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Tiersma JF, Evers B, Bakker BM, Reijngoud DJ, de Bruyn M, de Jong S, Jalving M. Targeting tumour metabolism in melanoma to enhance response to immune checkpoint inhibition: A balancing act. Cancer Treat Rev 2024; 129:102802. [PMID: 39029155 DOI: 10.1016/j.ctrv.2024.102802] [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: 05/22/2024] [Revised: 07/08/2024] [Accepted: 07/10/2024] [Indexed: 07/21/2024]
Abstract
Immune checkpoint inhibition has transformed the treatment landscape of advanced melanoma and long-term survival of patients is now possible. However, at least half of the patients do not benefit sufficiently. Metabolic reprogramming is a hallmark of cancer cells and may contribute to both tumour growth and immune evasion by the tumour. Preclinical studies have indeed demonstrated that modulating tumour metabolism can reduce tumour growth while improving the functionality of immune cells. Since metabolic pathways are commonly shared between immune and tumour cells, it is essential to understand how modulating tumour metabolism in patients influences the intricate balance of pro-and anti-tumour immune effects in the tumour microenvironment. The key question is whether modulating tumour metabolism can inhibit tumour cell growth as well as facilitate an anti-tumour immune response. Here, we review current knowledge on the effect of tumour metabolism on the immune response in melanoma. We summarise metabolic pathways in melanoma and non-cancerous cells in the tumour microenvironment and discuss models and techniques available to study the metabolic-immune interaction. Finally, we discuss clinical use of these techniques to improve our understanding of how metabolic interventions can tip the balance towards a favourable, immune permissive microenvironment in melanoma patients.
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Affiliation(s)
- J F Tiersma
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - B Evers
- Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signalling, and Center for Liver, Digestive and Metabolic Diseases, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - B M Bakker
- Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signalling, and Center for Liver, Digestive and Metabolic Diseases, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - D J Reijngoud
- Laboratory of Pediatrics, Section Systems Medicine of Metabolism and Signalling, and Center for Liver, Digestive and Metabolic Diseases, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - M de Bruyn
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - S de Jong
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - M Jalving
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
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2
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Jin H, Zhang C, Nagenborg J, Juhasz P, Ruder AV, Sikkink CJJM, Mees BME, Waring O, Sluimer JC, Neumann D, Goossens P, Donners MMPC, Mardinoglu A, Biessen EAL. Genome-scale metabolic network of human carotid plaque reveals the pivotal role of glutamine/glutamate metabolism in macrophage modulating plaque inflammation and vulnerability. Cardiovasc Diabetol 2024; 23:240. [PMID: 38978031 PMCID: PMC11232311 DOI: 10.1186/s12933-024-02339-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 06/26/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Metabolism is increasingly recognized as a key regulator of the function and phenotype of the primary cellular constituents of the atherosclerotic vascular wall, including endothelial cells, smooth muscle cells, and inflammatory cells. However, a comprehensive analysis of metabolic changes associated with the transition of plaque from a stable to a hemorrhaged phenotype is lacking. METHODS In this study, we integrated two large mRNA expression and protein abundance datasets (BIKE, n = 126; MaasHPS, n = 43) from human atherosclerotic carotid artery plaque to reconstruct a genome-scale metabolic network (GEM). Next, the GEM findings were linked to metabolomics data from MaasHPS, providing a comprehensive overview of metabolic changes in human plaque. RESULTS Our study identified significant changes in lipid, cholesterol, and inositol metabolism, along with altered lysosomal lytic activity and increased inflammatory activity, in unstable plaques with intraplaque hemorrhage (IPH+) compared to non-hemorrhaged (IPH-) plaques. Moreover, topological analysis of this network model revealed that the conversion of glutamine to glutamate and their flux between the cytoplasm and mitochondria were notably compromised in hemorrhaged plaques, with a significant reduction in overall glutamate levels in IPH+ plaques. Additionally, reduced glutamate availability was associated with an increased presence of macrophages and a pro-inflammatory phenotype in IPH+ plaques, suggesting an inflammation-prone microenvironment. CONCLUSIONS This study is the first to establish a robust and comprehensive GEM for atherosclerotic plaque, providing a valuable resource for understanding plaque metabolism. The utility of this GEM was illustrated by its ability to reliably predict dysregulation in the cholesterol hydroxylation, inositol metabolism, and the glutamine/glutamate pathway in rupture-prone hemorrhaged plaques, a finding that may pave the way to new diagnostic or therapeutic measures.
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Affiliation(s)
- Han Jin
- Central Laboratory, Tianjin Medical University General Hospital, Tianjin, China
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
- Science for Life Laboratory (SciLifeLab), KTH-Royal Institute of Technology, Solna, Sweden
| | - Cheng Zhang
- Science for Life Laboratory (SciLifeLab), KTH-Royal Institute of Technology, Solna, Sweden
| | - Jan Nagenborg
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
| | | | - Adele V Ruder
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
| | | | - Barend M E Mees
- Department of Surgery, Maastricht UMC+, Maastricht, the Netherlands
| | - Olivia Waring
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
| | - Judith C Sluimer
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, Scotland
| | - Dietbert Neumann
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
| | - Pieter Goossens
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
| | - Marjo M P C Donners
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands
| | - Adil Mardinoglu
- Science for Life Laboratory (SciLifeLab), KTH-Royal Institute of Technology, Solna, Sweden.
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London, UK.
| | - Erik A L Biessen
- Department of Pathology, Cardiovascular Research Institute Maastricht (CARIM), Maastricht UMC+, Maastricht, the Netherlands.
- Institute for Molecular Cardiovascular Research, RWTH Aachen University, Aachen, Germany.
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Chen K, Wang F. Cell-specific genome-scale metabolic modeling of SARS-CoV-2-infected lung to identify antiviral enzymes. FEBS Open Bio 2023; 13:2172-2186. [PMID: 37734920 PMCID: PMC10699103 DOI: 10.1002/2211-5463.13710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 08/09/2023] [Accepted: 09/19/2023] [Indexed: 09/23/2023] Open
Abstract
Computational systems biology plays a key role in the discovery of suitable antiviral targets. We designed a cell-specific, constraint-based modeling technique for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-infected lungs. We used the gene sequence of the alpha variant of SARS-CoV-2 to build a viral biomass reaction (VBR). We also used the mass proportion of lipids between the viral biomass and its host cell to estimate the stoichiometric coefficients of viral lipids in the reaction. We then integrated the VBR, the gene expression of the alpha variant of SARS-CoV-2, and the generic human metabolic network Recon3D to reconstruct a cell-specific genome-scale metabolic model. An antiviral target discovery (AVTD) platform was introduced using this model to identify therapeutic drug targets for combating COVID-19. The AVTD platform not only identified antiviral genes for eliminating viral replication but also predicted side effects of treatments. Our computational results revealed that knocking out dihydroorotate dehydrogenase (DHODH) might reduce the synthesis rate of cytidine-5'-triphosphate and uridine-5'-triphosphate, which terminate the viral building blocks of DNA and RNA for SARS-CoV-2 replication. Our results also indicated that DHODH is a promising antiviral target that causes minor side effects, which is consistent with the results of recent reports. Moreover, we discovered that the genes that participate in the de novo biosynthesis of glycerophospholipids and ceramides become unidentifiable if the VBR does not involve the stoichiometry of lipids.
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Affiliation(s)
- Ke‐Lin Chen
- Department of Chemical EngineeringNational Chung Cheng UniversityChiayiTaiwan
| | - Feng‐Sheng Wang
- Department of Chemical EngineeringNational Chung Cheng UniversityChiayiTaiwan
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4
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Gelbach PE, Finley SD. Genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer. iScience 2023; 26:107569. [PMID: 37664588 PMCID: PMC10474475 DOI: 10.1016/j.isci.2023.107569] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 05/24/2023] [Accepted: 08/07/2023] [Indexed: 09/05/2023] Open
Abstract
Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment (TME), which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the TME. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.
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Affiliation(s)
- Patrick E. Gelbach
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Stacey D. Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA
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Yang G, Huang S, Hu K, Lu A, Yang J, Meroueh N, Dang P, Wang Y, Zhu H, Cao S, Zhang C. Flux estimation analysis systematically characterizes the metabolic shifts of the central metabolism pathway in human cancer. Front Oncol 2023; 13:1117810. [PMID: 37377905 PMCID: PMC10291142 DOI: 10.3389/fonc.2023.1117810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 05/02/2023] [Indexed: 06/29/2023] Open
Abstract
Introduction Glucose and glutamine are major carbon and energy sources that promote the rapid proliferation of cancer cells. Metabolic shifts observed on cell lines or mouse models may not reflect the general metabolic shifts in real human cancer tissue. Method In this study, we conducted a computational characterization of the flux distribution and variations of the central energy metabolism and key branches in a pan-cancer analysis, including the glycolytic pathway, production of lactate, tricarboxylic acid (TCA) cycle, nucleic acid synthesis, glutaminolysis, glutamate, glutamine, and glutathione metabolism, and amino acid synthesis, in 11 cancer subtypes and nine matched adjacent normal tissue types using TCGA transcriptomics data. Result Our analysis confirms the increased influx in glucose uptake and glycolysis and decreased upper part of the TCA cycle, i.e., the Warburg effect, in almost all the analyzed cancer. However, increased lactate production and the second half of the TCA cycle were only seen in certain cancer types. More interestingly, we failed to detect significantly altered glutaminolysis in cancer tissues compared to their adjacent normal tissues. A systems biology model of metabolic shifts through cancer and tissue types is further developed and analyzed. We observed that (1) normal tissues have distinct metabolic phenotypes; (2) cancer types have drastically different metabolic shifts compared to their adjacent normal controls; and (3) the different shifts in tissue-specific metabolic phenotypes result in a converged metabolic phenotype through cancer types and cancer progression. Discussion This study strongly suggests the possibility of having a unified framework for studies of cancer-inducing stressors, adaptive metabolic reprogramming, and cancerous behaviors.
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Affiliation(s)
- Grace Yang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Carmel High School, Carmel, IN, United States
| | - Shaoyang Huang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Carmel High School, Carmel, IN, United States
| | - Kevin Hu
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Carmel High School, Carmel, IN, United States
| | - Alex Lu
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Park Tudor School, Indianapolis, IN, United States
| | - Jonathan Yang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Carmel High School, Carmel, IN, United States
| | - Noah Meroueh
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Carmel High School, Carmel, IN, United States
| | - Pengtao Dang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Department of Electrical and Computer Engineering, Purdue University, Indianapolis, IN, United States
| | - Yijie Wang
- Department of Computer Science, Indiana University, Bloomington, IN, United States
| | - Haiqi Zhu
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Department of Computer Science, Indiana University, Bloomington, IN, United States
| | - Sha Cao
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, United States
| | - Chi Zhang
- Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, United States
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, United States
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Çubuk C, Loucera C, Peña-Chilet M, Dopazo J. Crosstalk between Metabolite Production and Signaling Activity in Breast Cancer. Int J Mol Sci 2023; 24:ijms24087450. [PMID: 37108611 PMCID: PMC10138666 DOI: 10.3390/ijms24087450] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/11/2023] [Accepted: 04/13/2023] [Indexed: 04/29/2023] Open
Abstract
The reprogramming of metabolism is a recognized cancer hallmark. It is well known that different signaling pathways regulate and orchestrate this reprogramming that contributes to cancer initiation and development. However, recent evidence is accumulating, suggesting that several metabolites could play a relevant role in regulating signaling pathways. To assess the potential role of metabolites in the regulation of signaling pathways, both metabolic and signaling pathway activities of Breast invasive Carcinoma (BRCA) have been modeled using mechanistic models. Gaussian Processes, powerful machine learning methods, were used in combination with SHapley Additive exPlanations (SHAP), a recent methodology that conveys causality, to obtain potential causal relationships between the production of metabolites and the regulation of signaling pathways. A total of 317 metabolites were found to have a strong impact on signaling circuits. The results presented here point to the existence of a complex crosstalk between signaling and metabolic pathways more complex than previously was thought.
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Affiliation(s)
- Cankut Çubuk
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain
- Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London E1 4NS, UK
| | - Carlos Loucera
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBiS), University Hospital Virgen del Rocío, Consejo Superior de Investigaciones Científicas, University of Seville, 41013 Sevilla, Spain
| | - María Peña-Chilet
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBiS), University Hospital Virgen del Rocío, Consejo Superior de Investigaciones Científicas, University of Seville, 41013 Sevilla, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 41013 Sevilla, Spain
- FPS, ELIXIR-es, Hospital Virgen del Rocío, 42013 Sevilla, Spain
| | - Joaquin Dopazo
- Computational Medicine Platform, Andalusian Public Foundation Progress and Health-FPS, 41013 Sevilla, Spain
- Computational Systems Medicine, Institute of Biomedicine of Seville (IBiS), University Hospital Virgen del Rocío, Consejo Superior de Investigaciones Científicas, University of Seville, 41013 Sevilla, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 41013 Sevilla, Spain
- FPS, ELIXIR-es, Hospital Virgen del Rocío, 42013 Sevilla, Spain
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7
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Miller HA, Miller DM, van Berkel VH, Frieboes HB. Evaluation of Lung Cancer Patient Response to First-Line Chemotherapy by Integration of Tumor Core Biopsy Metabolomics with Multiscale Modeling. Ann Biomed Eng 2023; 51:820-832. [PMID: 36224485 PMCID: PMC10023290 DOI: 10.1007/s10439-022-03096-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Accepted: 10/02/2022] [Indexed: 11/28/2022]
Abstract
The standard of care for intermediate (Stage II) and advanced (Stages III and IV) non-small cell lung cancer (NSCLC) involves chemotherapy with taxane/platinum derivatives, with or without radiation. Ideally, patients would be screened a priori to allow non-responders to be initially treated with second-line therapies. This evaluation is non-trivial, however, since tumors behave as complex multiscale systems. To address this need, this study employs a multiscale modeling approach to evaluate first-line chemotherapy response of individual patient tumors based on metabolomic analysis of tumor core biopsies obtained during routine clinical evaluation. Model parameters were calculated for a patient cohort as a function of these metabolomic profiles, previously obtained from high-resolution 2DLC-MS/MS analysis. Evaluation metrics were defined to classify patients as Disease-Control (DC) [encompassing complete-response (CR), partial-response (PR), and stable-disease (SD)] and Progressive-Disease (PD) following first-line chemotherapy. Response was simulated for each patient and compared to actual response. The results show that patient classifications were significantly separated from each other, and also when grouped as DC vs. PD and as CR/PR vs. SD/PD, by fraction of initial tumor radius metric at 6 days post simulated bolus drug injection. This study shows that patient first-line chemotherapy response can in principle be evaluated from multiscale modeling integrated with tumor tissue metabolomic data, offering a first step towards individualized lung cancer treatment prognosis.
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Affiliation(s)
- Hunter A Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA
| | - Donald M Miller
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA
- Department of Medicine, University of Louisville, Louisville, KY, USA
| | - Victor H van Berkel
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA
- Department of Cardiovascular and Thoracic Surgery, University of Louisville, Louisville, KY, USA
| | - Hermann B Frieboes
- Department of Pharmacology and Toxicology, University of Louisville, Louisville, KY, USA.
- James Graham Brown Cancer Center, University of Louisville, Louisville, KY, USA.
- Department of Bioengineering, University of Louisville, Lutz Hall 419, Louisville, KY, 40292, USA.
- Center for Predictive Medicine, University of Louisville, Louisville, KY, USA.
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Mathematical Modeling of Eicosanoid Metabolism in Macrophage Cells: Cybernetic Framework Combined with Novel Information-Theoretic Approaches. Processes (Basel) 2023. [DOI: 10.3390/pr11030874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023] Open
Abstract
Cellular response to inflammatory stimuli leads to the production of eicosanoids—prostanoids (PRs) and leukotrienes (LTs)—and signaling molecules—cytokines and chemokines—by macrophages. Quantitative modeling of the inflammatory response is challenging owing to a lack of knowledge of the complex regulatory processes involved. Cybernetic models address these challenges by utilizing a well-defined cybernetic goal and optimizing a coarse-grained model toward this goal. We developed a cybernetic model to study arachidonic acid (AA) metabolism, which included two branches, PRs and LTs. We utilized a priori biological knowledge to define the branch-specific cybernetic goals for PR and LT branches as the maximization of TNFα and CCL2, respectively. We estimated the model parameters by fitting data from three experimental conditions. With these parameters, we were able to capture a novel fourth independent experimental condition as part of the model validation. The cybernetic model enhanced our understanding of enzyme dynamics by predicting their profiles. The success of the model implies that the cell regulates the synthesis and activity of the associated enzymes, through cybernetic control variables, to accomplish the chosen biological goal. The results indicated that the dominant metabolites are PGD2 (a PR) and LTB4 (an LT), aligning with their corresponding known prominent biological roles during inflammation. Using heuristic arguments, we also infer that eicosanoid overproduction can lead to increased secretion of cytokines/chemokines. This novel model integrates mechanistic knowledge, known biological understanding of signaling pathways, and data-driven methods to study the dynamics of eicosanoid metabolism.
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9
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Gelbach PE, Finley SD. Ensemble-based genome-scale modeling predicts metabolic differences between macrophage subtypes in colorectal cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.09.532000. [PMID: 36993493 PMCID: PMC10052244 DOI: 10.1101/2023.03.09.532000] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
1Colorectal cancer (CRC) shows high incidence and mortality, partly due to the tumor microenvironment, which is viewed as an active promoter of disease progression. Macrophages are among the most abundant cells in the tumor microenvironment. These immune cells are generally categorized as M1, with inflammatory and anti-cancer properties, or M2, which promote tumor proliferation and survival. Although the M1/M2 subclassification scheme is strongly influenced by metabolism, the metabolic divergence between the subtypes remains poorly understood. Therefore, we generated a suite of computational models that characterize the M1- and M2-specific metabolic states. Our models show key differences between the M1 and M2 metabolic networks and capabilities. We leverage the models to identify metabolic perturbations that cause the metabolic state of M2 macrophages to more closely resemble M1 cells. Overall, this work increases understanding of macrophage metabolism in CRC and elucidates strategies to promote the metabolic state of anti-tumor macrophages.
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Affiliation(s)
- Patrick E. Gelbach
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
| | - Stacey D. Finley
- Alfred E. Mann Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA 90089, USA
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10
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Yang G, Mishra M, Perera MA. Multi-Omics Studies in Historically Excluded Populations: The Road to Equity. Clin Pharmacol Ther 2023; 113:541-556. [PMID: 36495075 DOI: 10.1002/cpt.2818] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 12/01/2022] [Indexed: 12/14/2022]
Abstract
Over the past few decades, genomewide association studies (GWASs) have identified the specific genetics variants contributing to many complex diseases by testing millions of genetic variations across the human genome against a variety of phenotypes. However, GWASs are limited in their ability to uncover mechanistic insight given that most significant associations are found in non-coding region of the genome. Furthermore, the lack of diversity in studies has stymied the advance of precision medicine for many historically excluded populations. In this review, we summarize most popular multi-omics approaches (genomics, transcriptomics, proteomics, and metabolomics) related to precision medicine and highlight if diverse populations have been included and how their findings have advance biological understanding of disease and drug response. New methods that incorporate local ancestry have been to improve the power of GWASs for admixed populations (such as African Americans and Latinx). Because most signals from GWAS are in the non-coding region, other machine learning and omics approaches have been developed to identify the potential causative single-nucleotide polymorphisms and genes that explain these phenotypes. These include polygenic risk scores, expression quantitative trait locus mapping, and transcriptome-wide association studies. Analogous protein methods, such as proteins quantitative trait locus mapping, proteome-wide association studies, and metabolomic approaches provide insight into the consequences of genetic variation on protein abundance. Whereas, integrated multi-omics studies have improved our understanding of the mechanisms for genetic association, we still lack the datasets and cohorts for historically excluded populations to provide equity in precision medicine and pharmacogenomics.
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Affiliation(s)
- Guang Yang
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Mrinal Mishra
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Minoli A Perera
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
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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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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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13
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Granata I, Manipur I, Giordano M, Maddalena L, Guarracino MR. TumorMet: A repository of tumor metabolic networks derived from context-specific Genome-Scale Metabolic Models. Sci Data 2022; 9:607. [PMID: 36207341 PMCID: PMC9547001 DOI: 10.1038/s41597-022-01702-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 09/15/2022] [Indexed: 11/25/2022] Open
Abstract
Studies about the metabolic alterations during tumorigenesis have increased our knowledge of the underlying mechanisms and consequences, which are important for diagnostic and therapeutic investigations. In this scenario and in the era of systems biology, metabolic networks have become a powerful tool to unravel the complexity of the cancer metabolic machinery and the heterogeneity of this disease. Here, we present TumorMet, a repository of tumor metabolic networks extracted from context-specific Genome-Scale Metabolic Models, as a benchmark for graph machine learning algorithms and network analyses. This repository has an extended scope for use in graph classification, clustering, community detection, and graph embedding studies. Along with the data, we developed and provided Met2Graph, an R package for creating three different types of metabolic graphs, depending on the desired nodes and edges: Metabolites-, Enzymes-, and Reactions-based graphs. This package allows the easy generation of datasets for downstream analysis. Measurement(s) | gene expression, metabolic relationships | Technology Type(s) | Genome Scale Metabolic Models; Computational network biology | Sample Characteristic - Organism | Homo sapiens |
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14
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Bafiti V, Katsila T. Pharmacometabolomics-Based Translational Biomarkers: How to Navigate the Data Ocean. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:542-551. [PMID: 36149303 DOI: 10.1089/omi.2022.0097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Metabolome is the end point of the genome-environment interplay, and enables an important holistic overview of individual adaptability and host responses to environmental, ecological, as well as endogenous changes such as disease. Pharmacometabolomics is the application of metabolome knowledge to decipher the mechanisms of interindividual and intraindividual variations in drug efficacy and safety. Pharmacometabolomics also contributes to prediction of drug treatment outcomes on the basis of baseline (predose) and postdose metabotypes through mathematical modeling. Thus, pharmacometabolomics is a strong asset for a diverse community of stakeholders interested in theory and practice of evidence-based and precision/personalized medicine: academic researchers, public health scholars, health professionals, pharmaceutical, diagnostics, and biotechnology industries, among others. In this expert review, we discuss pharmacometabolomics in four contexts: (1) an interdisciplinary omics tool and field to map the mechanisms and scale of interindividual variability in drug effects, (2) discovery and development of translational biomarkers, (3) advance digital biomarkers, and (4) empower drug repurposing, a field that is increasingly proving useful in the current era of Covid-19. As the applications of pharmacometabolomics are growing rapidly in the current postgenome era, next-generation proteomics and metabolomics follow the example of next-generation sequencing analyses. Pharmacometabolomics can also empower data reliability and reproducibility through multiomics integration strategies, which use each data layer to correct, connect with, and inform each other. Finally, we underscore here that contextual data remain crucial for precision medicine and drug development that stand the test of time and clinical relevance.
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Affiliation(s)
- Vivi Bafiti
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
| | - Theodora Katsila
- Institute of Chemical Biology, National Hellenic Research Foundation, Athens, Greece
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15
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Wang FS, Chen PR, Chen TY, Zhang HX. Fuzzy optimization for identifying anti-cancer targets with few side effects in constraint-based models of head and neck cancer. ROYAL SOCIETY OPEN SCIENCE 2022; 9:220633. [PMID: 36303939 PMCID: PMC9597175 DOI: 10.1098/rsos.220633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Computer-aided methods can be used to screen potential candidate targets and to reduce the time and cost of drug development. In most of these methods, synthetic lethality is used as a therapeutic criterion to identify drug targets. However, these methods do not consider the side effects during the identification stage. This study developed a fuzzy multi-objective optimization for identifying anti-cancer targets that not only evaluated cancer cell mortality, but also minimized side effects due to treatment. We identified potential anti-cancer enzymes and antimetabolites for the treatment of head and neck cancer (HNC). The identified one- and two-target enzymes were primarily involved in six major pathways, namely, purine and pyrimidine metabolism and the pentose phosphate pathway. Most of the identified targets can be regulated by approved drugs; thus, these drugs are potential candidates for drug repurposing as a treatment for HNC. Furthermore, we identified antimetabolites involved in pathways similar to those identified using a gene-centric approach. Moreover, HMGCR knockdown could not block the growth of HNC cells. However, the two-target combinations of (UMPS, HMGCR) and (CAD, HMGCR) could achieve cell mortality and improve metabolic deviation grades over 22% without reducing the cell viability grade.
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Affiliation(s)
- Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Pei-Rong Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Ting-Yu Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Hao-Xiang Zhang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
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16
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Genome-Scale Metabolic Model Analysis of Metabolic Differences between Lauren Diffuse and Intestinal Subtypes in Gastric Cancer. Cancers (Basel) 2022; 14:cancers14092340. [PMID: 35565469 PMCID: PMC9104812 DOI: 10.3390/cancers14092340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 05/05/2022] [Indexed: 01/01/2023] Open
Abstract
Gastric cancer (GC) is one of the most lethal cancers worldwide; it has a high mortality rate, particularly in East Asia. Recently, genetic events (e.g., mutations and copy number alterations) and molecular signaling associated with histologically different GC subtypes (diffuse and intestinal) have been elucidated. However, metabolic differences among the histological GC subtypes have not been studied systematically. In this study, we utilized transcriptome-based genome-scale metabolic models (GEMs) to identify differential metabolic pathways between Lauren diffuse and intestinal subtypes. We found that diverse metabolic pathways, including cholesterol homeostasis, xenobiotic metabolism, fatty acid metabolism, the MTORC1 pathway, and glycolysis, were dysregulated between the diffuse and intestinal subtypes. Our study provides an overview of the metabolic differences between the two subtypes, possibly leading to an understanding of metabolism in GC heterogeneity.
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Yadav S, Virk R, Chung CH, Eduardo MB, VanDerway D, Chen D, Burdett K, Gao H, Zeng Z, Ranjan M, Cottone G, Xuei X, Chandrasekaran S, Backman V, Chatterton R, Khan SA, Clare SE. Lipid exposure activates gene expression changes associated with estrogen receptor negative breast cancer. NPJ Breast Cancer 2022; 8:59. [PMID: 35508495 PMCID: PMC9068822 DOI: 10.1038/s41523-022-00422-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Accepted: 03/31/2022] [Indexed: 12/13/2022] Open
Abstract
Improved understanding of local breast biology that favors the development of estrogen receptor negative (ER-) breast cancer (BC) would foster better prevention strategies. We have previously shown that overexpression of specific lipid metabolism genes is associated with the development of ER- BC. We now report results of exposure of MCF-10A and MCF-12A cells, and mammary organoids to representative medium- and long-chain polyunsaturated fatty acids. This exposure caused a dynamic and profound change in gene expression, accompanied by changes in chromatin packing density, chromatin accessibility, and histone posttranslational modifications (PTMs). We identified 38 metabolic reactions that showed significantly increased activity, including reactions related to one-carbon metabolism. Among these reactions are those that produce S-adenosyl-L-methionine for histone PTMs. Utilizing both an in-vitro model and samples from women at high risk for ER- BC, we show that lipid exposure engenders gene expression, signaling pathway activation, and histone marks associated with the development of ER- BC.
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Affiliation(s)
- Shivangi Yadav
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Ranya Virk
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, 60208-2850, USA
| | - Carolina H Chung
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | | | - David VanDerway
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, 60208-2850, USA
| | - Duojiao Chen
- Center of for Medical Genomics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Kirsten Burdett
- Department of Preventive Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Hongyu Gao
- Center of for Medical Genomics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Zexian Zeng
- Department of Data Sciences, Dana Farber Cancer Institute, Harvard T.H. Chan School of Public Health, Boston, MA, 02215, USA
| | - Manish Ranjan
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Gannon Cottone
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA
| | - Xiaoling Xuei
- Center of for Medical Genomics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, 48109, USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
- Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Vadim Backman
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, 60208-2850, USA
| | - Robert Chatterton
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, IL, 60611, USA
| | - Seema Ahsan Khan
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.
| | - Susan E Clare
- Department of Surgery, Feinberg School of Medicine, Northwestern University, Chicago, IL, 60611, USA.
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18
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Li G, Han F, Xiao F, Gu K, Shen Q, Xu W, Li W, Wang Y, Liang B, Huang J, Xiao W, Kong Q. System-level metabolic modeling facilitates unveiling metabolic signature in exceptional longevity. Aging Cell 2022; 21:e13595. [PMID: 35343058 PMCID: PMC9009231 DOI: 10.1111/acel.13595] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 02/17/2022] [Accepted: 03/10/2022] [Indexed: 12/29/2022] Open
Abstract
Although it is well known that metabolic control plays a crucial role in regulating the health span and life span of various organisms, little is known for the systems metabolic profile of centenarians, the paradigm of human healthy aging and longevity. Meanwhile, how to well characterize the system‐level metabolic states in an organism of interest remains to be a major challenge in systems metabolism research. To address this challenge and better understand the metabolic mechanisms of healthy aging, we developed a method of genome‐wide precision metabolic modeling (GPMM) which is able to quantitatively integrate transcriptome, proteome and kinetome data in predictive modeling of metabolic networks. Benchmarking analysis showed that GPMM successfully characterized metabolic reprogramming in the NCI‐60 cancer cell lines; it dramatically improved the performance of the modeling with an R2 of 0.86 between the predicted and experimental measurements over the performance of existing methods. Using this approach, we examined the metabolic networks of a Chinese centenarian cohort and identified the elevated fatty acid oxidation (FAO) as the most significant metabolic feature in these long‐lived individuals. Evidence from serum metabolomics supports this observation. Given that FAO declines with normal aging and is impaired in many age‐related diseases, our study suggests that the elevated FAO has potential to be a novel signature of healthy aging of humans.
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Affiliation(s)
- Gong‐Hua Li
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
- Kunming Key Laboratory of Healthy Aging Study Kunming Yunnan China
| | - Feifei Han
- Harvard Medical School Immune and Metabolic Computational Center Massachusetts General Hospital Boston Massachusetts USA
| | - Fu‐Hui Xiao
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
- Kunming Key Laboratory of Healthy Aging Study Kunming Yunnan China
| | - Kang‐Su‐Yun Gu
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
- Kunming Key Laboratory of Healthy Aging Study Kunming Yunnan China
| | - Qiu Shen
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
| | - Weihong Xu
- Harvard Medical School Immune and Metabolic Computational Center Massachusetts General Hospital Boston Massachusetts USA
| | - Wen‐Xing Li
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
| | - Yan‐Li Wang
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
- School of Life Sciences Center for Life Sciences Yunnan University Kunming Yunnan China
| | - Bin Liang
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
- School of Life Sciences Center for Life Sciences Yunnan University Kunming Yunnan China
| | - Jing‐Fei Huang
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
| | - Wenzhong Xiao
- Harvard Medical School Immune and Metabolic Computational Center Massachusetts General Hospital Boston Massachusetts USA
| | - Qing‐Peng Kong
- State Key Laboratory of Genetic Resources and Evolution/Key Laboratory of Healthy Aging Research of Yunnan Province Kunming Institute of Zoology Chinese Academy of Sciences Kunming Yunnan China
- Kunming Key Laboratory of Healthy Aging Study Kunming Yunnan China
- CAS Center for Excellence in Animal Evolution and Genetics Chinese Academy of Sciences Kunming Yunnan China
- KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases Kunming Yunnan China
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19
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Akasiadis C, Ponce‐de‐Leon M, Montagud A, Michelioudakis E, Atsidakou A, Alevizos E, Artikis A, Valencia A, Paliouras G. Parallel model exploration for tumor treatment simulations. Comput Intell 2022. [DOI: 10.1111/coin.12515] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Charilaos Akasiadis
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
| | | | - Arnau Montagud
- Life Sciences Department Barcelona Supercomputing Center Barcelona Spain
| | - Evangelos Michelioudakis
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
- Department of Informatics and Telecommunications University of Athens Athens Greece
| | - Alexia Atsidakou
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
| | - Elias Alevizos
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
| | - Alexander Artikis
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
| | - Alfonso Valencia
- Life Sciences Department Barcelona Supercomputing Center Barcelona Spain
| | - Georgios Paliouras
- Institute of Informatics & Telecommunications NCSR ‘Demokritos’ Agia Paraskevi Greece
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20
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Modeling of Tumor Growth with Input from Patient-Specific Metabolomic Data. Ann Biomed Eng 2022; 50:314-329. [PMID: 35083584 PMCID: PMC9743982 DOI: 10.1007/s10439-022-02904-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Accepted: 01/01/2022] [Indexed: 12/15/2022]
Abstract
Advances in omic technologies have provided insight into cancer progression and treatment response. However, the nonlinear characteristics of cancer growth present a challenge to bridge from the molecular- to the tissue-scale, as tumor behavior cannot be encapsulated by the sum of the individual molecular details gleaned experimentally. Mathematical modeling and computational simulation have been traditionally employed to facilitate analysis of nonlinear systems. In this study, for the first time tumor metabolomic data are linked via mathematical modeling to the tumor tissue-scale behavior, showing the capability to mechanistically simulate cancer progression personalized to omic information obtainable from patient tumor core biopsy analysis. Generally, a higher degree of metabolic dysregulation has been correlated with more aggressive tumor behavior. Accordingly, key parameters influenced by metabolomic data in this model include tumor proliferation, vascularization, aggressiveness, lactic acid production, monocyte infiltration and macrophage polarization, and drug effect. The model enables evaluating interactions of interest between these parameters which drive tumor growth based on the metabolomic data. The results show that the model can group patients consistently with the clinically observed outcomes of response/non-response to chemotherapy. This modeling approach provides a first step towards evaluation of tumor growth based on tumor-specific metabolomic data.
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21
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Wang FS, Chen KL, Chu SW. Human/SARS-CoV-2 Genome-Scale Metabolic Modeling to Discover Potential Antiviral Targets for COVID-19. J Taiwan Inst Chem Eng 2022; 133:104273. [PMID: 35186172 PMCID: PMC8843340 DOI: 10.1016/j.jtice.2022.104273] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 02/06/2022] [Accepted: 02/11/2022] [Indexed: 12/20/2022]
Affiliation(s)
- Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan
| | - Ke-Lin Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan
| | - Sz-Wei Chu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi 621301, Taiwan
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22
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Tripathi S, Park JH, Pudakalakatti S, Bhattacharya PK, Kaipparettu BA, Levine H. A mechanistic modeling framework reveals the key principles underlying tumor metabolism. PLoS Comput Biol 2022; 18:e1009841. [PMID: 35148308 PMCID: PMC8870510 DOI: 10.1371/journal.pcbi.1009841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 02/24/2022] [Accepted: 01/15/2022] [Indexed: 01/12/2023] Open
Abstract
While aerobic glycolysis, or the Warburg effect, has for a long time been considered a hallmark of tumor metabolism, recent studies have revealed a far more complex picture. Tumor cells exhibit widespread metabolic heterogeneity, not only in their presentation of the Warburg effect but also in the nutrients and the metabolic pathways they are dependent on. Moreover, tumor cells can switch between different metabolic phenotypes in response to environmental cues and therapeutic interventions. A framework to analyze the observed metabolic heterogeneity and plasticity is, however, lacking. Using a mechanistic model that includes the key metabolic pathways active in tumor cells, we show that the inhibition of phosphofructokinase by excess ATP in the cytoplasm can drive a preference for aerobic glycolysis in fast-proliferating tumor cells. The differing rates of ATP utilization by tumor cells can therefore drive heterogeneity with respect to the presentation of the Warburg effect. Building upon this idea, we couple the metabolic phenotype of tumor cells to their migratory phenotype, and show that our model predictions are in agreement with previous experiments. Next, we report that the reliance of proliferating cells on different anaplerotic pathways depends on the relative availability of glucose and glutamine, and can further drive metabolic heterogeneity. Finally, using treatment of melanoma cells with a BRAF inhibitor as an example, we show that our model can be used to predict the metabolic and gene expression changes in cancer cells in response to drug treatment. By making predictions that are far more generalizable and interpretable as compared to previous tumor metabolism modeling approaches, our framework identifies key principles that govern tumor cell metabolism, and the reported heterogeneity and plasticity. These principles could be key to targeting the metabolic vulnerabilities of cancer.
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Affiliation(s)
- Shubham Tripathi
- PhD Program in Systems, Synthetic, and Physical Biology, Rice University, Houston, Texas, United States of America
- Center for Theoretical Biological Physics and Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
| | - Jun Hyoung Park
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
| | - Shivanand Pudakalakatti
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Pratip K. Bhattacharya
- Department of Cancer Systems Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America
| | - Benny Abraham Kaipparettu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, United States of America
- Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas, United States of America
| | - Herbert Levine
- Center for Theoretical Biological Physics and Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
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23
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Lee SM, Lee G, Kim HU. Machine learning-guided evaluation of extraction and simulation methods for cancer patient-specific metabolic models. Comput Struct Biotechnol J 2022; 20:3041-3052. [PMID: 35782748 PMCID: PMC9218235 DOI: 10.1016/j.csbj.2022.06.027] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/11/2022] [Accepted: 06/12/2022] [Indexed: 11/30/2022] Open
Abstract
Genome-scale metabolic model (GEM) has been established as an important tool to study cellular metabolism at a systems level by predicting intracellular fluxes. With the advent of generic human GEMs, they have been increasingly applied to a range of diseases, often for the objective of predicting effective metabolic drug targets. Cancer is a representative disease where the use of GEMs has proved to be effective, partly due to the massive availability of patient-specific RNA-seq data. When using a human GEM, so-called context-specific GEM needs to be developed first by using cell-specific RNA-seq data. Biological validity of a context-specific GEM highly depends on both model extraction method (MEM) and model simulation method (MSM). However, while MEMs have been thoroughly examined, MSMs have not been systematically examined, especially, when studying cancer metabolism. In this study, the effects of pairwise combinations of three MEMs and five MSMs were evaluated by examining biological features of the resulting cancer patient-specific GEMs. For this, a total of 1,562 patient-specific GEMs were reconstructed, and subjected to machine learning-guided and biological evaluations to draw robust conclusions. Noteworthy observations were made from the evaluation, including the high performance of two MEMs, namely rank-based ‘task-driven Integrative Network Inference for Tissues’ (tINIT) or ‘Gene Inactivity Moderated by Metabolism and Expression’ (GIMME), paired with least absolute deviation (LAD) as a MSM, and relatively poorer performance of flux balance analysis (FBA) and parsimonious FBA (pFBA). Insights from this study can be considered as a reference when studying cancer metabolism using patient-specific GEMs.
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24
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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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25
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Lee SM, Kim HU. Development of computational models using omics data for the identification of effective cancer metabolic biomarkers. Mol Omics 2021; 17:881-893. [PMID: 34608924 DOI: 10.1039/d1mo00337b] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Identification of novel biomarkers has been an active area of study for the effective diagnosis, prognosis and treatment of cancers. Among various types of cancer biomarkers, metabolic biomarkers, including enzymes, metabolites and metabolic genes, deserve attention as they can serve as a reliable source for diagnosis, prognosis and treatment of cancers. In particular, efforts to identify novel biomarkers have been greatly facilitated by a rapid increase in the volume of multiple omics data generated for a range of cancer cells. These omics data in turn serve as ingredients for developing computational models that can help derive deeper insights into the biology of cancer cells, and identify metabolic biomarkers. In this review, we provide an overview of omics data generated for cancer cells, and discuss recent studies on computational models that were developed using omics data in order to identify effective cancer metabolic biomarkers.
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Affiliation(s)
- Sang Mi Lee
- Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea.
| | - Hyun Uk Kim
- Systems Biology and Medicine Laboratory, Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea. .,KAIST Institute for Artificial Intelligence, KAIST, Daejeon 34141, Republic of Korea.,BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon 34141, Republic of Korea
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Kishk A, Pacheco MP, Sauter T. DCcov: Repositioning of drugs and drug combinations for SARS-CoV-2 infected lung through constraint-based modeling. iScience 2021; 24:103331. [PMID: 34723158 PMCID: PMC8536485 DOI: 10.1016/j.isci.2021.103331] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Revised: 07/29/2021] [Accepted: 10/19/2021] [Indexed: 12/15/2022] Open
Abstract
The 2019 coronavirus disease (COVID-19) became a worldwide pandemic with currently no approved effective antiviral drug. Flux balance analysis (FBA) is an efficient method to analyze metabolic networks. Here, FBA was applied on human lung cells infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to reposition metabolic drugs and drug combinations against the virus replication within the host tissue. Making use of expression datasets of infected lung tissue, genome-scale COVID-19-specific metabolic models were reconstructed. Then, host-specific essential genes and gene pairs were determined through in silico knockouts that permit reducing the viral biomass production without affecting the host biomass. Key pathways that are associated with COVID-19 severity in lung tissue are related to oxidative stress, ferroptosis, and pyrimidine metabolism. By in silico screening of Food and Drug Administration (FDA)-approved drugs on the putative disease-specific essential genes and gene pairs, 85 drugs and 52 drug combinations were predicted as promising candidates for COVID-19 (https://github.com/sysbiolux/DCcov).
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Affiliation(s)
- Ali Kishk
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Maria Pires Pacheco
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Thomas Sauter
- Systems Biology Group, Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
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27
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Comparison of metabolic states using genome-scale metabolic models. PLoS Comput Biol 2021; 17:e1009522. [PMID: 34748535 PMCID: PMC8601616 DOI: 10.1371/journal.pcbi.1009522] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 11/18/2021] [Accepted: 10/04/2021] [Indexed: 11/25/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are comprehensive knowledge bases of cellular metabolism and serve as mathematical tools for studying biological phenotypes and metabolic states or conditions in various organisms and cell types. Given the sheer size and complexity of human metabolism, selecting parameters for existing analysis methods such as metabolic objective functions and model constraints is not straightforward in human GEMs. In particular, comparing several conditions in large GEMs to identify condition- or disease-specific metabolic features is challenging. In this study, we showcase a scalable, model-driven approach for an in-depth investigation and comparison of metabolic states in large GEMs which enables identifying the underlying functional differences. Using a combination of flux space sampling and network analysis, our approach enables extraction and visualisation of metabolically distinct network modules. Importantly, it does not rely on known or assumed objective functions. We apply this novel approach to extract the biochemical differences in adipocytes arising due to unlimited vs blocked uptake of branched-chain amino acids (BCAAs, considered as biomarkers in obesity) using a human adipocyte GEM (iAdipocytes1809). The biological significance of our approach is corroborated by literature reports confirming our identified metabolic processes (TCA cycle and Fatty acid metabolism) to be functionally related to BCAA metabolism. Additionally, our analysis predicts a specific altered uptake and secretion profile indicating a compensation for the unavailability of BCAAs. Taken together, our approach facilitates determining functional differences between any metabolic conditions of interest by offering a versatile platform for analysing and comparing flux spaces of large metabolic networks. Cellular metabolism is a highly complex and interconnected system. As many lifestyle diseases in humans have a strong metabolic component, it is important to understand metabolic differences between healthy and diseased states. In systems biology, metabolic behaviours are investigated using genome-scale metabolic models. In addition to the sheer size and complexity of the genome-scale metabolic models of human systems, using existing analysis methods is challenging and the parameter selection is not straightforward. Therefore, novel methodological frameworks are necessary for analysing metabolic conditions despite the challenges posed by human models. Particularly, an ongoing challenge has been that of comparing several phenotypes for identifying condition- or disease-specific metabolic signatures. We address this significant challenge by developing a scalable and model-driven approach, ComMet (Comparison of Metabolic states). ComMet enables an in-depth investigation and comparison of metabolic phenotypes in large models while also identifying the underlying functional differences. Novel hypotheses can be generated using ComMet for not only understanding known metabolic phenotypes better but also for guiding the design of new experiments to validate the processes predicted by ComMet.
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28
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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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Frades I, Foguet C, Cascante M, Araúzo-Bravo MJ. Genome Scale Modeling to Study the Metabolic Competition between Cells in the Tumor Microenvironment. Cancers (Basel) 2021; 13:4609. [PMID: 34572839 PMCID: PMC8470216 DOI: 10.3390/cancers13184609] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 09/06/2021] [Accepted: 09/09/2021] [Indexed: 12/31/2022] Open
Abstract
The tumor's physiology emerges from the dynamic interplay of numerous cell types, such as cancer cells, immune cells and stromal cells, within the tumor microenvironment. Immune and cancer cells compete for nutrients within the tumor microenvironment, leading to a metabolic battle between these cell populations. Tumor cells can reprogram their metabolism to meet the high demand of building blocks and ATP for proliferation, and to gain an advantage over the action of immune cells. The study of the metabolic reprogramming mechanisms underlying cancer requires the quantification of metabolic fluxes which can be estimated at the genome-scale with constraint-based or kinetic modeling. Constraint-based models use a set of linear constraints to simulate steady-state metabolic fluxes, whereas kinetic models can simulate both the transient behavior and steady-state values of cellular fluxes and concentrations. The integration of cell- or tissue-specific data enables the construction of context-specific models that reflect cell-type- or tissue-specific metabolic properties. While the available modeling frameworks enable limited modeling of the metabolic crosstalk between tumor and immune cells in the tumor stroma, future developments will likely involve new hybrid kinetic/stoichiometric formulations.
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Affiliation(s)
- Itziar Frades
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, 20009 San Sebastian, Spain;
| | - Carles Foguet
- Department of Biochemistry and Molecular Biomedicine, Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain; (C.F.); (M.C.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) (CB17/04/00023) and Metabolomics Node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), 28020 Madrid, Spain
| | - Marta Cascante
- Department of Biochemistry and Molecular Biomedicine, Institute of Biomedicine of University of Barcelona, Faculty of Biology, Universitat de Barcelona, Av. Diagonal 643, 08028 Barcelona, Spain; (C.F.); (M.C.)
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD) (CB17/04/00023) and Metabolomics Node at Spanish National Bioinformatics Institute (INB-ISCIII-ES-ELIXIR), Instituto de Salud Carlos III (ISCIII), 28020 Madrid, Spain
| | - Marcos J. Araúzo-Bravo
- Computational Biology and Systems Biomedicine Group, Biodonostia Health Research Institute, 20009 San Sebastian, Spain;
- Max Planck Institute of Molecular Biomedicine, 48167 Münster, Germany
- Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERfes), 28015 Madrid, Spain
- Translational Bioinformatics Network (TransBioNet), 8001 Barcelona, Spain
- Ikerbasque, Basque Foundation for Science, 48012 Bilbao, Spain
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30
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Herrmann HA, Rusz M, Baier D, Jakupec MA, Keppler BK, Berger W, Koellensperger G, Zanghellini J. Thermodynamic Genome-Scale Metabolic Modeling of Metallodrug Resistance in Colorectal Cancer. Cancers (Basel) 2021; 13:cancers13164130. [PMID: 34439283 PMCID: PMC8391396 DOI: 10.3390/cancers13164130] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Revised: 07/23/2021] [Accepted: 08/03/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary Cancer, but also its treatment, can lead to a reprogramming of cellular metabolism. These changes are observable in metabolite abundances, which can be unbiasedly measured via mass spectrometry metabolomics. However, even when the metabolome changes strongly, a (mechanistic) interpretation is difficult as metabolite levels do not necessarily directly correspond to pathway activities. Here we measure the changes of the cellular metabolome in colorectal cancer cell lines sensitive and resistant to the ruthenium-based drug BOLD-100/KP1339 and the platinum-based drug oxaliplatin. We map these changes onto a cancer-specific genome-scale metabolic model, which allows us not only to compute intracellular flux distributions, but also to disentangle drug-specific effects from growth differences from differences in metabolic adaptations due to resistance. Specifically, we find that resistance to BOLD-100/KP1339 induces more extensive reprogramming than oxaliplatin, especially with respect to fatty acid and amino acid metabolism. Abstract Background: Mass spectrometry-based metabolomics approaches provide an immense opportunity to enhance our understanding of the mechanisms that underpin the cellular reprogramming of cancers. Accurate comparative metabolic profiling of heterogeneous conditions, however, is still a challenge. Methods: Measuring both intracellular and extracellular metabolite concentrations, we constrain four instances of a thermodynamic genome-scale metabolic model of the HCT116 colorectal carcinoma cell line to compare the metabolic flux profiles of cells that are either sensitive or resistant to ruthenium- or platinum-based treatments with BOLD-100/KP1339 and oxaliplatin, respectively. Results: Normalizing according to growth rate and normalizing resistant cells according to their respective sensitive controls, we are able to dissect metabolic responses specific to the drug and to the resistance states. We find the normalization steps to be crucial in the interpretation of the metabolomics data and show that the metabolic reprogramming in resistant cells is limited to a select number of pathways. Conclusions: Here, we elucidate the key importance of normalization steps in the interpretation of metabolomics data, allowing us to uncover drug-specific metabolic reprogramming during acquired metal-drug resistance.
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Affiliation(s)
- Helena A. Herrmann
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
| | - Mate Rusz
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
| | - Dina Baier
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
| | - Michael A. Jakupec
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
- Research Cluster Translational Cancer Therapy Research, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria;
| | - Bernhard K. Keppler
- Institute of Inorganic Chemistry, University of Vienna, 1090 Vienna, Austria; (D.B.); (M.A.J.); (B.K.K.)
- Research Cluster Translational Cancer Therapy Research, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria;
| | - Walter Berger
- Research Cluster Translational Cancer Therapy Research, University of Vienna and Medical University of Vienna, 1090 Vienna, Austria;
- Institute of Cancer Research and Comprehensive Cancer Center, Medical University of Vienna, 1090 Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
- Vienna Metabolomics Center (VIME), University of Vienna, 1090 Vienna, Austria
- Research Network Chemistry Meets Microbiology, University of Vienna, 1090 Vienna, Austria
- Correspondence: (G.K.); (J.Z.)
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
- Correspondence: (G.K.); (J.Z.)
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31
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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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32
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Wang H, Robinson JL, Kocabas P, Gustafsson J, Anton M, Cholley PE, Huang S, Gobom J, Svensson T, Uhlen M, Zetterberg H, Nielsen J. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc Natl Acad Sci U S A 2021; 118:e2102344118. [PMID: 34282017 PMCID: PMC8325244 DOI: 10.1073/pnas.2102344118] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are used extensively for analysis of mechanisms underlying human diseases and metabolic malfunctions. However, the lack of comprehensive and high-quality GEMs for model organisms restricts translational utilization of omics data accumulating from the use of various disease models. Here we present a unified platform of GEMs that covers five major model animals, including Mouse1 (Mus musculus), Rat1 (Rattus norvegicus), Zebrafish1 (Danio rerio), Fruitfly1 (Drosophila melanogaster), and Worm1 (Caenorhabditis elegans). These GEMs represent the most comprehensive coverage of the metabolic network by considering both orthology-based pathways and species-specific reactions. All GEMs can be interactively queried via the accompanying web portal Metabolic Atlas. Specifically, through integrative analysis of Mouse1 with RNA-sequencing data from brain tissues of transgenic mice we identified a coordinated up-regulation of lysosomal GM2 ganglioside and peptide degradation pathways which appears to be a signature metabolic alteration in Alzheimer's disease (AD) mouse models with a phenotype of amyloid precursor protein overexpression. This metabolic shift was further validated with proteomics data from transgenic mice and cerebrospinal fluid samples from human patients. The elevated lysosomal enzymes thus hold potential to be used as a biomarker for early diagnosis of AD. Taken together, we foresee that this evolving open-source platform will serve as an important resource to facilitate the development of systems medicines and translational biomedical applications.
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Affiliation(s)
- Hao Wang
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
- Wallenberg Center for Molecular and Translational Medicine, University of Gothenburg, 405 30 Gothenburg, Sweden
| | - Jonathan L Robinson
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Pinar Kocabas
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Johan Gustafsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Pierre-Etienne Cholley
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Shan Huang
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Johan Gobom
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, 431 30 Mölndal, Sweden
| | - Thomas Svensson
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Mattias Uhlen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, SE-100 44 Stockholm, Sweden
- Wallenberg Center for Protein Research, KTH-Royal Institute of Technology, SE-100 44 Stockholm, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, 431 30 Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, 431 30 Mölndal, Sweden
- Department of Neurodegenerative Disease, University College London Queen Square Institute of Neurology, London WC1E 6BT, United Kingdom
- UK Dementia Research Institute, University College London, London WC1E 6BT, United Kingdom
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden;
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
- BioInnovation Institute, DK2200 Copenhagen, Denmark
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Wang YT, Lin MR, Chen WC, Wu WH, Wang FS. Optimization of a modeling platform to predict oncogenes from genome-scale metabolic networks of non-small-cell lung cancers. FEBS Open Bio 2021. [PMID: 34137202 PMCID: PMC8329960 DOI: 10.1002/2211-5463.13231] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 05/19/2021] [Accepted: 06/16/2021] [Indexed: 12/25/2022] Open
Abstract
Cancer cell dysregulations result in the abnormal regulation of cellular metabolic pathways. By simulating this metabolic reprogramming using constraint-based modeling approaches, oncogenes can be predicted, and this knowledge can be used in prognosis and treatment. We introduced a trilevel optimization problem describing metabolic reprogramming for inferring oncogenes. First, this study used RNA-Seq expression data of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) samples and their healthy counterparts to reconstruct tissue-specific genome-scale metabolic models and subsequently build the flux distribution pattern that provided a measure for the oncogene inference optimization problem for determining tumorigenesis. The platform detected 45 genes for LUAD and 84 genes for LUSC that lead to tumorigenesis. A high level of differentially expressed genes was not an essential factor for determining tumorigenesis. The platform indicated that pyruvate kinase (PKM), a well-known oncogene with a low level of differential gene expression in LUAD and LUSC, had the highest fitness among the predicted oncogenes based on computation. By contrast, pyruvate kinase L/R (PKLR), an isozyme of PKM, had a high level of differential gene expression in both cancers. Phosphatidylserine synthase 1 (PTDSS1), an oncogene in LUAD, was inferred to have a low level of differential gene expression, and overexpression could significantly reduce survival probability. According to the factor analysis, PTDSS1 characteristics were close to those of the template, but they were unobvious in LUSC. Angiotensin-converting enzyme 2 (ACE2) has recently garnered widespread interest as the SARS-CoV-2 virus receptor. Moreover, we determined that ACE2 is an oncogene of LUSC but not of LUAD. The platform developed in this study can identify oncogenes with low levels of differential expression and be used to identify potential therapeutic targets for cancer treatment.
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Affiliation(s)
- You-Tyun Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Min-Ru Lin
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Wei-Chen Chen
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Wu-Hsiung Wu
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
| | - Feng-Sheng Wang
- Department of Chemical Engineering, National Chung Cheng University, Chiayi, Taiwan
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34
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Bushnell GG, Deshmukh AP, den Hollander P, Luo M, Soundararajan R, Jia D, Levine H, Mani SA, Wicha MS. Breast cancer dormancy: need for clinically relevant models to address current gaps in knowledge. NPJ Breast Cancer 2021; 7:66. [PMID: 34050189 PMCID: PMC8163741 DOI: 10.1038/s41523-021-00269-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Accepted: 04/08/2021] [Indexed: 02/04/2023] Open
Abstract
Breast cancer is the most commonly diagnosed cancer in the USA. Although advances in treatment over the past several decades have significantly improved the outlook for this disease, most women who are diagnosed with estrogen receptor positive disease remain at risk of metastatic relapse for the remainder of their life. The cellular source of late relapse in these patients is thought to be disseminated tumor cells that reactivate after a long period of dormancy. The biology of these dormant cells and their natural history over a patient's lifetime is largely unclear. We posit that research on tumor dormancy has been significantly limited by the lack of clinically relevant models. This review will discuss existing dormancy models, gaps in biological understanding, and propose criteria for future models to enhance their clinical relevance.
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Affiliation(s)
- Grace G Bushnell
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Abhijeet P Deshmukh
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Petra den Hollander
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ming Luo
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Rama Soundararajan
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Dongya Jia
- Center for Theoretical Biological Physics, Rice University, Houston, TX, USA
| | - Herbert Levine
- Center for Theoretical Biological Physics and Departments of Physics and Bioengineering, Northeastern University, Boston, MA, USA.
| | - Sendurai A Mani
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Max S Wicha
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
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35
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Schinn SM, Morrison C, Wei W, Zhang L, Lewis NE. Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells. Metab Eng 2021; 66:21-30. [PMID: 33771719 DOI: 10.1016/j.ymben.2021.03.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 11/25/2020] [Accepted: 03/17/2021] [Indexed: 10/21/2022]
Abstract
Genome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells. To address this, we enumerated model parameters that describe key features of cultured mammalian cells - including cellular composition, bioprocess performance metrics, mammalian-specific pathways, and biological assumptions behind model formulation approaches. We tested these parameters by building thousands of metabolic models and evaluating their ability to predict the growth rates of a panel of phenotypically diverse Chinese Hamster Ovary cell clones. We found the following considerations to be most critical for accurate parameterization: (1) cells limit metabolic activity to maintain homeostasis, (2) cell morphology and viability change dynamically during a growth curve, and (3) cellular biomass has a particular macromolecular composition. Depending on parameterization, models predicted different metabolic phenotypes, including contrasting mechanisms of nutrient utilization and energy generation, leading to varying accuracies of growth rate predictions. Notably, accurate parameter values broadly agreed with experimental measurements. These insights will guide future investigations of mammalian metabolism.
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Affiliation(s)
- Song-Min Schinn
- Department of Pediatrics, University of California, San Diego, USA
| | - Carly Morrison
- Pfizer, Biotherapeutics Pharmaceutical Sciences, Andover, MA, USA
| | - Wei Wei
- Pfizer, Biotherapeutics Pharmaceutical Sciences, Andover, MA, USA
| | - Lin Zhang
- Pfizer, Biotherapeutics Pharmaceutical Sciences, Andover, MA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, USA; Department of Bioengineering, University of California, San Diego, USA; Novo Nordisk Foundation Center for Biosustainability at UC, San Diego, USA.
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36
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Turanli B, Yildirim E, Gulfidan G, Arga KY, Sinha R. Current State of "Omics" Biomarkers in Pancreatic Cancer. J Pers Med 2021; 11:127. [PMID: 33672926 PMCID: PMC7918884 DOI: 10.3390/jpm11020127] [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: 01/22/2021] [Revised: 02/08/2021] [Accepted: 02/11/2021] [Indexed: 02/06/2023] Open
Abstract
Pancreatic cancer is one of the most fatal malignancies and the seventh leading cause of cancer-related deaths related to late diagnosis, poor survival rates, and high incidence of metastasis. Unfortunately, pancreatic cancer is predicted to become the third leading cause of cancer deaths in the future. Therefore, diagnosis at the early stages of pancreatic cancer for initial diagnosis or postoperative recurrence is a great challenge, as well as predicting prognosis precisely in the context of biomarker discovery. From the personalized medicine perspective, the lack of molecular biomarkers for patient selection confines tailored therapy options, including selecting drugs and their doses or even diet. Currently, there is no standardized pancreatic cancer screening strategy using molecular biomarkers, but CA19-9 is the most well known marker for the detection of pancreatic cancer. In contrast, recent innovations in high-throughput techniques have enabled the discovery of specific biomarkers of cancers using genomics, transcriptomics, proteomics, metabolomics, glycomics, and metagenomics. Panels combining CA19-9 with other novel biomarkers from different "omics" levels might represent an ideal strategy for the early detection of pancreatic cancer. The systems biology approach may shed a light on biomarker identification of pancreatic cancer by integrating multi-omics approaches. In this review, we provide background information on the current state of pancreatic cancer biomarkers from multi-omics stages. Furthermore, we conclude this review on how multi-omics data may reveal new biomarkers to be used for personalized medicine in the future.
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Affiliation(s)
- Beste Turanli
- Department of Bioengineering, Marmara University, 34722 Istanbul, Turkey; (B.T.); (E.Y.); (G.G.)
| | - Esra Yildirim
- Department of Bioengineering, Marmara University, 34722 Istanbul, Turkey; (B.T.); (E.Y.); (G.G.)
| | - Gizem Gulfidan
- Department of Bioengineering, Marmara University, 34722 Istanbul, Turkey; (B.T.); (E.Y.); (G.G.)
| | - Kazim Yalcin Arga
- Department of Bioengineering, Marmara University, 34722 Istanbul, Turkey; (B.T.); (E.Y.); (G.G.)
- Turkish Institute of Public Health and Chronic Diseases, 34718 Istanbul, Turkey
| | - Raghu Sinha
- Department of Biochemistry and Molecular Biology, Penn State College of Medicine, Hershey, PA 17033, USA
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Cysteine and Folate Metabolism Are Targetable Vulnerabilities of Metastatic Colorectal Cancer. Cancers (Basel) 2021; 13:cancers13030425. [PMID: 33498690 PMCID: PMC7866204 DOI: 10.3390/cancers13030425] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Revised: 01/09/2021] [Accepted: 01/20/2021] [Indexed: 01/17/2023] Open
Abstract
Simple Summary In this work, we studied the metabolic reprogramming of same-patient-derived cell lines with increasing metastatic potential to develop new therapeutic approaches against metastatic colorectal cancer. Using a novel systems biology approach to integrate multiple layers of omics data, we predicted and validated that cystine uptake and folate metabolism, two key pathways related to redox metabolism, are potential targets against metastatic colorectal cancer. Our findings indicate that metastatic cell lines are selectively dependent on redox homeostasis, paving the way for new targeted therapies. Abstract With most cancer-related deaths resulting from metastasis, the development of new therapeutic approaches against metastatic colorectal cancer (mCRC) is essential to increasing patient survival. The metabolic adaptations that support mCRC remain undefined and their elucidation is crucial to identify potential therapeutic targets. Here, we employed a strategy for the rational identification of targetable metabolic vulnerabilities. This strategy involved first a thorough metabolic characterisation of same-patient-derived cell lines from primary colon adenocarcinoma (SW480), its lymph node metastasis (SW620) and a liver metastatic derivative (SW620-LiM2), and second, using a novel multi-omics integration workflow, identification of metabolic vulnerabilities specific to the metastatic cell lines. We discovered that the metastatic cell lines are selectively vulnerable to the inhibition of cystine import and folate metabolism, two key pathways in redox homeostasis. Specifically, we identified the system xCT and MTHFD1 genes as potential therapeutic targets, both individually and combined, for combating mCRC.
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38
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Exploring gene knockout strategies to identify potential drug targets using genome-scale metabolic models. Sci Rep 2021; 11:213. [PMID: 33420254 PMCID: PMC7794450 DOI: 10.1038/s41598-020-80561-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 12/11/2020] [Indexed: 01/29/2023] Open
Abstract
Research on new cancer drugs is performed either through gene knockout studies or phenotypic screening of drugs in cancer cell-lines. Both of these approaches are costly and time-consuming. Computational framework, e.g., genome-scale metabolic models (GSMMs), could be a good alternative to find potential drug targets. The present study aims to investigate the applicability of gene knockout strategies to be used as the finding of drug targets using GSMMs. We performed single-gene knockout studies on existing GSMMs of the NCI-60 cell-lines obtained from 9 tissue types. The metabolic genes responsible for the growth of cancerous cells were identified and then ranked based on their cellular growth reduction. The possible growth reduction mechanisms, which matches with the gene knockout results, were described. Gene ranking was used to identify potential drug targets, which reduce the growth rate of cancer cells but not of the normal cells. The gene ranking results were also compared with existing shRNA screening data. The rank-correlation results for most of the cell-lines were not satisfactory for a single-gene knockout, but it played a significant role in deciding the activity of drug against cell proliferation, whereas multiple gene knockout analysis gave better correlation results. We validated our theoretical results experimentally and showed that the drugs mitotane and myxothiazol can inhibit the growth of at least four cell-lines of NCI-60 database.
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39
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Uppal K. Models of Metabolomic Networks. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11615-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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40
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Baloni P, Dinalankara W, Earls JC, Knijnenburg TA, Geman D, Marchionni L, Price ND. Identifying Personalized Metabolic Signatures in Breast Cancer. Metabolites 2020; 11:20. [PMID: 33396819 PMCID: PMC7823382 DOI: 10.3390/metabo11010020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 12/23/2020] [Accepted: 12/28/2020] [Indexed: 01/04/2023] Open
Abstract
Cancer cells are adept at reprogramming energy metabolism, and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism, and reactive oxygen species (ROS) detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.
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Affiliation(s)
- Priyanka Baloni
- Institute for Systems Biology, Seattle, WA 98109, USA; (P.B.); (J.C.E.); (T.A.K.)
| | - Wikum Dinalankara
- Department of Oncology, Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - John C. Earls
- Institute for Systems Biology, Seattle, WA 98109, USA; (P.B.); (J.C.E.); (T.A.K.)
| | - Theo A. Knijnenburg
- Institute for Systems Biology, Seattle, WA 98109, USA; (P.B.); (J.C.E.); (T.A.K.)
| | - Donald Geman
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD 21205, USA;
| | - Luigi Marchionni
- Department of Oncology, Sydney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA;
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY 10021, USA
| | - Nathan D. Price
- Institute for Systems Biology, Seattle, WA 98109, USA; (P.B.); (J.C.E.); (T.A.K.)
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41
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Flux variability analysis reveals a tragedy of commons in cancer cells. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-03762-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
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42
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Aboulmouna L, Raja R, Khanum S, Gupta S, Maurya MR, Grama A, Subramaniam S, Ramkrishna D. Cybernetic modeling of biological processes in mammalian systems. Curr Opin Chem Eng 2020. [DOI: 10.1016/j.coche.2020.100660] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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43
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Kim G, Kim J, Cha H, Park WY, Ahn JS, Ahn MJ, Park K, Park YJ, Choi JY, Lee KH, Lee SH, Moon SH. Metabolic radiogenomics in lung cancer: associations between FDG PET image features and oncogenic signaling pathway alterations. Sci Rep 2020; 10:13231. [PMID: 32764738 PMCID: PMC7411040 DOI: 10.1038/s41598-020-70168-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 07/24/2020] [Indexed: 12/22/2022] Open
Abstract
This study investigated the associations between image features extracted from tumor 18F-fluorodeoxyglucose (FDG) uptake and genetic alterations in patients with lung cancer. A total of 137 patients (age, 62.7 ± 10.2 years) who underwent FDG positron emission tomography/computed tomography (PET/CT) and targeted deep sequencing analysis for a tumor lesion, comprising 61 adenocarcinoma (ADC), 31 squamous cell carcinoma (SQCC), and 45 small cell lung cancer (SCLC) patients, were enrolled in this study. From the tumor lesions, 86 image features were extracted, and 381 genes were assessed. PET features were associated with genetic mutations: 41 genes with 24 features in ADC; 35 genes with 22 features in SQCC; and 43 genes with 25 features in SCLC (FDR < 0.05). Clusters based on PET features showed an association with alterations in oncogenic signaling pathways: Cell cycle and WNT signaling pathways in ADC (p = 0.023, p = 0.035, respectively); Cell cycle, p53, and WNT in SQCC (p = 0.045, 0.009, and 0.029, respectively); and TGFβ in SCLC (p = 0.030). In addition, SUVpeak and SUVmax were associated with a mutation of the TGFβ signaling pathway in ADC (FDR = 0.001, < 0.001). In this study, PET image features had significant associations with alterations in genes and oncogenic signaling pathways in patients with lung cancer.
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Affiliation(s)
- Gahyun Kim
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Jinho Kim
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea
| | - Hongui Cha
- Samsung Genome Institute, Samsung Medical Center, Seoul, Republic of Korea.,Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Samsung Advanced Institute of Health Science and Technology, Department of Molecular Cell Biology, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jin Seok Ahn
- Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Myung-Ju Ahn
- Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Keunchil Park
- Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Yong-Jin Park
- Department of Nuclear Medicine and Molecular Imaging, Samsung Medical Center, Seoul, Republic of Korea
| | - Joon Young Choi
- Department of Nuclear Medicine and Molecular Imaging, Samsung Medical Center, Seoul, Republic of Korea
| | - Kyung-Han Lee
- Department of Nuclear Medicine and Molecular Imaging, Samsung Medical Center, Seoul, Republic of Korea
| | - Se-Hoon Lee
- Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea. .,Division of Hematology/Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Seung Hwan Moon
- Department of Nuclear Medicine and Molecular Imaging, Samsung Medical Center, Seoul, Republic of Korea.
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44
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Dattilo R, Mottini C, Camera E, Lamolinara A, Auslander N, Doglioni G, Muscolini M, Tang W, Planque M, Ercolani C, Buglioni S, Manni I, Trisciuoglio D, Boe A, Grande S, Luciani AM, Iezzi M, Ciliberto G, Ambs S, De Maria R, Fendt SM, Ruppin E, Cardone L. Pyrvinium Pamoate Induces Death of Triple-Negative Breast Cancer Stem-Like Cells and Reduces Metastases through Effects on Lipid Anabolism. Cancer Res 2020; 80:4087-4102. [PMID: 32718996 DOI: 10.1158/0008-5472.can-19-1184] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2019] [Revised: 05/18/2020] [Accepted: 07/20/2020] [Indexed: 12/19/2022]
Abstract
Cancer stem-like cells (CSC) induce aggressive tumor phenotypes such as metastasis formation, which is associated with poor prognosis in triple-negative breast cancer (TNBC). Repurposing of FDA-approved drugs that can eradicate the CSC subcompartment in primary tumors may prevent metastatic disease, thus representing an effective strategy to improve the prognosis of TNBC. Here, we investigated spheroid-forming cells in a metastatic TNBC model. This strategy enabled us to specifically study a population of long-lived tumor cells enriched in CSCs, which show stem-like characteristics and induce metastases. To repurpose FDA-approved drugs potentially toxic for CSCs, we focused on pyrvinium pamoate (PP), an anthelmintic drug with documented anticancer activity in preclinical models. PP induced cytotoxic effects in CSCs and prevented metastasis formation. Mechanistically, the cell killing effects of PP were a result of inhibition of lipid anabolism and, more specifically, the impairment of anabolic flux from glucose to cholesterol and fatty acids. CSCs were strongly dependent upon activation of lipid biosynthetic pathways; activation of these pathways exhibited an unfavorable prognostic value in a cohort of breast cancer patients, where it predicted high probability of metastatic dissemination and tumor relapse. Overall, this work describes a new approach to target aggressive CSCs that may substantially improve clinical outcomes for patients with TNBC, who currently lack effective targeted therapeutic options. SIGNIFICANCE: These findings provide preclinical evidence that a drug repurposing approach to prevent metastatic disease in TNBC exploits lipid anabolism as a metabolic vulnerability against CSCs in primary tumors.
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Affiliation(s)
- Rosanna Dattilo
- Department of Research, Advanced Diagnostics, and Technological Innovations, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Carla Mottini
- Department of Research, Advanced Diagnostics, and Technological Innovations, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Emanuela Camera
- Laboratory of Cutaneous Physiopathology and Integrated Center for Metabolomics Research, San Gallicano Dermatological Institute (ISG)-IRCCS, Rome, Italy
| | - Alessia Lamolinara
- Department of Medicine and Aging Science, CAST, "G. D'Annunzio" University, Chieti-Pescara, Italy
| | - Noam Auslander
- Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Ginevra Doglioni
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | | | - Wei Tang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Melanie Planque
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Cristiana Ercolani
- S.C. Anatomia Patologica, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Simonetta Buglioni
- S.C. Anatomia Patologica, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Isabella Manni
- Department of Research, Advanced Diagnostics, and Technological Innovations, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Daniela Trisciuoglio
- Department of Research, Advanced Diagnostics, and Technological Innovations, IRCCS Regina Elena National Cancer Institute, Rome, Italy
- Institute of Molecular Biology and Pathology, CNR National Research Council, Rome, Italy
| | - Alessandra Boe
- Core Facilities, Italian National Institute of Health, Rome, Italy
| | - Sveva Grande
- Centro Nazionale per le Tecnologie Innovative in Sanità Pubblica, Istituto Superiore di Sanità, Rome, Italy
- Istituto Nazionale di Fisica Nucleare INFN Sez. di Roma, Rome, Italy
| | - Anna Maria Luciani
- Centro Nazionale per le Tecnologie Innovative in Sanità Pubblica, Istituto Superiore di Sanità, Rome, Italy
- Istituto Nazionale di Fisica Nucleare INFN Sez. di Roma, Rome, Italy
| | - Manuela Iezzi
- Department of Medicine and Aging Science, CAST, "G. D'Annunzio" University, Chieti-Pescara, Italy
| | - Gennaro Ciliberto
- Scientific Directorate, IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Stefan Ambs
- Laboratory of Human Carcinogenesis, Center for Cancer Research, NCI, NIH, Bethesda, Maryland
| | - Ruggero De Maria
- Dipartimento di Medicina e Chirurgia traslazionale, Università Cattolica del Sacro Cuore, Rome, Italy
- Fondazione Policlinico Universitario "A. Gemelli" - IRCCS, Rome, Italy
| | - Sarah-Maria Fendt
- Laboratory of Cellular Metabolism and Metabolic Regulation, VIB Center for Cancer Biology, VIB, Leuven, Belgium
- Laboratory of Cellular Metabolism and Metabolic Regulation, Department of Oncology, KU Leuven and Leuven Cancer Institute (LKI), Leuven, Belgium
| | - Eytan Ruppin
- Center for Cancer Research, NCI, NIH, Bethesda, Maryland.
| | - Luca Cardone
- Department of Research, Advanced Diagnostics, and Technological Innovations, IRCCS Regina Elena National Cancer Institute, Rome, Italy.
- Institute of Biochemistry and Cellular Biology, CNR National Research Council, Rome, Italy
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45
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Campit SE, Meliki A, Youngson NA, Chandrasekaran S. Nutrient Sensing by Histone Marks: Reading the Metabolic Histone Code Using Tracing, Omics, and Modeling. Bioessays 2020; 42:e2000083. [PMID: 32638413 DOI: 10.1002/bies.202000083] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 05/23/2020] [Indexed: 12/19/2022]
Abstract
Several metabolites serve as substrates for histone modifications and communicate changes in the metabolic environment to the epigenome. Technologies such as metabolomics and proteomics have allowed us to reconstruct the interactions between metabolic pathways and histones. These technologies have shed light on how nutrient availability can have a dramatic effect on various histone modifications. This metabolism-epigenome cross talk plays a fundamental role in development, immune function, and diseases like cancer. Yet, major challenges remain in understanding the interactions between cellular metabolism and the epigenome. How the levels and fluxes of various metabolites impact epigenetic marks is still unclear. Discussed herein are recent applications and the potential of systems biology methods such as flux tracing and metabolic modeling to address these challenges and to uncover new metabolic-epigenetic interactions. These systems approaches can ultimately help elucidate how nutrients shape the epigenome of microbes and mammalian cells.
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Affiliation(s)
- Scott E Campit
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Alia Meliki
- Center for Bioinformatics and Computational Medicine, Ann Arbor, MI, 48109, USA
| | - Neil A Youngson
- Institute of Hepatology, Foundation for Liver Research, London, SE5 9NT, UK.,Faculty of Life Sciences and Medicine, King's College London, London, WC2R 2LS, UK.,School of Medical Sciences, UNSW Sydney, Sydney, 2052, Australia
| | - Sriram Chandrasekaran
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI, 48109, USA.,Center for Bioinformatics and Computational Medicine, Ann Arbor, MI, 48109, USA.,Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA.,Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
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46
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Oruganty K, Campit SE, Mamde S, Lyssiotis CA, Chandrasekaran S. Common biochemical properties of metabolic genes recurrently dysregulated in tumors. Cancer Metab 2020; 8:5. [PMID: 32411371 PMCID: PMC7206696 DOI: 10.1186/s40170-020-0211-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 02/03/2020] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Tumor initiation and progression are associated with numerous metabolic alterations. However, the biochemical drivers and constraints that contribute to metabolic gene dysregulation are unclear. METHODS Here, we present MetOncoFit, a computational model that integrates 142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topology, and reaction flux. MetOncoFit uses genome-scale metabolic modeling and machine-learning to quantify the relative importance of various metabolic features in predicting cancer metabolic gene expression, copy number variation, and survival data. RESULTS Using MetOncoFit, we performed a meta-analysis of 9 cancer types and over 4500 samples from TCGA, Prognoscan, and COSMIC tumor databases. MetOncoFit accurately predicted enzyme differential expression and its impact on patient survival using the 142 attributes of metabolic enzymes. Our analysis revealed that enzymes with high catalytic activity were frequently upregulated in many tumors and associated with poor survival. Topological analysis also identified specific metabolites that were hot spots of dysregulation. CONCLUSIONS MetOncoFit integrates a broad range of datasets to understand how biochemical and topological features influence metabolic gene dysregulation across various cancer types. MetOncoFit was able to achieve significantly higher accuracy in predicting differential expression, copy number variation, and patient survival than traditional modeling approaches. Overall, MetOncoFit illuminates how enzyme activity and metabolic network architecture influences tumorigenesis.
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Affiliation(s)
- Krishnadev Oruganty
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48105 USA
- Present Address: Genpact, New York, NY 10036 USA
| | - Scott Edward Campit
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48105 USA
| | - Sainath Mamde
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48105 USA
| | - Costas A. Lyssiotis
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48105 USA
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Michigan Medical School, Ann Arbor, MI 48109 USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109 USA
| | - Sriram Chandrasekaran
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48105 USA
- Program in Chemical Biology, University of Michigan, Ann Arbor, MI 48105 USA
- Rogel Cancer Center, University of Michigan Medical School, Ann Arbor, MI 48109 USA
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Gysi DM, Nowick K. Construction, comparison and evolution of networks in life sciences and other disciplines. J R Soc Interface 2020; 17:20190610. [PMID: 32370689 PMCID: PMC7276545 DOI: 10.1098/rsif.2019.0610] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Accepted: 04/09/2020] [Indexed: 12/12/2022] Open
Abstract
Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.
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Affiliation(s)
- Deisy Morselli Gysi
- Department of Computer Science, Interdisciplinary Center of Bioinformatics, University of Leipzig, 04109 Leipzig, Germany
- Swarm Intelligence and Complex Systems Group, Faculty of Mathematics and Computer Science, University of Leipzig, 04109 Leipzig, Germany
- Center for Complex Networks Research, Northeastern University, 177 Huntington Avenue, Boston, MA 02115, USA
| | - Katja Nowick
- Human Biology Group, Institute for Biology, Faculty of Biology, Chemistry, Pharmacy, Freie Universität Berlin, Königin-Luise-Straβe 1-3, 14195 Berlin, Germany
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Schroeder WL, Harris SD, Saha R. Computation-Driven Analysis of Model Polyextremo-tolerant Fungus Exophiala dermatitidis: Defensive Pigment Metabolic Costs and Human Applications. iScience 2020; 23:100980. [PMID: 32240950 PMCID: PMC7115120 DOI: 10.1016/j.isci.2020.100980] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2020] [Revised: 02/28/2020] [Accepted: 03/09/2020] [Indexed: 02/06/2023] Open
Abstract
The polyextremotolerant black yeast Exophiala dermatitidis is a tractable model system for investigation of adaptations that support growth under extreme conditions. Foremost among these adaptations are melanogenesis and carotenogenesis. A particularly important question is their metabolic production cost. However, investigation of this issue has been hindered by a relatively poor systems-level understanding of E. dermatitidis metabolism. To address this challenge, a genome-scale model (iEde2091) was developed. Using iEde2091, carotenoids were found to be more expensive to produce than melanins. Given their overlapping protective functions, this suggests that carotenoids have an underexplored yet important role in photo-protection. Furthermore, multiple defensive pigments with overlapping functions might allow E. dermatitidis to minimize cost. Because iEde2091 revealed that E. dermatitidis synthesizes the same melanins as humans and the active sites of the key tyrosinase enzyme are highly conserved this model may enable a broader understanding of melanin production across kingdoms.
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Affiliation(s)
- Wheaton L Schroeder
- Department of Chemical and Biomolecular Engineering, University of Nebraska - Lincoln, Lincoln, NE 68588, USA
| | - Steven D Harris
- Department of Biological Sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Rajib Saha
- Department of Chemical and Biomolecular Engineering, University of Nebraska - Lincoln, Lincoln, NE 68588, USA.
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Li W, Wang J. Uncovering the Underlying Mechanisms of Cancer Metabolism through the Landscapes and Probability Flux Quantifications. iScience 2020; 23:101002. [PMID: 32276228 PMCID: PMC7150521 DOI: 10.1016/j.isci.2020.101002] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 11/03/2019] [Accepted: 03/17/2020] [Indexed: 02/07/2023] Open
Abstract
Cancer metabolism is critical for understanding the mechanism of tumorigenesis, yet the understanding is still challenging. We studied gene-metabolism regulatory interactions and quantified the global driving forces for cancer-metabolism dynamics as the underlying landscape and probability flux. We uncovered four steady-state attractors: a normal state attractor, a cancer OXPHOS state attractor, a cancer glycolysis state attractor, and an intermediate cancer state attractor. We identified the key regulatory interactions through global sensitivity analysis based on the landscape topography. Different landscape topographies of glycolysis switch between normal cells and cancer cells were identified. We uncovered that the normal state to cancer state transformation is associated with the peaks of the probability flux and the thermodynamic dissipation, giving dynamical and thermodynamic origin of cancer formation. We found that cancer metabolism oscillations consume more energy to support cancer malignancy. This study provides a quantitative understanding of cancer metabolism and suggests a metabolic therapeutic strategy.
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Affiliation(s)
- Wenbo Li
- State Key Laboratory of Electroanalytical Chemistry, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, Jilin 130022, China
| | - Jin Wang
- Department of Chemistry and Physics, State University of New York at Stony Brook, Stony Brook, NY 11794-3400, USA.
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50
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Damiani C, Rovida L, Maspero D, Sala I, Rosato L, Di Filippo M, Pescini D, Graudenzi A, Antoniotti M, Mauri G. MaREA4Galaxy: Metabolic reaction enrichment analysis and visualization of RNA-seq data within Galaxy. Comput Struct Biotechnol J 2020; 18:993-999. [PMID: 32373287 PMCID: PMC7191582 DOI: 10.1016/j.csbj.2020.04.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 03/20/2020] [Accepted: 04/08/2020] [Indexed: 12/21/2022] Open
Abstract
We present MaREA4Galaxy, a user-friendly tool that allows a user to characterize and to graphically compare groups of samples with different transcriptional regulation of metabolism, as estimated from cross-sectional RNA-seq data. The tool is available as plug-in for the widely-used Galaxy platform for comparative genomics and bioinformatics analyses. MaREA4Galaxy combines three modules. The Expression2RAS module, which, for each reaction of a specified set, computes a Reaction Activity Score (RAS) as a function of the expression level of genes encoding for the associated enzyme. The MaREA (Metabolic Reaction Enrichment Analysis) module that allows to highlight significant differences in reaction activities between specified groups of samples. The Clustering module which employs the RAS computed before as a metric for unsupervised clustering of samples into distinct metabolic subgroups; the Clustering tool provides different clustering techniques and implements standard methods to evaluate the goodness of the results.
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Affiliation(s)
- Chiara Damiani
- Dept. of Biotechnology and Biosciences, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- SYSBE-IT/SYSBIO Centre of Systems Biology, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Lorenzo Rovida
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Davide Maspero
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Irene Sala
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Luca Rosato
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Marzia Di Filippo
- SYSBE-IT/SYSBIO Centre of Systems Biology, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- Department of Statistics and Quantitative Methods, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Dario Pescini
- SYSBE-IT/SYSBIO Centre of Systems Biology, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- Department of Statistics and Quantitative Methods, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Alex Graudenzi
- Institute of Molecular Bioimaging and Physiology, Consiglio Nazionale delle Ricerche (IBFM-CNR), Segrate, Milan, Italy
| | - Marco Antoniotti
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
| | - Giancarlo Mauri
- Dept. of Informatics, Systems and Communication, Universitá degli Studi di Milano-Bicocca, Milan, Italy
- SYSBE-IT/SYSBIO Centre of Systems Biology, Universitá degli Studi di Milano-Bicocca, Milan, Italy
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