251
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Zhang C, Ji B, Mardinoglu A, Nielsen J, Hua Q. Logical transformation of genome-scale metabolic models for gene level applications and analysis. Bioinformatics 2015; 31:2324-31. [DOI: 10.1093/bioinformatics/btv134] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2014] [Accepted: 02/25/2015] [Indexed: 01/10/2023] Open
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252
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Modelling the effect of SPION size in a stent assisted magnetic drug targeting system with interparticle interactions. ScientificWorldJournal 2015; 2015:618658. [PMID: 25815370 PMCID: PMC4359871 DOI: 10.1155/2015/618658] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2014] [Accepted: 02/01/2015] [Indexed: 01/21/2023] Open
Abstract
Cancer is a leading cause of death worldwide and it is caused by the interaction of genomic, environmental, and lifestyle factors. Although chemotherapy is one way of treating cancers, it also damages healthy cells and may cause severe side effects. Therefore, it is beneficial in drug delivery in the human body to increase the proportion of the drugs at the target site while limiting its exposure at the rest of body through Magnetic Drug Targeting (MDT). Superparamagnetic iron oxide nanoparticles (SPIONs) are derived from polyol methods and coated with oleic acid and can be used as magnetic drug carrier particles (MDCPs) in an MDT system. Here, we develop a mathematical model for studying the interactions between the MDCPs enriched with three different diameters of SPIONs (6.6, 11.6, and 17.8 nm) in the MDT system with an implanted magnetizable stent using different magnetic field strengths and blood velocities. Our computational analysis allows for the optimal design of the SPIONs enriched MDCPs to be used in clinical applications.
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253
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Ghaffari P, Mardinoglu A, Asplund A, Shoaie S, Kampf C, Uhlen M, Nielsen J. Identifying anti-growth factors for human cancer cell lines through genome-scale metabolic modeling. Sci Rep 2015; 5:8183. [PMID: 25640694 PMCID: PMC4313100 DOI: 10.1038/srep08183] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2014] [Accepted: 12/29/2014] [Indexed: 12/25/2022] Open
Abstract
Human cancer cell lines are used as important model systems to study molecular mechanisms associated with tumor growth, hereunder how genomic and biological heterogeneity found in primary tumors affect cellular phenotypes. We reconstructed Genome scale metabolic models (GEMs) for eleven cell lines based on RNA-Seq data and validated the functionality of these models with data from metabolite profiling. We used cell line-specific GEMs to analyze the differences in the metabolism of cancer cell lines, and to explore the heterogeneous expression of the metabolic subsystems. Furthermore, we predicted 85 antimetabolites that can inhibit growth of, or even kill, any of the cell lines, while at the same time not being toxic for 83 different healthy human cell types. 60 of these antimetabolites were found to inhibit growth in all cell lines. Finally, we experimentally validated one of the predicted antimetabolites using two cell lines with different phenotypic origins, and found that it is effective in inhibiting the growth of these cell lines. Using immunohistochemistry, we also showed high or moderate expression levels of proteins targeted by the validated antimetabolite. Identified anti-growth factors for inhibition of cell growth may provide leads for the development of efficient cancer treatment strategies.
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Affiliation(s)
- Pouyan Ghaffari
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - Adil Mardinoglu
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - Anna Asplund
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85, Uppsala, Sweden
| | - Saeed Shoaie
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden
| | - Caroline Kampf
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85, Uppsala, Sweden
| | - Mathias Uhlen
- 1] Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21, Stockholm, Sweden [2] Department of Proteomics, KTH - Royal Institute of Technology, SE-106 91, Stockholm, Sweden
| | - Jens Nielsen
- 1] Department of Biology and Biological Engineering, Chalmers University of Technology, SE-412 96, Gothenburg, Sweden [2] Science for Life Laboratory, KTH - Royal Institute of Technology, SE-171 21, Stockholm, Sweden
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254
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Oberhardt MA, Gianchandani EP. Genome-scale modeling and human disease: an overview. Front Physiol 2015; 5:527. [PMID: 25667572 PMCID: PMC4304257 DOI: 10.3389/fphys.2014.00527] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2014] [Accepted: 12/23/2014] [Indexed: 12/11/2022] Open
Affiliation(s)
- Matthew A Oberhardt
- Department of Molecular Microbiology and Biotechnology, Faculty of Life Sciences, School of Computer Sciences, and Sackler School of Medicine, Tel Aviv University Tel Aviv, Israel
| | - Erwin P Gianchandani
- Division of Computer and Network Systems, United States National Science Foundation Arlington, VA, USA
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255
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Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto CAK, Odeberg J, Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist PH, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Pontén F. Tissue-based map of the human proteome. Science 2015; 347:1260419. [PMID: 25613900 DOI: 10.1126/science.1260419] [Citation(s) in RCA: 9460] [Impact Index Per Article: 1051.1] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Mathias Uhlén
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden. Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden. Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2970 Hørsholm, Denmark.
| | - Linn Fagerberg
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Björn M Hallström
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden. Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden
| | - Cecilia Lindskog
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
| | - Per Oksvold
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Adil Mardinoglu
- Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Åsa Sivertsson
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Caroline Kampf
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
| | - Evelina Sjöstedt
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden. Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
| | - Anna Asplund
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
| | - IngMarie Olsson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
| | - Karolina Edlund
- Leibniz Research Centre for Working Environment and Human Factors (IfADo) at Dortmund TU, D-44139 Dortmund, Germany
| | - Emma Lundberg
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | | | | | - Jacob Odeberg
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Dijana Djureinovic
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
| | - Jenny Ottosson Takanen
- Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden
| | - Sophia Hober
- Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden
| | - Tove Alm
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Per-Henrik Edqvist
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
| | - Holger Berling
- Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden
| | - Hanna Tegel
- Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden
| | - Jan Mulder
- Science for Life Laboratory, Department of Neuroscience, Karolinska Institute, SE-171 77 Stockholm, Sweden
| | - Johan Rockberg
- Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden
| | - Peter Nilsson
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Jochen M Schwenk
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Marica Hamsten
- Department of Proteomics, KTH-Royal Institute of Technology, SE-106 91 Stockholm, Sweden
| | - Kalle von Feilitzen
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Mattias Forsberg
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Lukas Persson
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Fredric Johansson
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Martin Zwahlen
- Science for Life Laboratory, KTH-Royal Institute of Technology, SE-171 21 Stockholm, Sweden
| | - Gunnar von Heijne
- Center for Biomembrane Research, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
| | - Jens Nielsen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2970 Hørsholm, Denmark. Department of Chemical and Biological Engineering, Chalmers University of Technology, SE-412 96 Gothenburg, Sweden
| | - Fredrik Pontén
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, SE-751 85 Uppsala, Sweden·
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256
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Ryu JY, Kim HU, Lee SY. Human genes with a greater number of transcript variants tend to show biological features of housekeeping and essential genes. MOLECULAR BIOSYSTEMS 2015; 11:2798-807. [DOI: 10.1039/c5mb00322a] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Human genes with a greater number of transcript variants are more likely to play functionally important roles such as cellular maintenance and survival.
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Affiliation(s)
- Jae Yong Ryu
- Metabolic and Biomolecular Engineering National Research Laboratory
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program)
- Center for Systems and Synthetic Biotechnology
- Institute for the BioCentury
- Korea Advanced Institute of Science and Technology (KAIST)
| | - Hyun Uk Kim
- Metabolic and Biomolecular Engineering National Research Laboratory
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program)
- Center for Systems and Synthetic Biotechnology
- Institute for the BioCentury
- Korea Advanced Institute of Science and Technology (KAIST)
| | - Sang Yup Lee
- Metabolic and Biomolecular Engineering National Research Laboratory
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program)
- Center for Systems and Synthetic Biotechnology
- Institute for the BioCentury
- Korea Advanced Institute of Science and Technology (KAIST)
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257
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Correia S, Rocha M. A Critical Evaluation of Methods for the Reconstruction of Tissue-Specific Models. PROGRESS IN ARTIFICIAL INTELLIGENCE 2015. [DOI: 10.1007/978-3-319-23485-4_35] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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258
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Yizhak K, Gaude E, Le Dévédec S, Waldman YY, Stein GY, van de Water B, Frezza C, Ruppin E. Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer. eLife 2014; 3. [PMID: 25415239 PMCID: PMC4238051 DOI: 10.7554/elife.03641] [Citation(s) in RCA: 103] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2014] [Accepted: 10/28/2014] [Indexed: 12/11/2022] Open
Abstract
Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies.
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Affiliation(s)
- Keren Yizhak
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
| | - Edoardo Gaude
- MRC Cancer Unit, University of Cambridge, Cambridge, United Kingdom
| | - Sylvia Le Dévédec
- Division of Toxicology, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands
| | - Yedael Y Waldman
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
| | - Gideon Y Stein
- Department of Internal Medicine 'B', Beilinson Hospital, Rabin Medical Center, Petah-Tikva, Israel
| | - Bob van de Water
- Division of Toxicology, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands
| | - Christian Frezza
- MRC Cancer Unit, University of Cambridge, Cambridge, United Kingdom
| | - Eytan Ruppin
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
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259
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Hoshida Y, Fuchs BC, Bardeesy N, Baumert TF, Chung RT. Pathogenesis and prevention of hepatitis C virus-induced hepatocellular carcinoma. J Hepatol 2014; 61:S79-90. [PMID: 25443348 PMCID: PMC4435677 DOI: 10.1016/j.jhep.2014.07.010] [Citation(s) in RCA: 153] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2014] [Revised: 07/03/2014] [Accepted: 07/10/2014] [Indexed: 02/08/2023]
Abstract
Hepatitis C virus (HCV) is one of the major aetiologic agents that causes hepatocellular carcinoma (HCC) by generating an inflammatory, fibrogenic, and carcinogenic tissue microenvironment in the liver. HCV-induced HCC is a rational target for cancer preventive intervention because of the clear-cut high-risk condition, cirrhosis, associated with high cancer incidence (1% to 7% per year). Studies have elucidated direct and indirect carcinogenic effects of HCV, which have in turn led to the identification of candidate HCC chemoprevention targets. Selective molecular targeted agents may enable personalized strategies for HCC chemoprevention. In addition, multiple experimental and epidemiological studies suggest the potential value of generic drugs or dietary supplements targeting inflammation, oxidant stress, or metabolic derangements as possible HCC chemopreventive agents. While the successful use of highly effective direct-acting antiviral agents will make important inroads into reducing long-term HCC risk, there will remain an important role for HCC chemoprevention even after viral cure, given the persistence of HCC risk in persons with advanced HCV fibrosis, as shown in recent studies. The successful development of cancer preventive therapies will be more challenging compared to cancer therapeutics because of the requirement for larger and longer clinical trials and the need for a safer toxicity profile given its use as a preventive agent. Molecular biomarkers to selectively identify high-risk population could help mitigate these challenges. Genome-wide, unbiased molecular characterization, high-throughput drug/gene screening, experimental model-based functional analysis, and systems-level in silico modelling are expected to complement each other to facilitate discovery of new HCC chemoprevention targets and therapies.
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Affiliation(s)
- Yujin Hoshida
- Liver Cancer Program, Tisch Cancer Institute, Division of Liver Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, United States.
| | - Bryan C Fuchs
- Division of Surgical Oncology, Massachusetts General Hospital, Harvard Medical School, United States
| | - Nabeel Bardeesy
- Cancer Center, Massachusetts General Hospital, Harvard Medical School, United States
| | - Thomas F Baumert
- INSERM Unité 1110, Institut de Recherche sur les Maladies Virales et Hépatiques, Université de Strasbourg, and Institut Hospitalo-Universitaire, Pôle Hépato-digestif, Hôpitaux Universitaires de Strasbourg, France; Liver Center and Gastrointestinal Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, United States
| | - Raymond T Chung
- Liver Center and Gastrointestinal Division, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, United States.
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260
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Kampf C, Mardinoglu A, Fagerberg L, Hallström BM, Danielsson A, Nielsen J, Pontén F, Uhlen M. Defining the human gallbladder proteome by transcriptomics and affinity proteomics. Proteomics 2014; 14:2498-507. [PMID: 25175928 DOI: 10.1002/pmic.201400201] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Revised: 07/30/2014] [Accepted: 08/27/2014] [Indexed: 12/12/2022]
Abstract
Global protein analysis of human gallbladder tissue is vital for identification of molecular regulators and effectors of its physiological activity. Here, we employed a genome-wide deep RNA sequencing analysis in 28 human tissues to identify the genes overrepresented in the gallbladder and complemented it with antibody-based immunohistochemistry in 48 human tissues. We characterized human gallbladder proteins and identified 140 gallbladder-specific proteins with an elevated expression in the gallbladder as compared to the other analyzed tissues. Five genes were categorized as enriched, with at least fivefold higher levels in gallbladder, 60 genes were categorized as group enriched with elevated transcript levels in gallbladder shared with at least one other tissue and 75 genes were categorized as enhanced with higher expression than the average expression in other tissues. We explored the localization of the genes within the gallbladder through cell-type specific antibody-based protein profiling and the subcellular localization of the genes through immunofluorescent-based profiling. Finally, we revealed the biological processes and metabolic functions carried out by these genes through the use of GO, KEGG Pathway, and HMR2.0 that is compilation of the human metabolic reactions. We demonstrated the results of the combined analysis of the transcriptomics and affinity proteomics.
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Affiliation(s)
- Caroline Kampf
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
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261
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Mardinoglu A, Kampf C, Asplund A, Fagerberg L, Hallström BM, Edlund K, Blüher M, Pontén F, Uhlen M, Nielsen J. Defining the human adipose tissue proteome to reveal metabolic alterations in obesity. J Proteome Res 2014; 13:5106-19. [PMID: 25219818 DOI: 10.1021/pr500586e] [Citation(s) in RCA: 54] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
White adipose tissue (WAT) has a major role in the progression of obesity. Here, we combined data from RNA-Seq and antibody-based immunohistochemistry to describe the normal physiology of human WAT obtained from three female subjects and explored WAT-specific genes by comparing WAT to 26 other major human tissues. Using the protein evidence in WAT, we validated the content of a genome-scale metabolic model for adipocytes. We employed this high-quality model for the analysis of subcutaneous adipose tissue (SAT) gene expression data obtained from subjects included in the Swedish Obese Subjects Sib Pair study to reveal molecular differences between lean and obese individuals. We integrated SAT gene expression and plasma metabolomics data, investigated the contribution of the metabolic differences in the mitochondria of SAT to the occurrence of obesity, and eventually identified cytosolic branched-chain amino acid (BCAA) transaminase 1 as a potential target that can be used for drug development. We observed decreased glutaminolysis and alterations in the BCAAs metabolism in SAT of obese subjects compared to lean subjects. We also provided mechanistic explanations for the changes in the plasma level of BCAAs, glutamate, pyruvate, and α-ketoglutarate in obese subjects. Finally, we validated a subset of our model-based predictions in 20 SAT samples obtained from 10 lean and 10 obese male and female subjects.
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Affiliation(s)
- Adil Mardinoglu
- Department of Chemical and Biological Engineering, Chalmers University of Technology , 412 96 Gothenburg, Sweden
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262
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Yizhak K, Le Dévédec SE, Rogkoti VM, Baenke F, de Boer VC, Frezza C, Schulze A, van de Water B, Ruppin E. A computational study of the Warburg effect identifies metabolic targets inhibiting cancer migration. Mol Syst Biol 2014; 10:744. [PMID: 25086087 PMCID: PMC4299514 DOI: 10.15252/msb.20134993] [Citation(s) in RCA: 107] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Over the last decade, the field of cancer metabolism has mainly focused on studying the role of
tumorigenic metabolic rewiring in supporting cancer proliferation. Here, we perform the first
genome-scale computational study of the metabolic underpinnings of cancer migration. We build
genome-scale metabolic models of the NCI-60 cell lines that capture the Warburg effect (aerobic
glycolysis) typically occurring in cancer cells. The extent of the Warburg effect in each of these
cell line models is quantified by the ratio of glycolytic to oxidative ATP flux (AFR), which is
found to be highly positively associated with cancer cell migration. We hence predicted that
targeting genes that mitigate the Warburg effect by reducing the AFR may specifically inhibit cancer
migration. By testing the anti-migratory effects of silencing such 17 top predicted genes in four
breast and lung cancer cell lines, we find that up to 13 of these novel predictions significantly
attenuate cell migration either in all or one cell line only, while having almost no effect on cell
proliferation. Furthermore, in accordance with the predictions, a significant reduction is observed
in the ratio between experimentally measured ECAR and OCR levels following these perturbations.
Inhibiting anti-migratory targets is a promising future avenue in treating cancer since it may
decrease cytotoxic-related side effects that plague current anti-proliferative treatments.
Furthermore, it may reduce cytotoxic-related clonal selection of more aggressive cancer cells and
the likelihood of emerging resistance.
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Affiliation(s)
- Keren Yizhak
- The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
| | - Sylvia E Le Dévédec
- Division of Toxicology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Vasiliki Maria Rogkoti
- Division of Toxicology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Franziska Baenke
- Gene Expression Analysis Laboratory, Cancer Research UK, London Research Institute, London, UK
| | - Vincent C de Boer
- Laboratory Genetic Metabolic Diseases, Academic Medical Center, Amsterdam, The Netherlands
| | - Christian Frezza
- MRC Cancer Unit, Hutchison/MRC Research Centre, University of Cambridge, Cambridge, UK
| | - Almut Schulze
- Gene Expression Analysis Laboratory, Cancer Research UK, London Research Institute, London, UK
| | - Bob van de Water
- Division of Toxicology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, The Netherlands
| | - Eytan Ruppin
- The Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel The Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
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263
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Uhlen M. Integrating omics to study human biology and disease. N Biotechnol 2014. [DOI: 10.1016/j.nbt.2014.05.1723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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264
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Pinyol R, Llovet JM. Hepatocellular carcinoma: genome-scale metabolic models for hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 2014; 11:336-7. [PMID: 24840704 DOI: 10.1038/nrgastro.2014.70] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
A new study proposes a modelling strategy to identify reactions, genes and metabolites relevant in hepatocellular carcinoma using in silico and in vivo analyses. The proposed genome-scale metabolic model integrates genomic and proteomic information, and points to statins, among others, as potential chemopreventive and anticancer drugs.
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Affiliation(s)
- Roser Pinyol
- HCC Translational Research Laboratory, Barcelona Clinic Liver Cancer (BCLC) Group, Liver Unit, Hospital Clínic Barcelona, IDIBAPS, CIBERehd, University of Barcelona, Rosselló 153, 08036 Barcelona, Spain
| | - Josep M Llovet
- HCC Translational Research Laboratory, Barcelona Clinic Liver Cancer (BCLC) Group, Liver Unit, Hospital Clínic Barcelona, IDIBAPS, CIBERehd, University of Barcelona, Rosselló 153, 08036 Barcelona, Spain
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265
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Shoaie S, Nielsen J. Elucidating the interactions between the human gut microbiota and its host through metabolic modeling. Front Genet 2014; 5:86. [PMID: 24795748 PMCID: PMC4000997 DOI: 10.3389/fgene.2014.00086] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2014] [Accepted: 03/31/2014] [Indexed: 01/03/2023] Open
Abstract
Increased understanding of the interactions between the gut microbiota, diet and environmental effects may allow us to design efficient treatment strategies for addressing global health problems. Existence of symbiotic microorganisms in the human gut provides different functions for the host such as conversion of nutrients, training of the immune system, and resistance to pathogens. The gut microbiome also plays an influential role in maintaining human health, and it is a potential target for prevention and treatment of common disorders including obesity, type 2 diabetes, and atherosclerosis. Due to the extreme complexity of such disorders, it is necessary to develop mathematical models for deciphering the role of its individual elements as well as the entire system and such models may assist in better understanding of the interactions between the bacteria in the human gut and the host by use of genome-scale metabolic models (GEMs). Recently, GEMs have been employed to explore the interactions between predominant bacteria in the gut ecosystems. Additionally, these models enabled analysis of the contribution of each species to the overall metabolism of the microbiota through the integration of omics data. The outcome of these studies can be used for proposing optimal conditions for desired microbiome phenotypes. Here, we review the recent progress and challenges for elucidating the interactions between the human gut microbiota and host through metabolic modeling. We discuss how these models may provide scaffolds for analyzing high-throughput data, developing probiotics and prebiotics, evaluating the effects of probiotics and prebiotics and eventually designing clinical interventions.
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Affiliation(s)
- Saeed Shoaie
- Department of Chemical and Biological Engineering, Chalmers University of Technology Gothenburg, Sweden
| | - Jens Nielsen
- Department of Chemical and Biological Engineering, Chalmers University of Technology Gothenburg, Sweden
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266
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Kampf C, Mardinoglu A, Fagerberg L, Hallström BM, Edlund K, Lundberg E, Pontén F, Nielsen J, Uhlen M. The human liver-specific proteome defined by transcriptomics and antibody-based profiling. FASEB J 2014; 28:2901-14. [PMID: 24648543 DOI: 10.1096/fj.14-250555] [Citation(s) in RCA: 65] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Human liver physiology and the genetic etiology of the liver diseases can potentially be elucidated through the identification of proteins with enriched expression in the liver. Here, we combined data from RNA sequencing (RNA-Seq) and antibody-based immunohistochemistry across all major human tissues to explore the human liver proteome with enriched expression, as well as the cell type-enriched expression in hepatocyte and bile duct cells. We identified in total 477 protein-coding genes with elevated expression in the liver: 179 genes have higher expression as compared to all the other analyzed tissues; 164 genes have elevated transcript levels in the liver shared with at least one other tissue type; and an additional 134 genes have a mild level of increased expression in the liver. We identified the precise localization of these proteins through antibody-based protein profiling and the subcellular localization of these proteins through immunofluorescent-based profiling. We also identified the biological processes and metabolic functions associated with these proteins, investigated their contribution in the occurrence of liver diseases, and identified potential targets for their treatment. Our study demonstrates the use of RNA-Seq and antibody-based immunohistochemistry for characterizing the human liver proteome, as well as the use of tissue-specific proteins in identification of novel drug targets and discovery of biomarkers.-Kampf, C., Mardinoglu, A., Fagerberg, L., Hallström, B. M., Edlund, K., Lundberg, E., Pontén, F., Nielsen, J., Uhlen, M. The human liver-specific proteome defined by transcriptomics and antibody-based profiling.
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Affiliation(s)
- Caroline Kampf
- Department of Immunology, Genetics, and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Adil Mardinoglu
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; and
| | | | | | - Karolina Edlund
- Department of Immunology, Genetics, and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | | | - Fredrik Pontén
- Department of Immunology, Genetics, and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Jens Nielsen
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden; and Science for Life Laboratory and
| | - Mathias Uhlen
- Science for Life Laboratory and Department of Proteomics, School of Biotechnology, Royal Institute of Technology (KTH), Stockholm, Sweden
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267
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Agren R, Mardinoglu A, Asplund A, Kampf C, Uhlen M, Nielsen J. Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol Syst Biol 2014; 10:721. [PMID: 24646661 PMCID: PMC4017677 DOI: 10.1002/msb.145122] [Citation(s) in RCA: 266] [Impact Index Per Article: 26.6] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Genome-scale metabolic models (GEMs) have proven useful as scaffolds for the integration of omics data for understanding the genotype-phenotype relationship in a mechanistic manner. Here, we evaluated the presence/absence of proteins encoded by 15,841 genes in 27 hepatocellular carcinoma (HCC) patients using immunohistochemistry. We used this information to reconstruct personalized GEMs for six HCC patients based on the proteomics data, HMR 2.0, and a task-driven model reconstruction algorithm (tINIT). The personalized GEMs were employed to identify anticancer drugs using the concept of antimetabolites; i.e., drugs that are structural analogs to metabolites. The toxicity of each antimetabolite was predicted by assessing the in silico functionality of 83 healthy cell type-specific GEMs, which were also reconstructed with the tINIT algorithm. We predicted 101 antimetabolites that could be effective in preventing tumor growth in all HCC patients, and 46 antimetabolites which were specific to individual patients. Twenty-two of the 101 predicted antimetabolites have already been used in different cancer treatment strategies, while the remaining antimetabolites represent new potential drugs. Finally, one of the identified targets was validated experimentally, and it was confirmed to attenuate growth of the HepG2 cell line.
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Affiliation(s)
- Rasmus Agren
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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268
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Robaina Estévez S, Nikoloski Z. Generalized framework for context-specific metabolic model extraction methods. FRONTIERS IN PLANT SCIENCE 2014; 5:491. [PMID: 25285097 PMCID: PMC4168813 DOI: 10.3389/fpls.2014.00491] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/10/2014] [Accepted: 09/03/2014] [Indexed: 05/21/2023]
Abstract
Genome-scale metabolic models (GEMs) are increasingly applied to investigate the physiology not only of simple prokaryotes, but also eukaryotes, such as plants, characterized with compartmentalized cells of multiple types. While genome-scale models aim at including the entirety of known metabolic reactions, mounting evidence has indicated that only a subset of these reactions is active in a given context, including: developmental stage, cell type, or environment. As a result, several methods have been proposed to reconstruct context-specific models from existing genome-scale models by integrating various types of high-throughput data. Here we present a mathematical framework that puts all existing methods under one umbrella and provides the means to better understand their functioning, highlight similarities and differences, and to help users in selecting a most suitable method for an application.
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Affiliation(s)
| | - Zoran Nikoloski
- *Correspondence: Zoran Nikoloski, Systems Biology and Mathematical Modeling Group, Max-Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14424 Potsdam, Germany e-mail:
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