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Cortese N, Procopio A, Merola A, Zaffino P, Cosentino C. Applications of genome-scale metabolic models to the study of human diseases: A systematic review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 256:108397. [PMID: 39232376 DOI: 10.1016/j.cmpb.2024.108397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 08/25/2024] [Accepted: 08/25/2024] [Indexed: 09/06/2024]
Abstract
BACKGROUND AND OBJECTIVES Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases. METHODS This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined. RESULTS The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models. CONCLUSIONS The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.
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Affiliation(s)
- Nicola Cortese
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Anna Procopio
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Alessio Merola
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Paolo Zaffino
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy
| | - Carlo Cosentino
- Department of Experimental and Clinical Medicine, Università degli Studi Magna Græcia, Catanzaro, 88100, Italy.
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Turanli B, Gulfidan G, Aydogan OO, Kula C, Selvaraj G, Arga KY. Genome-scale metabolic models in translational medicine: the current status and potential of machine learning in improving the effectiveness of the models. Mol Omics 2024; 20:234-247. [PMID: 38444371 DOI: 10.1039/d3mo00152k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
The genome-scale metabolic model (GEM) has emerged as one of the leading modeling approaches for systems-level metabolic studies and has been widely explored for a broad range of organisms and applications. Owing to the development of genome sequencing technologies and available biochemical data, it is possible to reconstruct GEMs for model and non-model microorganisms as well as for multicellular organisms such as humans and animal models. GEMs will evolve in parallel with the availability of biological data, new mathematical modeling techniques and the development of automated GEM reconstruction tools. The use of high-quality, context-specific GEMs, a subset of the original GEM in which inactive reactions are removed while maintaining metabolic functions in the extracted model, for model organisms along with machine learning (ML) techniques could increase their applications and effectiveness in translational research in the near future. Here, we briefly review the current state of GEMs, discuss the potential contributions of ML approaches for more efficient and frequent application of these models in translational research, and explore the extension of GEMs to integrative cellular models.
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Affiliation(s)
- Beste Turanli
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gizem Gulfidan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ozge Onluturk Aydogan
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
| | - Ceyda Kula
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
| | - Gurudeeban Selvaraj
- Concordia University, Centre for Research in Molecular Modeling & Department of Chemistry and Biochemistry, Quebec, Canada
- Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha Dental College and Hospital, Department of Biomaterials, Bioinformatics Unit, Chennai, India
| | - Kazim Yalcin Arga
- Marmara University, Faculty of Engineering, Department of Bioengineering, Istanbul, Turkey.
- Health Biotechnology Joint Research and Application Center of Excellence, Istanbul, Turkey
- Marmara University, Genetic and Metabolic Diseases Research and Investigation Center, Istanbul, Turkey
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Key A, Haiman Z, Palsson BO, D’Alessandro A. Modeling Red Blood Cell Metabolism in the Omics Era. Metabolites 2023; 13:1145. [PMID: 37999241 PMCID: PMC10673375 DOI: 10.3390/metabo13111145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 10/23/2023] [Accepted: 11/08/2023] [Indexed: 11/25/2023] Open
Abstract
Red blood cells (RBCs) are abundant (more than 80% of the total cells in the human body), yet relatively simple, as they lack nuclei and organelles, including mitochondria. Since the earliest days of biochemistry, the accessibility of blood and RBCs made them an ideal matrix for the characterization of metabolism. Because of this, investigations into RBC metabolism are of extreme relevance for research and diagnostic purposes in scientific and clinical endeavors. The relative simplicity of RBCs has made them an eligible model for the development of reconstruction maps of eukaryotic cell metabolism since the early days of systems biology. Computational models hold the potential to deepen knowledge of RBC metabolism, but also and foremost to predict in silico RBC metabolic behaviors in response to environmental stimuli. Here, we review now classic concepts on RBC metabolism, prior work in systems biology of unicellular organisms, and how this work paved the way for the development of reconstruction models of RBC metabolism. Translationally, we discuss how the fields of metabolomics and systems biology have generated evidence to advance our understanding of the RBC storage lesion, a process of decline in storage quality that impacts over a hundred million blood units transfused every year.
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Affiliation(s)
- Alicia Key
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA;
| | - Zachary Haiman
- Department of Bioengineering, University of California, San Diego, CA 92093, USA (B.O.P.)
- Bioinformatics and Systems Biology Program, University of California, San Diego, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, CA 92161, USA
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, CA 92093, USA (B.O.P.)
- Bioinformatics and Systems Biology Program, University of California, San Diego, CA 92093, USA
- Department of Pediatrics, University of California, San Diego, CA 92161, USA
| | - Angelo D’Alessandro
- Department of Biochemistry and Molecular Genetics, University of Colorado Denver, Anschutz Medical Campus, Aurora, CO 80045, USA;
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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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Sen P, Orešič M. Integrating Omics Data in Genome-Scale Metabolic Modeling: A Methodological Perspective for Precision Medicine. Metabolites 2023; 13:855. [PMID: 37512562 PMCID: PMC10383060 DOI: 10.3390/metabo13070855] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Recent advancements in omics technologies have generated a wealth of biological data. Integrating these data within mathematical models is essential to fully leverage their potential. Genome-scale metabolic models (GEMs) provide a robust framework for studying complex biological systems. GEMs have significantly contributed to our understanding of human metabolism, including the intrinsic relationship between the gut microbiome and the host metabolism. In this review, we highlight the contributions of GEMs and discuss the critical challenges that must be overcome to ensure their reproducibility and enhance their prediction accuracy, particularly in the context of precision medicine. We also explore the role of machine learning in addressing these challenges within GEMs. The integration of omics data with GEMs has the potential to lead to new insights, and to advance our understanding of molecular mechanisms in human health and disease.
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Affiliation(s)
- Partho Sen
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
| | - Matej Orešič
- Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland
- School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 702 81 Örebro, Sweden
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Lüleci HB, Uzuner D, Çakır T, Thambisetty M. Computational Approaches to Assess Abnormal Metabolism in Alzheimer's Disease Using Transcriptomics. Methods Mol Biol 2023; 2561:173-189. [PMID: 36399270 DOI: 10.1007/978-1-0716-2655-9_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Transcriptome-integrated human genome-scale metabolic models (GEMs) have been used widely to assess alterations in metabolism in response to disease. Transcriptome integration leads to identification of metabolic reactions that are differentially inactivated in the tissue of interest. Among the methods available for mapping transcriptome data on GEMs, we focus here on an Integrative Metabolic Analysis Tool (iMAT), which we have recently applied to the analysis of Alzheimer's disease (AD). We provide a detailed protocol for applying iMAT to create models of personalized metabolic networks, which can be further processed to identify reactions associated with abnormal metabolism.
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Affiliation(s)
- Hatice Büşra Lüleci
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Dilara Uzuner
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Madhav Thambisetty
- Clinical and Translational Neuroscience Section, Laboratory of Behavioral Neuroscience, National Institute on Aging (NIA), National Institutes of Health (NIH), Baltimore, MD, USA.
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Kishk A, Pacheco MP, Heurtaux T, Sinkkonen L, Pang J, Fritah S, Niclou SP, Sauter T. Review of Current Human Genome-Scale Metabolic Models for Brain Cancer and Neurodegenerative Diseases. Cells 2022; 11:2486. [PMID: 36010563 PMCID: PMC9406599 DOI: 10.3390/cells11162486] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/28/2022] [Accepted: 08/08/2022] [Indexed: 11/16/2022] Open
Abstract
Brain disorders represent 32% of the global disease burden, with 169 million Europeans affected. Constraint-based metabolic modelling and other approaches have been applied to predict new treatments for these and other diseases. Many recent studies focused on enhancing, among others, drug predictions by generating generic metabolic models of brain cells and on the contextualisation of the genome-scale metabolic models with expression data. Experimental flux rates were primarily used to constrain or validate the model inputs. Bi-cellular models were reconstructed to study the interaction between different cell types. This review highlights the evolution of genome-scale models for neurodegenerative diseases and glioma. We discuss the advantages and drawbacks of each approach and propose improvements, such as building bi-cellular models, tailoring the biomass formulations for glioma and refinement of the cerebrospinal fluid composition.
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Affiliation(s)
- Ali Kishk
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Tony Heurtaux
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
- Luxembourg Center of Neuropathology, L-3555 Dudelange, Luxembourg
| | - Lasse Sinkkonen
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
| | - Jun Pang
- Department of Computer Science, University of Luxembourg, L-4364 Esch-sur-Alzette, Luxembourg
| | - Sabrina Fritah
- NORLUX Neuro-Oncology Laboratory, Luxembourg Institute of Health, Department of Cancer Research, L-1526 Luxembourg, Luxembourg
| | - Simone P. Niclou
- NORLUX Neuro-Oncology Laboratory, Luxembourg Institute of Health, Department of Cancer Research, L-1526 Luxembourg, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, L-4367 Belvaux, Luxembourg
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8
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Kori M, Cig D, Arga KY, Kasavi C. Multiomics Data Integration Identifies New Molecular Signatures for Abdominal Aortic Aneurysm and Aortic Occlusive Disease: Implications for Early Diagnosis, Prognosis, and Therapeutic Targets. OMICS : A JOURNAL OF INTEGRATIVE BIOLOGY 2022; 26:290-304. [PMID: 35447046 DOI: 10.1089/omi.2022.0021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Cardiovascular disease (CVD) is the leading cause of death among adults in developed countries. Among CVDs, abdominal aortic aneurysm (AAA) and aortic occlusive disease (AOD) are of great public health importance because of the high mortality rate in the elderly population. Despite significant molecular insights into AAA and AOD, the molecular mechanisms of these diseases remain unclear, and the current lack of robust diagnostic and prognostic biomarkers requires novel approaches to biomarker discovery and molecular targeting. In this study, we performed a comparative analysis of genome-wide expression data from patients with large AAA (n = 29), small AAA (n = 20), AOD (n = 9), and controls (n = 10). Specifically, we identified the differentially expressed genes and associated molecular pathways and biological processes (BPs) in each disease. Using a systems science approach, these data were linked to comprehensive human biological networks (i.e., protein-protein interaction, transcriptional regulatory, and metabolic networks) to identify molecular signatures of the salient mechanisms of AAA and AOD. Significant alterations in lipid metabolism and valine, leucine, and isoleucine metabolism, as well as neurodegenerative diseases and sex differences in the pathogenesis of AAA and AOD were identified. In the presence of aneurysm, size-dependent changes in lipid metabolism were observed. In addition, molecules and signaling pathways related to immunity, inflammation, infectious disease, and oxidative phosphorylation were identified in common. The results of the comparative and integrative analyzes revealed important clues to disease mechanisms and reporter molecules at various levels that warrant future development as potential prognostic biomarkers and putative therapeutic targets.
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Affiliation(s)
- Medi Kori
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
| | - Defne Cig
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
| | - Kazim Yalcin Arga
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
- Genetic and Metabolic Diseases Research and Investigation Center (GEMHAM), Marmara University, Istanbul, Turkey
| | - Ceyda Kasavi
- Department of Bioengineering, Faculty of Engineering, Marmara University, Istanbul, Turkey
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9
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ARBRE: Computational resource to predict pathways towards industrially important aromatic compounds. Metab Eng 2022; 72:259-274. [DOI: 10.1016/j.ymben.2022.03.013] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 03/15/2022] [Accepted: 03/26/2022] [Indexed: 12/16/2022]
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10
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Yuan M, Shong K, Li X, Ashraf S, Shi M, Kim W, Nielsen J, Turkez H, Shoaie S, Uhlen M, Zhang C, Mardinoglu A. A Gene Co-Expression Network-Based Drug Repositioning Approach Identifies Candidates for Treatment of Hepatocellular Carcinoma. Cancers (Basel) 2022; 14:cancers14061573. [PMID: 35326724 PMCID: PMC8946504 DOI: 10.3390/cancers14061573] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/15/2022] [Accepted: 03/16/2022] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Hepatocellular carcinoma (HCC) is the most common malignancy of liver cancer. However, treatment of HCC is still severely limited due to limitation of drug therapy. We aimed to screen more possible target genes and candidate drugs for HCC, exploring the possibility of drug treatments from systems biological perspective. We identified ten candidate target genes, which are hub genes in HCC co-expression networks, which also possess significant prognostic value in two independent HCC cohorts. The rationality of these target genes was well demonstrated through variety analyses of patient expression profiles. We then screened candidate drugs for target genes and finally identified withaferin-a and mitoxantrone as the candidate drug for HCC treatment. The drug effectiveness was validated in in vitro model and computational analysis, providing more evidence for our drug repositioning method and results. Abstract Hepatocellular carcinoma (HCC) is a malignant liver cancer that continues to increase deaths worldwide owing to limited therapies and treatments. Computational drug repurposing is a promising strategy to discover potential indications of existing drugs. In this study, we present a systematic drug repositioning method based on comprehensive integration of molecular signatures in liver cancer tissue and cell lines. First, we identify robust prognostic genes and two gene co-expression modules enriched in unfavorable prognostic genes based on two independent HCC cohorts, which showed great consistency in functional and network topology. Then, we screen 10 genes as potential target genes for HCC on the bias of network topology analysis in these two modules. Further, we perform a drug repositioning method by integrating the shRNA and drug perturbation of liver cancer cell lines and identifying potential drugs for every target gene. Finally, we evaluate the effects of the candidate drugs through an in vitro model and observe that two identified drugs inhibited the protein levels of their corresponding target genes and cell migration, also showing great binding affinity in protein docking analysis. Our study demonstrates the usefulness and efficiency of network-based drug repositioning approach to discover potential drugs for cancer treatment and precision medicine approach.
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Affiliation(s)
- Meng Yuan
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
| | - Koeun Shong
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
| | - Xiangyu Li
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
- Bash Biotech Inc., 600 West Broadway, Suite 700, San Diego, CA 92101, USA
| | - Sajda Ashraf
- Heka Lab, Camlik Mah. Hearty, Sk. No:4 Heka Human Plaza Umraniye, Istanbul 34774, Turkey;
| | - Mengnan Shi
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
| | - Woonghee Kim
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE-41296 Gothenburg, Sweden;
- BioInnovation Institute, DK-2200 Copenhagen, Denmark
| | - Hasan Turkez
- Department of Medical Biology, Faculty of Medicine, Atatürk University, Erzurum 25240, Turkey;
| | - Saeed Shoaie
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK
| | - Mathias Uhlen
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
| | - Cheng Zhang
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
- Key Laboratory of Advanced Drug Preparation Technologies, School of Pharmaceutical Sciences, Ministry of Education, Zhengzhou University, Zhengzhou 450001, China
- Correspondence: (C.Z.); (A.M.)
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH—Royal Institute of Technology, SE-17165 Stockholm, Sweden; (M.Y.); (K.S.); (X.L.); (M.S.); (W.K.); (S.S.); (M.U.)
- Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London SE1 9RT, UK
- Correspondence: (C.Z.); (A.M.)
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Collin CB, Gebhardt T, Golebiewski M, Karaderi T, Hillemanns M, Khan FM, Salehzadeh-Yazdi A, Kirschner M, Krobitsch S, Kuepfer L. Computational Models for Clinical Applications in Personalized Medicine—Guidelines and Recommendations for Data Integration and Model Validation. J Pers Med 2022; 12:jpm12020166. [PMID: 35207655 PMCID: PMC8879572 DOI: 10.3390/jpm12020166] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 01/14/2022] [Accepted: 01/20/2022] [Indexed: 12/12/2022] Open
Abstract
The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.
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Affiliation(s)
- Catherine Bjerre Collin
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
| | - Tom Gebhardt
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies gGmbH, 69118 Heidelberg, Germany;
| | - Tugce Karaderi
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark; (C.B.C.); (T.K.)
- Center for Health Data Science, Faculty of Health and Medical Sciences, University of Copenhagen, 2200 N Copenhagen, Denmark
| | - Maximilian Hillemanns
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | - Faiz Muhammad Khan
- Department of Systems Biology and Bioinformatics, University of Rostock, 18057 Rostock, Germany; (T.G.); (M.H.); (F.M.K.)
| | | | - Marc Kirschner
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | - Sylvia Krobitsch
- Forschungszentrum Jülich GmbH, Project Management Jülich, 52425 Jülich, Germany; (M.K.); (S.K.)
| | | | - Lars Kuepfer
- Institute for Systems Medicine with Focus on Organ Interaction, University Hospital RWTH Aachen, 52074 Aachen, Germany
- Correspondence: ; Tel.: +49-241-8085900
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12
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Redhu N, Thakur Z. Network biology and applications. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00024-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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13
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Farha S, Comhair S, Hou Y, Park MM, Sharp J, Peterson L, Willard B, Zhang R, DiFilippo FP, Neumann D, Tang WHW, Cheng F, Erzurum S. Metabolic endophenotype associated with right ventricular glucose uptake in pulmonary hypertension. Pulm Circ 2021; 11:20458940211054325. [PMID: 34888034 PMCID: PMC8649443 DOI: 10.1177/20458940211054325] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 10/01/2021] [Indexed: 11/25/2022] Open
Abstract
Alterations in metabolism and bioenergetics are hypothesized in the mechanisms
leading to pulmonary vascular remodeling and heart failure in pulmonary
hypertension (PH). To test this, we performed metabolomic analyses on 30 PH
individuals and 12 controls. Furthermore, using 2-[18F]fluoro-2-deoxy-D-glucose
positron emission tomography, we dichotomized PH patients into metabolic
phenotypes of high and low right ventricle (RV) glucose uptake and followed them
longitudinally. In support of metabolic alterations in PH and its progression,
the high RV glucose group had higher RV systolic pressure (p < 0.001), worse
RV function as measured by RV fractional area change and peak global
longitudinal strain (both p < 0.05) and may be associated with poorer
outcomes (33% death or transplantation in the high glucose RV uptake group
compared to 7% in the low RV glucose uptake group at five years follow-up,
log-ranked p = 0.07). Pathway enrichment analysis identified key metabolic
pathways including fructose catabolism, arginine-nitric oxide metabolism,
tricarboxylic acid cycle, and ketones metabolism. Integrative human
protein-protein interactome network analysis of metabolomic and transcriptomic
data identified key pathobiological pathways: arginine biosynthesis,
tricarboxylic acid cycle, purine metabolism, hypoxia-inducible factor 1, and
apelin signaling. These findings identify a PH metabolomic endophenotype, and
for the first time link this to disease severity and outcomes.
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Affiliation(s)
- Samar Farha
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.,Respiratory Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Suzy Comhair
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Yuan Hou
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Margaret M Park
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.,Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Jacqueline Sharp
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.,Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Laura Peterson
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Belinda Willard
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Renliang Zhang
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | | | | | - W H Wilson Tang
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.,Heart Vascular and Thoracic Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Feixiong Cheng
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Serpil Erzurum
- Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.,Respiratory Institute, Cleveland Clinic, Cleveland, OH, USA
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14
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MohammadiPeyhani H, Chiappino-Pepe A, Haddadi K, Hafner J, Hadadi N, Hatzimanikatis V. NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism. eLife 2021; 10:e65543. [PMID: 34340747 PMCID: PMC8331181 DOI: 10.7554/elife.65543] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Accepted: 07/07/2021] [Indexed: 12/30/2022] Open
Abstract
The discovery of a drug requires over a decade of intensive research and financial investments - and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.
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Affiliation(s)
- Homa MohammadiPeyhani
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Anush Chiappino-Pepe
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Kiandokht Haddadi
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Jasmin Hafner
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Noushin Hadadi
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne, EPFLLausanneSwitzerland
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15
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Pomyen Y, Budhu A, Chaisaingmongkol J, Forgues M, Dang H, Ruchirawat M, Mahidol C, Wang XW. Tumor metabolism and associated serum metabolites define prognostic subtypes of Asian hepatocellular carcinoma. Sci Rep 2021; 11:12097. [PMID: 34103600 PMCID: PMC8187378 DOI: 10.1038/s41598-021-91560-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 05/24/2021] [Indexed: 12/20/2022] Open
Abstract
Treatment effectiveness in hepatocellular carcinoma (HCC) depends on early detection and precision-medicine-based patient stratification for targeted therapies. However, the lack of robust biomarkers, particularly a non-invasive diagnostic tool, precludes significant improvement of clinical outcomes for HCC patients. Serum metabolites are one of the best non-invasive means for determining patient prognosis, as they are stable end-products of biochemical processes in human body. In this study, we aimed to identify prognostic serum metabolites in HCC. To determine serum metabolites that were relevant and representative of the tissue status, we performed a two-step correlation analysis to first determine associations between metabolic genes and tissue metabolites, and second, between tissue metabolites and serum metabolites among 49 HCC patients, which were then validated in 408 additional Asian HCC patients with mixed etiologies. We found that certain metabolic genes, tissue metabolites and serum metabolites can independently stratify HCC patients into prognostic subgroups, which are consistent across these different data types and our previous findings. The metabolic subtypes are associated with β-oxidation process in fatty acid metabolism, where patients with worse survival outcome have dysregulated fatty acid metabolism. These serum metabolites may be used as non-invasive biomarkers to define prognostic tumor molecular subtypes for HCC.
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Affiliation(s)
- Yotsawat Pomyen
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA.,Translational Research Unit, Chulabhorn Research Institute, Bangkok, 10210, Thailand
| | - Anuradha Budhu
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA.,Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Jittiporn Chaisaingmongkol
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA.,Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, 10210, Thailand.,Center of Excellence on Environmental Health and Toxicology, Office of Higher Education Commission, Ministry of Higher Education, Science, Research and Innovation, Bangkok, 10400, Thailand
| | - Marshonna Forgues
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA
| | - Hien Dang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA.,Division of Surgery, Thomas Jefferson University, Philadelphia, PA, 19107, USA
| | - Mathuros Ruchirawat
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, 10210, Thailand.,Center of Excellence on Environmental Health and Toxicology, Office of Higher Education Commission, Ministry of Higher Education, Science, Research and Innovation, Bangkok, 10400, Thailand
| | - Chulabhorn Mahidol
- Laboratory of Chemical Carcinogenesis, Chulabhorn Research Institute, Bangkok, 10210, Thailand
| | - Xin Wei Wang
- Laboratory of Human Carcinogenesis, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA. .,Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, MD, 20892, USA.
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16
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O’Garro C, Igbineweka L, Ali Z, Mezei M, Mujtaba S. The Biological Significance of Targeting Acetylation-Mediated Gene Regulation for Designing New Mechanistic Tools and Potential Therapeutics. Biomolecules 2021; 11:biom11030455. [PMID: 33803759 PMCID: PMC8003229 DOI: 10.3390/biom11030455] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 03/15/2021] [Accepted: 03/16/2021] [Indexed: 01/13/2023] Open
Abstract
The molecular interplay between nucleosomal packaging and the chromatin landscape regulates the transcriptional programming and biological outcomes of downstream genes. An array of epigenetic modifications plays a pivotal role in shaping the chromatin architecture, which controls DNA access to the transcriptional machinery. Acetylation of the amino acid lysine is a widespread epigenetic modification that serves as a marker for gene activation, which intertwines the maintenance of cellular homeostasis and the regulation of signaling during stress. The biochemical horizon of acetylation ranges from orchestrating the stability and cellular localization of proteins that engage in the cell cycle to DNA repair and metabolism. Furthermore, lysine acetyltransferases (KATs) modulate the functions of transcription factors that govern cellular response to microbial infections, genotoxic stress, and inflammation. Due to their central role in many biological processes, mutations in KATs cause developmental and intellectual challenges and metabolic disorders. Despite the availability of tools for detecting acetylation, the mechanistic knowledge of acetylation-mediated cellular processes remains limited. This review aims to integrate molecular and structural bases of KAT functions, which would help design highly selective tools for understanding the biology of KATs toward developing new disease treatments.
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Affiliation(s)
- Chenise O’Garro
- Department of Biology, Medgar Evers College, City University of New York, Brooklyn, NY 11225, USA; (C.O.); (L.I.); (Z.A.)
| | - Loveth Igbineweka
- Department of Biology, Medgar Evers College, City University of New York, Brooklyn, NY 11225, USA; (C.O.); (L.I.); (Z.A.)
| | - Zonaira Ali
- Department of Biology, Medgar Evers College, City University of New York, Brooklyn, NY 11225, USA; (C.O.); (L.I.); (Z.A.)
| | - Mihaly Mezei
- Department of Pharmaceutical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Shiraz Mujtaba
- Department of Biology, Medgar Evers College, City University of New York, Brooklyn, NY 11225, USA; (C.O.); (L.I.); (Z.A.)
- Correspondence:
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17
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Common Transcriptional Program of Liver Fibrosis in Mouse Genetic Models and Humans. Int J Mol Sci 2021; 22:ijms22020832. [PMID: 33467660 PMCID: PMC7830925 DOI: 10.3390/ijms22020832] [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: 12/07/2020] [Revised: 01/08/2021] [Accepted: 01/12/2021] [Indexed: 02/06/2023] Open
Abstract
Multifactorial metabolic diseases, such as non-alcoholic fatty liver disease, are a major burden to modern societies, and frequently present with no clearly defined molecular biomarkers. Herein we used system medicine approaches to decipher signatures of liver fibrosis in mouse models with malfunction in genes from unrelated biological pathways: cholesterol synthesis-Cyp51, notch signaling-Rbpj, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling-Ikbkg, and unknown lysosomal pathway-Glmp. Enrichment analyses of Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome and TRANScription FACtor (TRANSFAC) databases complemented with genome-scale metabolic modeling revealed fibrotic signatures highly similar to liver pathologies in humans. The diverse genetic models of liver fibrosis exposed a common transcriptional program with activated estrogen receptor alpha (ERα) signaling, and a network of interactions between regulators of lipid metabolism and transcription factors from cancer pathways and the immune system. The novel hallmarks of fibrosis are downregulated lipid pathways, including fatty acid, bile acid, and steroid hormone metabolism. Moreover, distinct metabolic subtypes of liver fibrosis were proposed, supported by unique enrichment of transcription factors based on the type of insult, disease stage, or potentially, also sex. The discovered novel features of multifactorial liver fibrotic pathologies could aid also in improved stratification of other fibrosis related pathologies.
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18
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Zhou Y, Hou Y, Shen J, Mehra R, Kallianpur A, Culver DA, Gack MU, Farha S, Zein J, Comhair S, Fiocchi C, Stappenbeck T, Chan T, Eng C, Jung JU, Jehi L, Erzurum S, Cheng F. A network medicine approach to investigation and population-based validation of disease manifestations and drug repurposing for COVID-19. PLoS Biol 2020; 18:e3000970. [PMID: 33156843 PMCID: PMC7728249 DOI: 10.1371/journal.pbio.3000970] [Citation(s) in RCA: 113] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 12/10/2020] [Accepted: 10/28/2020] [Indexed: 01/08/2023] Open
Abstract
The global coronavirus disease 2019 (COVID-19) pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to unprecedented social and economic consequences. The risk of morbidity and mortality due to COVID-19 increases dramatically in the presence of coexisting medical conditions, while the underlying mechanisms remain unclear. Furthermore, there are no approved therapies for COVID-19. This study aims to identify SARS-CoV-2 pathogenesis, disease manifestations, and COVID-19 therapies using network medicine methodologies along with clinical and multi-omics observations. We incorporate SARS-CoV-2 virus-host protein-protein interactions, transcriptomics, and proteomics into the human interactome. Network proximity measurement revealed underlying pathogenesis for broad COVID-19-associated disease manifestations. Analyses of single-cell RNA sequencing data show that co-expression of ACE2 and TMPRSS2 is elevated in absorptive enterocytes from the inflamed ileal tissues of Crohn disease patients compared to uninflamed tissues, revealing shared pathobiology between COVID-19 and inflammatory bowel disease. Integrative analyses of metabolomics and transcriptomics (bulk and single-cell) data from asthma patients indicate that COVID-19 shares an intermediate inflammatory molecular profile with asthma (including IRAK3 and ADRB2). To prioritize potential treatments, we combined network-based prediction and a propensity score (PS) matching observational study of 26,779 individuals from a COVID-19 registry. We identified that melatonin usage (odds ratio [OR] = 0.72, 95% CI 0.56-0.91) is significantly associated with a 28% reduced likelihood of a positive laboratory test result for SARS-CoV-2 confirmed by reverse transcription-polymerase chain reaction assay. Using a PS matching user active comparator design, we determined that melatonin usage was associated with a reduced likelihood of SARS-CoV-2 positive test result compared to use of angiotensin II receptor blockers (OR = 0.70, 95% CI 0.54-0.92) or angiotensin-converting enzyme inhibitors (OR = 0.69, 95% CI 0.52-0.90). Importantly, melatonin usage (OR = 0.48, 95% CI 0.31-0.75) is associated with a 52% reduced likelihood of a positive laboratory test result for SARS-CoV-2 in African Americans after adjusting for age, sex, race, smoking history, and various disease comorbidities using PS matching. In summary, this study presents an integrative network medicine platform for predicting disease manifestations associated with COVID-19 and identifying melatonin for potential prevention and treatment of COVID-19.
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Affiliation(s)
- Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Yuan Hou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Jiayu Shen
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Reena Mehra
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Neurological Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Asha Kallianpur
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
| | - Daniel A. Culver
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Michaela U. Gack
- Florida Research and Innovation Center, Cleveland Clinic, Port Saint Lucie, Florida, United States of America
| | - Samar Farha
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Pulmonary Medicine, Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Joe Zein
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Suzy Comhair
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Claudio Fiocchi
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Thaddeus Stappenbeck
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Timothy Chan
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
| | - Jae U. Jung
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Lara Jehi
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Neurological Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Serpil Erzurum
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, Ohio, United States of America
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, Ohio, United States of America
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19
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Man AW, Zhou Y, Xia N, Li H. Involvement of Gut Microbiota, Microbial Metabolites and Interaction with Polyphenol in Host Immunometabolism. Nutrients 2020; 12:E3054. [PMID: 33036205 PMCID: PMC7601750 DOI: 10.3390/nu12103054] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 09/30/2020] [Accepted: 10/01/2020] [Indexed: 12/11/2022] Open
Abstract
Immunological and metabolic processes are inextricably linked and important for maintaining tissue and organismal health. Manipulation of cellular metabolism could be beneficial to immunity and prevent metabolic and degenerative diseases including obesity, diabetes, and cancer. Maintenance of a normal metabolism depends on symbiotic consortium of gut microbes. Gut microbiota contributes to certain xenobiotic metabolisms and bioactive metabolites production. Gut microbiota-derived metabolites have been shown to be involved in inflammatory activation of macrophages and contribute to metabolic diseases. Recent studies have focused on how nutrients affect immunometabolism. Polyphenols, the secondary metabolites of plants, are presented in many foods and beverages. Several studies have demonstrated the antioxidant and anti-inflammatory properties of polyphenols. Many clinical trials and epidemiological studies have also shown that long-term consumption of polyphenol-rich diet protects against chronic metabolic diseases. It is known that polyphenols can modulate the composition of core gut microbiota and interact with the immunometabolism. In the present article, we review the mechanisms of gut microbiota and its metabolites on immunometabolism, summarize recent findings on how the interaction between microbiota and polyphenol modulates host immunometabolism, and discuss future research directions.
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Affiliation(s)
| | | | | | - Huige Li
- Department of Pharmacology, Johannes Gutenberg University Medical Center, Langenbeckstr. 1, 55131 Mainz, Germany; (A.W.C.M.); (Y.Z.); (N.X.)
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20
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González J, Pinzón A, Angarita-Rodríguez A, Aristizabal AF, Barreto GE, Martín-Jiménez C. Advances in Astrocyte Computational Models: From Metabolic Reconstructions to Multi-omic Approaches. Front Neuroinform 2020; 14:35. [PMID: 32848690 PMCID: PMC7426703 DOI: 10.3389/fninf.2020.00035] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2020] [Accepted: 07/14/2020] [Indexed: 12/12/2022] Open
Abstract
The growing importance of astrocytes in the field of neuroscience has led to a greater number of computational models devoted to the study of astrocytic functions and their metabolic interactions with neurons. The modeling of these interactions demands a combined understanding of brain physiology and the development of computational frameworks based on genomic-scale reconstructions, system biology, and dynamic models. These computational approaches have helped to highlight the neuroprotective mechanisms triggered by astrocytes and other glial cells, both under normal conditions and during neurodegenerative processes. In the present review, we evaluate some of the most relevant models of astrocyte metabolism, including genome-scale reconstructions and astrocyte-neuron interactions developed in the last few years. Additionally, we discuss novel strategies from the multi-omics perspective and computational models of other glial cell types that will increase our knowledge in brain metabolism and its association with neurodegenerative diseases.
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Affiliation(s)
- Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Andrés Pinzón
- Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia Bogotá, Bogotá, Colombia
| | - Andrea Angarita-Rodríguez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia.,Laboratorio de Bioinformática y Biología de Sistemas, Universidad Nacional de Colombia Bogotá, Bogotá, Colombia
| | - Andrés Felipe Aristizabal
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - George E Barreto
- Department of Biological Sciences, University of Limerick, Limerick, Ireland.,Health Research Institute, University of Limerick, Limerick, Ireland
| | - Cynthia Martín-Jiménez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana, Bogotá, Colombia
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21
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Novel molecular signatures and potential therapeutics in renal cell carcinomas: Insights from a comparative analysis of subtypes. Genomics 2020; 112:3166-3178. [PMID: 32512143 DOI: 10.1016/j.ygeno.2020.06.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Revised: 05/20/2020] [Accepted: 06/02/2020] [Indexed: 01/05/2023]
Abstract
Renal cell carcinomas (RCCs) are among the highest causes of cancer mortality. Although transcriptome profiling studies in the last decade have made significant molecular findings on RCCs, effective diagnosis and treatment strategies have yet to be achieved due to lack of adequate screening and comparative profiling of RCC subtypes. In this study, a comparative analysis was performed on RNA-seq based transcriptome data from each RCC subtype, namely clear cell RCC (KIRC), papillary RCC (KIRP) and kidney chromophobe (KICH), and mutual or subtype-specific reporter biomolecules were identified at RNA, protein, and metabolite levels by the integration of expression profiles with genome-scale biomolecular networks. This approach revealed already-known biomarkers in RCCs as well as novel biomarker candidates and potential therapeutic targets. Our findings also pointed out the incorporation of the molecular mechanisms of KIRC and KIRP, whereas KICH was shown to have distinct molecular signatures. Furthermore, considering the Dipeptidyl Peptidase 4 (DPP4) receptor as a potential therapeutic target specific to KICH, several drug candidates such as ZINC6745464 were identified through virtual screening of ZINC molecules. In this study, we reported valuable data for further experimental and clinical efforts, since the proposed molecules have significant potential for screening and therapeutic purposes in RCCs.
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22
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Larsson I, Uhlén M, Zhang C, Mardinoglu A. Genome-Scale Metabolic Modeling of Glioblastoma Reveals Promising Targets for Drug Development. Front Genet 2020; 11:381. [PMID: 32362913 PMCID: PMC7181968 DOI: 10.3389/fgene.2020.00381] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2019] [Accepted: 03/27/2020] [Indexed: 01/23/2023] Open
Abstract
Glioblastoma (GBM) is an aggressive type of brain cancer with a poor prognosis for affected patients. The current line of treatment only gives the patients a survival time of on average 15 months. In this work, we use genome-scale metabolic models (GEMs) together with other systems biology tools to examine the global transcriptomics-data of GBM-patients obtained from The Cancer Genome Atlas (TCGA). We reveal the molecular mechanisms underlying GBM and identify potential therapeutic targets for effective treatment of patients. The work presented consists of two main parts. The first part stratifies the patients into two groups, high and low survival, and compares their gene expression. The second part uses GBM and healthy brain tissue GEMs to simulate gene knockout in a GBM cell model to find potential therapeutic targets and predict their side effect in healthy brain tissue. We (1) find that genes upregulated in the patients with low survival are linked to various stages of the glioma invasion process, and (2) identify five essential genes for GBM, whose inhibition is non-toxic to healthy brain tissue, therefore promising to investigate further as therapeutic targets.
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Affiliation(s)
- Ida Larsson
- Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.,Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlén
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.,Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, United Kingdom
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23
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Pandey N, Lanke V, Vinod PK. Network-based metabolic characterization of renal cell carcinoma. Sci Rep 2020; 10:5955. [PMID: 32249812 PMCID: PMC7136214 DOI: 10.1038/s41598-020-62853-8] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 03/21/2020] [Indexed: 12/31/2022] Open
Abstract
An emerging hallmark of cancer is metabolic reprogramming, which presents opportunities for cancer diagnosis and treatment based on metabolism. We performed a comprehensive metabolic network analysis of major renal cell carcinoma (RCC) subtypes including clear cell, papillary and chromophobe by integrating transcriptomic data with the human genome-scale metabolic model to understand the coordination of metabolic pathways in cancer cells. We identified metabolic alterations of each subtype with respect to tumor-adjacent normal samples and compared them to understand the differences between subtypes. We found that genes of amino acid metabolism and redox homeostasis are significantly altered in RCC subtypes. Chromophobe showed metabolic divergence compared to other subtypes with upregulation of genes involved in glutamine anaplerosis and aspartate biosynthesis. A difference in transcriptional regulation involving HIF1A is observed between subtypes. We identified E2F1 and FOXM1 as other major transcriptional activators of metabolic genes in RCC. Further, the co-expression pattern of metabolic genes in each patient showed the variations in metabolism within RCC subtypes. We also found that co-expression modules of each subtype have tumor stage-specific behavior, which may have clinical implications.
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Affiliation(s)
- Nishtha Pandey
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Vinay Lanke
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
- TCS Innovation Labs, Hyderabad, India
| | - P K Vinod
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
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24
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Robinson JL, Kocabaş P, Wang H, Cholley PE, Cook D, Nilsson A, Anton M, Ferreira R, Domenzain I, Billa V, Limeta A, Hedin A, Gustafsson J, Kerkhoven EJ, Svensson LT, Palsson BO, Mardinoglu A, Hansson L, Uhlén M, Nielsen J. An atlas of human metabolism. Sci Signal 2020; 13:13/624/eaaz1482. [PMID: 32209698 DOI: 10.1126/scisignal.aaz1482] [Citation(s) in RCA: 175] [Impact Index Per Article: 43.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Genome-scale metabolic models (GEMs) are valuable tools to study metabolism and provide a scaffold for the integrative analysis of omics data. Researchers have developed increasingly comprehensive human GEMs, but the disconnect among different model sources and versions impedes further progress. We therefore integrated and extensively curated the most recent human metabolic models to construct a consensus GEM, Human1. We demonstrated the versatility of Human1 through the generation and analysis of cell- and tissue-specific models using transcriptomic, proteomic, and kinetic data. We also present an accompanying web portal, Metabolic Atlas (https://www.metabolicatlas.org/), which facilitates further exploration and visualization of Human1 content. Human1 was created using a version-controlled, open-source model development framework to enable community-driven curation and refinement. This framework allows Human1 to be an evolving shared resource for future studies of human health and disease.
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Affiliation(s)
- Jonathan L Robinson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Pınar Kocabaş
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Hao Wang
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Molecular and Translational Medicine, University of Gothenburg, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Pierre-Etienne Cholley
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Daniel Cook
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Avlant Nilsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Mihail Anton
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Raphael Ferreira
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Iván Domenzain
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Virinchi Billa
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Angelo Limeta
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Alex Hedin
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Johan Gustafsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Eduard J Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - L Thomas Svensson
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden
| | - Bernhard O Palsson
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.,Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA.,Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Adil Mardinoglu
- Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, SE-10044 Stockholm, Sweden.,Centre for Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King's College London, London WC2R 2LS, UK
| | - Lena Hansson
- Department of Biology and Biological Engineering, National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Novo Nordisk Research Centre Oxford, Oxford OX3 7FZ, UK
| | - Mathias Uhlén
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.,Department of Protein Science, Science for Life Laboratory, KTH-Royal Institute of Technology, SE-10044 Stockholm, Sweden.,Wallenberg Center for Protein Research, KTH-Royal Institute of Technology, SE-10044 Stockholm, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden. .,Wallenberg Center for Protein Research, Chalmers University of Technology, Kemivägen 10, SE-41258 Gothenburg, Sweden.,Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.,BioInnovation Institute, Ole Maaløes Vej 3, DK-2200 Copenhagen, Denmark
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25
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Abstract
The integration of high-throughput data to build predictive computational models of cellular metabolism is a major challenge of systems biology. These models are needed to predict cellular responses to genetic and environmental perturbations. Typically, this response involves both metabolic regulations related to the kinetic properties of enzymes and a genetic regulation affecting their concentrations. Thus, the integration of the transcriptional regulatory information is required to improve the accuracy and predictive ability of metabolic models. Integrative modeling is of primary importance to guide the search for various applications such as discovering novel potential drug targets to develop efficient therapeutic strategies for various diseases. In this paper, we propose an integrative predictive model based on techniques combining semantic web, probabilistic modeling, and constraint-based modeling methods. We applied our approach to human cancer metabolism to predict in silico the growth response of specific cancer cells under approved drug effects. Our method has proven successful in predicting the biomass rates of human liver cancer cells under drug-induced transcriptional perturbations.
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26
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Ansari AF, Acharya NIS, Kumaran S, Ravindra K, Reddy YBS, Dixit NM, Raut J. 110th Anniversary: High-Order Interactions Can Eclipse Pairwise Interactions in Shaping the Structure of Microbial Communities. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b03190] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Aamir Faisal Ansari
- Department of Chemical Engineering, Indian Institute of Science, Bangalore 560012, India
| | | | | | | | | | - Narendra M. Dixit
- Department of Chemical Engineering, Indian Institute of Science, Bangalore 560012, India
- Centre for Biosystems Science and Engineering, Indian Institute of Science, Bangalore 560012, India
| | - Janhavi Raut
- Unilever R&D India Pvt, Ltd., Bangalore 560066, India
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27
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Xu W, Comhair SAA, Chen R, Hu B, Hou Y, Zhou Y, Mavrakis LA, Janocha AJ, Li L, Zhang D, Willard BB, Asosingh K, Cheng F, Erzurum SC. Integrative proteomics and phosphoproteomics in pulmonary arterial hypertension. Sci Rep 2019; 9:18623. [PMID: 31819116 PMCID: PMC6901481 DOI: 10.1038/s41598-019-55053-6] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 11/21/2019] [Indexed: 02/06/2023] Open
Abstract
Pulmonary arterial endothelial cells (PAEC) are mechanistically linked to origins of pulmonary arterial hypertension (PAH). Here, global proteomics and phosphoproteomics of PAEC from PAH (n = 4) and healthy lungs (n = 5) were performed using LC-MS/MS to confirm known pathways and identify new areas of investigation in PAH. Among PAH and control cells, 170 proteins and 240 phosphopeptides were differentially expressed; of these, 45 proteins and 18 phosphopeptides were located in the mitochondria. Pathologic pathways were identified with integrative bioinformatics and human protein-protein interactome network analyses, then confirmed with targeted proteomics in PAH PAEC and non-targeted metabolomics and targeted high-performance liquid chromatography of metabolites in plasma from PAH patients (n = 30) and healthy controls (n = 12). Dysregulated pathways in PAH include accelerated one carbon metabolism, abnormal tricarboxylic acid (TCA) cycle flux and glutamate metabolism, dysfunctional arginine and nitric oxide pathways, and increased oxidative stress. Functional studies in cells confirmed abnormalities in glucose metabolism, mitochondrial oxygen consumption, and production of reactive oxygen species in PAH. Altogether, the findings indicate that PAH is typified by changes in metabolic pathways that are primarily found in mitochondria.
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Affiliation(s)
- Weiling Xu
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America.
| | - Suzy A A Comhair
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Ruoying Chen
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Bo Hu
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Yuan Hou
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Yadi Zhou
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Lori A Mavrakis
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Allison J Janocha
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Ling Li
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Dongmei Zhang
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Belinda B Willard
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Kewal Asosingh
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Feixiong Cheng
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America
| | - Serpil C Erzurum
- Lerner Research Institute, Cleveland Clinic, Cleveland, Ohio, United States of America. .,Respiratory Institute, Cleveland Clinic, Cleveland, Ohio, United States of America.
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28
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Yao D, Zhan X, Zhan X, Kwoh CK, Sun Y. ncRNA2MetS: a manually curated database for non-coding RNAs associated with metabolic syndrome. PeerJ 2019; 7:e7909. [PMID: 31637139 PMCID: PMC6798904 DOI: 10.7717/peerj.7909] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Accepted: 09/17/2019] [Indexed: 12/19/2022] Open
Abstract
Metabolic syndrome is a cluster of the most dangerous heart attack risk factors (diabetes and raised fasting plasma glucose, abdominal obesity, high cholesterol and high blood pressure), and has become a major global threat to human health. A number of studies have demonstrated that hundreds of non-coding RNAs, including miRNAs and lncRNAs, are involved in metabolic syndrome-related diseases such as obesity, type 2 diabetes mellitus, hypertension, etc. However, these research results are distributed in a large number of literature, which is not conducive to analysis and use. There is an urgent need to integrate these relationship data between metabolic syndrome and non-coding RNA into a specialized database. To address this need, we developed a metabolic syndrome-associated non-coding RNA database (ncRNA2MetS) to curate the associations between metabolic syndrome and non-coding RNA. Currently, ncRNA2MetS contains 1,068 associations between five metabolic syndrome traits and 627 non-coding RNAs (543 miRNAs and 84 lncRNAs) in four species. Each record in ncRNA2MetS database represents a pair of disease-miRNA (lncRNA) association consisting of non-coding RNA category, miRNA (lncRNA) name, name of metabolic syndrome trait, expressive patterns of non-coding RNA, method for validation, specie involved, a brief introduction to the association, the article referenced, etc. We also developed a user-friendly website so that users can easily access and download all data. In short, ncRNA2MetS is a complete and high-quality data resource for exploring the role of non-coding RNA in the pathogenesis of metabolic syndrome and seeking new treatment options. The website is freely available at http://www.biomed-bigdata.com:50020/index.html
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Affiliation(s)
- Dengju Yao
- School of Software and Microelectronics, Harbin University of Science and Technology, Harbin, Heilongjiang, China.,School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.,College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang, China
| | - Xiaojuan Zhan
- College of Computer Science and Technology, Heilongjiang Institute of Technology, Harbin, Heilongjiang, China.,School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang, China
| | - Xiaorong Zhan
- Department of Endocrinology and Metabolism, the First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Chee Keong Kwoh
- School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore
| | - Yuezhongyi Sun
- School of Software and Microelectronics, Harbin University of Science and Technology, Harbin, Heilongjiang, China.,School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, Heilongjiang, China
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29
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Marín de Mas I, Torrents L, Bedia C, Nielsen LK, Cascante M, Tauler R. Stoichiometric gene-to-reaction associations enhance model-driven analysis performance: Metabolic response to chronic exposure to Aldrin in prostate cancer. BMC Genomics 2019; 20:652. [PMID: 31416420 PMCID: PMC6694502 DOI: 10.1186/s12864-019-5979-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 07/16/2019] [Indexed: 01/06/2023] Open
Abstract
BACKGROUND Genome-scale metabolic models (GSMM) integrating transcriptomics have been widely used to study cancer metabolism. This integration is achieved through logical rules that describe the association between genes, proteins, and reactions (GPRs). However, current gene-to-reaction formulation lacks the stoichiometry describing the transcript copies necessary to generate an active catalytic unit, which limits our understanding of how genes modulate metabolism. The present work introduces a new state-of-the-art GPR formulation that considers the stoichiometry of the transcripts (S-GPR). As case of concept, this novel gene-to-reaction formulation was applied to investigate the metabolic effects of the chronic exposure to Aldrin, an endocrine disruptor, on DU145 prostate cancer cells. To this aim we integrated the transcriptomic data from Aldrin-exposed and non-exposed DU145 cells through S-GPR or GPR into a human GSMM by applying different constraint-based-methods. RESULTS Our study revealed a significant improvement of metabolite consumption/production predictions when S-GPRs are implemented. Furthermore, our computational analysis unveiled important alterations in carnitine shuttle and prostaglandine biosynthesis in Aldrin-exposed DU145 cells that is supported by bibliographic evidences of enhanced malignant phenotype. CONCLUSIONS The method developed in this work enables a more accurate integration of gene expression data into model-driven methods. Thus, the presented approach is conceptually new and paves the way for more in-depth studies of aberrant cancer metabolism and other diseases with strong metabolic component with important environmental and clinical implications.
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Affiliation(s)
- Igor Marín de Mas
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Jordi Girona 18-24, 08034 Barcelona, Spain
| | - Laura Torrents
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Jordi Girona 18-24, 08034 Barcelona, Spain
| | - Carmen Bedia
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Jordi Girona 18-24, 08034 Barcelona, Spain
| | - Lars K. Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Marta Cascante
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Institute of Biomedicine of University of Barcelona (IBUB), Networked Center for Research in Liver and Digestive Diseases (CIBEREHD- CB17/04/00023)) and metabolomics node at INB-Bioinformatics Platform, Instituto de Salud Carlos III (ISCIII, 28029 Madrid), Diagonal 645, 08028 Barcelona, Spain
| | - Romà Tauler
- Department of Environmental Chemistry, Institute of Environmental Assessment and Water Research (IDAEA-CSIC), Jordi Girona 18-24, 08034 Barcelona, Spain
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30
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Lee S, Zhang C, Arif M, Liu Z, Benfeitas R, Bidkhori G, Deshmukh S, Al Shobky M, Lovric A, Boren J, Nielsen J, Uhlen M, Mardinoglu A. TCSBN: a database of tissue and cancer specific biological networks. Nucleic Acids Res 2019; 46:D595-D600. [PMID: 29069445 PMCID: PMC5753183 DOI: 10.1093/nar/gkx994] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 10/12/2017] [Indexed: 12/15/2022] Open
Abstract
Biological networks provide new opportunities for understanding the cellular biology in both health and disease states. We generated tissue specific integrated networks (INs) for liver, muscle and adipose tissues by integrating metabolic, regulatory and protein-protein interaction networks. We also generated human co-expression networks (CNs) for 46 normal tissues and 17 cancers to explore the functional relationships between genes as well as their relationships with biological functions, and investigate the overlap between functional and physical interactions provided by CNs and INs, respectively. These networks can be employed in the analysis of omics data, provide detailed insight into disease mechanisms by identifying the key biological components and eventually can be used in the development of efficient treatment strategies. Moreover, comparative analysis of the networks may allow for the identification of tissue-specific targets that can be used in the development of drugs with the minimum toxic effect to other human tissues. These context-specific INs and CNs are presented in an interactive website http://inetmodels.com without any limitation.
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Affiliation(s)
- Sunjae Lee
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Zhengtao Liu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Rui Benfeitas
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Sumit Deshmukh
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Mohamed Al Shobky
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Alen Lovric
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, SE-413 45, Sweden
| | - Jens Nielsen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, SE-171 21, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE-412 96, Sweden
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31
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Abstract
Genome-scale metabolic models (GEMs) computationally describe gene-protein-reaction associations for entire metabolic genes in an organism, and can be simulated to predict metabolic fluxes for various systems-level metabolic studies. Since the first GEM for Haemophilus influenzae was reported in 1999, advances have been made to develop and simulate GEMs for an increasing number of organisms across bacteria, archaea, and eukarya. Here, we review current reconstructed GEMs and discuss their applications, including strain development for chemicals and materials production, drug targeting in pathogens, prediction of enzyme functions, pan-reactome analysis, modeling interactions among multiple cells or organisms, and understanding human diseases.
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Affiliation(s)
- Changdai Gu
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Gi Bae Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Won Jun Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
| | - Hyun Uk Kim
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Systems Biology and Medicine Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
| | - Sang Yup Lee
- Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Metabolic and Biomolecular Engineering National Research Laboratory, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea.
- Systems Metabolic Engineering and Systems Healthcare Cross-Generation Collaborative Laboratory, KAIST, Daejeon, 34141, Republic of Korea.
- BioProcess Engineering Research Center and BioInformatics Research Center, KAIST, Daejeon, 34141, Republic of Korea.
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32
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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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33
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Karakitsou E, Foguet C, de Atauri P, Kultima K, Khoonsari PE, Martins dos Santos VA, Saccenti E, Rosato A, Cascante M. Metabolomics in systems medicine: an overview of methods and applications. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.coisb.2019.03.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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34
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Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A, Heinken A, Haraldsdóttir HS, Wachowiak J, Keating SM, Vlasov V, Magnusdóttir S, Ng CY, Preciat G, Žagare A, Chan SHJ, Aurich MK, Clancy CM, Modamio J, Sauls JT, Noronha A, Bordbar A, Cousins B, El Assal DC, Valcarcel LV, Apaolaza I, Ghaderi S, Ahookhosh M, Ben Guebila M, Kostromins A, Sompairac N, Le HM, Ma D, Sun Y, Wang L, Yurkovich JT, Oliveira MAP, Vuong PT, El Assal LP, Kuperstein I, Zinovyev A, Hinton HS, Bryant WA, Aragón Artacho FJ, Planes FJ, Stalidzans E, Maass A, Vempala S, Hucka M, Saunders MA, Maranas CD, Lewis NE, Sauter T, Palsson BØ, Thiele I, Fleming RMT. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat Protoc 2019; 14:639-702. [PMID: 30787451 PMCID: PMC6635304 DOI: 10.1038/s41596-018-0098-2] [Citation(s) in RCA: 609] [Impact Index Per Article: 121.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Constraint-based reconstruction and analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental molecular systems biology data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive desktop software suite of interoperable COBRA methods. It has found widespread application in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. This protocol is an update to the COBRA Toolbox v.1.0 and v.2.0. Version 3.0 includes new methods for quality-controlled reconstruction, modeling, topological analysis, strain and experimental design, and network visualization, as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimization solvers for multi-scale, multi-cellular, and reaction kinetic modeling, respectively. This protocol provides an overview of all these new features and can be adapted to generate and analyze constraint-based models in a wide variety of scenarios. The COBRA Toolbox v.3.0 provides an unparalleled depth of COBRA methods.
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Affiliation(s)
- Laurent Heirendt
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Sylvain Arreckx
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Thomas Pfau
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Sebastián N Mendoza
- Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile
- Mathomics, Center for Mathematical Modeling, University of Chile, Santiago, Chile
| | - Anne Richelle
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, USA
| | - Almut Heinken
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Hulda S Haraldsdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Jacek Wachowiak
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Sarah M Keating
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridge, UK
| | - Vanja Vlasov
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Stefania Magnusdóttir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Chiam Yu Ng
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - German Preciat
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Alise Žagare
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Siu H J Chan
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Maike K Aurich
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Catherine M Clancy
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Jennifer Modamio
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - John T Sauls
- Department of Physics, and Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Alberto Noronha
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | | | - Benjamin Cousins
- Algorithms and Randomness Center, School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Diana C El Assal
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Luis V Valcarcel
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Iñigo Apaolaza
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Susan Ghaderi
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Masoud Ahookhosh
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Marouen Ben Guebila
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Andrejs Kostromins
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Nicolas Sompairac
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Hoai M Le
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ding Ma
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Yuekai Sun
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - James T Yurkovich
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
| | - Miguel A P Oliveira
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Phan T Vuong
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Lemmer P El Assal
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Inna Kuperstein
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, Mines Paris Tech, Inserm, U900, Paris, France
| | - H Scott Hinton
- Utah State University Research Foundation, North Logan, UT, USA
| | - William A Bryant
- Centre for Integrative Systems Biology and Bioinformatics, Department of Life Sciences, Imperial College London, London, UK
| | | | - Francisco J Planes
- Biomedical Engineering and Sciences Department, TECNUN, University of Navarra, San Sebastián, Spain
| | - Egils Stalidzans
- Institute of Microbiology and Biotechnology, University of Latvia, Riga, Latvia
| | - Alejandro Maass
- Center for Genome Regulation (Fondap 15090007), University of Chile, Santiago, Chile
- Mathomics, Center for Mathematical Modeling, University of Chile, Santiago, Chile
| | - Santosh Vempala
- Algorithms and Randomness Center, School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA
| | - Michael Hucka
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Michael A Saunders
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, State College, PA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, University of California, San Diego, La Jolla, CA, USA
| | - Thomas Sauter
- Life Sciences Research Unit, University of Luxembourg, Belvaux, Luxembourg
| | - Bernhard Ø Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kemitorvet, Lyngby, Denmark
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg
| | - Ronan M T Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belvaux, Luxembourg.
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Faculty of Science, Leiden University, Leiden, The Netherlands.
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35
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Sen P, Orešič M. Metabolic Modeling of Human Gut Microbiota on a Genome Scale: An Overview. Metabolites 2019; 9:E22. [PMID: 30695998 PMCID: PMC6410263 DOI: 10.3390/metabo9020022] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2018] [Revised: 01/23/2019] [Accepted: 01/24/2019] [Indexed: 12/18/2022] Open
Abstract
There is growing interest in the metabolic interplay between the gut microbiome and host metabolism. Taxonomic and functional profiling of the gut microbiome by next-generation sequencing (NGS) has unveiled substantial richness and diversity. However, the mechanisms underlying interactions between diet, gut microbiome and host metabolism are still poorly understood. Genome-scale metabolic modeling (GSMM) is an emerging approach that has been increasingly applied to infer diet⁻microbiome, microbe⁻microbe and host⁻microbe interactions under physiological conditions. GSMM can, for example, be applied to estimate the metabolic capabilities of microbes in the gut. Here, we discuss how meta-omics datasets such as shotgun metagenomics, can be processed and integrated to develop large-scale, condition-specific, personalized microbiota models in healthy and disease states. Furthermore, we summarize various tools and resources available for metagenomic data processing and GSMM, highlighting the experimental approaches needed to validate the model predictions.
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Affiliation(s)
- Partho Sen
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden.
| | - Matej Orešič
- Turku Centre for Biotechnology, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland.
- School of Medical Sciences, Örebro University, 702 81 Örebro, Sweden.
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36
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Gut Microbiome Dysbiosis and Immunometabolism: New Frontiers for Treatment of Metabolic Diseases. Mediators Inflamm 2018; 2018:2037838. [PMID: 30622429 PMCID: PMC6304917 DOI: 10.1155/2018/2037838] [Citation(s) in RCA: 160] [Impact Index Per Article: 26.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2018] [Accepted: 10/23/2018] [Indexed: 02/06/2023] Open
Abstract
Maintenance of healthy human metabolism depends on a symbiotic consortium among bacteria, archaea, viruses, fungi, and host eukaryotic cells throughout the human gastrointestinal tract. Microbial communities provide the enzymatic machinery and the metabolic pathways that contribute to food digestion, xenobiotic metabolism, and production of a variety of bioactive molecules. These include vitamins, amino acids, short-chain fatty acids (SCFAs), and metabolites, which are essential for the interconnected pathways of glycolysis, the tricarboxylic acid/Krebs cycle, oxidative phosphorylation (OXPHOS), and amino acid and fatty acid metabolism. Recent studies have been elucidating how nutrients that fuel the metabolic processes impact on the ways immune cells, in particular, macrophages, respond to different stimuli under physiological and pathological conditions and become activated and acquire a specialized function. The two major inflammatory phenotypes of macrophages are controlled through differential consumption of glucose, glutamine, and oxygen. M1 phenotype is triggered by polarization signal from bacterial lipopolysaccharide (LPS) and Th1 proinflammatory cytokines such as interferon-γ, TNF-α, and IL-1β, or both, whereas M2 phenotype is triggered by Th2 cytokines such as interleukin-4 and interleukin-13 as well as anti-inflammatory cytokines, IL-10 and TGFβ, or glucocorticoids. Glucose utilization and production of chemical mediators including ATP, reactive oxygen species (ROS), nitric oxide (NO), and NADPH support effector activities of M1 macrophages. Dysbiosis is an imbalance of commensal and pathogenic bacteria and the production of microbial antigens and metabolites. It is now known that the gut microbiota-derived products induce low-grade inflammatory activation of tissue-resident macrophages and contribute to metabolic and degenerative diseases, including diabetes, obesity, metabolic syndrome, and cancer. Here, we update the potential interplay of host gut microbiome dysbiosis and metabolic diseases. We also summarize on advances on fecal therapy, probiotics, prebiotics, symbiotics, and nutrients and small molecule inhibitors of metabolic pathway enzymes as prophylactic and therapeutic agents for metabolic diseases.
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37
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Zhang C, Bidkhori G, Benfeitas R, Lee S, Arif M, Uhlén M, Mardinoglu A. ESS: A Tool for Genome-Scale Quantification of Essentiality Score for Reaction/Genes in Constraint-Based Modeling. Front Physiol 2018; 9:1355. [PMID: 30323767 PMCID: PMC6173058 DOI: 10.3389/fphys.2018.01355] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2018] [Accepted: 09/07/2018] [Indexed: 11/17/2022] Open
Abstract
Genome-scale metabolic models (GEMs) are comprehensive descriptions of cell metabolism and have been extensively used to understand biological responses in health and disease. One such application is in determining metabolic adaptation to the absence of a gene or reaction, i.e., essentiality analysis. However, current methods do not permit efficiently and accurately quantifying reaction/gene essentiality. Here, we present Essentiality Score Simulator (ESS), a tool for quantification of gene/reaction essentialities in GEMs. ESS quantifies and scores essentiality of each reaction/gene and their combinations based on the stoichiometric balance using synthetic lethal analysis. This method provides an option to weight metabolic models which currently rely mostly on topologic parameters, and is potentially useful to investigate the metabolic pathway differences between different organisms, cells, tissues, and/or diseases. We benchmarked the proposed method against multiple network topology parameters, and observed that our method displayed higher accuracy based on experimental evidence. In addition, we demonstrated its application in the wild-type and ldh knock-out E. coli core model, as well as two human cell lines, and revealed the changes of essentiality in metabolic pathways based on the reactions essentiality score. ESS is available without any limitation at https://sourceforge.net/projects/essentiality-score-simulator.
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Affiliation(s)
- Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Gholamreza Bidkhori
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Rui Benfeitas
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Sunjae Lee
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Mathias Uhlén
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden.,Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.,Centre for Host-Microbiome Interactions, Dental Institute, King's College London, London, United Kingdom
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38
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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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39
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Abstract
An important hallmark of the human gut microbiota is its species diversity and complexity. Various diseases have been associated with a decreased diversity leading to reduced metabolic functionalities. Common approaches to investigate the human microbiota include high-throughput sequencing with subsequent correlative analyses. However, to understand the ecology of the human gut microbiota and consequently design novel treatments for diseases, it is important to represent the different interactions between microbes with their associated metabolites. Computational systems biology approaches can give further mechanistic insights by constructing data- or knowledge-driven networks that represent microbe interactions. In this minireview, we will discuss current approaches in systems biology to analyze the human gut microbiota, with a particular focus on constraint-based modeling. We will discuss various community modeling techniques with their advantages and differences, as well as their application to predict the metabolic mechanisms of intestinal microbial communities. Finally, we will discuss future perspectives and current challenges of simulating realistic and comprehensive models of the human gut microbiota.
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Affiliation(s)
- Eugen Bauer
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, Universite du Luxembourg, Esch-sur-Alzette, Luxembourg
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40
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Shubham K, Vinay L, Vinod PK. Systems-level organization of non-alcoholic fatty liver disease progression network. MOLECULAR BIOSYSTEMS 2018; 13:1898-1911. [PMID: 28745372 DOI: 10.1039/c7mb00013h] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Non-Alcoholic Fatty Liver Disease (NAFLD) is a complex spectrum of diseases ranging from simple steatosis to Non-Alcoholic Steatohepatitis (NASH) with fibrosis, which can progress to cirrhosis and hepatocellular carcinoma. The pathogenesis of NAFLD is complex, involving crosstalk between multiple organs, cell-types, and environmental and genetic factors. Dysfunction of the adipose tissue plays a central role in NAFLD progression. Here, we analysed transcriptomics data obtained from the Visceral Adipose Tissue (VAT) of NAFLD patients to understand how the VAT metabolism is altered at the genome scale and co-regulated with other cellular processes during the progression from obesity to NASH with fibrosis. For this purpose, we performed Weighted Gene Co-expression Network Analysis (WGCNA), a method that organizes the disease transcriptome into functional modules of cellular processes and pathways. Our analysis revealed the coordination of metabolic and inflammatory modules (termed "immunometabolism") in the VAT of NAFLD patients. We found that genes of arachidonic acid, sphingolipid and glycosphingolipid metabolism were upregulated and co-expressed with genes of proinflammatory signalling pathways and hypoxia in NASH/NASH with fibrosis. We hypothesize that these metabolic alterations might play a role in sustaining VAT inflammation. Furthermore, immunometabolism related genes were also co-expressed with genes involved in Extracellular Matrix (ECM) degradation. Our analysis indicates that upregulation of both ECM degrading enzymes and their inhibitors (incoherent feedforward loop) potentially leads to the ECM deposition in the VAT of NASH with fibrosis patients.
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Affiliation(s)
- K Shubham
- Centre for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad-500032, India.
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41
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Brunk E, Sahoo S, Zielinski DC, Altunkaya A, Dräger A, Mih N, Gatto F, Nilsson A, Gonzalez GAP, Aurich MK, Prlić A, Sastry A, Danielsdottir AD, Heinken A, Noronha A, Rose PW, Burley SK, Fleming RM, Nielsen J, Thiele I, Palsson BO. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat Biotechnol 2018; 36:272-281. [PMID: 29457794 PMCID: PMC5840010 DOI: 10.1038/nbt.4072] [Citation(s) in RCA: 390] [Impact Index Per Article: 65.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Accepted: 01/10/2018] [Indexed: 12/14/2022]
Abstract
Genome-scale network reconstructions have helped uncover the molecular basis of metabolism. Here we present Recon3D, a computational resource that includes three-dimensional (3D) metabolite and protein structure data and enables integrated analyses of metabolic functions in humans. We use Recon3D to functionally characterize mutations associated with disease, and identify metabolic response signatures that are caused by exposure to certain drugs. Recon3D represents the most comprehensive human metabolic network model to date, accounting for 3,288 open reading frames (representing 17% of functionally annotated human genes), 13,543 metabolic reactions involving 4,140 unique metabolites, and 12,890 protein structures. These data provide a unique resource for investigating molecular mechanisms of human metabolism. Recon3D is available at http://vmh.life.
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Affiliation(s)
- Elizabeth Brunk
- Department of Bioengineering, University of California San Diego CA 92093
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
| | - Swagatika Sahoo
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | | | - Ali Altunkaya
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Andreas Dräger
- Applied Bioinformatics Group, Center for Bioinformatics Tübingen (ZBIT), University of Tübingen, 72076 Tübingen, Germany
| | - Nathan Mih
- Department of Bioengineering, University of California San Diego CA 92093
| | - Francesco Gatto
- Department of Bioengineering, University of California San Diego CA 92093
- Department of Biology and Biological Engineering, Chalmers University of Technology, Sweden
| | - Avlant Nilsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Sweden
| | | | - Maike Kathrin Aurich
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Andreas Prlić
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Anand Sastry
- Department of Bioengineering, University of California San Diego CA 92093
| | - Anna D. Danielsdottir
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Almut Heinken
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Alberto Noronha
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Peter W. Rose
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
| | - Stephen K. Burley
- RCSB Protein Data Bank, San Diego Supercomputer Center, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Chemistry and Chemical Biology, Center for Integrative Proteomics Research, Institute for Quantitative Biomedicine, and Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, Piscataway, NJ 08854, USA
| | - Ronan M.T. Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Jens Nielsen
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
- Department of Biology and Biological Engineering, Chalmers University of Technology, Sweden
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego CA 92093
- The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, 2800 Lyngby, Denmark
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42
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Zhang X, Mardinoglu A, Joosten LAB, Kuivenhoven JA, Li Y, Netea MG, Groen AK. Identification of Discriminating Metabolic Pathways and Metabolites in Human PBMCs Stimulated by Various Pathogenic Agents. Front Physiol 2018. [PMID: 29535640 PMCID: PMC5835230 DOI: 10.3389/fphys.2018.00139] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Immunity and cellular metabolism are tightly interconnected but it is not clear whether different pathogens elicit specific metabolic responses. To address this issue, we studied differential metabolic regulation in peripheral blood mononuclear cells (PBMCs) of healthy volunteers challenged by Candida albicans, Borrelia burgdorferi, lipopolysaccharide, and Mycobacterium tuberculosis in vitro. By integrating gene expression data of stimulated PBMCs of healthy individuals with the KEGG pathways, we identified both common and pathogen-specific regulated pathways depending on the time of incubation. At 4 h of incubation, pathogenic agents inhibited expression of genes involved in both the glycolysis and oxidative phosphorylation pathways. In contrast, at 24 h of incubation, particularly glycolysis was enhanced while genes involved in oxidative phosphorylation remained unaltered in the PBMCs. In general, differential gene expression was less pronounced at 4 h compared to 24 h of incubation. KEGG pathway analysis allowed differentiation between effects induced by Candida and bacterial stimuli. Application of genome-scale metabolic model further generated a Candida-specific set of 103 reporter metabolites (e.g., desmosterol) that might serve as biomarkers discriminating Candida-stimulated PBMCs from bacteria-stimuated PBMCs. Our analysis also identified a set of 49 metabolites that allowed discrimination between the effects of Borrelia burgdorferi, lipopolysaccharide and Mycobacterium tuberculosis. We conclude that analysis of pathogen-induced effects on PBMCs by a combination of KEGG pathways and genome-scale metabolic model provides deep insight in the metabolic changes coupled to host defense.
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Affiliation(s)
- Xiang Zhang
- Department of Experimental Vascular Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands
| | - Adil Mardinoglu
- Department of Chemical and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.,Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Leo A B Joosten
- Department of Internal Medicine, Radboud University Nijmegen Medical Center, Nijmegen, Netherlands
| | - Jan A Kuivenhoven
- Section Molecular Genetics, Department of Pediatrics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Yang Li
- Department of Genetics, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Mihai G Netea
- Department of Internal Medicine, Radboud University Nijmegen Medical Center, Nijmegen, Netherlands.,Department for Genomics & Immunoregulation, Life and Medical Sciences Institute, University of Bonn, Bonn, Germany
| | - Albert K Groen
- Department of Experimental Vascular Medicine, Academic Medical Center, University of Amsterdam, Amsterdam, Netherlands.,Department of Laboratory Medicine, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
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43
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Abstract
The gastrointestinal (GI) tract is the residence of trillions of microorganisms that include bacteria, archaea, fungi and viruses. The collective genomes of whole microbial communities (microbiota) integrate the gut microbiome. Up to 100 genera and 1000 distinct bacterial species were identified in digestive tube niches. Gut microbiomes exert permanent pivotal functions by promoting food digestion, xenobiotic metabolism and regulation of innate and adaptive immunological processes. Proteins, peptides and metabolites released locally and at distant sites trigger many cell signalling and pathways. This intense crosstalk maintains the host-microbial homeostasis. Diet, age, diet, stress and diseases cause increases or decreases in relative abundance and diversity bacterial specie of GI and other body sites. Studies in animal models and humans have shown that a persistent imbalance of gut's microbial community, named dysbiosis, relates to inflammatory bowel diseases (IBD), irritable bowel syndrome (IBS), diabetes, obesity, cancer, cardiovascular and central nervous system disorders. Notably specific bacterial communities are promising clinical target to treat inflammatory and infectious diseases. In this context, intestinal microbiota transplantation (IMT) is one optional treatment for IBD, in particular to patients with recurrent Clostridium difficile-induced pseudo-membrane colitis. Here we discuss on recent discoveries linking whole gut microbiome dysbiosis to metabolic and inflammatory diseases and potential prophylactic and therapeutic applications of faecal and phage therapy, probiotic and prebiotic diets.
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Affiliation(s)
- José E Belizário
- Department of Pharmacology, Institute of Biomedical Sciences, University of São Paulo, São Paulo, Brazil.
| | - Joel Faintuch
- Department of Gastroenterology of Medical School, University of Sao Paulo, São Paulo, Brazil
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44
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Özcan E, Çakır T. Genome-Scale Brain Metabolic Networks as Scaffolds for the Systems Biology of Neurodegenerative Diseases: Mapping Metabolic Alterations. ADVANCES IN NEUROBIOLOGY 2018; 21:195-217. [PMID: 30334223 DOI: 10.1007/978-3-319-94593-4_7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Systems-based investigation of diseases requires integrated analysis of cellular networks and high-throughput data of gene products. The use of genome-scale metabolic networks for such integration has led to the elucidation of cellular mechanisms for several cell types from microorganisms to plants. It has become easier and cheaper to generate high-throughput data over years in the form of transcriptome, proteome and metabolome. This has tremendously improved the quality and quantity of information extracted from such data enabling the documentation of active pathways and reactions in cell metabolism. A number of omics-based datasets for several neurodegenerative diseases are now available in public repositories. This increases the potential of using genome-scale brain metabolic networks as a scaffold for this type of data to map metabolic alterations for the purpose of elucidating disease mechanisms and for the diagnosis and treatment of such disorders. This chapter first reviews omics data collected for neurodegenerative diseases to map their effect on metabolism. Later, the potential for genome-scale metabolic modeling of such data is reviewed and discussed in light of recently reconstructed brain metabolic networks at genome-scale.
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Affiliation(s)
- Emrah Özcan
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Gebze, Kocaeli, Turkey.
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Gutierrez Najera NA, Resendis-Antonio O, Nicolini H. "Gestaltomics": Systems Biology Schemes for the Study of Neuropsychiatric Diseases. Front Physiol 2017; 8:286. [PMID: 28536537 PMCID: PMC5422874 DOI: 10.3389/fphys.2017.00286] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2016] [Accepted: 04/19/2017] [Indexed: 01/28/2023] Open
Abstract
The integration of different sources of biological information about what defines a behavioral phenotype is difficult to unify in an entity that reflects the arithmetic sum of its individual parts. In this sense, the challenge of Systems Biology for understanding the “psychiatric phenotype” is to provide an improved vision of the shape of the phenotype as it is visualized by “Gestalt” psychology, whose fundamental axiom is that the observed phenotype (behavior or mental disorder) will be the result of the integrative composition of every part. Therefore, we propose the term “Gestaltomics” as a term from Systems Biology to integrate data coming from different sources of information (such as the genome, transcriptome, proteome, epigenome, metabolome, phenome, and microbiome). In addition to this biological complexity, the mind is integrated through multiple brain functions that receive and process complex information through channels and perception networks (i.e., sight, ear, smell, memory, and attention) that in turn are programmed by genes and influenced by environmental processes (epigenetic). Today, the approach of medical research in human diseases is to isolate one disease for study; however, the presence of an additional disease (co-morbidity) or more than one disease (multimorbidity) adds complexity to the study of these conditions. This review will present the challenge of integrating psychiatric disorders at different levels of information (Gestaltomics). The implications of increasing the level of complexity, for example, studying the co-morbidity with another disease such as cancer, will also be discussed.
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Affiliation(s)
| | - Osbaldo Resendis-Antonio
- Instituto Nacional de Medicina GenómicaMexico City, Mexico.,Human Systems Biology Laboratory, Coordinación de la Investigación Científica - Red de Apoyo a la Investigación, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, National Autonomous University of Mexico (UNAM)Mexico City, Mexico
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Martín-Jiménez CA, Salazar-Barreto D, Barreto GE, González J. Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network. Front Aging Neurosci 2017; 9:23. [PMID: 28243200 PMCID: PMC5303712 DOI: 10.3389/fnagi.2017.00023] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 01/27/2017] [Indexed: 12/22/2022] Open
Abstract
Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states.
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Affiliation(s)
- Cynthia A Martín-Jiménez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana Bogotá, Colombia
| | - Diego Salazar-Barreto
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana Bogotá, Colombia
| | - George E Barreto
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad JaverianaBogotá, Colombia; Instituto de Ciencias Biomédicas, Universidad Autónoma de ChileSantiago, Chile
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana Bogotá, Colombia
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Nilsson A, Mardinoglu A, Nielsen J. Predicting growth of the healthy infant using a genome scale metabolic model. NPJ Syst Biol Appl 2017; 3:3. [PMID: 28649430 PMCID: PMC5460126 DOI: 10.1038/s41540-017-0004-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Revised: 12/15/2016] [Accepted: 01/07/2017] [Indexed: 12/28/2022] Open
Abstract
An estimated 165 million children globally have stunted growth, and extensive growth data are available. Genome scale metabolic models allow the simulation of molecular flux over each metabolic enzyme, and are well adapted to analyze biological systems. We used a human genome scale metabolic model to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body composition. The model corroborates the finding that essential amino and fatty acids do not limit growth, but that energy is the main growth limiting factor. Disruptions to the supply and demand of energy markedly affected the predicted growth, indicating that elevated energy expenditure may be detrimental. The model was used to simulate the metabolic effect of mineral deficiencies, and showed the greatest growth reduction for deficiencies in copper, iron, and magnesium ions which affect energy production through oxidative phosphorylation. The model and simulation method were integrated to a platform and shared with the research community. The growth model constitutes another step towards the complete representation of human metabolism, and may further help improve the understanding of the mechanisms underlying stunting.
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Affiliation(s)
- Avlant Nilsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE41296 Sweden
| | - Adil Mardinoglu
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE41296 Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE41296 Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, DK2970 Denmark
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Diener C, Resendis-Antonio O. Personalized Prediction of Proliferation Rates and Metabolic Liabilities in Cancer Biopsies. Front Physiol 2016; 7:644. [PMID: 28082911 PMCID: PMC5186797 DOI: 10.3389/fphys.2016.00644] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Accepted: 12/09/2016] [Indexed: 12/13/2022] Open
Abstract
Cancer is a heterogeneous disease and its genetic and metabolic mechanism may manifest differently in each patient. This creates a demand for studies that can characterize phenotypic traits of cancer on a per-sample basis. Combining two large data sets, the NCI60 cancer cell line panel, and The Cancer Genome Atlas, we used a linear interaction model to predict proliferation rates for more than 12,000 cancer samples across 33 different cancers from The Cancer Genome Atlas. The predicted proliferation rates are associated with patient survival and cancer stage and show a strong heterogeneity in proliferative capacity within and across different cancer panels. We also show how the obtained proliferation rates can be incorporated into genome-scale metabolic reconstructions to obtain the metabolic fluxes for more than 3000 cancer samples that identified specific metabolic liabilities for nine cancer panels. Here we found that affected pathways coincided with the literature, with pentose phosphate pathway, retinol, and branched-chain amino acid metabolism being the most panel-specific alterations and fatty acid metabolism and ROS detoxification showing homogeneous metabolic activities across all cancer panels. The presented strategy has potential applications in personalized medicine since it can leverage gene expression signatures for cell line based prediction of additional metabolic properties which might help in constraining personalized metabolic models and improve the identification of metabolic alterations in cancer for individual patients.
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Affiliation(s)
- Christian Diener
- Human Systems Biology Laboratory, Instituto Nacional de Medicina GenómicaMéxico City, Mexico
| | - Osbaldo Resendis-Antonio
- Human Systems Biology Laboratory, Instituto Nacional de Medicina GenómicaMéxico City, Mexico
- Coordinación de la Investigación Científica, Red de Apoyo a la Investigación, UNAMMéxico City, Mexico
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De Sanctis G, Spinelli M, Vanoni M, Sacco E. K-Ras Activation Induces Differential Sensitivity to Sulfur Amino Acid Limitation and Deprivation and to Oxidative and Anti-Oxidative Stress in Mouse Fibroblasts. PLoS One 2016; 11:e0163790. [PMID: 27685888 PMCID: PMC5042513 DOI: 10.1371/journal.pone.0163790] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2016] [Accepted: 09/14/2016] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Cancer cells have an increased demand for amino acids and require transport even of non-essential amino acids to support their increased proliferation rate. Besides their major role as protein synthesis precursors, the two proteinogenic sulfur-containing amino acids, methionine and cysteine, play specific biological functions. In humans, methionine is essential for cell growth and development and may act as a precursor for cysteine synthesis. Cysteine is a precursor for the biosynthesis of glutathione, the major scavenger for reactive oxygen species. METHODOLOGY AND PRINCIPAL FINDINGS We study the effect of K-ras oncogene activation in NIH3T3 mouse fibroblasts on transport and metabolism of cysteine and methionine. We show that cysteine limitation and deprivation cause apoptotic cell death (cytotoxic effect) in both normal and K-ras-transformed fibroblasts, due to accumulation of reactive oxygen species and a decrease in reduced glutathione. Anti-oxidants glutathione and MitoTEMPO inhibit apoptosis, but only cysteine-containing glutathione partially rescues the cell growth defect induced by limiting cysteine. Methionine limitation and deprivation has a cytostatic effect on mouse fibroblasts, unaffected by glutathione. K-ras-transformed cells-but not their parental NIH3T3-are extremely sensitive to methionine limitation. This fragility correlates with decreased expression of the Slc6a15 gene-encoding the nutrient transporter SBAT1, known to exhibit a strong preference for methionine-and decreased methionine uptake. CONCLUSIONS AND SIGNIFICANCE Overall, limitation of sulfur-containing amino acids results in a more dramatic perturbation of the oxido-reductive balance in K-ras-transformed cells compared to NIH3T3 cells. Growth defects induced by cysteine limitation in mouse fibroblasts are largely-though not exclusively-due to cysteine utilization in the synthesis of glutathione, mouse fibroblasts requiring an exogenous cysteine source for protein synthesis. Therapeutic regimens of cancer involving modulation of methionine metabolism could be more effective in cells with limited methionine transport capability.
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Affiliation(s)
- Gaia De Sanctis
- SYSBIO, Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126, Milan, Italy
| | - Michela Spinelli
- SYSBIO, Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126, Milan, Italy
| | - Marco Vanoni
- SYSBIO, Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126, Milan, Italy
| | - Elena Sacco
- SYSBIO, Centre of Systems Biology, Milan, Italy
- Department of Biotechnology and Biosciences, University of Milano-Bicocca, Piazza della Scienza 2, 20126, Milan, Italy
- * E-mail:
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Shen F, Li J, Zhu Y, Wang Z. Systematic investigation of metabolic reprogramming in different cancers based on tissue-specific metabolic models. J Bioinform Comput Biol 2016; 14:1644001. [PMID: 27760488 DOI: 10.1142/s0219720016440017] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Cancer cells have different metabolism in contrast to normal cells. The advancement in omics measurement technology enables the genome-wide characterization of altered cellular processes in cancers, but the metabolic flux landscape of cancer is still far from understood. In this study, we compared the well-reconstructed tissue-specific models of five cancers, including breast, liver, lung, renal, and urothelial cancer, and their corresponding normal cells. There are similar patterns in majority of significantly regulated pathways and enriched pathways in correlated reaction sets. But the differences among cancers are also explicit. The renal cancer demonstrates more dramatic difference with other cancer models, including the smallest number of reactions, flux distribution patterns, and specifically correlated pathways. We also validated the predicted essential genes and revealed the Warburg effect by in silico simulation in renal cancer, which are consistent with the measurements for renal cancer. In conclusion, the tissue-specific metabolic model is more suitable to investigate the cancer metabolism. The similarity and heterogenicity of metabolic reprogramming in different cancers are crucial for understanding the aberrant mechanisms of cancer proliferation, which is fundamental for identifying drug targets and biomarkers.
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Affiliation(s)
- Fangzhou Shen
- 1 School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P. R. China
| | - Jian Li
- 1 School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P. R. China
| | - Ying Zhu
- 1 School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P. R. China
| | - Zhuo Wang
- 1 School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, P. R. China
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