1
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Lee JH, Kim J, Kim HS, Kang YJ. Unraveling Connective Tissue Growth Factor as a Therapeutic Target and Assessing Kahweol as a Potential Drug Candidate in Triple-Negative Breast Cancer Treatment. Int J Mol Sci 2023; 24:16307. [PMID: 38003505 PMCID: PMC10671558 DOI: 10.3390/ijms242216307] [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: 08/17/2023] [Revised: 11/02/2023] [Accepted: 11/12/2023] [Indexed: 11/26/2023] Open
Abstract
Triple-negative breast cancer (TNBC) is characterized by aggressive behavior and limited treatment options, necessitating the identification of novel therapeutic targets. In this study, we investigated the clinical significance of connective tissue growth factor (CTGF) as a prognostic marker and explored the potential therapeutic effects of kahweol, a coffee diterpene molecule, in TNBC treatment. Initially, through a survival analysis on breast cancer patients from The Cancer Genome Atlas (TCGA) database, we found that CTGF exhibited significant prognostic effects exclusively in TNBC patients. To gain mechanistic insights, we performed the functional annotation and gene set enrichment analyses, revealing the involvement of CTGF in migratory pathways relevant to TNBC treatment. Subsequently, in vitro experiments using MDA-MB 231 cells, a representative TNBC cell line, demonstrated that recombinant CTGF (rCTGF) administration enhanced cell motility, whereas CTGF knockdown using CTGF siRNA resulted in reduced motility. Notably, rCTGF restored kahweol-reduced cell motility, providing compelling evidence for the role of CTGF in mediating kahweol's effects. At the molecular level, kahweol downregulated the protein expression of CTGF as well as critical signaling molecules, such as p-ERK, p-P38, p-PI3K/AKT, and p-FAK, associated with cell motility. In summary, our findings propose CTGF as a potential prognostic marker for guiding TNBC treatment and suggest kahweol as a promising antitumor compound capable of regulating CTGF expression to suppress cell motility in TNBC. These insights hold promise for the development of targeted therapies and improved clinical outcomes for TNBC patients.
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Affiliation(s)
- Jeong Hee Lee
- Department of Biological Sciences, Sungkyunkwan University, Suwon 16419, Republic of Korea; (J.H.L.); (J.K.)
| | - Jongsu Kim
- Department of Biological Sciences, Sungkyunkwan University, Suwon 16419, Republic of Korea; (J.H.L.); (J.K.)
| | - Hong Sook Kim
- Department of Biological Sciences, Sungkyunkwan University, Suwon 16419, Republic of Korea; (J.H.L.); (J.K.)
| | - Young Jin Kang
- Department of Pharmacology, College of Medicine, Yeungnam University, Daegu 42415, Republic of Korea
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2
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Cesur MF, Basile A, Patil KR, Çakır T. A new metabolic model of Drosophila melanogaster and the integrative analysis of Parkinson's disease. Life Sci Alliance 2023; 6:e202201695. [PMID: 37236669 PMCID: PMC10215973 DOI: 10.26508/lsa.202201695] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 05/28/2023] Open
Abstract
High conservation of the disease-associated genes between flies and humans facilitates the common use of Drosophila melanogaster to study metabolic disorders under controlled laboratory conditions. However, metabolic modeling studies are highly limited for this organism. We here report a comprehensively curated genome-scale metabolic network model of Drosophila using an orthology-based approach. The gene coverage and metabolic information of the draft model derived from a reference human model were expanded via Drosophila-specific KEGG and MetaCyc databases, with several curation steps to avoid metabolic redundancy and stoichiometric inconsistency. Furthermore, we performed literature-based curations to improve gene-reaction associations, subcellular metabolite locations, and various metabolic pathways. The performance of the resulting Drosophila model (8,230 reactions, 6,990 metabolites, and 2,388 genes), iDrosophila1 (https://github.com/SysBioGTU/iDrosophila), was assessed using flux balance analysis in comparison with the other currently available fly models leading to superior or comparable results. We also evaluated the transcriptome-based prediction capacity of iDrosophila1, where differential metabolic pathways during Parkinson's disease could be successfully elucidated. Overall, iDrosophila1 is promising to investigate system-level metabolic alterations in response to genetic and environmental perturbations.
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Affiliation(s)
- Müberra Fatma Cesur
- Systems Biology and Bioinformatics Program, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Arianna Basile
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Kiran Raosaheb Patil
- Medical Research Council Toxicology Unit, University of Cambridge, Cambridge, UK
| | - Tunahan Çakır
- Systems Biology and Bioinformatics Program, Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
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3
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Bar-Peled L, Kory N. Principles and functions of metabolic compartmentalization. Nat Metab 2022; 4:1232-1244. [PMID: 36266543 PMCID: PMC10155461 DOI: 10.1038/s42255-022-00645-2] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/24/2022] [Indexed: 01/20/2023]
Abstract
Metabolism has historically been studied at the levels of whole cells, whole tissues and whole organisms. As a result, our understanding of how compartmentalization-the spatial and temporal separation of pathways and components-shapes organismal metabolism remains limited. At its essence, metabolic compartmentalization fulfils three important functions or 'pillars': establishing unique chemical environments, providing protection from reactive metabolites and enabling the regulation of metabolic pathways. However, how these pillars are established, regulated and maintained at both the cellular and systemic levels remains unclear. Here we discuss how the three pillars are established, maintained and regulated within the cell and discuss the consequences of dysregulation of metabolic compartmentalization in human disease. Organelles are increasingly emerging as 'command-and-control centres' and the increased understanding of metabolic compartmentalization is revealing new aspects of metabolic homeostasis, with this knowledge being translated into therapies for the treatment of cancer and certain neurodegenerative diseases.
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Affiliation(s)
- Liron Bar-Peled
- Center for Cancer Research, Massachusetts General Hospital and Department of Medicine, Harvard Medical School, Boston, MA, USA.
| | - Nora Kory
- Department of Molecular Metabolism, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
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4
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Gao C, Yang J, Hao T, Li J, Sun J. Reconstruction of Litopenaeus vannamei Genome-Scale Metabolic Network Model and Nutritional Requirements Analysis of Different Shrimp Commercial Varieties. Front Genet 2021; 12:658109. [PMID: 34054922 PMCID: PMC8149995 DOI: 10.3389/fgene.2021.658109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 03/29/2021] [Indexed: 11/22/2022] Open
Abstract
As an important tool for systematic analysis, genome-scale metabolic network (GSMN) model has been widely used in various organisms. However, there are few reports on the GSMNs of aquatic crustaceans. Litopenaeus vannamei is the largest and most productive shrimp species. Feed improvement is one of the important methods to improve the yield of L. vannamei and control water pollution caused by the inadequate absorption of feed. In this work, the first L. vannamei GSMN named iGH3005 was reconstructed and applied to the optimization of feed. iGH3005 was reconstructed based on the genomic data. The model includes 2,292 reactions and 3,005 genes. iGH3005 was used to analyze the nutritional requirements of five different L. vannamei commercial varieties and the genes influencing the metabolism of the nutrients. Based on the simulation, we found that tyrosine-protein kinase src64b like may catalyze different reactions in different commercial varieties. The preference of carbohydrate utilization is different in various commercial varieties, which may due to the different expressions of some genes. In addition, this investigation suggests that a rational and targeted modification in the macronutrient content of shrimp feed would lead to an increase in growth and feed conversion rate. The feed for different commercial varieties should be adjusted accordingly, and possible adjustment schemes were provided. The results of this work provided important information for physiological research and optimization of the components in feed of L. vannamei.
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Affiliation(s)
- Chenchen Gao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Jiarui Yang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Tong Hao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Jingjing Li
- Tianjin Fisheries Research Institute, Tianjin, China
| | - Jinsheng Sun
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
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5
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Ghulam A, Lei X, Guo M, Bian C. A Review of Pathway Databases and Related Methods Analysis. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191018162505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Pathway analysis integrates most of the computational tools for the investigation of
high-level and complex human diseases. In the field of bioinformatics research, biological pathways
analysis is an important part of systems biology. The molecular complexities of biological
pathways are difficult to understand in human diseases, which can be explored through pathway
analysis. In this review, we describe essential information related to pathway databases and their
mechanisms, algorithms and methods. In the pathway database analysis, we present a brief introduction
on how to gain knowledge from fundamental pathway data in regard to specific human
pathways and how to use pathway databases and pathway analysis to predict diseases during an
experiment. We also provide detailed information related to computational tools that are used in
complex pathway data analysis, the roles of these tools in the bioinformatics field and how to store
the pathway data. We illustrate various methodological difficulties that are faced during pathway
analysis. The main ideas and techniques for the pathway-based examination approaches are presented.
We provide the list of pathway databases and analytical tools. This review will serve as a
helpful manual for pathway analysis databases.
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Affiliation(s)
- Ali Ghulam
- School of Computer Science, Shaanxi Normal University, Xian, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xian, China
| | - Min Guo
- School of Computer Science, Shaanxi Normal University, Xian, China
| | - Chen Bian
- School of Computer Science, Shaanxi Normal University, Xian, China
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6
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Wang B, Yang J, Gao C, Hao T, Li J, Sun J. Reconstruction of Eriocheir sinensis Y-organ Genome-Scale Metabolic Network and Differential Analysis After Eyestalk Ablation. Front Genet 2020; 11:532492. [PMID: 33101373 PMCID: PMC7545369 DOI: 10.3389/fgene.2020.532492] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 09/07/2020] [Indexed: 12/23/2022] Open
Abstract
Genome-scale metabolic network (GSMN) has been proven to be a useful tool for the system analysis of organism metabolism and applied to deeply explore the metabolic functions or mechanisms in many organisms, including model or non-model organisms. However, the systematic studies on the metabolisms of aquatic animals are seldom reported, especially the aquatic crustaceans. In this work, we reconstructed an Eriocheir sinensis Y-organ GSMN based on the transcriptome sequencing of Y-organ, which includes 1,645 reactions, 1,885 unigenes, and 1,524 metabolites distributed in 100 pathways and 11 subsystems. Functional module and centrality analysis of the GSMN show the main metabolic functions of Y-organ. Further analysis of the differentially expressed unigenes in Y-organ after eyestalk ablation reveals that 191 genes in the network were up-regulated and 283 were down-regulated. The unigenes associated with the ecdysone synthetic pathway were all up-regulated, which is consistent with the report on the increasing secretion of ecdysone after eyestalk ablation. Besides, we compared the Y-organ GSMN with that of E. sinensis eyestalk and hepatopancreas, and we analyzed the specific metabolisms in each organ. The specific metabolisms and pathways of these three networks are closely related to their corresponding metabolic functions. The GSMN reconstructed in this work provides a new method and many novel clues for further understanding the physiological function of Y-organ. It also supplies a new platform for the investigation of the interactions among different organs in the growth process of E. sinensis.
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Affiliation(s)
- Bin Wang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Jiarui Yang
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Chenchen Gao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Tong Hao
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
| | - Jingjing Li
- Tianjin Fisheries Research Institute, Tianjin, China
| | - Jinsheng Sun
- Tianjin Key Laboratory of Animal and Plant Resistance, College of Life Sciences, Tianjin Normal University, Tianjin, China
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7
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Abstract
Recent advances in analytical techniques, particularly LC-MS, generate increasingly large and complex metabolomics datasets. Pathway analysis tools help place the experimental observations into relevant biological or disease context. This chapter provides an overview of the general concepts and common tools for pathway analysis, including Mummichog for untargeted metabolomics. Examples of pathway mapping, MetScape, and Mummichog are explained. This serves as both a practical tutorial and a timely survey of pathway analysis for label-free metabolomics data.
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8
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Waller TC, Berg JA, Lex A, Chapman BE, Rutter J. Compartment and hub definitions tune metabolic networks for metabolomic interpretations. Gigascience 2020; 9:giz137. [PMID: 31972021 PMCID: PMC6977586 DOI: 10.1093/gigascience/giz137] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 08/31/2019] [Accepted: 10/27/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism's cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments. RESULTS We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications. CONCLUSIONS Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.
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Affiliation(s)
- T Cameron Waller
- Division of Medical Genetics, Department of Medicine, School of Medicine, University of California San Diego, Room 1318A, 9500 Gilman Drive #0606, La Jolla, California 92093-0606, United States of America
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
| | - Jordan A Berg
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
| | - Alexander Lex
- School of Computing, University of Utah, Room 3190, 50 South Central Campus Drive, Salt Lake City, Utah 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Room 3750, 72 South Central Campus Drive, Salt Lake City, Utah 84112, USA
| | - Brian E Chapman
- Department of Radiology and Imaging Sciences, School of Medicine, University of Utah, Room 1A071, 30 North 1900 East, Salt Lake City, Utah 84132, USA
- Department of Biomedical Informatics, School of Medicine, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, Utah 84108, USA
| | - Jared Rutter
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
- Howard Hughes Medical Institute, School of Medicine, University of Utah, Room AC101, 30 North 1900 East, Salt Lake City, Utah 84132, USA
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9
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Chu SH, Huang M, Kelly RS, Benedetti E, Siddiqui JK, Zeleznik OA, Pereira A, Herrington D, Wheelock CE, Krumsiek J, McGeachie M, Moore SC, Kraft P, Mathé E, Lasky-Su J. Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective. Metabolites 2019; 9:E117. [PMID: 31216675 PMCID: PMC6630728 DOI: 10.3390/metabo9060117] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 06/12/2019] [Accepted: 06/14/2019] [Indexed: 12/30/2022] Open
Abstract
It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.
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Affiliation(s)
- Su H Chu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Mengna Huang
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Elisa Benedetti
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Jalal K Siddiqui
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Alexandre Pereira
- Department of Genetics and Molecular Medicine, University of Sao Paulo Medical School, Sao Paulo 01246-903, Brazil.
| | - David Herrington
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA.
| | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 77 Stockholm, Sweden.
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Michael McGeachie
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA.
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
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10
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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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11
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Elucidation of Novel Therapeutic Targets for Acute Myeloid Leukemias with RUNX1- RUNX1T1 Fusion. Int J Mol Sci 2019; 20:ijms20071717. [PMID: 30959925 PMCID: PMC6480444 DOI: 10.3390/ijms20071717] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 04/02/2019] [Accepted: 04/04/2019] [Indexed: 11/17/2022] Open
Abstract
The RUNX1-RUNX1T1 fusion is a frequent chromosomal alteration in acute myeloid leukemias (AMLs). Although RUNX1-RUNX1T1 fusion protein has pivotal roles in the development of AMLs with the fusion, RUNX1-RUNX1T1, fusion protein is difficult to target, as it lacks kinase activities. Here, we used bioinformatic tools to elucidate targetable signaling pathways in AMLs with RUNX1-RUNX1T1 fusion. After analysis of 93 AML cases from The Cancer Genome Atlas (TCGA) database, we found expression of 293 genes that correlated to the expression of the RUNX1-RUNX1T1 fusion gene. Based on these 293 genes, the cyclooxygenase (COX), vascular endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), and fibroblast growth factor receptor (FGFR) pathways were predicted to be specifically activated in AMLs with RUNX1-RUNX1T1 fusion. Moreover, the in vitro proliferation of AML cells with RUNX1-RUNX1T1 fusion decreased significantly more than that of AML cells without the fusion, when the pathways were inhibited pharmacologically. The results indicate that novel targetable signaling pathways could be identified by the analysis of the gene expression features of AMLs with non-targetable genetic alterations. The elucidation of specific molecular targets for AMLs that have a specific genetic alteration would promote personalized treatment of AMLs and improve clinical outcomes.
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12
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Jeffrey MG, Nathanson L, Aenlle K, Barnes ZM, Baig M, Broderick G, Klimas NG, Fletcher MA, Craddock TJA. Treatment Avenues in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Split-gender Pharmacogenomic Study of Gene-expression Modules. Clin Ther 2019; 41:815-835.e6. [PMID: 30851951 DOI: 10.1016/j.clinthera.2019.01.011] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Revised: 01/09/2019] [Accepted: 01/18/2019] [Indexed: 12/13/2022]
Abstract
PURPOSE Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a debilitating multisymptom illness impacting up to 1 million people in the United States. As the pathogenesis and etiology of this complex condition are unclear, prospective treatments are limited. Identifying US Food and Drug Administration-approved drugs that may be repositioned as treatments for ME/CFS may offer a rapid and cost-effective solution. METHODS Here we used gene-expression data from 33 patients with Fukuda-defined ME/CFS (23 females, 10 males) and 21 healthy demographically comparable controls (15 females, 6 males) to identify differential expression of predefined gene-module sets based on nonparametric statistics. Differentially expressed gene modules were then annotated via over-representation analysis using the Consensus Pathway database. Differentially expressed modules were then regressed onto measures of fatigue and cross-referenced with drug atlas and pharmacogenomics databases to identify putative treatment agents. FINDINGS The top 1% of modules identified in males indicated small effect sizes in modules associated with immune regulation and mitochondrial dysfunction. In females, modules identified included those related to immune factors and cardiac/blood factors, returning effect sizes ranging from very small to intermediate (0.147 < Cohen δ < 0.532). Regression analysis indicated that B-cell receptors, T-cell receptors, tumor necrosis factor α, transforming growth factor β, and metabolic and cardiac modules were strongly correlated with multiple composite measures of fatigue. Cross-referencing identified genes with pharmacogenomics data indicated immunosuppressants as potential treatments of ME/CFS symptoms. IMPLICATIONS The findings from our analysis suggest that ME/CFS symptoms are perpetuated by immune dysregulation that may be approached via immune modulation-based treatment strategies.
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Affiliation(s)
- Mary G Jeffrey
- Institute for Neuro-Immune Medicine, Nova Southeastern University, Ft. Lauderdale, FL, USA; College of Psychology, Nova Southeastern University, Ft. Lauderdale, FL, USA
| | - Lubov Nathanson
- Institute for Neuro-Immune Medicine, Nova Southeastern University, Ft. Lauderdale, FL, USA; Department of Clinical Immunology, Nova Southeastern University, Ft. Lauderdale, FL, USA
| | - Kristina Aenlle
- Institute for Neuro-Immune Medicine, Nova Southeastern University, Ft. Lauderdale, FL, USA; Department of Clinical Immunology, Nova Southeastern University, Ft. Lauderdale, FL, USA; Miami Veterans Affairs Medical Center, Miami, FL, USA
| | - Zachary M Barnes
- Institute for Neuro-Immune Medicine, Nova Southeastern University, Ft. Lauderdale, FL, USA; Miami Veterans Affairs Medical Center, Miami, FL, USA; Miller School of Medicine, University of Miami, Miami, FL, USA; Diabetes Research Institute, University of Miami, Miami, FL, USA
| | - Mirza Baig
- Institute for Neuro-Immune Medicine, Nova Southeastern University, Ft. Lauderdale, FL, USA
| | - Gordon Broderick
- Institute for Neuro-Immune Medicine, Nova Southeastern University, Ft. Lauderdale, FL, USA; College of Psychology, Nova Southeastern University, Ft. Lauderdale, FL, USA; Department of Clinical Immunology, Nova Southeastern University, Ft. Lauderdale, FL, USA; Department of Medicine, University of Alberta, Edmonton, Alberta, Canada; Center for Clinical Systems Biology, Rochester General Hospital, Rochester, NY, USA
| | - Nancy G Klimas
- Institute for Neuro-Immune Medicine, Nova Southeastern University, Ft. Lauderdale, FL, USA; College of Psychology, Nova Southeastern University, Ft. Lauderdale, FL, USA; Department of Clinical Immunology, Nova Southeastern University, Ft. Lauderdale, FL, USA
| | - Mary Ann Fletcher
- Institute for Neuro-Immune Medicine, Nova Southeastern University, Ft. Lauderdale, FL, USA; Department of Clinical Immunology, Nova Southeastern University, Ft. Lauderdale, FL, USA; Miami Veterans Affairs Medical Center, Miami, FL, USA
| | - Travis J A Craddock
- Institute for Neuro-Immune Medicine, Nova Southeastern University, Ft. Lauderdale, FL, USA; College of Psychology, Nova Southeastern University, Ft. Lauderdale, FL, USA; Department of Clinical Immunology, Nova Southeastern University, Ft. Lauderdale, FL, USA; Department of Computer Science, Nova Southeastern University, Ft. Lauderdale, FL, USA.
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13
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Perez M, Jaundoo R, Hilton K, Del Alamo A, Gemayel K, Klimas NG, Craddock TJA, Nathanson L. Genetic Predisposition for Immune System, Hormone, and Metabolic Dysfunction in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome: A Pilot Study. Front Pediatr 2019; 7:206. [PMID: 31179255 PMCID: PMC6542994 DOI: 10.3389/fped.2019.00206] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Accepted: 05/03/2019] [Indexed: 12/25/2022] Open
Abstract
Introduction: Myalgic Encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS) is a multifactorial illness of unknown etiology with considerable social and economic impact. To investigate a putative genetic predisposition to ME/CFS we conducted genome-wide single-nucleotide polymorphism (SNP) analysis to identify possible variants. Methods: 383 ME/CFS participants underwent DNA testing using the commercial company 23andMe. The deidentified genetic data was then filtered to include only non-synonymous and nonsense SNPs from exons and microRNAs, and SNPs close to splice sites. The frequencies of each SNP were calculated within our cohort and compared to frequencies from the Kaviar reference database. Functional annotation of pathway sets containing SNP genes with high frequency in ME/CFS was performed using over-representation analysis via ConsensusPathDB. Furthermore, these SNPs were also scored using the Combined Annotation Dependent Depletion (CADD) algorithm to gauge their deleteriousness. Results: 5693 SNPs were found to have at least 10% frequency in at least one cohort (ME/CFS or reference) and at least two-fold absolute difference for ME/CFS. Functional analysis identified the majority of SNPs as related to immune system, hormone, metabolic, and extracellular matrix organization. CADD scoring identified 517 SNPs in these pathways that are among the 10% most deleteriousness substitutions to the human genome.
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Affiliation(s)
- Melanie Perez
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States
| | - Rajeev Jaundoo
- Department of Psychology and Neuroscience, Nova Southeastern University, Fort Lauderdale, FL, United States.,Institute for Neuro Immune Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States
| | - Kelly Hilton
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States
| | - Ana Del Alamo
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States.,Institute for Neuro Immune Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States
| | - Kristina Gemayel
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States
| | - Nancy G Klimas
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States.,Institute for Neuro Immune Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States.,Veterans Affairs Medical Center, Miami, FL, United States
| | - Travis J A Craddock
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States.,Department of Psychology and Neuroscience, Nova Southeastern University, Fort Lauderdale, FL, United States.,Institute for Neuro Immune Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States.,Department of Computer Science, Nova Southeastern University, Fort Lauderdale, FL, United States
| | - Lubov Nathanson
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States.,Institute for Neuro Immune Medicine, Nova Southeastern University, Fort Lauderdale, FL, United States
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14
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Sertbas M, Ulgen KO. Unlocking Human Brain Metabolism by Genome-Scale and Multiomics Metabolic Models: Relevance for Neurology Research, Health, and Disease. ACTA ACUST UNITED AC 2018; 22:455-467. [DOI: 10.1089/omi.2018.0088] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Affiliation(s)
- Mustafa Sertbas
- Department of Chemical Engineering, Bogazici University, Istanbul, Turkey
| | - Kutlu O. Ulgen
- Department of Chemical Engineering, Bogazici University, Istanbul, Turkey
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15
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Basu S, Duren W, Evans CR, Burant CF, Michailidis G, Karnovsky A. Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data. Bioinformatics 2018; 33:1545-1553. [PMID: 28137712 DOI: 10.1093/bioinformatics/btx012] [Citation(s) in RCA: 91] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2016] [Accepted: 01/11/2017] [Indexed: 02/01/2023] Open
Abstract
Motivation Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data. Results Leveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds. Availability and Implementation http://metscape.med.umich.edu. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Sumanta Basu
- Department of Statistics, University of California, Berkeley, CA, USA.,Department of Genome Dynamics, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - William Duren
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Charles R Evans
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Charles F Burant
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
| | | | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan Medical School, Ann Arbor, MI, USA
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16
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Eyassu F, Angione C. Modelling pyruvate dehydrogenase under hypoxia and its role in cancer metabolism. ROYAL SOCIETY OPEN SCIENCE 2017; 4:170360. [PMID: 29134060 PMCID: PMC5666243 DOI: 10.1098/rsos.170360] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2017] [Accepted: 09/25/2017] [Indexed: 05/18/2023]
Abstract
Metabolism is the only biological system that can be fully modelled at genome scale. As a result, metabolic models have been increasingly used to study the molecular mechanisms of various diseases. Hypoxia, a low-oxygen tension, is a well-known characteristic of many cancer cells. Pyruvate dehydrogenase (PDH) controls the flux of metabolites between glycolysis and the tricarboxylic acid cycle and is a key enzyme in metabolic reprogramming in cancer metabolism. Here, we develop and manually curate a constraint-based metabolic model to investigate the mechanism of pyruvate dehydrogenase under hypoxia. Our results characterize the activity of pyruvate dehydrogenase and its decline during hypoxia. This results in lactate accumulation, consistent with recent hypoxia studies and a well-known feature in cancer metabolism. We apply machine-learning techniques on the flux datasets to identify reactions that drive these variations. We also identify distinct features on the structure of the variables and individual metabolic components in the switch from normoxia to hypoxia. Our results provide a framework for future studies by integrating multi-omics data to predict condition-specific metabolic phenotypes under hypoxia.
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17
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Alvarez-Silva MC, Álvarez-Yela AC, Gómez-Cano F, Zambrano MM, Husserl J, Danies G, Restrepo S, González-Barrios AF. Compartmentalized metabolic network reconstruction of microbial communities to determine the effect of agricultural intervention on soils. PLoS One 2017; 12:e0181826. [PMID: 28767679 PMCID: PMC5540551 DOI: 10.1371/journal.pone.0181826] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2016] [Accepted: 07/09/2017] [Indexed: 01/02/2023] Open
Abstract
Soil microbial communities are responsible for a wide range of ecological processes and have an important economic impact in agriculture. Determining the metabolic processes performed by microbial communities is crucial for understanding and managing ecosystem properties. Metagenomic approaches allow the elucidation of the main metabolic processes that determine the performance of microbial communities under different environmental conditions and perturbations. Here we present the first compartmentalized metabolic reconstruction at a metagenomics scale of a microbial ecosystem. This systematic approach conceives a meta-organism without boundaries between individual organisms and allows the in silico evaluation of the effect of agricultural intervention on soils at a metagenomics level. To characterize the microbial ecosystems, topological properties, taxonomic and metabolic profiles, as well as a Flux Balance Analysis (FBA) were considered. Furthermore, topological and optimization algorithms were implemented to carry out the curation of the models, to ensure the continuity of the fluxes between the metabolic pathways, and to confirm the metabolite exchange between subcellular compartments. The proposed models provide specific information about ecosystems that are generally overlooked in non-compartmentalized or non-curated networks, like the influence of transport reactions in the metabolic processes, especially the important effect on mitochondrial processes, as well as provide more accurate results of the fluxes used to optimize the metabolic processes within the microbial community.
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Affiliation(s)
- María Camila Alvarez-Silva
- Grupo de Diseño de Productos y Procesos (GDPP), Department of Chemical Engineering, Universidad de los Andes, Bogotá, Colombia
| | - Astrid Catalina Álvarez-Yela
- Grupo de Diseño de Productos y Procesos (GDPP), Department of Chemical Engineering, Universidad de los Andes, Bogotá, Colombia
| | - Fabio Gómez-Cano
- Laboratorio de Micología y Fitopatología (LAMFU), Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
| | - María Mercedes Zambrano
- Center for Genomics and Bioinformatics of Extreme Environments (Gebix), Bogotá, Colombia
- Corporación Corpogen Research Center, Bogotá, Colombia
| | - Johana Husserl
- Centro de Investigaciones en Ingeniería Ambiental, Department of Environmental Engineering, Universidad de los Andes, Bogotá, Colombia
| | - Giovanna Danies
- Laboratorio de Micología y Fitopatología (LAMFU), Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
| | - Silvia Restrepo
- Laboratorio de Micología y Fitopatología (LAMFU), Department of Biological Sciences, Universidad de los Andes, Bogotá, Colombia
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18
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Biomedical applications of cell- and tissue-specific metabolic network models. J Biomed Inform 2017; 68:35-49. [DOI: 10.1016/j.jbi.2017.02.014] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 02/21/2017] [Accepted: 02/23/2017] [Indexed: 12/17/2022]
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19
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Analysing Algorithms and Data Sources for the Tissue-Specific Reconstruction of Liver Healthy and Cancer Cells. Interdiscip Sci 2017; 9:36-45. [PMID: 28255832 DOI: 10.1007/s12539-017-0214-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Revised: 12/12/2016] [Accepted: 01/02/2017] [Indexed: 01/27/2023]
Abstract
Genome-Scale Metabolic Models (GSMMs), mathematical representations of the cell metabolism in different organisms including humans, are resourceful tools to simulate metabolic phenotypes and understand associated diseases, such as obesity, diabetes and cancer. In the last years, different algorithms have been developed to generate tissue-specific metabolic models that simulate different phenotypes for distinct cell types. Hepatocyte cells are one of the main sites of metabolic conversions, mainly due to their diverse physiological functions. Most of the liver's tissue is formed by hepatocytes, being one of the largest and most important organs regarding its biological functions. Hepatocellular carcinoma is, also, one of the most important human cancers with high mortality rates. In this study, we will analyze four different algorithms (MBA, mCADRE, tINIT and FASTCORE) for tissue-specific model reconstruction, based on a template model and two types of data sources: transcriptomics and proteomics. These methods will be applied to the reconstruction of metabolic models for hepatocyte cells and HepG2 cancer cell line. The models will be analyzed and compared under different perspectives, emphasizing their functional analysis considering a set of metabolic liver tasks. The results show that there is no "ideal" algorithm. However, with the current analysis, we were able to retrieve knowledge about the metabolism of the liver.
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20
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Ryu JY, Kim HU, Lee SY. Reconstruction of genome-scale human metabolic models using omics data. Integr Biol (Camb) 2016; 7:859-68. [PMID: 25730289 DOI: 10.1039/c5ib00002e] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The impact of genome-scale human metabolic models on human systems biology and medical sciences is becoming greater, thanks to increasing volumes of model building platforms and publicly available omics data. The genome-scale human metabolic models started with Recon 1 in 2007, and have since been used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic models. Integration of tissue/cell type-specific omics data with the generic human metabolic models is the key step, and we discuss omics data and their integration methods to achieve this task. The initial version of the tissue/cell type-specific human metabolic models can further be computationally refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model.
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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), Daejeon 305-701, Republic of Korea.
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21
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Di Filippo M, Colombo R, Damiani C, Pescini D, Gaglio D, Vanoni M, Alberghina L, Mauri G. Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models. Comput Biol Chem 2016; 62:60-9. [PMID: 27085310 DOI: 10.1016/j.compbiolchem.2016.03.002] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Revised: 02/25/2016] [Accepted: 03/04/2016] [Indexed: 01/10/2023]
Abstract
The metabolic rearrangements occurring in cancer cells can be effectively investigated with a Systems Biology approach supported by metabolic network modeling. We here present tissue-specific constraint-based core models for three different types of tumors (liver, breast and lung) that serve this purpose. The core models were extracted and manually curated from the corresponding genome-scale metabolic models in the Human Metabolic Atlas database with a focus on the pathways that are known to play a key role in cancer growth and proliferation. Along similar lines, we also reconstructed a core model from the original general human metabolic network to be used as a reference model. A comparative Flux Balance Analysis between the reference and the cancer models highlighted both a clear distinction between the two conditions and a heterogeneity within the three different cancer types in terms of metabolic flux distribution. These results emphasize the need for modeling approaches able to keep up with this tumoral heterogeneity in order to identify more suitable drug targets and develop effective treatments. According to this perspective, we identified key points able to reverse the tumoral phenotype toward the reference one or vice-versa.
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Affiliation(s)
- Marzia Di Filippo
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
| | - Riccardo Colombo
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
| | - Chiara Damiani
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
| | - Dario Pescini
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Statistica e Metodi Quantitativi, Università degli Studi di Milano-Bicocca, Via Bicocca degli Arcimboldi 8, 20126 Milano, Italy.
| | - Daniela Gaglio
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Istituto di Bioimmagini e Fisiologia Molecolare, Consiglio Nazionale delle Ricerche, Via F.lli Cervi 93, 20090 Segrate (MI), Italy
| | - Marco Vanoni
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy
| | - Lilia Alberghina
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy
| | - Giancarlo Mauri
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy; Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126 Milano, Italy
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Aurich MK, Thiele I. Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine. Methods Mol Biol 2016; 1386:253-81. [PMID: 26677187 DOI: 10.1007/978-1-4939-3283-2_12] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. Constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge base to studies that take advantage of the model's topology and, most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data.An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine.
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Affiliation(s)
- Maike K Aurich
- Luxembourg Center for Systems Biomedicine, University of Luxembourg, Campus Belval, 7, Avenue des Hauts-Fourneaux, Esch-sur-alzette, L-4362, Luxembourg
| | - Ines Thiele
- Luxembourg Center for Systems Biomedicine, University of Luxembourg, Campus Belval, 7, Avenue des Hauts-Fourneaux, Esch-sur-alzette, L-4362, Luxembourg.
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23
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Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Nat Commun 2015; 5:3083. [PMID: 24419221 DOI: 10.1038/ncomms4083] [Citation(s) in RCA: 380] [Impact Index Per Article: 42.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2013] [Accepted: 12/10/2013] [Indexed: 02/07/2023] Open
Abstract
Several liver disorders result from perturbations in the metabolism of hepatocytes, and their underlying mechanisms can be outlined through the use of genome-scale metabolic models (GEMs). Here we reconstruct a consensus GEM for hepatocytes, which we call iHepatocytes2322, that extends previous models by including an extensive description of lipid metabolism. We build iHepatocytes2322 using Human Metabolic Reaction 2.0 database and proteomics data in Human Protein Atlas, which experimentally validates the incorporated reactions. The reconstruction process enables improved annotation of the proteomics data using the network centric view of iHepatocytes2322. We then use iHepatocytes2322 to analyse transcriptomics data obtained from patients with non-alcoholic fatty liver disease. We show that blood concentrations of chondroitin and heparan sulphates are suitable for diagnosing non-alcoholic steatohepatitis and for the staging of non-alcoholic fatty liver disease. Furthermore, we observe serine deficiency in patients with NASH and identify PSPH, SHMT1 and BCAT1 as potential therapeutic targets for the treatment of non-alcoholic steatohepatitis.
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24
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Wang B, Ning Q, Hao T, Yu A, Sun J. Reconstruction and analysis of a genome-scale metabolic model for Eriocheir sinensis eyestalks. MOLECULAR BIOSYSTEMS 2015; 12:246-52. [PMID: 26588667 DOI: 10.1039/c5mb00571j] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
The eyestalk of Eriocheir sinensis has significant biological functions with many nerve peptide hormones expressed in the X-organ which exists in the eyestalk. A metabolic network model is an effective tool for the systematic study of E. sinensis eyestalks. In this work, we reconstructed a metabolic network model for E. sinensis eyestalks based on transcriptome sequencing. The model contains 1304 reactions, 1381 unigenes and 1243 metabolites distributing in 98 pathways. The reconstructed metabolic network model was used for the functional module and block metabolite analysis of eyestalks, which reveals that the function of the eyestalk network agrees with its function as the centre of the E. sinensis endocrine system. The difference expression analysis of reactions in the model indicates that the eyestalk mainly functions in the regulation of amino acids, carbohydrate and nucleotide metabolism.
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Affiliation(s)
- Bin Wang
- College of Life Sciences, Henan Normal University, Xinxiang 453007, Henan, P. R. China.
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25
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Whitmore LS, Ye P. Dissecting Germ Cell Metabolism through Network Modeling. PLoS One 2015; 10:e0137607. [PMID: 26367011 PMCID: PMC4721539 DOI: 10.1371/journal.pone.0137607] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2014] [Accepted: 08/20/2015] [Indexed: 11/18/2022] Open
Abstract
Metabolic pathways are increasingly postulated to be vital in programming cell fate, including stemness, differentiation, proliferation, and apoptosis. The commitment to meiosis is a critical fate decision for mammalian germ cells, and requires a metabolic derivative of vitamin A, retinoic acid (RA). Recent evidence showed that a pulse of RA is generated in the testis of male mice thereby triggering meiotic commitment. However, enzymes and reactions that regulate this RA pulse have yet to be identified. We developed a mouse germ cell-specific metabolic network with a curated vitamin A pathway. Using this network, we implemented flux balance analysis throughout the initial wave of spermatogenesis to elucidate important reactions and enzymes for the generation and degradation of RA. Our results indicate that primary RA sources in the germ cell include RA import from the extracellular region, release of RA from binding proteins, and metabolism of retinal to RA. Further, in silico knockouts of genes and reactions in the vitamin A pathway predict that deletion of Lipe, hormone-sensitive lipase, disrupts the RA pulse thereby causing spermatogenic defects. Examination of other metabolic pathways reveals that the citric acid cycle is the most active pathway. In addition, we discover that fatty acid synthesis/oxidation are the primary energy sources in the germ cell. In summary, this study predicts enzymes, reactions, and pathways important for germ cell commitment to meiosis. These findings enhance our understanding of the metabolic control of germ cell differentiation and will help guide future experiments to improve reproductive health.
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Affiliation(s)
- Leanne S. Whitmore
- School of Molecular Biosciences, Washington State University, PO Box 647520, Pullman, Washington, 99164, United States of America
| | - Ping Ye
- School of Molecular Biosciences, Washington State University, PO Box 647520, Pullman, Washington, 99164, United States of America
- Department of Molecular and Experimental Medicine, Avera Cancer Institute, 1000 E 23rd Street, Sioux Falls, South Dakota, 57105, United States of America
- * E-mail:
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26
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Craddock TJA, Harvey JM, Nathanson L, Barnes ZM, Klimas NG, Fletcher MA, Broderick G. Using gene expression signatures to identify novel treatment strategies in gulf war illness. BMC Med Genomics 2015; 8:36. [PMID: 26156520 PMCID: PMC4495687 DOI: 10.1186/s12920-015-0111-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2015] [Accepted: 06/26/2015] [Indexed: 12/12/2022] Open
Abstract
Background Gulf War Illness (GWI) is a complex multi-symptom disorder that affects up to one in three veterans of this 1991 conflict and for which no effective treatment has been found. Discovering novel treatment strategies for such a complex chronic illness is extremely expensive, carries a high probability of failure and a lengthy cycle time. Repurposing Food and Drug Administration approved drugs offers a cost-effective solution with a significantly abbreviated timeline. Methods Here, we explore drug re-purposing opportunities in GWI by combining systems biology and bioinformatics techniques with pharmacogenomic information to find overlapping elements in gene expression linking GWI to successfully treated diseases. Gene modules were defined based on cellular function and their activation estimated from the differential expression of each module’s constituent genes. These gene modules were then cross-referenced with drug atlas and pharmacogenomic databases to identify agents currently used successfully for treatment in other diseases. To explore the clinical use of these drugs in illnesses similar to GWI we compared gene expression patterns in modules that were significantly expressed in GWI with expression patterns in those same modules in other illnesses. Results We found 19 functional modules with significantly altered gene expression patterns in GWI. Within these modules, 45 genes were documented drug targets. Illnesses with highly correlated gene expression patterns overlapping considerably with GWI were found in 18 of the disease conditions studied. Brain, muscular and autoimmune disorders composed the bulk of these. Conclusion Of the associated drugs, immunosuppressants currently used in treating rheumatoid arthritis, and hormone based therapies were identified as the best available candidates for treating GWI symptoms. Electronic supplementary material The online version of this article (doi:10.1186/s12920-015-0111-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Travis J A Craddock
- Center for Psychological Studies, Nova Southeastern University, Fort Lauderdale, USA. .,Graduate School of Computer and Information Sciences, Nova Southeastern University, Fort Lauderdale, USA. .,Institute for Neuro-Immune Medicine, Nova Southeastern University, 3440 South University Drive, Fort Lauderdale, FL, 33328, USA. .,College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA. .,Department of Medicine, University of Alberta, Edmonton, Canada.
| | | | - Lubov Nathanson
- Institute for Neuro-Immune Medicine, Nova Southeastern University, 3440 South University Drive, Fort Lauderdale, FL, 33328, USA.,College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA
| | - Zachary M Barnes
- Institute for Neuro-Immune Medicine, Nova Southeastern University, 3440 South University Drive, Fort Lauderdale, FL, 33328, USA.,College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA.,Miller School of Medicine, University of Miami, Miami, USA.,Miami Veterans Affairs Medical Center, Miami, USA.,Diabetes Research Institute, University of Miami, Miami, USA
| | - Nancy G Klimas
- Institute for Neuro-Immune Medicine, Nova Southeastern University, 3440 South University Drive, Fort Lauderdale, FL, 33328, USA.,College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA.,Miller School of Medicine, University of Miami, Miami, USA.,Miami Veterans Affairs Medical Center, Miami, USA
| | - Mary Ann Fletcher
- Institute for Neuro-Immune Medicine, Nova Southeastern University, 3440 South University Drive, Fort Lauderdale, FL, 33328, USA.,College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA.,Miller School of Medicine, University of Miami, Miami, USA
| | - Gordon Broderick
- Center for Psychological Studies, Nova Southeastern University, Fort Lauderdale, USA.,Institute for Neuro-Immune Medicine, Nova Southeastern University, 3440 South University Drive, Fort Lauderdale, FL, 33328, USA.,College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA.,Department of Medicine, University of Alberta, Edmonton, Canada
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Asgari Y, Zabihinpour Z, Salehzadeh-Yazdi A, Schreiber F, Masoudi-Nejad A. Alterations in cancer cell metabolism: the Warburg effect and metabolic adaptation. Genomics 2015; 105:275-81. [PMID: 25773945 DOI: 10.1016/j.ygeno.2015.03.001] [Citation(s) in RCA: 73] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Revised: 02/09/2015] [Accepted: 03/04/2015] [Indexed: 12/13/2022]
Abstract
The Warburg effect means higher glucose uptake of cancer cells compared to normal tissues, whereas a smaller fraction of this glucose is employed for oxidative phosphorylation. With the advent of high throughput technologies and computational systems biology, cancer cell metabolism has been reinvestigated over the last decades toward identifying various events underlying "how" and "why" a cancer cell employs aerobic glycolysis. Significant progress has been shaped to revise the Warburg effect. In this study, we have integrated the gene expression of 13 different cancer cells with the genome-scale metabolic network of human (Recon1) based on the E-Flux method, and analyzed them based on constraint-based modeling. Results show that regardless of significant up- and down-regulated metabolic genes, the distribution of metabolic changes is similar in different cancer types. These findings support the theory that the Warburg effect is a consequence of metabolic adaptation in cancer cells.
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Affiliation(s)
- Yazdan Asgari
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Zahra Zabihinpour
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Ali Salehzadeh-Yazdi
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
| | - Falk Schreiber
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Halle, Germany; Clayton School of Information Technology, Monash University, Clayton, VIC, Australia.
| | - Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
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Abstract
Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field.
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Affiliation(s)
- Kelli M Sas
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI
| | | | - Subramaniam Pennathur
- Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI
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29
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Resendis-Antonio O, González-Torres C, Jaime-Muñoz G, Hernandez-Patiño CE, Salgado-Muñoz CF. Modeling metabolism: A window toward a comprehensive interpretation of networks in cancer. Semin Cancer Biol 2015; 30:79-87. [DOI: 10.1016/j.semcancer.2014.04.003] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2014] [Revised: 04/01/2014] [Accepted: 04/04/2014] [Indexed: 12/01/2022]
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30
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Chowdhury S, Sarkar RR. Comparison of human cell signaling pathway databases--evolution, drawbacks and challenges. Database (Oxford) 2015; 2015:bau126. [PMID: 25632107 PMCID: PMC4309023 DOI: 10.1093/database/bau126] [Citation(s) in RCA: 65] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2014] [Revised: 11/27/2014] [Accepted: 12/18/2014] [Indexed: 12/14/2022]
Abstract
Elucidating the complexities of cell signaling pathways is of immense importance to gain understanding about various biological phenomenon, such as dynamics of gene/protein expression regulation, cell fate determination, embryogenesis and disease progression. The successful completion of human genome project has also helped experimental and theoretical biologists to analyze various important pathways. To advance this study, during the past two decades, systematic collections of pathway data from experimental studies have been compiled and distributed freely by several databases, which also integrate various computational tools for further analysis. Despite significant advancements, there exist several drawbacks and challenges, such as pathway data heterogeneity, annotation, regular update and automated image reconstructions, which motivated us to perform a thorough review on popular and actively functioning 24 cell signaling databases. Based on two major characteristics, pathway information and technical details, freely accessible data from commercial and academic databases are examined to understand their evolution and enrichment. This review not only helps to identify some novel and useful features, which are not yet included in any of the databases but also highlights their current limitations and subsequently propose the reasonable solutions for future database development, which could be useful to the whole scientific community.
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Affiliation(s)
- Saikat Chowdhury
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhaba Road, Pune, Maharashtra 411008, India and Academy of Scientific & Innovative Research (AcSIR), New Delhi 110 001, India Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhaba Road, Pune, Maharashtra 411008, India and Academy of Scientific & Innovative Research (AcSIR), New Delhi 110 001, India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhaba Road, Pune, Maharashtra 411008, India and Academy of Scientific & Innovative Research (AcSIR), New Delhi 110 001, India Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Dr. Homi Bhaba Road, Pune, Maharashtra 411008, India and Academy of Scientific & Innovative Research (AcSIR), New Delhi 110 001, India
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31
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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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32
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Cazzaniga P, Damiani C, Besozzi D, Colombo R, Nobile MS, Gaglio D, Pescini D, Molinari S, Mauri G, Alberghina L, Vanoni M. Computational strategies for a system-level understanding of metabolism. Metabolites 2014; 4:1034-87. [PMID: 25427076 PMCID: PMC4279158 DOI: 10.3390/metabo4041034] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2014] [Revised: 11/05/2014] [Accepted: 11/12/2014] [Indexed: 12/20/2022] Open
Abstract
Cell metabolism is the biochemical machinery that provides energy and building blocks to sustain life. Understanding its fine regulation is of pivotal relevance in several fields, from metabolic engineering applications to the treatment of metabolic disorders and cancer. Sophisticated computational approaches are needed to unravel the complexity of metabolism. To this aim, a plethora of methods have been developed, yet it is generally hard to identify which computational strategy is most suited for the investigation of a specific aspect of metabolism. This review provides an up-to-date description of the computational methods available for the analysis of metabolic pathways, discussing their main advantages and drawbacks. In particular, attention is devoted to the identification of the appropriate scale and level of accuracy in the reconstruction of metabolic networks, and to the inference of model structure and parameters, especially when dealing with a shortage of experimental measurements. The choice of the proper computational methods to derive in silico data is then addressed, including topological analyses, constraint-based modeling and simulation of the system dynamics. A description of some computational approaches to gain new biological knowledge or to formulate hypotheses is finally provided.
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Affiliation(s)
- Paolo Cazzaniga
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Chiara Damiani
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Daniela Besozzi
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Riccardo Colombo
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Marco S Nobile
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Daniela Gaglio
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Dario Pescini
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Sara Molinari
- Dipartimento di Biotecnologie e Bioscienze, Università degli Studi di Milano-Bicocca, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Giancarlo Mauri
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Lilia Alberghina
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
| | - Marco Vanoni
- SYSBIO Centre of Systems Biology, Piazza della Scienza 2, 20126 Milano, Italy.
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Fisher CP, Kierzek AM, Plant NJ, Moore JB. Systems biology approaches for studying the pathogenesis of non-alcoholic fatty liver disease. World J Gastroenterol 2014; 20:15070-15078. [PMID: 25386055 PMCID: PMC4223240 DOI: 10.3748/wjg.v20.i41.15070] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Accepted: 03/13/2014] [Indexed: 02/06/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a progressive disease of increasing public health concern. In western populations the disease has an estimated prevalence of 20%-40%, rising to 70%-90% in obese and type II diabetic individuals. Simplistically, NAFLD is the macroscopic accumulation of lipid in the liver, and is viewed as the hepatic manifestation of the metabolic syndrome. However, the molecular mechanisms mediating both the initial development of steatosis and its progression through non-alcoholic steatohepatitis to debilitating and potentially fatal fibrosis and cirrhosis are only partially understood. Despite increased research in this field, the development of non-invasive clinical diagnostic tools and the discovery of novel therapeutic targets has been frustratingly slow. We note that, to date, NAFLD research has been dominated by in vivo experiments in animal models and human clinical studies. Systems biology tools and novel computational simulation techniques allow the study of large-scale metabolic networks and the impact of their dysregulation on health. Here we review current systems biology tools and discuss the benefits to their application to the study of NAFLD. We propose that a systems approach utilising novel in silico modelling and simulation techniques is key to a more comprehensive, better targeted NAFLD research strategy. Such an approach will accelerate the progress of research and vital translation into clinic.
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35
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Anomalies in network bridges involved in bile Acid metabolism predict outcomes of colorectal cancer patients. PLoS One 2014; 9:e107925. [PMID: 25259881 PMCID: PMC4178056 DOI: 10.1371/journal.pone.0107925] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2014] [Accepted: 08/18/2014] [Indexed: 12/21/2022] Open
Abstract
Biomarkers prognostic for colorectal cancer (CRC) would be highly desirable in clinical practice. Proteins that regulate bile acid (BA) homeostasis, by linking metabolic sensors and metabolic enzymes, also called bridge proteins, may be reliable prognostic biomarkers for CRC. Based on a devised metric, "bridgeness," we identified bridge proteins involved in the regulation of BA homeostasis and identified their prognostic potentials. The expression patterns of these bridge proteins could distinguish between normal and diseased tissues, suggesting that these proteins are associated with CRC pathogenesis. Using a supervised classification system, we found that these bridge proteins were reproducibly prognostic, with high prognostic ability compared to other known markers.
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36
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Shang D, Li C, Yao Q, Yang H, Xu Y, Han J, Li J, Su F, Zhang Y, Zhang C, Li D, Li X. Prioritizing candidate disease metabolites based on global functional relationships between metabolites in the context of metabolic pathways. PLoS One 2014; 9:e104934. [PMID: 25153931 PMCID: PMC4143229 DOI: 10.1371/journal.pone.0104934] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2014] [Accepted: 07/14/2014] [Indexed: 11/18/2022] Open
Abstract
Identification of key metabolites for complex diseases is a challenging task in today's medicine and biology. A special disease is usually caused by the alteration of a series of functional related metabolites having a global influence on the metabolic network. Moreover, the metabolites in the same metabolic pathway are often associated with the same or similar disease. Based on these functional relationships between metabolites in the context of metabolic pathways, we here presented a pathway-based random walk method called PROFANCY for prioritization of candidate disease metabolites. Our strategy not only takes advantage of the global functional relationships between metabolites but also sufficiently exploits the functionally modular nature of metabolic networks. Our approach proved successful in prioritizing known metabolites for 71 diseases with an AUC value of 0.895. We also assessed the performance of PROFANCY on 16 disease classes and found that 4 classes achieved an AUC value over 0.95. To investigate the robustness of the PROFANCY, we repeated all the analyses in two metabolic networks and obtained similar results. Then we applied our approach to Alzheimer's disease (AD) and found that a top ranked candidate was potentially related to AD but had not been reported previously. Furthermore, our method was applicable to prioritize the metabolites from metabolomic profiles of prostate cancer. The PROFANCY could identify prostate cancer related-metabolites that are supported by literatures but not considered to be significantly differential by traditional differential analysis. We also developed a freely accessible web-based and R-based tool at http://bioinfo.hrbmu.edu.cn/PROFANCY.
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Affiliation(s)
- Desi Shang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Chunquan Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, P. R. China
| | - Qianlan Yao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Haixiu Yang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Yanjun Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Jing Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Fei Su
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Yunpeng Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Chunlong Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
| | - Dongguo Li
- School of Biomedical Engineering, Capital Medical University, No. 10 You An Men Wai Xi Tou Tiao, Beijing, P.R. China
- * E-mail: (DL); (XL)
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, P. R. China
- * E-mail: (DL); (XL)
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Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, Arnold M, Erte I, Forgetta V, Yang TP, Walter K, Menni C, Chen L, Vasquez L, Valdes AM, Hyde CL, Wang V, Ziemek D, Roberts P, Xi L, Grundberg E, Waldenberger M, Richards JB, Mohney RP, Milburn MV, John SL, Trimmer J, Theis FJ, Overington JP, Suhre K, Brosnan MJ, Gieger C, Kastenmüller G, Spector TD, Soranzo N. An atlas of genetic influences on human blood metabolites. Nat Genet 2014; 46:543-550. [PMID: 24816252 PMCID: PMC4064254 DOI: 10.1038/ng.2982] [Citation(s) in RCA: 935] [Impact Index Per Article: 93.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2013] [Accepted: 04/14/2014] [Indexed: 02/07/2023]
Abstract
Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.
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Affiliation(s)
- So-Youn Shin
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton CB10 1HH, UK
| | - Eric B. Fauman
- Pfizer Worldwide Research and Development, Computational Sciences Center of Emphasis, 200 Cambridgepark Drive, Cambridge MA, 02140, USA
| | - Ann-Kristin Petersen
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany
| | - Jan Krumsiek
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany
| | - Rita Santos
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Jie Huang
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton CB10 1HH, UK
| | - Matthias Arnold
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany
| | - Idil Erte
- Department of Twin research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
| | - Vincenzo Forgetta
- Department of Human Genetics, Jewish General Hospital, Lady Davis Institute, McGill University, Montreal H3A 1A5, Canada
| | - Tsun-Po Yang
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton CB10 1HH, UK
| | - Klaudia Walter
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton CB10 1HH, UK
| | - Cristina Menni
- Department of Twin research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
| | - Lu Chen
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton CB10 1HH, UK
- Department of Hematology, University of Cambridge, Long Road, Cambridge CB2 2PT, UK
| | - Louella Vasquez
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton CB10 1HH, UK
| | - Ana M. Valdes
- Department of Twin research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
- School of Medicine, University of Nottingham, Nottingham NG5 1PB, UK
| | - Craig L. Hyde
- Pfizer Worldwide Research and Development, Clinical Research Statistics, 558 Eastern Point Rd, Groton CT 06340, USA
| | - Vicky Wang
- Pfizer Worldwide Research and Development, Computational Sciences Center of Emphasis, 200 Cambridgepark Drive, Cambridge MA, 02140, USA
| | - Daniel Ziemek
- Pfizer Worldwide Research and Development, Computational Sciences Center of Emphasis, 200 Cambridgepark Drive, Cambridge MA, 02140, USA
| | - Phoebe Roberts
- Pfizer Worldwide Research and Development, Computational Sciences Center of Emphasis, 200 Cambridgepark Drive, Cambridge MA, 02140, USA
| | - Li Xi
- Pfizer Worldwide Research and Development, Computational Sciences Center of Emphasis, 200 Cambridgepark Drive, Cambridge MA, 02140, USA
| | - Elin Grundberg
- Department of Human Genetics, Jewish General Hospital, Lady Davis Institute, McGill University, Montreal H3A 1A5, Canada
- Genome Quebec Innovation Centre, McGill University, Montreal QCH3A 1A5, Canada
| | | | - Melanie Waldenberger
- Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany
| | - J. Brent Richards
- Department of Human Genetics, Jewish General Hospital, Lady Davis Institute, McGill University, Montreal H3A 1A5, Canada
- Department of Medicine, Jewish General Hospital, Lady Davis Institute, McGill University, Montreal H3A 1A5, Canada
| | | | | | - Sally L. John
- Pfizer Worldwide Research and Development, Cardiovascular and Metabolic Diseases, 620 Memorial Drive, Cambridge, MA 02139, USA
| | - Jeff Trimmer
- Pfizer Worldwide Research and Development, Cardiovascular and Metabolic Diseases, 620 Memorial Drive, Cambridge, MA 02139, USA
| | - Fabian J. Theis
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany
- Department of Mathematics, Technische Universität München, Garching, Germany
| | - John P. Overington
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Karsten Suhre
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany
- Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Education City, Qatar Foundation, Doha, Qatar
| | - M. Julia Brosnan
- Pfizer Worldwide Research and Development, Clinical Research Statistics, 558 Eastern Point Rd, Groton CT 06340, USA
| | - Christian Gieger
- Institute of Genetic Epidemiology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany
| | - Gabi Kastenmüller
- Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Ingolstädter Landstraße 1, Neuherberg, 85764, Germany
| | - Tim D Spector
- Department of Twin research and Genetic Epidemiology, Kings College London, London SE1 7EH, UK
| | - Nicole Soranzo
- Human Genetics, Wellcome Trust Sanger Institute, Hinxton CB10 1HH, UK
- Department of Hematology, Long Road, Cambridge CB2 0PT, UK
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38
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Duren W, Weymouth T, Hull T, Omenn GS, Athey B, Burant C, Karnovsky A. MetDisease--connecting metabolites to diseases via literature. ACTA ACUST UNITED AC 2014; 30:2239-41. [PMID: 24713438 DOI: 10.1093/bioinformatics/btu179] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
MOTIVATION In recent years, metabolomics has emerged as an approach to perform large-scale characterization of small molecules in biological systems. Metabolomics posed a number of bioinformatics challenges associated in data analysis and interpretation. Genome-based metabolic reconstructions have established a powerful framework for connecting metabolites to genes through metabolic reactions and enzymes that catalyze them. Pathway databases and bioinformatics tools that use this framework have proven to be useful for annotating experimental metabolomics data. This framework can be used to infer connections between metabolites and diseases through annotated disease genes. However, only about half of experimentally detected metabolites can be mapped to canonical metabolic pathways. We present a new Cytoscape 3 plug-in, MetDisease, which uses an alternative approach to link metabolites to disease information. MetDisease uses Medical Subject Headings (MeSH) disease terms mapped to PubChem compounds through literature to annotate compound networks. AVAILABILITY AND IMPLEMENTATION MetDisease can be downloaded from http://apps.cytoscape.org/apps/metdisease or installed via the Cytoscape app manager. Further information about MetDisease can be found at http://metdisease.ncibi.org CONTACT akarnovs@med.umich.edu SUPPLEMENTARY INFORMATION Supplementary Data are available at Bioinformatics online.
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Affiliation(s)
- William Duren
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Terry Weymouth
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Tim Hull
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Brian Athey
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Charles Burant
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alla Karnovsky
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA Departments of Medicine and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA and Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA
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Masoudi-Nejad A, Asgari Y. Metabolic cancer biology: structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment. Semin Cancer Biol 2014; 30:21-9. [PMID: 24495661 DOI: 10.1016/j.semcancer.2014.01.007] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2013] [Revised: 01/15/2014] [Accepted: 01/18/2014] [Indexed: 12/21/2022]
Abstract
The cancer cell metabolism or the Warburg effect discovery goes back to 1924 when, for the first time Otto Warburg observed, in contrast to the normal cells, cancer cells have different metabolism. With the initiation of high throughput technologies and computational systems biology, cancer cell metabolism renaissances and many attempts were performed to revise the Warburg effect. The development of experimental and analytical tools which generate high-throughput biological data including lots of information could lead to application of computational models in biological discovery and clinical medicine especially for cancer. Due to the recent availability of tissue-specific reconstructed models, new opportunities in studying metabolic alteration in various kinds of cancers open up. Structural approaches at genome-scale levels seem to be suitable for developing diagnostic and prognostic molecular signatures, as well as in identifying new drug targets. In this review, we have considered these recent advances in structural-based analysis of cancer as a metabolic disease view. Two different structural approaches have been described here: topological and constraint-based methods. The ultimate goal of this type of systems analysis is not only the discovery of novel drug targets but also the development of new systems-based therapy strategies.
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Affiliation(s)
- Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - Yazdan Asgari
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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Kell DB, Goodacre R. Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery. Drug Discov Today 2014; 19:171-82. [PMID: 23892182 PMCID: PMC3989035 DOI: 10.1016/j.drudis.2013.07.014] [Citation(s) in RCA: 118] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Revised: 07/03/2013] [Accepted: 07/16/2013] [Indexed: 02/06/2023]
Abstract
Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.
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Affiliation(s)
- Douglas B Kell
- School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
| | - Royston Goodacre
- School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
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Bekaert M, Conant GC. Gene duplication and phenotypic changes in the evolution of mammalian metabolic networks. PLoS One 2014; 9:e87115. [PMID: 24489850 PMCID: PMC3904969 DOI: 10.1371/journal.pone.0087115] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2013] [Accepted: 12/23/2013] [Indexed: 11/18/2022] Open
Abstract
Metabolic networks attempt to describe the complete suite of biochemical reactions available to an organism. One notable feature of these networks in mammals is the large number of distinct proteins that catalyze the same reaction. While the existence of these isoenzymes has long been known, their evolutionary significance is still unclear. Using a phylogenetically-aware comparative genomics approach, we infer enzyme orthology networks for sixteen mammals as well as for their common ancestors. We find that the pattern of isoenzymes copy-number alterations (CNAs) in these networks is suggestive of natural selection acting on the retention of certain gene duplications. When further analyzing these data with a machine-learning approach, we found that that the pattern of CNAs is also predictive of several important phenotypic traits, including milk composition and geographic range. Integrating tools from network analyses, phylogenetics and comparative genomics both allows the prediction of phenotypes from genetic data and represents a means of unifying distinct biological disciplines.
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Affiliation(s)
- Michaël Bekaert
- Institute of Aquaculture, University of Stirling, Stirling, United Kingdom
| | - Gavin C. Conant
- Division of Animal Sciences, University of Missouri, Columbia, Missouri, United States of America
- Informatics Institute, University of Missouri, Columbia, Missouri, United States of America
- * E-mail:
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Cardinal-Fernández P, Nin N, Ruíz-Cabello J, Lorente JA. Systems medicine: a new approach to clinical practice. Arch Bronconeumol 2014; 50:444-51. [PMID: 24397963 DOI: 10.1016/j.arbres.2013.10.010] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2013] [Revised: 10/13/2013] [Accepted: 10/31/2013] [Indexed: 10/25/2022]
Abstract
Most respiratory diseases are considered complex diseases as their susceptibility and outcomes are determined by the interaction between host-dependent factors (genetic factors, comorbidities, etc.) and environmental factors (exposure to microorganisms or allergens, treatments received, etc.) The reductionist approach in the study of diseases has been of fundamental importance for the understanding of the different components of a system. Systems biology or systems medicine is a complementary approach aimed at analyzing the interactions between the different components within one organizational level (genome, transcriptome, proteome), and then between the different levels. Systems medicine is currently used for the interpretation and understanding of the pathogenesis and pathophysiology of different diseases, biomarker discovery, design of innovative therapeutic targets, and the drawing up of computational models for different biological processes. In this review we discuss the most relevant concepts of the theory underlying systems medicine, as well as its applications in the various biological processes in humans.
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Affiliation(s)
- Pablo Cardinal-Fernández
- Servicio de Medicina Intensiva, Hospital Universitario de Getafe, Madrid, España; CIBER de Enfermedades Respiratorias, Madrid, España
| | - Nicolás Nin
- CIBER de Enfermedades Respiratorias, Madrid, España; Servicio de Medicina Intensiva, Hospital Universitario de Torrejón, Madrid, España
| | - Jesús Ruíz-Cabello
- CIBER de Enfermedades Respiratorias, Madrid, España; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, España; Universidad Complutense de Madrid, Madrid, España
| | - José A Lorente
- Servicio de Medicina Intensiva, Hospital Universitario de Getafe, Madrid, España; CIBER de Enfermedades Respiratorias, Madrid, España; Universidad Europea de Madrid, Madrid, España.
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Vlassis N, Pacheco MP, Sauter T. Fast reconstruction of compact context-specific metabolic network models. PLoS Comput Biol 2014; 10:e1003424. [PMID: 24453953 PMCID: PMC3894152 DOI: 10.1371/journal.pcbi.1003424] [Citation(s) in RCA: 150] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2013] [Accepted: 11/20/2013] [Indexed: 12/14/2022] Open
Abstract
Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms.
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Affiliation(s)
- Nikos Vlassis
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Luxembourg City, Luxembourg
| | - Maria Pires Pacheco
- Life Sciences Research Unit, University of Luxembourg, Luxembourg City, Luxembourg
| | - Thomas Sauter
- Life Sciences Research Unit, University of Luxembourg, Luxembourg City, Luxembourg
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Tang J, Aittokallio T. Network pharmacology strategies toward multi-target anticancer therapies: from computational models to experimental design principles. Curr Pharm Des 2014; 20:23-36. [PMID: 23530504 PMCID: PMC3894695 DOI: 10.2174/13816128113199990470] [Citation(s) in RCA: 84] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2013] [Accepted: 03/18/2013] [Indexed: 12/12/2022]
Abstract
Polypharmacology has emerged as novel means in drug discovery for improving treatment response in clinical use. However, to really capitalize on the polypharmacological effects of drugs, there is a critical need to better model and understand how the complex interactions between drugs and their cellular targets contribute to drug efficacy and possible side effects. Network graphs provide a convenient modeling framework for dealing with the fact that most drugs act on cellular systems through targeting multiple proteins both through on-target and off-target binding. Network pharmacology models aim at addressing questions such as how and where in the disease network should one target to inhibit disease phenotypes, such as cancer growth, ideally leading to therapies that are less vulnerable to drug resistance and side effects by means of attacking the disease network at the systems level through synergistic and synthetic lethal interactions. Since the exponentially increasing number of potential drug target combinations makes pure experimental approach quickly unfeasible, this review depicts a number of computational models and algorithms that can effectively reduce the search space for determining the most promising combinations for experimental evaluation. Such computational-experimental strategies are geared toward realizing the full potential of multi-target treatments in different disease phenotypes. Our specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where we give representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies.
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Investigating host-pathogen behavior and their interaction using genome-scale metabolic network models. Methods Mol Biol 2014; 1184:523-62. [PMID: 25048144 DOI: 10.1007/978-1-4939-1115-8_29] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023]
Abstract
Genome Scale Metabolic Modeling methods represent one way to compute whole cell function starting from the genome sequence of an organism and contribute towards understanding and predicting the genotype-phenotype relationship. About 80 models spanning all the kingdoms of life from archaea to eukaryotes have been built till date and used to interrogate cell phenotype under varying conditions. These models have been used to not only understand the flux distribution in evolutionary conserved pathways like glycolysis and the Krebs cycle but also in applications ranging from value added product formation in Escherichia coli to predicting inborn errors of Homo sapiens metabolism. This chapter describes a protocol that delineates the process of genome scale metabolic modeling for analysing host-pathogen behavior and interaction using flux balance analysis (FBA). The steps discussed in the process include (1) reconstruction of a metabolic network from the genome sequence, (2) its representation in a precise mathematical framework, (3) its translation to a model, and (4) the analysis using linear algebra and optimization. The methods for biological interpretations of computed cell phenotypes in the context of individual host and pathogen models and their integration are also discussed.
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Controllability in cancer metabolic networks according to drug targets as driver nodes. PLoS One 2013; 8:e79397. [PMID: 24282504 PMCID: PMC3839908 DOI: 10.1371/journal.pone.0079397] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Accepted: 09/30/2013] [Indexed: 11/24/2022] Open
Abstract
Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.
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Hamilton JJ, Reed JL. Software platforms to facilitate reconstructing genome-scale metabolic networks. Environ Microbiol 2013; 16:49-59. [PMID: 24148076 DOI: 10.1111/1462-2920.12312] [Citation(s) in RCA: 62] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2013] [Accepted: 10/12/2013] [Indexed: 12/24/2022]
Abstract
System-level analyses of microbial metabolism are facilitated by genome-scale reconstructions of microbial biochemical networks. A reconstruction provides a structured representation of the biochemical transformations occurring within an organism, as well as the genes necessary to carry out these transformations, as determined by the annotated genome sequence and experimental data. Network reconstructions also serve as platforms for constraint-based computational techniques, which facilitate biological studies in a variety of applications, including evaluation of network properties, metabolic engineering and drug discovery. Bottom-up metabolic network reconstructions have been developed for dozens of organisms, but until recently, the pace of reconstruction has failed to keep up with advances in genome sequencing. To address this problem, a number of software platforms have been developed to automate parts of the reconstruction process, thereby alleviating much of the manual effort previously required. Here, we review four such platforms in the context of established guidelines for network reconstruction. While many steps of the reconstruction process have been successfully automated, some manual evaluation of the results is still required to ensure a high-quality reconstruction. Widespread adoption of these platforms by the scientific community is underway and will be further enabled by exchangeable formats across platforms.
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Affiliation(s)
- Joshua J Hamilton
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, Madison, WI, 53706, USA
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Kotze HL, Armitage EG, Sharkey KJ, Allwood JW, Dunn WB, Williams KJ, Goodacre R. A novel untargeted metabolomics correlation-based network analysis incorporating human metabolic reconstructions. BMC SYSTEMS BIOLOGY 2013; 7:107. [PMID: 24153255 PMCID: PMC3874763 DOI: 10.1186/1752-0509-7-107] [Citation(s) in RCA: 57] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Accepted: 09/04/2013] [Indexed: 12/24/2022]
Abstract
BACKGROUND Metabolomics has become increasingly popular in the study of disease phenotypes and molecular pathophysiology. One branch of metabolomics that encompasses the high-throughput screening of cellular metabolism is metabolic profiling. In the present study, the metabolic profiles of different tumour cells from colorectal carcinoma and breast adenocarcinoma were exposed to hypoxic and normoxic conditions and these have been compared to reveal the potential metabolic effects of hypoxia on the biochemistry of the tumour cells; this may contribute to their survival in oxygen compromised environments. In an attempt to analyse the complex interactions between metabolites beyond routine univariate and multivariate data analysis methods, correlation analysis has been integrated with a human metabolic reconstruction to reveal connections between pathways that are associated with normoxic or hypoxic oxygen environments. RESULTS Correlation analysis has revealed statistically significant connections between metabolites, where differences in correlations between cells exposed to different oxygen levels have been highlighted as markers of hypoxic metabolism in cancer. Network mapping onto reconstructed human metabolic models is a novel addition to correlation analysis. Correlated metabolites have been mapped onto the Edinburgh human metabolic network (EHMN) with the aim of interlinking metabolites found to be regulated in a similar fashion in response to oxygen. This revealed novel pathways within the metabolic network that may be key to tumour cell survival at low oxygen. Results show that the metabolic responses to lowering oxygen availability can be conserved or specific to a particular cell line. Network-based correlation analysis identified conserved metabolites including malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate. In this way, this method has revealed metabolites not previously linked, or less well recognised, with respect to hypoxia before. Lactate fermentation is one of the key themes discussed in the field of hypoxia; however, malate, pyruvate, 2-oxoglutarate, glutamate and fructose-6-phosphate, which are connected by a single pathway, may provide a more significant marker of hypoxia in cancer. CONCLUSIONS Metabolic networks generated for each cell line were compared to identify conserved metabolite pathway responses to low oxygen environments. Furthermore, we believe this methodology will have general application within metabolomics.
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Affiliation(s)
| | | | | | | | | | | | - Royston Goodacre
- School of Chemistry, Manchester Institute of Biotechnology, University of Manchester, Manchester M1 7DN, UK.
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Stobbe MD, Swertz MA, Thiele I, Rengaw T, van Kampen AHC, Moerland PD. Consensus and conflict cards for metabolic pathway databases. BMC SYSTEMS BIOLOGY 2013; 7:50. [PMID: 23803311 PMCID: PMC3703255 DOI: 10.1186/1752-0509-7-50] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2012] [Accepted: 06/20/2013] [Indexed: 01/04/2023]
Abstract
Background The metabolic network of H. sapiens and many other organisms is described in multiple pathway databases. The level of agreement between these descriptions, however, has proven to be low. We can use these different descriptions to our advantage by identifying conflicting information and combining their knowledge into a single, more accurate, and more complete description. This task is, however, far from trivial. Results We introduce the concept of Consensus and Conflict Cards (C2Cards) to provide concise overviews of what the databases do or do not agree on. Each card is centered at a single gene, EC number or reaction. These three complementary perspectives make it possible to distinguish disagreements on the underlying biology of a metabolic process from differences that can be explained by different decisions on how and in what detail to represent knowledge. As a proof-of-concept, we implemented C2CardsHuman, as a web application http://www.molgenis.org/c2cards, covering five human pathway databases. Conclusions C2Cards can contribute to ongoing reconciliation efforts by simplifying the identification of consensus and conflicts between pathway databases and lowering the threshold for experts to contribute. Several case studies illustrate the potential of the C2Cards in identifying disagreements on the underlying biology of a metabolic process. The overviews may also point out controversial biological knowledge that should be subject of further research. Finally, the examples provided emphasize the importance of manual curation and the need for a broad community involvement.
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Affiliation(s)
- Miranda D Stobbe
- Bioinformatics Laboratory, Academic Medical Center, University of Amsterdam, PO Box 22700, Amsterdam 1100 DE, the Netherlands
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Väremo L, Nookaew I, Nielsen J. Novel insights into obesity and diabetes through genome-scale metabolic modeling. Front Physiol 2013; 4:92. [PMID: 23630502 PMCID: PMC3635026 DOI: 10.3389/fphys.2013.00092] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2013] [Accepted: 04/11/2013] [Indexed: 12/19/2022] Open
Abstract
The growing prevalence of metabolic diseases, such as obesity and diabetes, are putting a high strain on global healthcare systems as well as increasing the demand for efficient treatment strategies. More than 360 million people worldwide are suffering from type 2 diabetes (T2D) and, with the current trends, the projection is that 10% of the global adult population will be affected by 2030. In light of the systemic properties of metabolic diseases as well as the interconnected nature of metabolism, it is necessary to begin taking a holistic approach to study these diseases. Human genome-scale metabolic models (GEMs) are topological and mathematical representations of cell metabolism and have proven to be valuable tools in the area of systems biology. Successful applications of GEMs include the process of gaining further biological and mechanistic understanding of diseases, finding potential biomarkers, and identifying new drug targets. This review will focus on the modeling of human metabolism in the field of obesity and diabetes, showing its vast range of applications of clinical importance as well as point out future challenges.
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Affiliation(s)
- Leif Väremo
- Systems and Synthetic Biology, Department of Chemical and Biological Engineering, Chalmers University of Technology Gothenburg, Sweden
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