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Meijer NWF, Zwakenberg S, Gerrits J, Westland D, Ardisasmita AI, Fuchs SA, Verhoeven-Duif NM, Jans JJM, Zwartkruis FJT. Direct Infusion Mass Spectrometry to Rapidly Map Metabolic Flux of Substrates Labeled with Stable Isotopes. Metabolites 2024; 14:246. [PMID: 38786724 PMCID: PMC11122925 DOI: 10.3390/metabo14050246] [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: 03/11/2024] [Revised: 04/16/2024] [Accepted: 04/20/2024] [Indexed: 05/25/2024] Open
Abstract
Direct infusion-high-resolution mass spectrometry (DI-HRMS) allows for rapid profiling of complex mixtures of metabolites in blood, cerebrospinal fluid, tissue samples and cultured cells. Here, we present a DI-HRMS method suitable for the rapid determination of metabolic fluxes of isotopically labeled substrates in cultured cells and organoids. We adapted an automated annotation pipeline by selecting labeled adducts that best represent the majority of 13C and/or 15N-labeled glycolytic and tricarboxylic acid cycle intermediates as well as a number of their derivatives. Furthermore, valine, leucine and several of their degradation products were included. We show that DI-HRMS can determine anticipated and unanticipated alterations in metabolic fluxes along these pathways that result from the genetic alteration of single metabolic enzymes, including pyruvate dehydrogenase (PDHA1) and glutaminase (GLS). In addition, it can precisely pinpoint metabolic adaptations to the loss of methylmalonyl-CoA mutase in patient-derived liver organoids. Our results highlight the power of DI-HRMS in combination with stable isotopically labeled compounds as an efficient screening method for fluxomics.
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Affiliation(s)
- Nils W. F. Meijer
- Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands; (N.W.F.M.); (N.M.V.-D.)
| | - Susan Zwakenberg
- Center for Molecular Medicine, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands (D.W.); (F.J.T.Z.)
| | - Johan Gerrits
- Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands; (N.W.F.M.); (N.M.V.-D.)
| | - Denise Westland
- Center for Molecular Medicine, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands (D.W.); (F.J.T.Z.)
| | - Arif I. Ardisasmita
- Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands; (N.W.F.M.); (N.M.V.-D.)
| | - Sabine A. Fuchs
- Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands; (N.W.F.M.); (N.M.V.-D.)
| | - Nanda M. Verhoeven-Duif
- Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands; (N.W.F.M.); (N.M.V.-D.)
| | - Judith J. M. Jans
- Department of Genetics, Section Metabolic Diagnostics, University Medical Center Utrecht, Lundlaan 6, 3584 EA Utrecht, The Netherlands; (N.W.F.M.); (N.M.V.-D.)
| | - Fried J. T. Zwartkruis
- Center for Molecular Medicine, University Medical Center Utrecht, Universiteitsweg 100, 3584 CG Utrecht, The Netherlands (D.W.); (F.J.T.Z.)
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2
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Yang J, Bahcecioglu G, Ronan G, Zorlutuna P. Aged breast matrix bound vesicles promote breast cancer invasiveness. Biomaterials 2024; 306:122493. [PMID: 38330741 PMCID: PMC11202350 DOI: 10.1016/j.biomaterials.2024.122493] [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: 01/19/2024] [Accepted: 01/25/2024] [Indexed: 02/10/2024]
Abstract
Aging is one of the inherent risk factors for breast cancer. Although the influence of age-related cellular alterations on breast cancer development has been extensively explored, little is known about the alterations in the aging breast tissue microenvironment, specifically the extracellular matrix (ECM). Here, for the first time in literature, we have identified tissue resident matrix bound vesicles (MBVs) within the healthy mouse breast ECM, investigated and compared their characteristics in young and aged healthy breast tissues, and studied the effects of these MBVs on normal (KTB21) and cancerous (MDA-MB-231) human mammary epithelial cells with respect to the tissue age that they are extracted from. Using vesicle labeling technology, we were able to visualize cellular uptake of the MBVs directly from the native decellularized tissue sections, showing that these MBVs have regulatory roles in the tissue microenvironment. We mimicked the ECM by embedding the MBVs in collagen gels, and showed that MBVs could be taken up by the cells. The miRNA and cytokine profiling showed that MBVs shifted towards a more tumorigenic and invasive phenotype with age, as evidenced by the more pronounced presence of cancer-associated cytokines, and higher expression levels of oncomiRs miR-10b, miR-30e, and miR-210 in MBVs isolated from aged mice. When treated with MBVs or these upregulated factors, KTB21 and MDA-MB-231 cells showed significantly higher motility and invasion compared to untreated controls. Treatment of cells with a cocktail of miRNAs (miR-10b, miR-30e, and miR-210) or with the agonist of adiponectin (AdipoRon), which both were enriched in the aged MBVs, recapitulated the effect of aged MBVs on cells. This study shows for the first time that the MBVs have a regulatory role in the tissue microenvironment and that the MBV contents change towards cancer-promoting upon aging. Studying the effects of MBVs and their cargos on cellular behavior could lead to a better understanding of the critical roles of MBVs played in breast cancer progression and metastasis.
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Affiliation(s)
- Jun Yang
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA.
| | - Gokhan Bahcecioglu
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA; Harper Cancer Research Institute, University of Notre Dame, Notre Dame, 46556, USA.
| | - George Ronan
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA; Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN, 46556, USA.
| | - Pinar Zorlutuna
- Department of Chemical and Biomolecular Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA; Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN, 46556, USA; Harper Cancer Research Institute, University of Notre Dame, Notre Dame, 46556, USA; Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN, 46556, USA.
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3
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Shen Y, Dinh HV, Cruz ER, Chen Z, Bartman CR, Xiao T, Call CM, Ryseck RP, Pratas J, Weilandt D, Baron H, Subramanian A, Fatma Z, Wu ZY, Dwaraknath S, Hendry JI, Tran VG, Yang L, Yoshikuni Y, Zhao H, Maranas CD, Wühr M, Rabinowitz JD. Mitochondrial ATP generation is more proteome efficient than glycolysis. Nat Chem Biol 2024:10.1038/s41589-024-01571-y. [PMID: 38448734 DOI: 10.1038/s41589-024-01571-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 02/05/2024] [Indexed: 03/08/2024]
Abstract
Metabolic efficiency profoundly influences organismal fitness. Nonphotosynthetic organisms, from yeast to mammals, derive usable energy primarily through glycolysis and respiration. Although respiration is more energy efficient, some cells favor glycolysis even when oxygen is available (aerobic glycolysis, Warburg effect). A leading explanation is that glycolysis is more efficient in terms of ATP production per unit mass of protein (that is, faster). Through quantitative flux analysis and proteomics, we find, however, that mitochondrial respiration is actually more proteome efficient than aerobic glycolysis. This is shown across yeast strains, T cells, cancer cells, and tissues and tumors in vivo. Instead of aerobic glycolysis being valuable for fast ATP production, it correlates with high glycolytic protein expression, which promotes hypoxic growth. Aerobic glycolytic yeasts do not excel at aerobic growth but outgrow respiratory cells during oxygen limitation. We accordingly propose that aerobic glycolysis emerges from cells maintaining a proteome conducive to both aerobic and hypoxic growth.
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Affiliation(s)
- Yihui Shen
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Hoang V Dinh
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Edward R Cruz
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Zihong Chen
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton, NJ, USA
| | - Caroline R Bartman
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton, NJ, USA
| | - Tianxia Xiao
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Catherine M Call
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Rolf-Peter Ryseck
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Jimmy Pratas
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Daniel Weilandt
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Heide Baron
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Arjuna Subramanian
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Zia Fatma
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Zong-Yen Wu
- US Department of Energy Joint Genome Institute and Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Sudharsan Dwaraknath
- US Department of Energy Joint Genome Institute and Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - John I Hendry
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Vinh G Tran
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Lifeng Yang
- Department of Chemistry, Princeton University, Princeton, NJ, USA
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Yasuo Yoshikuni
- US Department of Energy Joint Genome Institute and Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Huimin Zhao
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Martin Wühr
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
| | - Joshua D Rabinowitz
- Department of Chemistry, Princeton University, Princeton, NJ, USA.
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
- Ludwig Institute for Cancer Research, Princeton Branch, Princeton, NJ, USA.
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4
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Barata T, Pereira V, Pires das Neves R, Rocha M. Reconstruction of cell-specific models capturing the influence of metabolism on DNA methylation in cancer. Comput Biol Med 2024; 170:108052. [PMID: 38308868 DOI: 10.1016/j.compbiomed.2024.108052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 01/18/2024] [Accepted: 01/26/2024] [Indexed: 02/05/2024]
Abstract
The imbalance of epigenetic regulatory mechanisms such as DNA methylation, which can promote aberrant gene expression profiles without affecting the DNA sequence, may cause the deregulation of signaling, regulatory, and metabolic processes, contributing to a cancerous phenotype. Since some metabolites are substrates and cofactors of epigenetic regulators, their availability can be affected by characteristic cancer cell metabolic shifts, feeding cancer onset and progression through epigenetic deregulation. Hence, there is a need to study the influence of cancer metabolic reprogramming in DNA methylation to design new effective treatments. In this study, a generic Genome-Scale Metabolic Model (GSMM) of a human cell, integrating DNA methylation or demethylation reactions, was obtained and used for the reconstruction of Genome-Scale Metabolic Models enhanced with Enzymatic Constraints using Kinetic and Omics data (GECKOs) of 31 cancer cell lines. Furthermore, cell-line-specific DNA methylation levels were included in the models, as coefficients of a DNA composition pseudo-reaction, to depict the influence of metabolism over global DNA methylation in each of the cancer cell lines. Flux simulations demonstrated the ability of these models to provide simulated fluxes of exchange reactions similar to the equivalent experimentally measured uptake/secretion rates and to make good functional predictions. In addition, simulations found metabolic pathways, reactions and enzymes directly or inversely associated with the gene promoter methylation. Two potential candidates for targeted cancer epigenetic therapy were identified.
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Affiliation(s)
- Tânia Barata
- CNC - Center for Neuroscience and Cell Biology, University of Coimbra, 3004-517 Coimbra, Portugal; CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-517 Coimbra, Portugal
| | - Vítor Pereira
- Centre of Biological Engineering, University of Minho - Campus de Gualtar, Braga, Portugal; LABBELS - Associate Laboratory, Braga/Guimarães, Portugal
| | - Ricardo Pires das Neves
- CNC - Center for Neuroscience and Cell Biology, University of Coimbra, 3004-517 Coimbra, Portugal; CIBB - Centre for Innovative Biomedicine and Biotechnology, University of Coimbra, 3004-517 Coimbra, Portugal; IIIUC-Institute of Interdisciplinary Research, University of Coimbra, 3030-789 Coimbra, Portugal
| | - Miguel Rocha
- Centre of Biological Engineering, University of Minho - Campus de Gualtar, Braga, Portugal; LABBELS - Associate Laboratory, Braga/Guimarães, Portugal; Department of Informatics, University of Minho, Portugal.
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5
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Tan ML, Jenkins-Johnston N, Huang S, Schutrum B, Vadhin S, Adhikari A, Williams RM, Zipfel WR, Lammerding J, Varner JD, Fischbach C. Endothelial cells metabolically regulate breast cancer invasion toward a microvessel. APL Bioeng 2023; 7:046116. [PMID: 38058993 PMCID: PMC10697723 DOI: 10.1063/5.0171109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 11/06/2023] [Indexed: 12/08/2023] Open
Abstract
Breast cancer metastasis is initiated by invasion of tumor cells into the collagen type I-rich stroma to reach adjacent blood vessels. Prior work has identified that metabolic plasticity is a key requirement of tumor cell invasion into collagen. However, it remains largely unclear how blood vessels affect this relationship. Here, we developed a microfluidic platform to analyze how tumor cells invade collagen in the presence and absence of a microvascular channel. We demonstrate that endothelial cells secrete pro-migratory factors that direct tumor cell invasion toward the microvessel. Analysis of tumor cell metabolism using metabolic imaging, metabolomics, and computational flux balance analysis revealed that these changes are accompanied by increased rates of glycolysis and oxygen consumption caused by broad alterations of glucose metabolism. Indeed, restricting glucose availability decreased endothelial cell-induced tumor cell invasion. Our results suggest that endothelial cells promote tumor invasion into the stroma due, in part, to reprogramming tumor cell metabolism.
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Affiliation(s)
- Matthew L. Tan
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York 14853, USA
| | - Niaa Jenkins-Johnston
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York 14853, USA
| | - Sarah Huang
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, USA
| | - Brittany Schutrum
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York 14853, USA
| | - Sandra Vadhin
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, USA
| | - Abhinav Adhikari
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, USA
| | - Rebecca M. Williams
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York 14853, USA
| | - Warren R. Zipfel
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York 14853, USA
| | - Jan Lammerding
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York 14853, USA
| | - Jeffrey D. Varner
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York 14853, USA
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Alberghina L. The Warburg Effect Explained: Integration of Enhanced Glycolysis with Heterogeneous Mitochondria to Promote Cancer Cell Proliferation. Int J Mol Sci 2023; 24:15787. [PMID: 37958775 PMCID: PMC10648413 DOI: 10.3390/ijms242115787] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 10/19/2023] [Accepted: 10/25/2023] [Indexed: 11/15/2023] Open
Abstract
The Warburg effect is the long-standing riddle of cancer biology. How does aerobic glycolysis, inefficient in producing ATP, confer a growth advantage to cancer cells? A new evaluation of a large set of literature findings covering the Warburg effect and its yeast counterpart, the Crabtree effect, led to an innovative working hypothesis presented here. It holds that enhanced glycolysis partially inactivates oxidative phosphorylation to induce functional rewiring of a set of TCA cycle enzymes to generate new non-canonical metabolic pathways that sustain faster growth rates. The hypothesis has been structured by constructing two metabolic maps, one for cancer metabolism and the other for the yeast Crabtree effect. New lines of investigation, suggested by these maps, are discussed as instrumental in leading toward a better understanding of cancer biology in order to allow the development of more efficient metabolism-targeted anticancer drugs.
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Affiliation(s)
- Lilia Alberghina
- Centre of Systems Biology, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy
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7
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Khilwani R, Singh S. Systems Biology and Cytokines Potential Role in Lung Cancer Immunotherapy Targeting Autophagic Axis. Biomedicines 2023; 11:2706. [PMID: 37893079 PMCID: PMC10604646 DOI: 10.3390/biomedicines11102706] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 10/29/2023] Open
Abstract
Lung cancer accounts for the highest number of deaths among men and women worldwide. Although extensive therapies, either alone or in conjunction with some specific drugs, continue to be the principal regimen for evolving lung cancer, significant improvements are still needed to understand the inherent biology behind progressive inflammation and its detection. Unfortunately, despite every advancement in its treatment, lung cancer patients display different growth mechanisms and continue to die at significant rates. Autophagy, which is a physiological defense mechanism, serves to meet the energy demands of nutrient-deprived cancer cells and sustain the tumor cells under stressed conditions. In contrast, autophagy is believed to play a dual role during different stages of tumorigenesis. During early stages, it acts as a tumor suppressor, degrading oncogenic proteins; however, during later stages, autophagy supports tumor cell survival by minimizing stress in the tumor microenvironment. The pivotal role of the IL6-IL17-IL23 signaling axis has been observed to trigger autophagic events in lung cancer patients. Since the obvious roles of autophagy are a result of different immune signaling cascades, systems biology can be an effective tool to understand these interconnections and enhance cancer treatment and immunotherapy. In this review, we focus on how systems biology can be exploited to target autophagic processes that resolve inflammatory responses and contribute to better treatment in carcinogenesis.
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Affiliation(s)
| | - Shailza Singh
- Systems Medicine Laboratory, National Centre for Cell Science, SPPU Campus, Ganeshkhind Road, Pune 411007, India;
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8
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Zhao J, Lan G. TFAP2A activates HMGA1 to promote glycolysis and lung adenocarcinoma progression. Pathol Res Pract 2023; 249:154759. [PMID: 37586214 DOI: 10.1016/j.prp.2023.154759] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/03/2023] [Accepted: 08/08/2023] [Indexed: 08/18/2023]
Abstract
BACKGROUND Lung cancer is the most common cancer in the world. High Mobility Group AT-Hook 1 (HMGA1) is found to be associated with the glycolytic pathway in a variety of cancers, and abnormal glycolysis function is one of the important characteristics of cancer cells. Therefore, this paper discusses the effect of HMGA1 on glycolysis of lung adenocarcinoma (LUAD) cells METHODS: The mRNA expression data were downloaded from TCGA-LUAD database. Groups were set according to the median expression of HMGA1, followed by GSEA enrichment analysis. The upstream transcriptional regulators of HMGA1 were predicted by bioinformatics. The correlation between HMGA1 and Transcription Factor AP-2 Alpha (TFAP2A) and their expression in LUAD tissues were analyzed as well. mRNA expression levels of HMGA1 and TFAP2A were detected by qRT-PCR. The binding of HMGA1 and TFAP2A was demonstrated by ChIP and dual luciferase reporter assays. Cell function experiments were utilized to assay proliferation, apoptosis, glycolysis ability of LUAD cells, and glycolysis-related protein expression in each treatment group. RESULTS HMGA1 was highly expressed in LUAD patients' tissues and enriched in the glycolytic pathway. Additionally, silencing HMGA1 markedly hampered cell proliferation and glycolysis, and promoted cell apoptosis. The upstream transcriptional regulator TFAP2A was predicted to be highly expressed in LUAD. ChIP and dual luciferase reporter assays confirmed the targeted relationship between HMGA1 and TFAP2A. Cell rescue assay confirmed that TFAP2A promoted glycolysis and LUAD progression by activating HMGA1. CONCLUSION TFAP2A promotes glycolysis, proliferation and hampers apoptosis of LUAD cells by stimulating HMGA1. Hence, TFAP2A/HMGA1 may be a feasible therapeutic target for LUAD. AVAILABILITY OF DATA AND MATERIALS All the data within this manuscript could be gotten from corresponding author at reasonable request.
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Affiliation(s)
- Junjie Zhao
- Department of Thoracic Surgery, The First Hospital of Jiaxing, Affiliated Hospital of Jiaxing University, Jiaxing, Zhejiang Province 314000, China
| | - Gang Lan
- Department of Thoracic Surgery, Zhejiang Rongjun Hospital, Jiaxing, Zhejiang Province 314000, China.
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9
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Griesemer M, Navid A. Uses of Multi-Objective Flux Analysis for Optimization of Microbial Production of Secondary Metabolites. Microorganisms 2023; 11:2149. [PMID: 37763993 PMCID: PMC10536367 DOI: 10.3390/microorganisms11092149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/07/2023] [Accepted: 08/16/2023] [Indexed: 09/29/2023] Open
Abstract
Secondary metabolites are not essential for the growth of microorganisms, but they play a critical role in how microbes interact with their surroundings. In addition to this important ecological role, secondary metabolites also have a variety of agricultural, medicinal, and industrial uses, and thus the examination of secondary metabolism of plants and microbes is a growing scientific field. While the chemical production of certain secondary metabolites is possible, industrial-scale microbial production is a green and economically attractive alternative. This is even more true, given the advances in bioengineering that allow us to alter the workings of microbes in order to increase their production of compounds of interest. This type of engineering requires detailed knowledge of the "chassis" organism's metabolism. Since the resources and the catalytic capacity of enzymes in microbes is finite, it is important to examine the tradeoffs between various bioprocesses in an engineered system and alter its working in a manner that minimally perturbs the robustness of the system while allowing for the maximum production of a product of interest. The in silico multi-objective analysis of metabolism using genome-scale models is an ideal method for such examinations.
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Affiliation(s)
| | - Ali Navid
- Lawrence Livermore National Laboratory, Biosciences & Biotechnology Division, Physical & Life Sciences Directorate, Livermore, CA 94550, USA
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10
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Huang Y, Mohanty V, Dede M, Tsai K, Daher M, Li L, Rezvani K, Chen K. Characterizing cancer metabolism from bulk and single-cell RNA-seq data using METAFlux. Nat Commun 2023; 14:4883. [PMID: 37573313 PMCID: PMC10423258 DOI: 10.1038/s41467-023-40457-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 07/26/2023] [Indexed: 08/14/2023] Open
Abstract
Cells often alter metabolic strategies under nutrient-deprived conditions to support their survival and growth. Characterizing metabolic reprogramming in the tumor microenvironment (TME) is of emerging importance in cancer research and patient care. However, recent technologies only measure a subset of metabolites and cannot provide in situ measurements. Computational methods such as flux balance analysis (FBA) have been developed to estimate metabolic flux from bulk RNA-seq data and can potentially be extended to single-cell RNA-seq (scRNA-seq) data. However, it is unclear how reliable current methods are, particularly in TME characterization. Here, we present a computational framework METAFlux (METAbolic Flux balance analysis) to infer metabolic fluxes from bulk or single-cell transcriptomic data. Large-scale experiments using cell-lines, the cancer genome atlas (TCGA), and scRNA-seq data obtained from diverse cancer and immunotherapeutic contexts, including CAR-NK cell therapy, have validated METAFlux's capability to characterize metabolic heterogeneity and metabolic interaction amongst cell types.
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Affiliation(s)
- Yuefan Huang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
- Department of Biostatistics & Data Science, School of Public Health, The University of Texas Health Science Center at Houston (UTHealth), Houston, TX, 77030, USA
| | - Vakul Mohanty
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Merve Dede
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Kyle Tsai
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - May Daher
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Li Li
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Katayoun Rezvani
- Department of Stem Cell Transplantation and Cellular Therapy, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, 77030, USA.
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11
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Shimpi AA, Tan ML, Vilkhovoy M, Dai D, Roberts LM, Kuo J, Huang L, Varner JD, Paszek M, Fischbach C. Convergent Approaches to Delineate the Metabolic Regulation of Tumor Invasion by Hyaluronic Acid Biosynthesis. Adv Healthc Mater 2023; 12:e2202224. [PMID: 36479976 PMCID: PMC10238572 DOI: 10.1002/adhm.202202224] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/21/2022] [Indexed: 12/13/2022]
Abstract
Metastasis is the leading cause of breast cancer-related deaths and is often driven by invasion and cancer-stem like cells (CSCs). Both the CSC phenotype and invasion are associated with increased hyaluronic acid (HA) production. How these independent observations are connected, and which role metabolism plays in this process, remains unclear due to the lack of convergent approaches integrating engineered model systems, computational tools, and cancer biology. Using microfluidic invasion models, metabolomics, computational flux balance analysis, and bioinformatic analysis of patient data, the functional links between the stem-like, invasive, and metabolic phenotype of breast cancer cells as a function of HA biosynthesis are investigated. These results suggest that CSCs are more invasive than non-CSCs and that broad metabolic changes caused by overproduction of HA play a role in this process. Accordingly, overexpression of hyaluronic acid synthases (HAS) 2 or 3 induces a metabolic phenotype that promotes cancer cell stemness and invasion in vitro and upregulates a transcriptomic signature predictive of increased invasion and worse patient survival. This study suggests that HA overproduction leads to metabolic adaptations to satisfy the energy demands for 3D invasion of breast CSCs highlighting the importance of engineered model systems and multidisciplinary approaches in cancer research.
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Affiliation(s)
- Adrian A. Shimpi
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, 14853, USA
| | - Matthew L. Tan
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, 14853, USA
| | - Michael Vilkhovoy
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, 14853, USA
| | - David Dai
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, 14853, USA
| | - L. Monet Roberts
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, 14853, USA
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, 14853, USA
| | - Joe Kuo
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, 14853, USA
| | - Lingting Huang
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, 14853, USA
| | - Jeffrey D. Varner
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, 14853, USA
| | - Matthew Paszek
- Robert Frederick Smith School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, New York, 14853, USA
- Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, New York, 14853, USA
| | - Claudia Fischbach
- Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, 14853, USA
- Kavli Institute at Cornell for Nanoscale Science, Cornell University, Ithaca, New York, 14853, USA
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12
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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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13
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The functions and molecular mechanisms of Tribbles homolog 3 (TRIB3) implicated in the pathophysiology of cancer. Int Immunopharmacol 2023; 114:109581. [PMID: 36527874 DOI: 10.1016/j.intimp.2022.109581] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 11/21/2022] [Accepted: 12/08/2022] [Indexed: 12/23/2022]
Abstract
Currently, cancer ranks as the second leading cause of death worldwide, and at the same time, the burden of cancer continues to increase. The underlying molecular pathways involved in the initiation and development of cancer are the subject of considerable research worldwide. Further understanding of these pathways may lead to new cancer treatments. Growing data suggest that Tribble's homolog 3 (TRIB3) is essential in oncogenesis in many types of cancer. The mammalian tribbles family's proteins regulate various cellular and physiological functions, such as the cell cycle, stress response, signal transduction, propagation, development, differentiation, immunity, inflammatory processes, and metabolism. To exert their activities, Tribbles proteins must alter key signaling pathways, including the mitogen-activated protein kinase (MAPK) and phosphatidylinositol 3 kinase (PI3K)/AKT pathways. Recent evidence supports that TRIB3 dysregulation has been linked to various diseases, including tumor development and chemoresistance. It has been speculated that TRIB3 may either promote or inhibit the onset and development of cancer. However, it is still unclear how TRIB3 performs this dual function in cancer. In this review, we present and discuss the most recent data on the role of TRIB3 in cancer pathophysiology and chemoresistance. Furthermore, we describe in detail the molecular mechanism TRIB3 regulates in cancer.
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14
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Gopalakrishnan S, Joshi CJ, Valderrama-Gómez MÁ, Icten E, Rolandi P, Johnson W, Kontoravdi C, Lewis NE. Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data. Metab Eng 2023; 75:181-191. [PMID: 36566974 PMCID: PMC10258867 DOI: 10.1016/j.ymben.2022.12.003] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 12/01/2022] [Accepted: 12/17/2022] [Indexed: 12/24/2022]
Abstract
Genome-scale metabolic models comprehensively describe an organism's metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organism's phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.
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Affiliation(s)
| | - Chintan J Joshi
- Department of Pediatrics, University of California San Diego, United States
| | | | - Elcin Icten
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Pablo Rolandi
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - William Johnson
- Digital Integration and Predictive Technologies, Amgen Inc, United States
| | - Cleo Kontoravdi
- Department of Chemical Engineering, Imperial College London, UK
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego, United States; Department of Bioengineering, University of California San Diego, United States.
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15
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Wang Y, Stancliffe E, Fowle-Grider R, Wang R, Wang C, Schwaiger-Haber M, Shriver LP, Patti GJ. Saturation of the mitochondrial NADH shuttles drives aerobic glycolysis in proliferating cells. Mol Cell 2022; 82:3270-3283.e9. [PMID: 35973426 PMCID: PMC10134440 DOI: 10.1016/j.molcel.2022.07.007] [Citation(s) in RCA: 65] [Impact Index Per Article: 32.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 05/12/2022] [Accepted: 07/14/2022] [Indexed: 12/21/2022]
Abstract
Proliferating cells exhibit a metabolic phenotype known as "aerobic glycolysis," which is characterized by an elevated rate of glucose fermentation to lactate irrespective of oxygen availability. Although several theories have been proposed, a rationalization for why proliferating cells seemingly waste glucose carbon by excreting it as lactate remains elusive. Using the NCI-60 cell lines, we determined that lactate excretion is strongly correlated with the activity of mitochondrial NADH shuttles, but not proliferation. Quantifying the fluxes of the malate-aspartate shuttle (MAS), the glycerol 3-phosphate shuttle (G3PS), and lactate dehydrogenase under various conditions demonstrated that proliferating cells primarily transform glucose to lactate when glycolysis outpaces the mitochondrial NADH shuttles. Increasing mitochondrial NADH shuttle fluxes decreased glucose fermentation but did not reduce the proliferation rate. Our results reveal that glucose fermentation, a hallmark of cancer, is a secondary consequence of MAS and G3PS saturation rather than a unique metabolic driver of cellular proliferation.
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Affiliation(s)
- Yahui Wang
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA; Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Ethan Stancliffe
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Ronald Fowle-Grider
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Rencheng Wang
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA; Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Cheng Wang
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA; Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Michaela Schwaiger-Haber
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA; Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Leah P Shriver
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO 63130, USA
| | - Gary J Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Medicine, Washington University in St. Louis, St. Louis, MO 63130, USA; Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO 63130, USA; Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO 63130, USA.
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16
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Kishk A, Pacheco MP, Heurtaux T, Sinkkonen L, Pang J, Fritah S, Niclou SP, Sauter T. Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases. Cells 2022; 11:cells11162486. [PMID: 36010563 PMCID: PMC9406599 DOI: 10.3390/cells11162486] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/28/2022] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.
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Affiliation(s)
- Ali Kishk
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Tony Heurtaux
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
- Luxembourg Center of Neuropathology, L-3555 Dudelange, Luxembourg
| | - Lasse Sinkkonen
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Jun Pang
- Department of Computer Science, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg
| | - Sabrina Fritah
- NORLUX Neuro-Oncology Laboratory, Luxembourg Institute of Health, Department of Cancer Research, L-1526 Luxembourg, Luxembourg
| | - Simone P. Niclou
- NORLUX Neuro-Oncology Laboratory, Luxembourg Institute of Health, Department of Cancer Research, L-1526 Luxembourg, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
- Correspondence:
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17
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Lapin A, Perfahl H, Jain HV, Reuss M. Integrating a dynamic central metabolism model of cancer cells with a hybrid 3D multiscale model for vascular hepatocellular carcinoma growth. Sci Rep 2022; 12:12373. [PMID: 35858953 PMCID: PMC9300625 DOI: 10.1038/s41598-022-15767-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 06/29/2022] [Indexed: 11/09/2022] Open
Abstract
We develop here a novel modelling approach with the aim of closing the conceptual gap between tumour-level metabolic processes and the metabolic processes occurring in individual cancer cells. In particular, the metabolism in hepatocellular carcinoma derived cell lines (HEPG2 cells) has been well characterized but implementations of multiscale models integrating this known metabolism have not been previously reported. We therefore extend a previously published multiscale model of vascular tumour growth, and integrate it with an experimentally verified network of central metabolism in HEPG2 cells. This resultant combined model links spatially heterogeneous vascular tumour growth with known metabolic networks within tumour cells and accounts for blood flow, angiogenesis, vascular remodelling and nutrient/growth factor transport within a growing tumour, as well as the movement of, and interactions between normal and cancer cells. Model simulations report for the first time, predictions of spatially resolved time courses of core metabolites in HEPG2 cells. These simulations can be performed at a sufficient scale to incorporate clinically relevant features of different tumour systems using reasonable computational resources. Our results predict larger than expected temporal and spatial heterogeneity in the intracellular concentrations of glucose, oxygen, lactate pyruvate, f16bp and Acetyl-CoA. The integrated multiscale model developed here provides an ideal quantitative framework in which to study the relationship between dosage, timing, and scheduling of anti-neoplastic agents and the physiological effects of tumour metabolism at the cellular level. Such models, therefore, have the potential to inform treatment decisions when drug response is dependent on the metabolic state of individual cancer cells.
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Affiliation(s)
- Alexey Lapin
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany.,Institute of Chemical Process Engineering, University Stuttgart, Stuttgart, Germany
| | - Holger Perfahl
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany
| | - Harsh Vardhan Jain
- Department of Mathematics and Statistics, University of Minnesota Duluth, Duluth, MN, USA
| | - Matthias Reuss
- Stuttgart Research Center Systems Biology, University Stuttgart, Stuttgart, Germany.
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18
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Metabolic modeling-based drug repurposing in Glioblastoma. Sci Rep 2022; 12:11189. [PMID: 35778411 PMCID: PMC9249780 DOI: 10.1038/s41598-022-14721-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 06/10/2022] [Indexed: 11/16/2022] Open
Abstract
The manifestation of intra- and inter-tumor heterogeneity hinders the development of ubiquitous cancer treatments, thus requiring a tailored therapy for each cancer type. Specifically, the reprogramming of cellular metabolism has been identified as a source of potential drug targets. Drug discovery is a long and resource-demanding process aiming at identifying and testing compounds early in the drug development pipeline. While drug repurposing efforts (i.e., inspecting readily available approved drugs) can be supported by a mechanistic rationale, strategies to further reduce and prioritize the list of potential candidates are still needed to facilitate feasible studies. Although a variety of ‘omics’ data are widely gathered, a standard integration method with modeling approaches is lacking. For instance, flux balance analysis is a metabolic modeling technique that mainly relies on the stoichiometry of the metabolic network. However, exploring the network’s topology typically neglects biologically relevant information. Here we introduce Transcriptomics-Informed Stoichiometric Modelling And Network analysis (TISMAN) in a recombinant innovation manner, allowing identification and validation of genes as targets for drug repurposing using glioblastoma as an exemplar.
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19
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Li Z, Ji BW, Dixit PD, Tchourine K, Lien EC, Hosios AM, Abbott KL, Rutter JC, Westermark AM, Gorodetsky EF, Sullivan LB, Vander Heiden MG, Vitkup D. Cancer cells depend on environmental lipids for proliferation when electron acceptors are limited. Nat Metab 2022; 4:711-723. [PMID: 35739397 DOI: 10.1038/s42255-022-00588-8] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 05/17/2022] [Indexed: 01/31/2023]
Abstract
Production of oxidized biomass, which requires regeneration of the cofactor NAD+, can be a proliferation bottleneck that is influenced by environmental conditions. However, a comprehensive quantitative understanding of metabolic processes that may be affected by NAD+ deficiency is currently missing. Here, we show that de novo lipid biosynthesis can impose a substantial NAD+ consumption cost in proliferating cancer cells. When electron acceptors are limited, environmental lipids become crucial for proliferation because NAD+ is required to generate precursors for fatty acid biosynthesis. We find that both oxidative and even net reductive pathways for lipogenic citrate synthesis are gated by reactions that depend on NAD+ availability. We also show that access to acetate can relieve lipid auxotrophy by bypassing the NAD+ consuming reactions. Gene expression analysis demonstrates that lipid biosynthesis strongly anti-correlates with expression of hypoxia markers across tumor types. Overall, our results define a requirement for oxidative metabolism to support biosynthetic reactions and provide a mechanistic explanation for cancer cell dependence on lipid uptake in electron acceptor-limited conditions, such as hypoxia.
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Affiliation(s)
- Zhaoqi Li
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Brian W Ji
- Department of Systems Biology, Columbia University, New York, NY, USA
- Physician-Scientist Training Pathway, Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Purushottam D Dixit
- Department of Systems Biology, Columbia University, New York, NY, USA
- Department of Physics, University of Florida, Gainesville, FL, USA
- University of Florida Genetics Institute, University of Florida, Gainesville, FL, USA
- Department of Chemical Engineering, University of Florida, Gainesville, FL, USA
- University of Florida Cancer Center, University of Florida, Gainesville, FL, USA
| | | | - Evan C Lien
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Aaron M Hosios
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Keene L Abbott
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Justine C Rutter
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Health Sciences and Technology (HST) and Harvard/MIT MD-PhD Program, Harvard Medical School, Boston, MA, USA
| | - Anna M Westermark
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Elizabeth F Gorodetsky
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lucas B Sullivan
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Human Biology Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Matthew G Vander Heiden
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Dana-Farber Cancer Institute, Boston, MA, USA.
| | - Dennis Vitkup
- Department of Systems Biology, Columbia University, New York, NY, USA.
- Department of Biomedical Informatics, Columbia University, New York, NY, USA.
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20
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Kutay M, Gozuacik D, Çakır T. Cancer Recurrence and Omics: Metabolic Signatures of Cancer Dormancy Revealed by Transcriptome Mapping of Genome-Scale Networks. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:270-279. [PMID: 35394340 DOI: 10.1089/omi.2022.0008] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
A major problem in medicine and oncology is cancer recurrence through the activation of dormant cancer cells. A system scale examination of metabolic dysregulations associated with the cancer dormancy offers promise for the discovery of novel molecular targets for cancer precision medicine, and importantly, for the prevention of cancer recurrence. In this study, we mapped the total mRNA sequencing-based transcriptomic data from dormant cancer cell lines and nondormant cancer controls onto a human genome-scale metabolic network by using a graph-based approach, and two mass balance-based approaches with one based on reaction activity/inactivity and the other one on flux changes. The gene expression datasets were accessed from Gene Expression Omnibus (GSE83142 and GSE114012). This analysis included two diverse cancer types, a liquid and a solid cancer, namely, acute lymphoblastic leukemia and colorectal cancer. For the dormant cancer state, we observed changes in major adenosine triphosphate-producing pathways, including the citric acid cycle, oxidative phosphorylation, and glycolysis/gluconeogenesis, indicating a reprogramming in the metabolism of dormant cells away from Warburg-based energy metabolism. All three computational approaches unanimously predicted that folate metabolism, pyruvate metabolism, and glutamate metabolism, as well as valine/leucine/isoleucine metabolism are likely dysregulated in cancer dormancy. These findings provide new insights and molecular pathway targets on cancer dormancy, comprehensively catalog dormancy-associated metabolic pathways, and inform future research aimed at prevention of cancer recurrence in particular.
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Affiliation(s)
- Merve Kutay
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Devrim Gozuacik
- Koç University Research Center for Translational Medicine (KUTTAM) and Koc, University School of Medicine, Istanbul, Turkey
- Sabancı University Nanotechnology Research and Application Center (SUNUM), Istanbul, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
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21
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Weiskittel TM, Ung CY, Correia C, Zhang C, Li H. De novo individualized disease modules reveal the synthetic penetrance of genes and inform personalized treatment regimens. Genome Res 2021; 32:124-134. [PMID: 34876496 PMCID: PMC8744682 DOI: 10.1101/gr.275889.121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 11/30/2021] [Indexed: 12/04/2022]
Abstract
Current understandings of individual disease etiology and therapeutics are limited despite great need. To fill the gap, we propose a novel computational pipeline that collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease modules de novo, which enables us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients. We reveal that importance of the notorious cancer drivers TP53 and PIK3CA fluctuate widely across breast cancers and peak in tumors with distinct numbers of mutations and that rarely mutated genes such as XPO1 and PLEKHA1 have high disease module importance in specific individuals. Furthermore, individualized module disruption enables us to devise customized singular and combinatorial target therapies that were highly varied across patients, showing the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals.
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Affiliation(s)
- Taylor M Weiskittel
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA
| | - Choong Y Ung
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA
| | - Cristina Correia
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA
| | - Cheng Zhang
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA
| | - Hu Li
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA
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22
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Wang J, Delfarah A, Gelbach PE, Fong E, Macklin P, Mumenthaler SM, Graham NA, Finley SD. Elucidating tumor-stromal metabolic crosstalk in colorectal cancer through integration of constraint-based models and LC-MS metabolomics. Metab Eng 2021; 69:175-187. [PMID: 34838998 PMCID: PMC8818109 DOI: 10.1016/j.ymben.2021.11.006] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 10/07/2021] [Accepted: 11/09/2021] [Indexed: 12/28/2022]
Abstract
Colorectal cancer (CRC) is a major cause of morbidity and mortality in the United States. Tumor-stromal metabolic crosstalk in the tumor microenvironment promotes CRC development and progression, but exactly how stromal cells, in particular cancer-associated fibroblasts (CAFs), affect the metabolism of tumor cells remains unknown. Here we take a data-driven approach to investigate the metabolic interactions between CRC cells and CAFs, integrating constraint-based modeling and metabolomic profiling. Using metabolomics data, we perform unsteady-state parsimonious flux balance analysis to infer flux distributions for central carbon metabolism in CRC cells treated with or without CAF-conditioned media. We find that CAFs reprogram CRC metabolism through stimulation of glycolysis, the oxidative arm of the pentose phosphate pathway (PPP), and glutaminolysis, as well as inhibition of the tricarboxylic acid cycle. To identify potential therapeutic targets, we simulate enzyme knockouts and find that CAF-treated CRC cells are especially sensitive to inhibitions of hexokinase and glucose-6-phosphate, the rate limiting steps of glycolysis and oxidative PPP. Our work gives mechanistic insights into the metabolic interactions between CRC cells and CAFs and provides a framework for testing hypotheses towards CRC-targeted therapies.
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Affiliation(s)
- Junmin Wang
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Alireza Delfarah
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | - Patrick E Gelbach
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, 90089, USA
| | - Emma Fong
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA, 90064, USA
| | - Paul Macklin
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, 46202, USA
| | - Shannon M Mumenthaler
- Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA, 90064, USA; Division of Medical Oncology, Norris Comprehensive Cancer Center, University of Southern California, Los Angeles, CA, 90033, USA
| | - Nicholas A Graham
- Mork Family Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, CA, 90089, USA
| | - Stacey D Finley
- Department of Biomedical Engineering, 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; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA, 90089, USA.
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23
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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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24
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Moscardó García M, Pacheco M, Bintener T, Presta L, Sauter T. Importance of the biomass formulation for cancer metabolic modeling and drug prediction. iScience 2021; 24:103110. [PMID: 34622163 PMCID: PMC8482493 DOI: 10.1016/j.isci.2021.103110] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 07/27/2021] [Accepted: 09/08/2021] [Indexed: 11/22/2022] Open
Abstract
Genome-scale metabolic reconstructions include all known biochemical reactions occurring in a cell. A typical application is the prediction of potential drug targets for cancer treatment. The precision of these predictions relies on the definition of the objective function. Generally, the biomass reaction is used to illustrate the growth capacity of a cancer cell. Today, seven human biomass reactions can be identified in published metabolic models. The impact of these differences on the metabolic model predictions has not been explored in detail. We explored this impact on cancer metabolic model predictions and showed that the metabolite composition and the associated coefficients had a large impact on the growth rate prediction accuracy, whereas gene essentiality predictions were mainly affected by the metabolite composition. Our results demonstrate the importance of defining a consensus biomass reaction compatible with most human models, which would contribute to ensuring the reproducibility and consistency of the results. The definition of the biomass reaction is of utmost importance for model predictions Growth rate predictions are affected by metabolite composition and their coefficients Gene essentiality predictions are mainly affected by the metabolite composition Need to find a standard biomass reaction for reproducibility and consistency purposes
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Affiliation(s)
- María Moscardó García
- Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Maria Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Tamara Bintener
- Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Luana Presta
- Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, 4367 Esch-sur-Alzette, Luxembourg
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25
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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; 13:4130. [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
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
| | - Jürgen Zanghellini
- Department of Analytical Chemistry, University of Vienna, 1090 Vienna, Austria; (H.A.H.); (M.R.)
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26
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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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27
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Karottki KJLC, Hefzi H, Li S, Pedersen LE, Spahn PN, Joshi C, Ruckerbauer D, Bort JAH, Thomas A, Lee JS, Borth N, Lee GM, Kildegaard HF, Lewis NE. A metabolic CRISPR-Cas9 screen in Chinese hamster ovary cells identifies glutamine-sensitive genes. Metab Eng 2021; 66:114-122. [PMID: 33813034 DOI: 10.1016/j.ymben.2021.03.017] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 03/25/2021] [Accepted: 03/28/2021] [Indexed: 12/20/2022]
Abstract
Media and feed optimization have fueled many-fold improvements in mammalian biopharmaceutical production, but genome editing offers an emerging avenue for further enhancing cell metabolism and bioproduction. However, the complexity of metabolism, involving thousands of genes, makes it unclear which engineering strategies will result in desired traits. Here we present a comprehensive pooled CRISPR screen for CHO cell metabolism, including ~16,000 gRNAs against ~2500 metabolic enzymes and regulators. Using this screen, we identified a glutamine response network in CHO cells. Glutamine is particularly important since it is often over-fed to drive increased TCA cycle flux, but toxic ammonia may accumulate. With the screen we found one orphan glutamine-responsive gene with no clear connection to our network. Knockout of this novel and poorly characterized lipase, Abhd11, substantially increased growth in glutamine-free media by altering the regulation of the TCA cycle. Thus, the screen provides an invaluable targeted platform to comprehensively study genes involved in any metabolic trait, and elucidate novel regulators of metabolism.
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Affiliation(s)
| | - Hooman Hefzi
- The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, USA; Department of Pediatrics, University of California, San Diego, USA; Department of Bioengineering, University of California, San Diego, USA
| | - Songyuan Li
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Denmark
| | - Lasse Ebdrup Pedersen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Denmark
| | - Philipp N Spahn
- The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, USA; Department of Pediatrics, University of California, San Diego, USA
| | - Chintan Joshi
- The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, USA; Department of Pediatrics, University of California, San Diego, USA
| | - David Ruckerbauer
- Austrian Centre of Industrial Biotechnology, Vienna, Austria; University of Natural Resources and Life Sciences, Vienna, Austria
| | | | - Alex Thomas
- The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, USA
| | - Jae Seong Lee
- Department of Molecular Science and Technology, Ajou University, Suwon, 16499, Republic of Korea
| | - Nicole Borth
- Austrian Centre of Industrial Biotechnology, Vienna, Austria; University of Natural Resources and Life Sciences, Vienna, Austria
| | - Gyun Min Lee
- Department of Biological Sciences, Kaist, 291 Daehak-Ro, Yuseong-Gu, Daejeon, 305-701, Republic of Korea
| | | | - Nathan E Lewis
- The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, USA; Department of Pediatrics, University of California, San Diego, USA; Department of Bioengineering, University of California, San Diego, USA; National Biologics Facility, Technical University of Denmark, Denmark.
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28
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Mattei F, Andreone S, Mencattini A, De Ninno A, Businaro L, Martinelli E, Schiavoni G. Oncoimmunology Meets Organs-on-Chip. Front Mol Biosci 2021; 8:627454. [PMID: 33842539 PMCID: PMC8032996 DOI: 10.3389/fmolb.2021.627454] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/04/2021] [Indexed: 01/04/2023] Open
Abstract
Oncoimmunology represents a biomedical research discipline coined to study the roles of immune system in cancer progression with the aim of discovering novel strategies to arm it against the malignancy. Infiltration of immune cells within the tumor microenvironment is an early event that results in the establishment of a dynamic cross-talk. Here, immune cells sense antigenic cues to mount a specific anti-tumor response while cancer cells emanate inhibitory signals to dampen it. Animals models have led to giant steps in this research context, and several tools to investigate the effect of immune infiltration in the tumor microenvironment are currently available. However, the use of animals represents a challenge due to ethical issues and long duration of experiments. Organs-on-chip are innovative tools not only to study how cells derived from different organs interact with each other, but also to investigate on the crosstalk between immune cells and different types of cancer cells. In this review, we describe the state-of-the-art of microfluidics and the impact of OOC in the field of oncoimmunology underlining the importance of this system in the advancements on the complexity of tumor microenvironment.
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Affiliation(s)
- Fabrizio Mattei
- Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy
| | - Sara Andreone
- Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy
| | - Arianna Mencattini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.,Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, Rome, Italy
| | - Adele De Ninno
- Institute for Photonics and Nanotechnologies, Italian National Research Council, Rome, Italy
| | - Luca Businaro
- Institute for Photonics and Nanotechnologies, Italian National Research Council, Rome, Italy
| | - Eugenio Martinelli
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.,Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, Rome, Italy
| | - Giovanna Schiavoni
- Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy
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29
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Feuerecker B, Biechl P, Seidl C, Bruchertseifer F, Morgenstern A, Schwaiger M, Eisenreich W. Diverse metabolic response of cancer cells treated with a 213Bi-anti-EGFR-immunoconjugate. Sci Rep 2021; 11:6227. [PMID: 33737524 PMCID: PMC7973706 DOI: 10.1038/s41598-021-84421-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 02/15/2021] [Indexed: 12/14/2022] Open
Abstract
Evaluation of treatment response is among the major challenges in modern oncology. We herein used a monoclonal antibody targeting the EGF receptor (EGFR) labelled with the alpha emitter 213Bi (213Bi-anti-EGFR-MAb). EJ28Luc (bladder) and LN18 (glioma) cancer cells, both overexpressing EGFR, were incubated for 3 h with the radioimmunoconjugate. To assess the responses in the core carbon metabolism upon this treatment, these cancer cell lines were subsequently cultivated for 18 h in the presence of [U-13C6]glucose. 13C-enrichment and isotopologue profiles of key amino acids were monitored by gas chromatography–mass spectrometry (GC/MS), in order to monitor the impacts of the radionuclide-treatment upon glucose metabolism. In comparison to untreated controls, treatment of EJ28Luc cells with 213Bi-anti-EGFR-MAb resulted in a significantly decreased incorporation of 13C from [U-13C6]glucose into alanine, aspartate, glutamate, glycine, proline and serine. In sharp contrast, the same amino acids did not display less 13C-enrichments during treatment of the LN18 cells. The data indicate early treatment response of the bladder cancer cells, but not of the glioma cells though cell lines were killed following 213Bi-anti-EGFR-MAb treatment. The pilot study shows that the 13C-labelling approach is a valid tool to assess the responsiveness of cancer cells upon radionuclide-treatment in considerable metabolic detail.
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Affiliation(s)
- Benedikt Feuerecker
- Department of Nuclear Medicine, School of Medicine, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany. .,Deutsches Konsortium für translationale Krebsforschung (DKTK), Heidelberg, partnersite München and German Cancer Research Center (DKFZ), Heidelberg, Germany. .,Department of Radiology, School of Medicine, Technische Universität München, Munich, Germany.
| | - Philipp Biechl
- Department of Chemistry, Bavarian NMR Center-Structural Membrane Biochemistry, Technische Universität München, Garching, Germany
| | - Christof Seidl
- Department of Nuclear Medicine, School of Medicine, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Frank Bruchertseifer
- European Commission, Joint Research Centre, Directorate for Nuclear Safety and Security, Karlsruhe, Germany
| | - Alfred Morgenstern
- European Commission, Joint Research Centre, Directorate for Nuclear Safety and Security, Karlsruhe, Germany
| | - Markus Schwaiger
- Department of Nuclear Medicine, School of Medicine, Technische Universität München, Ismaninger Straße 22, 81675, Munich, Germany
| | - Wolfgang Eisenreich
- Department of Chemistry, Bavarian NMR Center-Structural Membrane Biochemistry, Technische Universität München, Garching, Germany
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30
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Dick JM. Water as a reactant in the differential expression of proteins in cancer. COMPUTATIONAL AND SYSTEMS ONCOLOGY 2021. [DOI: 10.1002/cso2.1007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Jeffrey M. Dick
- Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Ministry of Education School of Geosciences and Info‐Physics Central South University Changsha China
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31
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Immanuel SRC, Ghanate AD, Parmar DS, Yadav R, Uthup R, Panchagnula V, Raghunathan A. Integrated genetic and metabolic landscapes predict vulnerabilities of temozolomide resistant glioblastoma cells. NPJ Syst Biol Appl 2021; 7:2. [PMID: 33420045 PMCID: PMC7794364 DOI: 10.1038/s41540-020-00161-7] [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: 05/19/2020] [Accepted: 11/06/2020] [Indexed: 01/22/2023] Open
Abstract
Metabolic reprogramming and its molecular underpinnings are critical to unravel the duality of cancer cell function and chemo-resistance. Here, we use a constraints-based integrated approach to delineate the interplay between metabolism and epigenetics, hardwired in the genome, to shape temozolomide (TMZ) resistance. Differential metabolism was identified in response to TMZ at varying concentrations in both the resistant neurospheroidal (NSP) and the susceptible (U87MG) glioblastoma cell-lines. The genetic basis of this metabolic adaptation was characterized by whole exome sequencing that identified mutations in signaling pathway regulators of growth and energy metabolism. Remarkably, our integrated approach identified rewiring in glycolysis, TCA cycle, malate aspartate shunt, and oxidative phosphorylation pathways. The differential killing of TMZ resistant NSP by Rotenone at low concentrations with an IC50 value of 5 nM, three orders of magnitude lower than for U87MG that exhibited an IC50 value of 1.8 mM was thus identified using our integrated systems-based approach.
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Affiliation(s)
- Selva Rupa Christinal Immanuel
- Chemical Engineering Division, CSIR-National Chemical Laboratory, Pune, 411008, India
- Academy of Scientific and Innovative Research (AcSIR), CSIR-National Chemical Laboratory, Pune, 411008, India
- Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109-5263, USA
| | - Avinash D Ghanate
- Chemical Engineering Division, CSIR-National Chemical Laboratory, Pune, 411008, India
- Academy of Scientific and Innovative Research (AcSIR), CSIR-National Chemical Laboratory, Pune, 411008, India
- The Francis Crick Institute, 1 Midland Road, London, NW1 1AT, UK
| | - Dharmeshkumar S Parmar
- Chemical Engineering Division, CSIR-National Chemical Laboratory, Pune, 411008, India
- Academy of Scientific and Innovative Research (AcSIR), CSIR-National Chemical Laboratory, Pune, 411008, India
| | - Ritu Yadav
- Chemical Engineering Division, CSIR-National Chemical Laboratory, Pune, 411008, India
- Academy of Scientific and Innovative Research (AcSIR), CSIR-National Chemical Laboratory, Pune, 411008, India
| | - Riya Uthup
- Chemical Engineering Division, CSIR-National Chemical Laboratory, Pune, 411008, India
| | - Venkateswarlu Panchagnula
- Chemical Engineering Division, CSIR-National Chemical Laboratory, Pune, 411008, India
- Academy of Scientific and Innovative Research (AcSIR), CSIR-National Chemical Laboratory, Pune, 411008, India
| | - Anu Raghunathan
- Chemical Engineering Division, CSIR-National Chemical Laboratory, Pune, 411008, India.
- Academy of Scientific and Innovative Research (AcSIR), CSIR-National Chemical Laboratory, Pune, 411008, India.
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33
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Xing Y, Luo P, Hu R, Wang D, Zhou G, Jiang J. TRIB3 Promotes Lung Adenocarcinoma Progression via an Enhanced Warburg Effect. Cancer Manag Res 2020; 12:13195-13206. [PMID: 33380827 PMCID: PMC7767749 DOI: 10.2147/cmar.s287956] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 12/10/2020] [Indexed: 02/05/2023] Open
Abstract
Background The pseudokinase Tribbles 3 (TRIB3) is involved in many cellular processes and various cancers. In recent years, the importance of metabolic transformation in the maintenance of malignant tumors has become increasingly prominent. Abnormal metabolism of cancer cells is considered a hallmark of cancer. However, the exact role and molecular mechanism of TRIB3 in lung adenocarcinoma (LUAD) cell reprogramming is largely unknown. Methods The oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) of cells were examined with a Seahorse XF Extracellular Flux Analyzer. In vitro and in vivo RT-qPCR, Western blotting, and functional assays were performed to explore the functional roles of TRIB3 in LUAD. Results In the present study, we demonstrated that TRIB3 is remarkably upregulated in LUAD cell lines as well as tissues. TRIB3 knockdown significantly inhibited LUAD cell growth and suppressed LUAD cell invasion, while TRIB3 overexpression conferred the opposite effects. Moreover, silencing TRIB3 suppressed the tumorigenesis and metastatic ability of LUAD cells. Mechanistically, we demonstrated that silencing TRIB3 significantly impaired aerobic glycolysis ability in LUAD cells. Furthermore, our data indicated that TRIB3 knockdown decreased hypoxia-inducible factor (HIF)1α levels and targeted the glycolytic genes regulated by HIF1α. Conclusion Together, our findings revealed a previously unappreciated function of TRIB3 in cancer cell metabolism and tumor progression, illustrating that TRIB3 could be considered a valuable therapeutic target for LUAD patients.
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Affiliation(s)
- Yutong Xing
- Department of Cardiothoracic Surgery, The Fifth Hospital of Xiamen, Xiamen, People's Republic of China.,Department of Cardiothoracic Surgery, The First Affiliated Hospital of Jiamusi University, Jiamusi, People's Republic of China
| | - Peng Luo
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Jiamusi University, Jiamusi, People's Republic of China
| | - Rui Hu
- Department of Cardiothoracic Surgery, The Fifth Hospital of Xiamen, Xiamen, People's Republic of China
| | - Duanduan Wang
- Department of Cardiothoracic Surgery, The Fifth Hospital of Xiamen, Xiamen, People's Republic of China
| | - Gang Zhou
- Department of Cardiothoracic Surgery, The First Affiliated Hospital of Jiamusi University, Jiamusi, People's Republic of China
| | - Jie Jiang
- Department of Thoracic Surgery, The First Affiliated Hospital of Xiamen University, Xiamen, People's Republic of China
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34
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Bharti S, Sengupta A, Chugh P, Narad P. PluriMetNet: A dynamic electronic model decrypting the metabolic variations in human embryonic stem cells (hESCs) at fluctuating oxygen concentrations. J Biomol Struct Dyn 2020; 40:4570-4578. [PMID: 33353496 DOI: 10.1080/07391102.2020.1860822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Stem cells are an excellent resource in translational medicine however much is known only in terms of transcriptional and epigenetic regulation of human embryonic stem cells (hESCs). Metabolic regulation of hESCs is still unexplored in many ways, particularly the role of energy metabolism, which is intrinsic to the maintenance of cell viability, however, is very little explored in the past years. Also, there exists no hESC specific core metabolic model of pluripotency as per our knowledge. Through our work, we establish such a metabolic model of hESC using combinatorial in-silico approach of genome scale model reduction and literature curation. Further, through perturbations taking oxygen as a parameter we propose that under lower levels of oxygen concentration there is a significant dynamic change in the energy metabolism of the hESC. We further investigated energy subsystem pathways and their respective reactions in order to locate the direction of energy production along with the dynamic of nutrient metabolites like glucose and glutamine. The output shows a steep increment/decrement at a certain oxygen range. These sharp increments/decrements under hypoxic conditions are termed here as a critical range for hESC metabolic pathway. The data also resonates with the previous experimental studies on hESC energy metabolism confirming the robustness of our model. The model helps to extract range for different pathways in the energy subsystem, making us a little closer in understanding the metabolism of hESC. We also demonstrated the possible range of pathway changes in hESC's energy metabolism that can serve as the crucial preliminary data for further prospective studies. The model also offers a promise in the prediction of the flux behaviour of various metabolites in hESC.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Samuel Bharti
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India
| | - Abhishek Sengupta
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India
| | - Parul Chugh
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India
| | - Priyanka Narad
- Amity Institute of Biotechnology, Amity University, Uttar Pradesh, India
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35
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Abstract
ATP is required for mammalian cells to remain viable and to perform genetically programmed functions. Maintenance of the ΔG′ATP hydrolysis of −56 kJ/mole is the endpoint of both genetic and metabolic processes required for life. Various anomalies in mitochondrial structure and function prevent maximal ATP synthesis through OxPhos in cancer cells. Little ATP synthesis would occur through glycolysis in cancer cells that express the dimeric form of pyruvate kinase M2. Mitochondrial substrate level phosphorylation (mSLP) in the glutamine-driven glutaminolysis pathway, substantiated by the succinate-CoA ligase reaction in the TCA cycle, can partially compensate for reduced ATP synthesis through both OxPhos and glycolysis. A protracted insufficiency of OxPhos coupled with elevated glycolysis and an auxiliary, fully operational mSLP, would cause a cell to enter its default state of unbridled proliferation with consequent dedifferentiation and apoptotic resistance, i.e., cancer. The simultaneous restriction of glucose and glutamine offers a therapeutic strategy for managing cancer.
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Affiliation(s)
- Thomas N Seyfried
- Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USA
| | - Gabriel Arismendi-Morillo
- Electron Microscopy Laboratory, Biological Researches Institute, Faculty of Medicine, University of Zulia, Maracaibo, Venezuela
| | - Purna Mukherjee
- Biology Department, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA 02467, USA
| | - Christos Chinopoulos
- Department of Medical Biochemistry, Semmelweis University, Budapest, 1094, Hungary
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36
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Masid M, Ataman M, Hatzimanikatis V. Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN. Nat Commun 2020; 11:2821. [PMID: 32499584 PMCID: PMC7272419 DOI: 10.1038/s41467-020-16549-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 05/07/2020] [Indexed: 01/31/2023] Open
Abstract
Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells.
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Affiliation(s)
- Maria Masid
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Meric Ataman
- Computational and Systems Biology, Biozentrum, University of Basel, Basel, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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Quantitative analysis of amino acid metabolism in liver cancer links glutamate excretion to nucleotide synthesis. Proc Natl Acad Sci U S A 2020; 117:10294-10304. [PMID: 32341162 PMCID: PMC7229649 DOI: 10.1073/pnas.1919250117] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
We used a combination of experimental measurements and computer simulations to understand how liver cancer cells rewire their metabolism to grow faster. We observed that glutamate is excreted by the cells, and our simulations suggest that this occurs because glutamate is formed in excess in the cytoplasm, when cells rapidly synthesize nucleotides, which are required for growth. Meanwhile, glutamate that is formed in the mitochondria is, on the other hand, not excreted. Treating glutamate as two distinct pools, a cytosolic and a mitochondrial, is useful to better understand why many cancer cells rapidly consume glutamine, the precursor of glutamate. The results point toward potential drug targets that could be used to reduce growth of liver cancer cells. Many cancer cells consume glutamine at high rates; counterintuitively, they simultaneously excrete glutamate, the first intermediate in glutamine metabolism. Glutamine consumption has been linked to replenishment of tricarboxylic acid cycle (TCA) intermediates and synthesis of adenosine triphosphate (ATP), but the reason for glutamate excretion is unclear. Here, we dynamically profile the uptake and excretion fluxes of a liver cancer cell line (HepG2) and use genome-scale metabolic modeling for in-depth analysis. We find that up to 30% of the glutamine is metabolized in the cytosol, primarily for nucleotide synthesis, producing cytosolic glutamate. We hypothesize that excreting glutamate helps the cell to increase the nucleotide synthesis rate to sustain growth. Indeed, we show experimentally that partial inhibition of glutamate excretion reduces cell growth. Our integrative approach thus links glutamine addiction to glutamate excretion in cancer and points toward potential drug targets.
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38
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Pandey N, Lanke V, Vinod PK. Network-based metabolic characterization of renal cell carcinoma. Sci Rep 2020; 10:5955. [PMID: 32249812 PMCID: PMC7136214 DOI: 10.1038/s41598-020-62853-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 03/21/2020] [Indexed: 12/31/2022] Open
Abstract
An emerging hallmark of cancer is metabolic reprogramming, which presents opportunities for cancer diagnosis and treatment based on metabolism. We performed a comprehensive metabolic network analysis of major renal cell carcinoma (RCC) subtypes including clear cell, papillary and chromophobe by integrating transcriptomic data with the human genome-scale metabolic model to understand the coordination of metabolic pathways in cancer cells. We identified metabolic alterations of each subtype with respect to tumor-adjacent normal samples and compared them to understand the differences between subtypes. We found that genes of amino acid metabolism and redox homeostasis are significantly altered in RCC subtypes. Chromophobe showed metabolic divergence compared to other subtypes with upregulation of genes involved in glutamine anaplerosis and aspartate biosynthesis. A difference in transcriptional regulation involving HIF1A is observed between subtypes. We identified E2F1 and FOXM1 as other major transcriptional activators of metabolic genes in RCC. Further, the co-expression pattern of metabolic genes in each patient showed the variations in metabolism within RCC subtypes. We also found that co-expression modules of each subtype have tumor stage-specific behavior, which may have clinical implications.
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Affiliation(s)
- Nishtha Pandey
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Vinay Lanke
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
- TCS Innovation Labs, Hyderabad, India
| | - P K Vinod
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
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Robinson JL, Kocabaş P, Wang H, Cholley PE, Cook D, Nilsson A, Anton M, Ferreira R, Domenzain I, Billa V, Limeta A, Hedin A, Gustafsson J, Kerkhoven EJ, Svensson LT, Palsson BO, Mardinoglu A, Hansson L, Uhlén M, Nielsen J. An atlas of human metabolism. Sci Signal 2020; 13:13/624/eaaz1482. [PMID: 32209698 DOI: 10.1126/scisignal.aaz1482] [Citation(s) in RCA: 164] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.
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Affiliation(s)
- Jonathan L Robinson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Pınar Kocabaş
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Hao Wang
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Molecular and Translational Medicine, University of Gothenburg, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Pierre-Etienne Cholley
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Daniel Cook
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Avlant Nilsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Raphael Ferreira
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Iván Domenzain
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Virinchi Billa
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Angelo Limeta
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Alex Hedin
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Johan Gustafsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - L Thomas Svensson
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Bernhard O Palsson
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.,Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.,Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Adil Mardinoglu
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, SE-10044 Stockholm, Sweden.,Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London WC2R 2LS, UK
| | - Lena Hansson
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Novo Nordisk Research Centre Oxford, Oxford OX3 7FZ, UK
| | - Mathias Uhlén
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.,Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, SE-10044 Stockholm, Sweden.,Wallenberg Center for Protein Research, KTH-Royal Institute of Technology, SE-10044 Stockholm, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden. .,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.,BioInnovation Institute, Ole Maaløes Vej 3, DK-2200 Copenhagen, Denmark
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40
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Abstract
The metabolic activity of a mammalian cell changes dynamically over time and is tied to the changing metabolic demands of cellular processes such as cell differentiation and proliferation. While experimental tools like time-course metabolomics and flux tracing can measure the dynamics of a few pathways, they are unable to infer fluxes at the whole network level. To address this limitation, we have developed the Dynamic Flux Activity (DFA) algorithm, a genome-scale modeling approach that uses time-course metabolomics to predict dynamic flux rewiring during transitions between metabolic states. This chapter provides a protocol for applying DFA to characterize the dynamic metabolic activity of various cancer cell lines.
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41
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Gatto F, Ferreira R, Nielsen J. Pan-cancer analysis of the metabolic reaction network. Metab Eng 2019; 57:51-62. [PMID: 31526853 DOI: 10.1016/j.ymben.2019.09.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 07/29/2019] [Accepted: 09/10/2019] [Indexed: 12/25/2022]
Abstract
Metabolic reprogramming is considered a hallmark of malignant transformation. However, it is not clear whether the network of metabolic reactions expressed by cancers of different origin differ from each other or from normal human tissues. In this study, we reconstructed functional and connected genome-scale metabolic models for 917 primary tumor samples across 13 types based on the probability of expression for 3765 reference metabolic genes in the sample. This network-centric approach revealed that tumor metabolic networks are largely similar in terms of accounted reactions, despite diversity in the expression of the associated genes. On average, each network contained 4721 reactions, of which 74% were core reactions (present in >95% of all models). Whilst 99.3% of the core reactions were classified as housekeeping also in normal tissues, we identified reactions catalyzed by ARG2, RHAG, SLC6 and SLC16 family gene members, and PTGS1 and PTGS2 as core exclusively in cancer. These findings were subsequently replicated in an independent validation set of 3388 genome-scale metabolic models. The remaining 26% of the reactions were contextual reactions. Their inclusion was dependent in one case (GLS2) on the absence of TP53 mutations and in 94.6% of cases on differences in cancer types. This dependency largely resembled differences in expression patterns in the corresponding normal tissues, with some exceptions like the presence of the NANP-encoded reaction in tumors not from the female reproductive system or of the SLC5A9-encoded reaction in kidney-pancreatic-colorectal tumors. In conclusion, tumors expressed a metabolic network virtually overlapping the matched normal tissues, raising the possibility that metabolic reprogramming simply reflects cancer cell plasticity to adapt to varying conditions thanks to redundancy and complexity of the underlying metabolic networks. At the same time, the here uncovered exceptions represent a resource to identify selective liabilities of tumor metabolism.
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Affiliation(s)
- Francesco Gatto
- Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Raphael Ferreira
- Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen N, Denmark.
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42
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Lagziel S, Lee WD, Shlomi T. Studying metabolic flux adaptations in cancer through integrated experimental-computational approaches. BMC Biol 2019; 17:51. [PMID: 31272436 PMCID: PMC6609376 DOI: 10.1186/s12915-019-0669-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022] Open
Affiliation(s)
| | | | - Tomer Shlomi
- Faculty of Computer Science, Technion, Haifa, Israel. .,Faculty of Biology, Technion, Haifa, Israel. .,Lokey Center for Life Science and Engineering, Technion, Haifa, Israel.
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43
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Schwartz JM, Otokuni H, Akutsu T, Nacher JC. Probabilistic controllability approach to metabolic fluxes in normal and cancer tissues. Nat Commun 2019; 10:2725. [PMID: 31221963 PMCID: PMC6586789 DOI: 10.1038/s41467-019-10616-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 05/17/2019] [Indexed: 02/06/2023] Open
Abstract
Recent research has shown that many types of cancers take control of specific metabolic processes. We compiled metabolic networks corresponding to four healthy and cancer tissues, and analysed the healthy–cancer transition from the metabolic flux change perspective. We used a Probabilistic Minimum Dominating Set (PMDS) model, which identifies a minimum set of nodes that act as driver nodes and control the entire network. The combination of control theory with flux correlation analysis shows that flux correlations substantially increase in cancer states of breast, kidney and urothelial tissues, but not in lung. No change in the network topology between healthy and cancer networks was observed, but PMDS analysis shows that cancer states require fewer controllers than their corresponding healthy states. These results indicate that cancer metabolism is characterised by more streamlined flux distributions, which may be focused towards a reduced set of objectives and controlled by fewer regulatory elements. Metabolic rewiring is a feature of many cancers. Here, the authors combine control theory and flux correlation analysis to study the transition of healthy metabolic networks to cancer states, and find that cancer metabolism is characterized by more streamlined flux distributions.
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Affiliation(s)
- Jean-Marc Schwartz
- Faculty of Biology, Medicine and Health, University of Manchester, Manchester, M13 9PT, UK.
| | - Hiroaki Otokuni
- Department of Information Science, Faculty of Science, Toho University, Funabashi, 274-8510, Japan
| | - Tatsuya Akutsu
- Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, 611-0011, Japan
| | - Jose C Nacher
- Department of Information Science, Faculty of Science, Toho University, Funabashi, 274-8510, Japan.
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44
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Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine. BIOMED RESEARCH INTERNATIONAL 2019; 2019:8304260. [PMID: 31281846 PMCID: PMC6590590 DOI: 10.1155/2019/8304260] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 02/07/2019] [Accepted: 05/20/2019] [Indexed: 01/06/2023]
Abstract
In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.
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45
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UHRF1 promotes aerobic glycolysis and proliferation via suppression of SIRT4 in pancreatic cancer. Cancer Lett 2019; 452:226-236. [PMID: 30905812 DOI: 10.1016/j.canlet.2019.03.024] [Citation(s) in RCA: 85] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 02/24/2019] [Accepted: 03/01/2019] [Indexed: 02/06/2023]
Abstract
UHRF1 (ubiquitin like with plant homeodomain and ring finger domains 1) is an epigenetic modifier that is overexpressed in some cancers, including pancreatic cancer, and mediates silencing of tumor suppressor genes. However, the role of UHRF1 in regulating pancreatic cancer metabolism and metastasis is not clear. In the present study, we demonstrated that silencing UHRF1 significantly inhibited aerobic glycolysis in pancreatic cancer cells. Furthermore, we demonstrated that UHRF1 knockdown decreased hypoxia inducible factor (HIF)1α levels and HIF1α targeted glycolytic genes. The Cancer Genome Atlas dataset analysis supported this observation. The Sirtuin (SIRT) family members regulate aerobic glycolysis in many cancers. We analyzed the correlation between UHRF1 and SIRT3-5 expression and found a significant negative correlation between UHRF1 and SIRT4. Further transcriptional and functional analysis demonstrates that SIRT4 is a downstream target of UHRF1 and negatively regulated aerobic glycolysis, cell proliferation and tumor growth. Our study identified a novel UHRF1/SIRT4 axis in regulation of pancreatic cancer cell proliferation, metabolism, and metastasis.
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46
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Tsai HJ, Nelliat AR, Choudhury MI, Kucharavy A, Bradford WD, Cook ME, Kim J, Mair DB, Sun SX, Schatz MC, Li R. Hypo-osmotic-like stress underlies general cellular defects of aneuploidy. Nature 2019; 570:117-121. [PMID: 31068692 PMCID: PMC6583789 DOI: 10.1038/s41586-019-1187-2] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Accepted: 04/10/2019] [Indexed: 01/06/2023]
Abstract
Aneuploidy, which refers to unbalanced chromosome numbers, represents a class of genetic variation that is associated with cancer, birth defects and eukaryotic micro-organisms1-4. Whereas it is known that each aneuploid chromosome stoichiometry can give rise to a distinct pattern of gene expression and phenotypic profile4,5, it remains a fundamental question as to whether there are common cellular defects that are associated with aneuploidy. Here we show the existence in budding yeast of a common aneuploidy gene-expression signature that is suggestive of hypo-osmotic stress, using a strategy that enables the observation of common transcriptome changes of aneuploidy by averaging out karyotype-specific dosage effects in aneuploid yeast-cell populations with random and diverse chromosome stoichiometry. Consistently, aneuploid yeast exhibited increased plasma-membrane stress that led to impaired endocytosis, and this defect was also observed in aneuploid human cells. Thermodynamic modelling showed that hypo-osmotic-like stress is a general outcome of the proteome imbalance that is caused by aneuploidy, and also predicted a relationship between ploidy and cell size that was observed in yeast and aneuploid cancer cells. A genome-wide screen uncovered a general dependency of aneuploid cells on a pathway of ubiquitin-mediated endocytic recycling of nutrient transporters. Loss of this pathway, coupled with the endocytic defect inherent to aneuploidy, leads to a marked alteration of intracellular nutrient homeostasis.
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Affiliation(s)
- Hung-Ji Tsai
- Center for Cell Dynamics, Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anjali R Nelliat
- Center for Cell Dynamics, Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Mohammad Ikbal Choudhury
- Department of Mechanical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Andrei Kucharavy
- Center for Cell Dynamics, Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | | | - Malcolm E Cook
- Stowers Institute for Medical Research, Kansas City, MO, USA
| | - Jisoo Kim
- Center for Cell Dynamics, Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Devin B Mair
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sean X Sun
- Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Mechanical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Michael C Schatz
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Rong Li
- Center for Cell Dynamics, Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Department of Chemical and Biomolecular Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
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47
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Dovrolis N, Filidou E, Kolios G. Systems biology in inflammatory bowel diseases: on the way to precision medicine. Ann Gastroenterol 2019; 32:233-246. [PMID: 31040620 PMCID: PMC6479645 DOI: 10.20524/aog.2019.0373] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2018] [Accepted: 02/25/2019] [Indexed: 02/07/2023] Open
Abstract
Inflammatory bowel diseases (IBD) are chronic and recurrent inflammatory disorders of the gastrointestinal tract. The elucidation of their etiopathology requires complex and multiple approaches. Systems biology has come to fulfill this need in approaching the pathogenetic mechanisms of IBD and its etiopathology, in a comprehensive way, by combining data from different scientific sources. In combination with bioinformatics and network medicine, it uses principles from computer science, mathematics, physics, chemistry, biology, medicine and computational tools to achieve its purposes. Systems biology utilizes scientific sources that provide data from omics studies (e.g., genomics, transcriptomics, etc.) and clinical observations, whose combined analysis leads to network formation and ultimately to a more integrative image of disease etiopathogenesis. In this review, we analyze the current literature on the methods and the tools utilized by systems biology in order to cover an innovative and exciting field: IBD-omics.
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Affiliation(s)
- Nikolas Dovrolis
- Laboratory of Pharmacology, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
| | - Eirini Filidou
- Laboratory of Pharmacology, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
| | - George Kolios
- Laboratory of Pharmacology, Faculty of Medicine, Democritus University of Thrace, Alexandroupolis, Greece
- Correspondence to: Prof. George Kolios, MD PhD, Laboratory of Pharmacology, Faculty of Medicine, Democritus University of Thrace, Dragana, Alexandroupolis, 68100, Greece, e-mail:
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48
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Jamialahmadi O, Hashemi-Najafabadi S, Motamedian E, Romeo S, Bagheri F. A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism. PLoS Comput Biol 2019; 15:e1006936. [PMID: 31009458 PMCID: PMC6497301 DOI: 10.1371/journal.pcbi.1006936] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 05/02/2019] [Accepted: 03/11/2019] [Indexed: 02/07/2023] Open
Abstract
Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.
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Affiliation(s)
- Oveis Jamialahmadi
- Department of Biotechnology, 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
- * E-mail: (SHN); (EM)
| | - Ehsan Motamedian
- Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
- * E-mail: (SHN); (EM)
| | - Stefano Romeo
- Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden
- Clinical Nutrition Unit, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
- Cardiology Department, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Fatemeh Bagheri
- Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
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49
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Richelle A, Chiang AWT, Kuo CC, Lewis NE. Increasing consensus of context-specific metabolic models by integrating data-inferred cell functions. PLoS Comput Biol 2019; 15:e1006867. [PMID: 30986217 PMCID: PMC6483243 DOI: 10.1371/journal.pcbi.1006867] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2018] [Revised: 04/25/2019] [Accepted: 02/13/2019] [Indexed: 12/26/2022] Open
Abstract
Genome-scale metabolic models provide a valuable context for analyzing data from diverse high-throughput experimental techniques. Models can quantify the activities of diverse pathways and cellular functions. Since some metabolic reactions are only catalyzed in specific environments, several algorithms exist that build context-specific models. However, these methods make differing assumptions that influence the content and associated predictive capacity of resulting models, such that model content varies more due to methods used than cell types. Here we overcome this problem with a novel framework for inferring the metabolic functions of a cell before model construction. For this, we curated a list of metabolic tasks and developed a framework to infer the activity of these functionalities from transcriptomic data. We protected the data-inferred tasks during the implementation of diverse context-specific model extraction algorithms for 44 cancer cell lines. We show that the protection of data-inferred metabolic tasks decreases the variability of models across extraction methods. Furthermore, resulting models better capture the actual biological variability across cell lines. This study highlights the potential of using biological knowledge, inferred from omics data, to obtain a better consensus between existing extraction algorithms. It further provides guidelines for the development of the next-generation of data contextualization methods.
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Affiliation(s)
- Anne Richelle
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
| | - Austin W. T. Chiang
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
| | - Chih-Chung Kuo
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
| | - Nathan E. Lewis
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, United States of America
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50
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Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, Haraldsdóttir HS, Wachowiak J, Keating SM, Vlasov V, Magnusdóttir S, Ng CY, Preciat G, Žagare A, Chan SHJ, Aurich MK, Clancy CM, Modamio J, Sauls JT, Noronha A, Bordbar A, Cousins B, El Assal DC, Valcarcel LV, Apaolaza I, Ghaderi S, Ahookhosh M, Ben Guebila M, Kostromins A, Sompairac N, Le HM, Ma D, Sun Y, Wang L, Yurkovich JT, Oliveira MAP, Vuong PT, El Assal LP, Kuperstein I, Zinovyev A, Hinton HS, Bryant WA, Aragón Artacho FJ, Planes FJ, Stalidzans E, Maass A, Vempala S, Hucka M, Saunders MA, Maranas CD, Lewis NE, Sauter T, Palsson BØ, Thiele I, Fleming RMT. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat Protoc 2019; 14:639-702. [PMID: 30787451 PMCID: PMC6635304 DOI: 10.1038/s41596-018-0098-2] [Citation(s) in RCA: 600] [Impact Index Per Article: 120.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.
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Affiliation(s)
- Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Sylvain Arreckx
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Thomas Pfau
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Sebastián N Mendoza
- Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile
- Mathomics, Center for Mathematical Modeling, University of Chile, Santiago, Chile
| | - Anne Richelle
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, USA
| | - Almut Heinken
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Hulda S Haraldsdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Jacek Wachowiak
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Sarah M Keating
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
| | - Vanja Vlasov
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Stefania Magnusdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Chiam Yu Ng
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - German Preciat
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Alise Žagare
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Siu H J Chan
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Maike K Aurich
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Catherine M Clancy
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Jennifer Modamio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - John T Sauls
- Department of Physics, and Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Alberto Noronha
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | | | - Benjamin Cousins
- Algorithms and Randomness Center, School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Diana C El Assal
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Luis V Valcarcel
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Iñigo Apaolaza
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Susan Ghaderi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Masoud Ahookhosh
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Marouen Ben Guebila
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Andrejs Kostromins
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Hoai M Le
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ding Ma
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Yuekai Sun
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - James T Yurkovich
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Miguel A P Oliveira
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Phan T Vuong
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Lemmer P El Assal
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Inna Kuperstein
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - H Scott Hinton
- Utah State University Research Foundation, North Logan, UT, USA
| | - William A Bryant
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, UK
| | | | - Francisco J Planes
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Egils Stalidzans
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Alejandro Maass
- Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile
- Mathomics, Center for Mathematical Modeling, University of Chile, Santiago, Chile
| | - Santosh Vempala
- Algorithms and Randomness Center, School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Michael Hucka
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Michael A Saunders
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, La Jolla, CA, USA
| | - Thomas Sauter
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Bernhard Ø Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Lyngby, Denmark
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ronan M T Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, The Netherlands.
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