Abstract
Metagenomic sequencing of complete microbial communities has greatly enhanced our understanding of the taxonomic composition of microbiotas. This has led to breakthrough developments in bioinformatic disciplines such as assembly, gene clustering, metagenomic binning of species genomes and the discovery of an incredible, so far undiscovered, taxonomic diversity. However, functional annotations and estimating metabolic processes from single species - or communities - is still challenging. Earlier approaches relied mostly on inferring the presence of key enzymes for metabolic pathways in the whole metagenome, ignoring the genomic context of such enzymes, resulting in the 'bag-of-genes' approach to estimate functional capacities of microbiotas. Here, we review recent developments in metagenomic bioinformatics, with a special focus on emerging technologies to simulate and estimate metabolic information, that can be derived from metagenomic assembled genomes. Genome-scale metabolic models can be used to model the emergent properties of microbial consortia and whole communities, and the progress in this area is reviewed. While this subfield of metagenomics is still in its infancy, it is becoming evident that there is a dire need for further bioinformatic tools to address the complex combinatorial problems in modelling the metabolism of large communities as a 'bag-of-genomes'.
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Affiliation(s)
- Clémence Frioux
- Inria, CNRS, INRAE Bordeaux, France
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
| | - Dipali Singh
- Microbes in the Food Chain, Quadram Institute Bioscience, Norwich, Norfolk, UK
| | - Tamas Korcsmaros
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
- Digital Biology, Earlham Institute, Norwich, Norfolk, UK
| | - Falk Hildebrand
- Gut Microbes and Health, Quadram Institute Bioscience, Norwich, Norfolk, UK
- Digital Biology, Earlham Institute, Norwich, Norfolk, UK
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50
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Masid M, Ataman M, Hatzimanikatis V. Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN. Nat Commun 2020; 11:2821. [PMID: 32499584 PMCID: PMC7272419 DOI: 10.1038/s41467-020-16549-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 05/07/2020] [Indexed: 01/31/2023] Open
Abstract
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.
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Affiliation(s)
- Maria Masid
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Meric Ataman
- Computational and Systems Biology, Biozentrum, University of Basel, Basel, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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