201
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Sim WC, Lee W, Sim H, Lee KY, Jung SH, Choi YJ, Kim HY, Kang KW, Lee JY, Choi YJ, Kim SK, Jun DW, Kim W, Lee BH. Downregulation of PHGDH expression and hepatic serine level contribute to the development of fatty liver disease. Metabolism 2020; 102:154000. [PMID: 31678070 DOI: 10.1016/j.metabol.2019.154000] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 10/08/2019] [Accepted: 10/19/2019] [Indexed: 01/07/2023]
Abstract
OBJECTIVE Supplementation with serine attenuates alcoholic fatty liver by regulating homocysteine metabolism and lipogenesis. However, little is known about serine metabolism in fatty liver disease (FLD). We aimed to investigate the changes in serine biosynthetic pathways in humans and animal models of fatty liver and their contribution to the development of FLD. METHODS High-fat diet (HFD)-induced steatosis and methionine-choline-deficient diet-induced steatohepatitis animal models were employed. Human serum samples were obtained from patients with FLD whose proton density fat fraction was estimated by magnetic resonance imaging. 3-Phosphoglycerate dehydrogenase (Phgdh)-knockout mouse embryonic fibroblasts (MEF) and transgenic mice overexpressing Phgdh (Tg-phgdh) were used to evaluate the role of serine metabolism in the development of FLD. RESULTS Expression of Phgdh was markedly reduced in the animal models. There were significant negative correlations of the serum serine with the liver fat fraction, serum alanine transaminase, and triglyceride levels among patients with FLD. Increased lipid accumulation and reduced NAD+ and SIRT1 activity were observed in Phgdh-knockout MEF and primary hepatocytes incubated with free fatty acids; these effects were reversed by overexpression of Phgdh. Tg-Phgdh mice showed significantly reduced hepatic triglyceride accumulation compared with wild-type littermates fed a HFD, which was accompanied by increased SIRT1 activity and reduced expression of lipogenic genes and proteins. CONCLUSIONS Human and experimental data suggest that reduced Phgdh expression and serine levels are closely associated with the development of FLD.
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Affiliation(s)
- Woo-Cheol Sim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Wonseok Lee
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyungtai Sim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Kang-Yo Lee
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Seung-Hwan Jung
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - You-Jin Choi
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Hyun Young Kim
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Keon Wook Kang
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea
| | - Ji-Yoon Lee
- College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea
| | - Young Jae Choi
- College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea
| | - Sang Kyum Kim
- College of Pharmacy, Chungnam National University, Daejeon, Republic of Korea
| | - Dae Won Jun
- Department of Internal Medicine, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Won Kim
- Department of Internal Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Byung-Hoon Lee
- College of Pharmacy and Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul, Republic of Korea.
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202
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Rawls K, Dougherty BV, Papin J. Metabolic Network Reconstructions to Predict Drug Targets and Off-Target Effects. Methods Mol Biol 2020; 2088:315-330. [PMID: 31893380 DOI: 10.1007/978-1-0716-0159-4_14] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
The drug development pipeline has stalled because of the difficulty in identifying new drug targets while minimizing off-target effects. Computational methods, such as the use of metabolic network reconstructions, may provide a cost-effective platform to test new hypotheses for drug targets and prevent off-target effects. Here, we summarize available methods to identify drug targets and off-target effects using either reaction-centric, gene-centric, or metabolite-centric approaches with genome-scale metabolic network reconstructions.
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Affiliation(s)
- Kristopher Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
| | - Jason Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA.
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203
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Waller TC, Berg JA, Lex A, Chapman BE, Rutter J. Compartment and hub definitions tune metabolic networks for metabolomic interpretations. Gigascience 2020; 9:giz137. [PMID: 31972021 PMCID: PMC6977586 DOI: 10.1093/gigascience/giz137] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2018] [Revised: 08/31/2019] [Accepted: 10/27/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism's cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments. RESULTS We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications. CONCLUSIONS Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.
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Affiliation(s)
- T Cameron Waller
- Division of Medical Genetics, Department of Medicine, School of Medicine, University of California San Diego, Room 1318A, 9500 Gilman Drive #0606, La Jolla, California 92093-0606, United States of America
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
| | - Jordan A Berg
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
| | - Alexander Lex
- School of Computing, University of Utah, Room 3190, 50 South Central Campus Drive, Salt Lake City, Utah 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Room 3750, 72 South Central Campus Drive, Salt Lake City, Utah 84112, USA
| | - Brian E Chapman
- Department of Radiology and Imaging Sciences, School of Medicine, University of Utah, Room 1A071, 30 North 1900 East, Salt Lake City, Utah 84132, USA
- Department of Biomedical Informatics, School of Medicine, University of Utah, Suite 140, 421 Wakara Way, Salt Lake City, Utah 84108, USA
| | - Jared Rutter
- Department of Biochemistry, School of Medicine, University of Utah, Room 4100, 15 North Medical Drive East, Salt Lake City, Utah 84112, USA
- Howard Hughes Medical Institute, School of Medicine, University of Utah, Room AC101, 30 North 1900 East, Salt Lake City, Utah 84132, USA
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204
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Satriano L, Lewinska M, Rodrigues PM, Banales JM, Andersen JB. Metabolic rearrangements in primary liver cancers: cause and consequences. Nat Rev Gastroenterol Hepatol 2019; 16:748-766. [PMID: 31666728 DOI: 10.1038/s41575-019-0217-8] [Citation(s) in RCA: 149] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/19/2019] [Indexed: 02/07/2023]
Abstract
Primary liver cancer (PLC) is the fourth most frequent cause of cancer-related death. The high mortality rates arise from late diagnosis and the limited accuracy of diagnostic and prognostic biomarkers. The liver is a major regulator, orchestrating the clearance of toxins, balancing glucose, lipid and amino acid uptake, managing whole-body metabolism and maintaining metabolic homeostasis. Tumour onset and progression is frequently accompanied by rearrangements of metabolic pathways, leading to dysregulation of metabolism. The limitation of current therapies targeting PLCs, such as hepatocellular carcinoma and cholangiocarcinoma, points towards the importance of deciphering this metabolic complexity. In this Review, we discuss the role of metabolic liver disruptions and the implications of these processes in PLCs, emphasizing their clinical relevance and value in early diagnosis and prognosis and as putative therapeutic targets. We also describe system biology approaches able to reconstruct the metabolic complexity of liver diseases. We also discuss whether metabolic rearrangements are a cause or consequence of PLCs, emphasizing the opportunity to clinically exploit the rewired metabolism. In line with this idea, we discuss circulating metabolites as promising biomarkers for PLCs.
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Affiliation(s)
- Letizia Satriano
- Biotech Research and Innovation Centre (BRIC) Department of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Monika Lewinska
- Biotech Research and Innovation Centre (BRIC) Department of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Pedro M Rodrigues
- Biodonostia Health Research Institute, Donostia University Hospital, San Sebastian, Spain
| | - Jesus M Banales
- Biodonostia Health Research Institute, Donostia University Hospital, San Sebastian, Spain.,National Institute for the Study of Liver and Gastrointestinal Diseases (CIBERehd), Carlos III National Institute of Health, Madrid, Spain.,IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
| | - Jesper B Andersen
- Biotech Research and Innovation Centre (BRIC) Department of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
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205
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Blencowe M, Karunanayake T, Wier J, Hsu N, Yang X. Network Modeling Approaches and Applications to Unravelling Non-Alcoholic Fatty Liver Disease. Genes (Basel) 2019; 10:E966. [PMID: 31771247 PMCID: PMC6947017 DOI: 10.3390/genes10120966] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 11/18/2019] [Accepted: 11/22/2019] [Indexed: 12/12/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a progressive condition of the liver encompassing a range of pathologies including steatosis, non-alcoholic steatohepatitis (NASH), cirrhosis, and hepatocellular carcinoma. Research into this disease is imperative due to its rapid growth in prevalence, economic burden, and current lack of FDA approved therapies. NAFLD involves a highly complex etiology that calls for multi-tissue multi-omics network approaches to uncover the pathogenic genes and processes, diagnostic biomarkers, and potential therapeutic strategies. In this review, we first present a basic overview of disease pathogenesis, risk factors, and remaining knowledge gaps, followed by discussions of the need and concepts of multi-tissue multi-omics approaches, various network methodologies and application examples in NAFLD research. We highlight the findings that have been uncovered thus far including novel biomarkers, genes, and biological pathways involved in different stages of NAFLD, molecular connections between NAFLD and its comorbidities, mechanisms underpinning sex differences, and druggable targets. Lastly, we outline the future directions of implementing network approaches to further improve our understanding of NAFLD in order to guide diagnosis and therapeutics.
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Affiliation(s)
- Montgomery Blencowe
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA; (M.B.); (T.K.); (J.W.); (N.H.)
- Molecular, Cellular, and Integrative Physiology Interdepartmental Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
| | - Tilan Karunanayake
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA; (M.B.); (T.K.); (J.W.); (N.H.)
| | - Julian Wier
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA; (M.B.); (T.K.); (J.W.); (N.H.)
| | - Neil Hsu
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA; (M.B.); (T.K.); (J.W.); (N.H.)
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA; (M.B.); (T.K.); (J.W.); (N.H.)
- Molecular, Cellular, and Integrative Physiology Interdepartmental Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
- Interdepartmental Program of Bioinformatics, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, USA
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206
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Moore JB, Thorne JL. Predicting and reducing hepatic lipotoxicity in non-alcoholic fatty liver disease. Lab Anim (NY) 2019; 48:143-144. [PMID: 31000821 DOI: 10.1038/s41684-019-0291-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
| | - James L Thorne
- School of Food Science & Nutrition, University of Leeds, Leeds, UK
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207
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Nilsson A, Björnson E, Flockhart M, Larsen FJ, Nielsen J. Complex I is bypassed during high intensity exercise. Nat Commun 2019; 10:5072. [PMID: 31699973 PMCID: PMC6838197 DOI: 10.1038/s41467-019-12934-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 10/10/2019] [Indexed: 12/28/2022] Open
Abstract
Human muscles are tailored towards ATP synthesis. When exercising at high work rates muscles convert glucose to lactate, which is less nutrient efficient than respiration. There is hence a trade-off between endurance and power. Metabolic models have been developed to study how limited catalytic capacity of enzymes affects ATP synthesis. Here we integrate an enzyme-constrained metabolic model with proteomics data from muscle fibers. We find that ATP synthesis is constrained by several enzymes. A metabolic bypass of mitochondrial complex I is found to increase the ATP synthesis rate per gram of protein compared to full respiration. To test if this metabolic mode occurs in vivo, we conduct a high resolved incremental exercise tests for five subjects. Their gas exchange at different work rates is accurately reproduced by a whole-body metabolic model incorporating complex I bypass. The study therefore shows how proteome allocation influences metabolism during high intensity exercise.
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Affiliation(s)
- Avlant Nilsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE41296, Sweden
| | - Elias Björnson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, SE41296, Sweden.,Department of Molecular and Clinical Medicine/Wallenberg Laboratory, University of Gothenburg, Gothenburg, Sweden
| | - Mikael Flockhart
- Åstrand Laboratory of Work Physiology, The Swedish School of Sport and Health Sciences, Stockholm, Sweden
| | - Filip J Larsen
- Åstrand Laboratory of Work Physiology, The Swedish School of Sport and Health Sciences, Stockholm, 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, DK2800, Kongens Lyngby, Denmark.
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208
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Marcišauskas S, Ji B, Nielsen J. Reconstruction and analysis of a Kluyveromyces marxianus genome-scale metabolic model. BMC Bioinformatics 2019; 20:551. [PMID: 31694544 PMCID: PMC6833147 DOI: 10.1186/s12859-019-3134-5] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Accepted: 10/09/2019] [Indexed: 12/29/2022] Open
Abstract
BACKGROUND Kluyveromyces marxianus is a thermotolerant yeast with multiple biotechnological potentials for industrial applications, which can metabolize a broad range of carbon sources, including less conventional sugars like lactose, xylose, arabinose and inulin. These phenotypic traits are sustained even up to 45 °C, what makes it a relevant candidate for industrial biotechnology applications, such as ethanol production. It is therefore of much interest to get more insight into the metabolism of this yeast. Recent studies suggested, that thermotolerance is achieved by reducing the number of growth-determining proteins or suppressing oxidative phosphorylation. Here we aimed to find related factors contributing to the thermotolerance of K. marxianus. RESULTS Here, we reported the first genome-scale metabolic model of Kluyveromyces marxianus, iSM996, using a publicly available Kluyveromyces lactis model as template. The model was manually curated and refined to include the missing species-specific metabolic capabilities. The iSM996 model includes 1913 reactions, associated with 996 genes and 1531 metabolites. It performed well to predict the carbon source utilization and growth rates under different growth conditions. Moreover, the model was coupled with transcriptomics data and used to perform simulations at various growth temperatures. CONCLUSIONS K. marxianus iSM996 represents a well-annotated metabolic model of thermotolerant yeast, which provides a new insight into theoretical metabolic profiles at different temperatures of K. marxianus. This could accelerate the integrative analysis of multi-omics data, leading to model-driven strain design and improvement.
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Affiliation(s)
- Simonas Marcišauskas
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Boyang Ji
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96, Gothenburg, Sweden.
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2800, Lyngby, Denmark.
- BioInnovation Institute, Ole Måløes Vej 3, DK2200, Copenhagen N, Denmark.
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209
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Toroghi MK, Cluett WR, Mahadevan R. A multi-scale model for low-density lipoprotein cholesterol (LDL-C) regulation in the human body: Application to quantitative systems pharmacology. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.06.032] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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210
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Léveillé M, Estall JL. Mitochondrial Dysfunction in the Transition from NASH to HCC. Metabolites 2019; 9:E233. [PMID: 31623280 PMCID: PMC6836234 DOI: 10.3390/metabo9100233] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 09/26/2019] [Accepted: 10/11/2019] [Indexed: 02/06/2023] Open
Abstract
The liver constantly adapts to meet energy requirements of the whole body. Despite its remarkable adaptative capacity, prolonged exposure of liver cells to harmful environmental cues (such as diets rich in fat, sugar, and cholesterol) results in the development of chronic liver diseases (including non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH)) that can progress to hepatocellular carcinoma (HCC). The pathogenesis of these diseases is extremely complex, multifactorial, and poorly understood. Emerging evidence suggests that mitochondrial dysfunction or maladaptation contributes to detrimental effects on hepatocyte bioenergetics, reactive oxygen species (ROS) homeostasis, endoplasmic reticulum (ER) stress, inflammation, and cell death leading to NASH and HCC. The present review highlights the potential contribution of altered mitochondria function to NASH-related HCC and discusses how agents targeting this organelle could provide interesting treatment strategies for these diseases.
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Affiliation(s)
- Mélissa Léveillé
- Institut de Recherches Cliniques de Montréal (IRCM), Montreal, Quebec, QC H2W 1R7, Canada.
- Faculty of Medicine, University of Montreal, Montreal, Quebec, QC H3G 2M1, Canada.
| | - Jennifer L Estall
- Institut de Recherches Cliniques de Montréal (IRCM), Montreal, Quebec, QC H2W 1R7, Canada.
- Faculty of Medicine, University of Montreal, Montreal, Quebec, QC H3G 2M1, Canada.
- Division of Experimental Medicine, McGill University, Montreal, Quebec, QC H4A 3J1, Canada.
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211
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Caussy C, Ajmera VH, Puri P, Li-Shin Hsu C, Bassirian S, Mgdsyan M, Singh S, Faulkner C, Valasek MA, Rizo E, Richards L, Brenner DA, Sirlin CB, Sanyal AJ, Loomba R. Serum metabolites detect the presence of advanced fibrosis in derivation and validation cohorts of patients with non-alcoholic fatty liver disease. Gut 2019; 68:1884-1892. [PMID: 30567742 PMCID: PMC8328048 DOI: 10.1136/gutjnl-2018-317584] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 10/22/2018] [Accepted: 11/29/2018] [Indexed: 12/16/2022]
Abstract
OBJECTIVE Non-invasive and accurate diagnostic tests for the screening of disease severity in non-alcoholic fatty liver disease (NAFLD) remain a major unmet need. Therefore, we aimed to examine if a combination of serum metabolites can accurately predict the presence of advanced fibrosis. DESIGN This is a cross-sectional analysis of a prospective derivation cohort including 156 well-characterised patients with biopsy-proven NAFLD and two validation cohorts, including (1) 142 patients assessed using MRI elastography (MRE) and(2) 59 patients with biopsy-proven NAFLD with untargeted serum metabolome profiling. RESULTS In the derivation cohort, 23 participants (15%) had advanced fibrosis and 32 of 652 analysed metabolites were significantly associated with advanced fibrosis after false-discovery rate adjustment. Among the top 10 metabolites, 8 lipids (5alpha-androstan-3beta monosulfate, pregnanediol-3-glucuronide, androsterone sulfate, epiandrosterone sulfate, palmitoleate, dehydroisoandrosterone sulfate, 5alpha-androstan-3beta disulfate, glycocholate), one amino acid (taurine) and one carbohydrate (fucose) were identified. The combined area under the receiver operating characteristic curve (AUROC) of the top 10 metabolite panel was higher than FIB--4 and NAFLD Fibrosis Score (NFS) for the detection of advanced fibrosis: 0.94 (95% CI 0.897 to 0.982) versus 0.78 (95% CI0.674 to 0.891), p=0.002 and versus 0.84 (95% CI 0.724 to 0.929), p=0.017, respectively. The AUROC of the top 10 metabolite panel remained excellent in the independent validation cohorts assessed by MRE or liver biopsy: c-statistic of 0.94 and 0.84, respectively. CONCLUSION A combination of 10 serum metabolites demonstrated excellent discriminatory ability for the detection of advanced fibrosis in an derivation and two independent validation cohorts with greater diagnostic accuracy than the FIB-4-index and NFS. This proof-of-concept study demonstrates that a non-invasive blood-based diagnostic test can provide excellent performance characteristics for the detection of advanced fibrosis.
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Affiliation(s)
- Cyrielle Caussy
- NAFLD Research Center, Department of Medicine, La Jolla, California, USA,Université Lyon 1, Hospices Civils de Lyon, Lyon, California, France
| | - Veeral H Ajmera
- NAFLD Research Center, Department of Medicine, La Jolla, California, USA
| | - Puneet Puri
- Virginia Commonwealth University, Richmond, Virginia, USA
| | | | - Shirin Bassirian
- NAFLD Research Center, Department of Medicine, La Jolla, California, USA
| | - Mania Mgdsyan
- NAFLD Research Center, Department of Medicine, La Jolla, California, USA
| | - Seema Singh
- NAFLD Research Center, Department of Medicine, La Jolla, California, USA
| | - Claire Faulkner
- NAFLD Research Center, Department of Medicine, La Jolla, California, USA
| | - Mark A Valasek
- Department of Pathology, University of California at San Diego, La Jolla, California, USA
| | - Emily Rizo
- NAFLD Research Center, Department of Medicine, La Jolla, California, USA
| | - Lisa Richards
- NAFLD Research Center, Department of Medicine, La Jolla, California, USA
| | - David A Brenner
- NAFLD Research Center, Department of Medicine, La Jolla, California, USA,Division of Gastroenterology, Department of Medicine, La Jolla, California, USA
| | - Claude B Sirlin
- Liver Imaging Group, Department of Radiology, University of California, San Diego, La Jolla, California, USA
| | - Arun J Sanyal
- Virginia Commonwealth University, Richmond, Virginia, USA
| | - Rohit Loomba
- NAFLD Research Center, Department of Medicine, La Jolla, California, USA,Division of Gastroenterology, Department of Medicine, La Jolla, California, USA,Division of Epidemiology, Department of Family and Preventive Medicine, University of California at San Diego, La Jolla, California, USA
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212
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Zeng L, Yu Y, Cai X, Xie S, Chen J, Zhong L, Zhang Y. Differences in Serum Amino Acid Phenotypes Among Patients with Diabetic Nephropathy, Hypertensive Nephropathy, and Chronic Nephritis. Med Sci Monit 2019; 25:7235-7242. [PMID: 31557143 PMCID: PMC6778409 DOI: 10.12659/msm.915735] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Abstract
Background We assessed levels of circulating amino acids in different etiologies of chronic kidney disease (CKD) and the association of amino acids with risk factors of CKD progression. Material/Methods High-performance liquid chromatography-based analysis was used to determine amino acid profiles in patients with diabetic nephropathy (DN, n=20), hypertensive nephropathy (HN, n=26), and chronic nephritis (CN, n=33), and in healthy controls (HC, n=25). Results All 3 types of CKD patients displayed decreased serum levels of serine, glycine, GABA, and tryptophan compared with healthy controls. Moreover, serine and tryptophan were positively correlated with glucose in DN cohorts. Total cholesterol was positively correlated with tryptophan levels in the DN cohort and negatively correlated with serine levels in the CN cohort. In the HN cohort, glycine was negatively correlated with triglyceride levels, and systolic blood pressure (SBP) was negatively correlated with GABA levels. Conclusions Patients with different etiologies of CKD have significantly different amino acids profiles, and this indicates specific supplementary nutritional needs in CKD patients.
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Affiliation(s)
- Li Zeng
- Department of Nephrology, Second Affiliated Hospital, Chongqing Medical University, Chongqing, China (mainland).,Chongqing Key Laboratory of Ultrasound Molecular Imaging, Ultrasound Department of Second Affiliated Hospital of Chongqing Medical University, Chongqing, China (mainland)
| | - Yuan Yu
- Department of Nephrology, Second Affiliated Hospital, Chongqing Medical University, Chongqing, China (mainland)
| | - Xi Cai
- Department of Nephrology, Second Affiliated Hospital, Chongqing Medical University, Chongqing, China (mainland)
| | - Shuqin Xie
- Department of Nephrology, Second Affiliated Hospital, Chongqing Medical University, Chongqing, China (mainland)
| | - Jianwei Chen
- Department of Nephrology, Second Affiliated Hospital, Chongqing Medical University, Chongqing, China (mainland)
| | - Ling Zhong
- Department of Nephrology, Second Affiliated Hospital, Chongqing Medical University, Chongqing, China (mainland)
| | - Ying Zhang
- Department of Nephrology, Second Affiliated Hospital, Chongqing Medical University, Chongqing, China (mainland)
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213
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Gatto F, Ferreira R, Nielsen J. Pan-cancer analysis of the metabolic reaction network. Metab Eng 2019; 57:51-62. [PMID: 31526853 DOI: 10.1016/j.ymben.2019.09.006] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 07/29/2019] [Accepted: 09/10/2019] [Indexed: 12/25/2022]
Abstract
Metabolic reprogramming is considered a hallmark of malignant transformation. However, it is not clear whether the network of metabolic reactions expressed by cancers of different origin differ from each other or from normal human tissues. In this study, we reconstructed functional and connected genome-scale metabolic models for 917 primary tumor samples across 13 types based on the probability of expression for 3765 reference metabolic genes in the sample. This network-centric approach revealed that tumor metabolic networks are largely similar in terms of accounted reactions, despite diversity in the expression of the associated genes. On average, each network contained 4721 reactions, of which 74% were core reactions (present in >95% of all models). Whilst 99.3% of the core reactions were classified as housekeeping also in normal tissues, we identified reactions catalyzed by ARG2, RHAG, SLC6 and SLC16 family gene members, and PTGS1 and PTGS2 as core exclusively in cancer. These findings were subsequently replicated in an independent validation set of 3388 genome-scale metabolic models. The remaining 26% of the reactions were contextual reactions. Their inclusion was dependent in one case (GLS2) on the absence of TP53 mutations and in 94.6% of cases on differences in cancer types. This dependency largely resembled differences in expression patterns in the corresponding normal tissues, with some exceptions like the presence of the NANP-encoded reaction in tumors not from the female reproductive system or of the SLC5A9-encoded reaction in kidney-pancreatic-colorectal tumors. In conclusion, tumors expressed a metabolic network virtually overlapping the matched normal tissues, raising the possibility that metabolic reprogramming simply reflects cancer cell plasticity to adapt to varying conditions thanks to redundancy and complexity of the underlying metabolic networks. At the same time, the here uncovered exceptions represent a resource to identify selective liabilities of tumor metabolism.
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Affiliation(s)
- Francesco Gatto
- Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Raphael Ferreira
- Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Göteborg, Sweden; BioInnovation Institute, Ole Maaløes Vej 3, DK2200, Copenhagen N, Denmark.
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214
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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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215
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Khusial RD, Cioffi CE, Caltharp SA, Krasinskas AM, Alazraki A, Knight-Scott J, Cleeton R, Castillo-Leon E, Jones DP, Pierpont B, Caprio S, Santoro N, Akil A, Vos MB. Development of a Plasma Screening Panel for Pediatric Nonalcoholic Fatty Liver Disease Using Metabolomics. Hepatol Commun 2019; 3:1311-1321. [PMID: 31592078 PMCID: PMC6771165 DOI: 10.1002/hep4.1417] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/21/2019] [Accepted: 06/28/2019] [Indexed: 12/14/2022] Open
Abstract
Nonalcoholic fatty liver disease (NAFLD) is the most common chronic liver disease in children, but diagnosis is challenging due to limited availability of noninvasive biomarkers. Machine learning applied to high-resolution metabolomics and clinical phenotype data offers a novel framework for developing a NAFLD screening panel in youth. Here, untargeted metabolomics by liquid chromatography-mass spectrometry was performed on plasma samples from a combined cross-sectional sample of children and adolescents ages 2-25 years old with NAFLD (n = 222) and without NAFLD (n = 337), confirmed by liver biopsy or magnetic resonance imaging. Anthropometrics, blood lipids, liver enzymes, and glucose and insulin metabolism were also assessed. A machine learning approach was applied to the metabolomics and clinical phenotype data sets, which were split into training and test sets, and included dimension reduction, feature selection, and classification model development. The selected metabolite features were the amino acids serine, leucine/isoleucine, and tryptophan; three putatively annotated compounds (dihydrothymine and two phospholipids); and two unknowns. The selected clinical phenotype variables were waist circumference, whole-body insulin sensitivity index (WBISI) based on the oral glucose tolerance test, and blood triglycerides. The highest performing classification model was random forest, which had an area under the receiver operating characteristic curve (AUROC) of 0.94, sensitivity of 73%, and specificity of 97% for detecting NAFLD cases. A second classification model was developed using the homeostasis model assessment of insulin resistance substituted for the WBISI. Similarly, the highest performing classification model was random forest, which had an AUROC of 0.92, sensitivity of 73%, and specificity of 94%. Conclusion: The identified screening panel consisting of both metabolomics and clinical features has promising potential for screening for NAFLD in youth. Further development of this panel and independent validation testing in other cohorts are warranted.
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Affiliation(s)
- Richard D Khusial
- Department of Pharmaceutical Sciences, College of Pharmacy Mercer University Atlanta GA
| | - Catherine E Cioffi
- Nutrition and Health Sciences, Laney Graduate School Emory University Atlanta GA
| | - Shelley A Caltharp
- Children's Healthcare of Atlanta Atlanta GA.,Department of Pathology and Laboratory Medicine Emory University School of Medicine Atlanta GA
| | - Alyssa M Krasinskas
- Department of Pathology and Laboratory Medicine Emory University School of Medicine Atlanta GA
| | - Adina Alazraki
- Children's Healthcare of Atlanta Atlanta GA.,Department of Radiology Emory University School of Medicine Atlanta GA
| | | | - Rebecca Cleeton
- Department of Pediatrics Emory University School of Medicine Atlanta GA
| | | | - Dean P Jones
- Department of Medicine Emory University School of Medicine Atlanta GA
| | | | - Sonia Caprio
- Department of Pediatrics Yale School of Medicine New Haven CT
| | - Nicola Santoro
- Department of Pediatrics Yale School of Medicine New Haven CT
| | - Ayman Akil
- Department of Pharmaceutical Sciences, College of Pharmacy Mercer University Atlanta GA
| | - Miriam B Vos
- Nutrition and Health Sciences, Laney Graduate School Emory University Atlanta GA.,Children's Healthcare of Atlanta Atlanta GA.,Department of Pediatrics Emory University School of Medicine Atlanta GA
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216
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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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217
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Abstract
The amount of omics data in the public domain is increasing every year. Modern science has become a data-intensive discipline. Innovative solutions for data management, data sharing, and for discovering novel datasets are therefore increasingly required. In 2016, we released the first version of the Omics Discovery Index (OmicsDI) as a light-weight system to aggregate datasets across multiple public omics data resources. OmicsDI aggregates genomics, transcriptomics, proteomics, metabolomics and multiomics datasets, as well as computational models of biological processes. Here, we propose a set of novel metrics to quantify the attention and impact of biomedical datasets. A complete framework (now integrated into OmicsDI) has been implemented in order to provide and evaluate those metrics. Finally, we propose a set of recommendations for authors, journals and data resources to promote an optimal quantification of the impact of datasets. Increasing amount of public omics data are important and valuable resources for the research community. Here, the authors develop a set of metrics to quantify the attention and impact of biomedical datasets and integrate them into the framework of Omics Discovery Index (OmicsDI).
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218
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Zhang C, Aldrees M, Arif M, Li X, Mardinoglu A, Aziz MA. Elucidating the Reprograming of Colorectal Cancer Metabolism Using Genome-Scale Metabolic Modeling. Front Oncol 2019; 9:681. [PMID: 31417867 PMCID: PMC6682621 DOI: 10.3389/fonc.2019.00681] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 07/10/2019] [Indexed: 12/16/2022] Open
Abstract
Colorectal cancer is the third most incidental cancer worldwide, and the response rate of current treatment for colorectal cancer is very low. Genome-scale metabolic models (GEMs) are systems biology platforms, and they had been used to assist researchers in understanding the metabolic alterations in different types of cancer. Here, we reconstructed a generic colorectal cancer GEM by merging 374 personalized GEMs from the Human Pathology Atlas and used it as a platform for systematic investigation of the difference between tumor and normal samples. The reconstructed model revealed the metabolic reprogramming in glutathione as well as the arginine and proline metabolism in response to tumor occurrence. In addition, six genes including ODC1, SMS, SRM, RRM2, SMOX, and SAT1 associated with arginine and proline metabolism were found to be key players in this metabolic alteration. We also investigated these genes in independent colorectal cancer patients and cell lines and found that many of these genes showed elevated level in colorectal cancer and exhibited adverse effect in patients. Therefore, these genes could be promising therapeutic targets for treatment of a specific colon cancer patient group.
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Affiliation(s)
- Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Mohammed Aldrees
- Department of Medical Genomics, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Saud Bin Abdul Aziz University for Health Sciences, Riyadh, Saudi Arabia
- Ministry of the National Guard- Health Affairs, Riyadh, Saudi Arabia
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Xiangyu Li
- 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
| | - Mohammad Azhar Aziz
- King Saud Bin Abdul Aziz University for Health Sciences, Riyadh, Saudi Arabia
- Ministry of the National Guard- Health Affairs, Riyadh, Saudi Arabia
- Colorectal Cancer Research Program, King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
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219
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The Potential Use of Metabolic Cofactors in Treatment of NAFLD. Nutrients 2019; 11:nu11071578. [PMID: 31336926 PMCID: PMC6682907 DOI: 10.3390/nu11071578] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 07/04/2019] [Accepted: 07/05/2019] [Indexed: 12/14/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is caused by the imbalance between lipid deposition and lipid removal from the liver, and its global prevalence continues to increase dramatically. NAFLD encompasses a spectrum of pathological conditions including simple steatosis and non-alcoholic steatohepatitis (NASH), which can progress to cirrhosis and liver cancer. Even though there is a multi-disciplinary effort for development of a treatment strategy for NAFLD, there is not an approved effective medication available. Single or combined metabolic cofactors can be supplemented to boost the metabolic processes altered in NAFLD. Here, we review the dosage and usage of metabolic cofactors including l-carnitine, Nicotinamide riboside (NR), l-serine, and N-acetyl-l-cysteine (NAC) in human clinical studies to improve the altered biological functions associated with different human diseases. We also discuss the potential use of these substances in treatment of NAFLD and other metabolic diseases including neurodegenerative and cardiovascular diseases of which pathogenesis is linked to mitochondrial dysfunction.
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220
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Baloni P, Sangar V, Yurkovich JT, Robinson M, Taylor S, Karbowski CM, Hamadeh HK, He YD, Price ND. Genome-scale metabolic model of the rat liver predicts effects of diet restriction. Sci Rep 2019; 9:9807. [PMID: 31285465 PMCID: PMC6614411 DOI: 10.1038/s41598-019-46245-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Accepted: 06/25/2019] [Indexed: 12/19/2022] Open
Abstract
Mapping network analysis in cells and tissues can provide insights into metabolic adaptations to changes in external environment, pathological conditions, and nutrient deprivation. Here, we reconstructed a genome-scale metabolic network of the rat liver that will allow for exploration of systems-level physiology. The resulting in silico model (iRatLiver) contains 1,882 reactions, 1,448 metabolites, and 994 metabolic genes. We then used this model to characterize the response of the liver’s energy metabolism to a controlled perturbation in diet. Transcriptomics data were collected from the livers of Sprague Dawley rats at 4 or 14 days of being subjected to 15%, 30%, or 60% diet restriction. These data were integrated with the iRatLiver model to generate condition-specific metabolic models, allowing us to explore network differences under each condition. We observed different pathway usage between early and late time points. Network analysis identified several highly connected “hub” genes (Pklr, Hadha, Tkt, Pgm1, Tpi1, and Eno3) that showed differing trends between early and late time points. Taken together, our results suggest that the liver’s response varied with short- and long-term diet restriction. More broadly, we anticipate that the iRatLiver model can be exploited further to study metabolic changes in the liver under other conditions such as drug treatment, infection, and disease.
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Affiliation(s)
- Priyanka Baloni
- Institute for Systems Biology, Seattle, WA, United States of America
| | - Vineet Sangar
- Institute for Systems Biology, Seattle, WA, United States of America
| | - James T Yurkovich
- Institute for Systems Biology, Seattle, WA, United States of America
| | - Max Robinson
- Institute for Systems Biology, Seattle, WA, United States of America
| | - Scott Taylor
- Department of Comparative Biology and Safety Sciences, Amgen Inc., Thousand Oaks, CA, United States of America
| | - Christine M Karbowski
- Department of Comparative Biology and Safety Sciences, Amgen Inc., Thousand Oaks, CA, United States of America
| | - Hisham K Hamadeh
- Department of Comparative Biology and Safety Sciences, Amgen Inc., Thousand Oaks, CA, United States of America.,Genmab, Princeton, NJ, United States of America
| | - Yudong D He
- Department of Comparative Biology and Safety Sciences, Amgen Inc., Thousand Oaks, CA, United States of America
| | - Nathan D Price
- Institute for Systems Biology, Seattle, WA, United States of America.
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221
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Richelle A, Joshi C, Lewis NE. Assessing key decisions for transcriptomic data integration in biochemical networks. PLoS Comput Biol 2019; 15:e1007185. [PMID: 31323017 PMCID: PMC6668847 DOI: 10.1371/journal.pcbi.1007185] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 07/31/2019] [Accepted: 06/14/2019] [Indexed: 12/21/2022] Open
Abstract
To gain insights into complex biological processes, genome-scale data (e.g., RNA-Seq) are often overlaid on biochemical networks. However, many networks do not have a one-to-one relationship between genes and network edges, due to the existence of isozymes and protein complexes. Therefore, decisions must be made on how to overlay data onto networks. For example, for metabolic networks, these decisions include (1) how to integrate gene expression levels using gene-protein-reaction rules, (2) the approach used for selection of thresholds on expression data to consider the associated gene as "active", and (3) the order in which these steps are imposed. However, the influence of these decisions has not been systematically tested. We compared 20 decision combinations using a transcriptomic dataset across 32 tissues and showed that definition of which reaction may be considered as active (i.e., reactions of the genome-scale metabolic network with a non-zero expression level after overlaying the data) is mainly influenced by thresholding approach used. To determine the most appropriate decisions, we evaluated how these decisions impact the acquisition of tissue-specific active reaction lists that recapitulate organ-system tissue groups. These results will provide guidelines to improve data analyses with biochemical networks and facilitate the construction of context-specific metabolic models.
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Affiliation(s)
- Anne Richelle
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, California, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, California, United States of America
| | - Chintan Joshi
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, California, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, California, United States of America
| | - Nathan E. Lewis
- Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, School of Medicine, La Jolla, California, United States of America
- Department of Pediatrics, University of California, San Diego, School of Medicine, La Jolla, California, United States of America
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
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222
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Lee DY, Kim EH. Therapeutic Effects of Amino Acids in Liver Diseases: Current Studies and Future Perspectives. J Cancer Prev 2019; 24:72-78. [PMID: 31360687 PMCID: PMC6619856 DOI: 10.15430/jcp.2019.24.2.72] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 06/18/2019] [Accepted: 06/19/2019] [Indexed: 12/20/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common primary malignant tumor of the liver and the third most common cause of cancer-related death worldwide. HCC is caused by infection of hepatitis B/C virus and liver dysfunctions, such as alcoholic liver disease, nonalcoholic fatty liver disease, and cirrhosis. Amino acids are organic substances containing amine and carboxylic acid functional groups. There are over 700 kinds of amino acids in nature, but only about 20 of them are used to synthesize proteins in cells. Liver is an important organ for protein synthesis, degradation and detoxification as well as amino acid metabolism. In the liver, there are abundant non-essential amino acids, such as alanine, aspartate, glutamate, glycine, and serine and essential amino acids, such as histidine and threonine. These amino acids are involved in various cellular metabolisms, the synthesis of lipids and nucleotides as well as detoxification reactions. Understanding the role of amino acids in the pathogenesis of liver and the effects of amino acid intake on liver disease can be a promising strategy for the prevention and treatment of liver disease. In this review, we describe the biochemical properties and functions of amino acids and to review how they have been applied to treatment of liver diseases.
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Affiliation(s)
- Da-Young Lee
- College of Pharmacy and Institute of Pharmaceutical Sciences, CHA University, Seongnam, Korea
| | - Eun-Hee Kim
- College of Pharmacy and Institute of Pharmaceutical Sciences, CHA University, Seongnam, Korea
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223
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Chu SH, Huang M, Kelly RS, Benedetti E, Siddiqui JK, Zeleznik OA, Pereira A, Herrington D, Wheelock CE, Krumsiek J, McGeachie M, Moore SC, Kraft P, Mathé E, Lasky-Su J. Integration of Metabolomic and Other Omics Data in Population-Based Study Designs: An Epidemiological Perspective. Metabolites 2019; 9:E117. [PMID: 31216675 PMCID: PMC6630728 DOI: 10.3390/metabo9060117] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 06/12/2019] [Accepted: 06/14/2019] [Indexed: 12/30/2022] Open
Abstract
It is not controversial that study design considerations and challenges must be addressed when investigating the linkage between single omic measurements and human phenotypes. It follows that such considerations are just as critical, if not more so, in the context of multi-omic studies. In this review, we discuss (1) epidemiologic principles of study design, including selection of biospecimen source(s) and the implications of the timing of sample collection, in the context of a multi-omic investigation, and (2) the strengths and limitations of various techniques of data integration across multi-omic data types that may arise in population-based studies utilizing metabolomic data.
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Affiliation(s)
- Su H Chu
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Mengna Huang
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Elisa Benedetti
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Jalal K Siddiqui
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Oana A Zeleznik
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Alexandre Pereira
- Department of Genetics and Molecular Medicine, University of Sao Paulo Medical School, Sao Paulo 01246-903, Brazil.
| | - David Herrington
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27101, USA.
| | - Craig E Wheelock
- Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institute, 171 77 Stockholm, Sweden.
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY 10021, USA.
| | - Michael McGeachie
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Steven C Moore
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA.
| | - Peter Kraft
- Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA.
| | - Ewy Mathé
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
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224
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Glycine Metabolism and Its Alterations in Obesity and Metabolic Diseases. Nutrients 2019; 11:nu11061356. [PMID: 31208147 PMCID: PMC6627940 DOI: 10.3390/nu11061356] [Citation(s) in RCA: 182] [Impact Index Per Article: 36.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Revised: 06/07/2019] [Accepted: 06/12/2019] [Indexed: 12/11/2022] Open
Abstract
Glycine is the proteinogenic amino-acid of lowest molecular weight, harboring a hydrogen atom as a side-chain. In addition to being a building-block for proteins, glycine is also required for multiple metabolic pathways, such as glutathione synthesis and regulation of one-carbon metabolism. Although generally viewed as a non-essential amino-acid, because it can be endogenously synthesized to a certain extent, glycine has also been suggested as a conditionally essential amino acid. In metabolic disorders associated with obesity, type 2 diabetes (T2DM), and non-alcoholic fatty liver disease (NAFLDs), lower circulating glycine levels have been consistently observed, and clinical studies suggest the existence of beneficial effects induced by glycine supplementation. The present review aims at synthesizing the recent advances in glycine metabolism, pinpointing its main metabolic pathways, identifying the causes leading to glycine deficiency-especially in obesity and associated metabolic disorders-and evaluating the potential benefits of increasing glycine availability to curb the progression of obesity and obesity-related metabolic disturbances. This study focuses on the importance of diet, gut microbiota, and liver metabolism in determining glycine availability in obesity and associated metabolic disorders.
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225
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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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226
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Abstract
Multi-omics multi-tissue data are used to interpret genome-wide association study results from mice to identify key driver genes of non-alcoholic fatty liver disease. Non-alcoholic fatty liver disease (NAFLD) is the accumulation of fat (steatosis) in the liver due to causes other than excessive alcohol consumption. The disease may progress to more severe forms of liver diseases, including non-alcoholic steatohepatitis, cirrhosis, and hepatocellular carcinoma. In this issue of Cell Systems, Krishnan et al. (2018) reveal mechanisms underlying NAFLD by generating multi-omics data using liver and adipose tissues obtained from the Hybrid Mouse Diversity Panel, consisting of 113 mouse strains with various degrees of NAFLD. The study identified key driver genes of NAFLD that can be used in the development of efficient treatment strategies and illustrates the potential utility of systematic analysis of multi-layer biological networks.
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Affiliation(s)
- 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.
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jan Borén
- Department of Molecular and Clinical Medicine/Wallenberg Laboratory, University of Gothenburg, and Sahlgrenska University Hospital, Gothenburg, Sweden
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227
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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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228
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Cho JS, Gu C, Han TH, Ryu JY, Lee SY. Reconstruction of context-specific genome-scale metabolic models using multiomics data to study metabolic rewiring. ACTA ACUST UNITED AC 2019. [DOI: 10.1016/j.coisb.2019.02.009] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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229
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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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230
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Identifying and targeting cancer-specific metabolism with network-based drug target prediction. EBioMedicine 2019; 43:98-106. [PMID: 31126892 PMCID: PMC6558238 DOI: 10.1016/j.ebiom.2019.04.046] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2018] [Revised: 04/24/2019] [Accepted: 04/24/2019] [Indexed: 12/12/2022] Open
Abstract
Background Metabolic rewiring allows cancer cells to sustain high proliferation rates. Thus, targeting only the cancer-specific cellular metabolism will safeguard healthy tissues. Methods We developed the very efficient FASTCORMICS RNA-seq workflow (rFASTCORMICS) to build 10,005 high-resolution metabolic models from the TCGA dataset to capture metabolic rewiring strategies in cancer cells. Colorectal cancer (CRC) was used as a test case for a repurposing workflow based on rFASTCORMICS. Findings Alternative pathways that are not required for proliferation or survival tend to be shut down and, therefore, tumours display cancer-specific essential genes that are significantly enriched for known drug targets. We identified naftifine, ketoconazole, and mimosine as new potential CRC drugs, which were experimentally validated. Interpretation The here presented rFASTCORMICS workflow successfully reconstructs a metabolic model based on RNA-seq data and successfully predicted drug targets and drugs not yet indicted for colorectal cancer. Fund This study was supported by the University of Luxembourg (IRP grant scheme; R-AGR-0755-12), the Luxembourg National Research Fund (FNR PRIDE PRIDE15/10675146/CANBIO), the Fondation Cancer (Luxembourg), the European Union‘s Horizon2020 research and innovation programme under the Marie Sklodowska- Curie grant agreement No 642295 (MEL-PLEX), and the German Federal Ministry of Education and Research (BMBF) within the project MelanomSensitivity (BMBF/BM/7643621).
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231
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The Fate of Glutamine in Human Metabolism. The Interplay with Glucose in Proliferating Cells. Metabolites 2019; 9:metabo9050081. [PMID: 31027329 PMCID: PMC6571637 DOI: 10.3390/metabo9050081] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2019] [Accepted: 04/23/2019] [Indexed: 01/13/2023] Open
Abstract
Genome-scale models of metabolism (GEM) are used to study how metabolism varies in different physiological conditions. However, the great number of reactions involved in GEM makes it difficult to understand these variations. In order to have a more understandable tool, we developed a reduced metabolic model of central carbon and nitrogen metabolism, C2M2N with 77 reactions, 54 internal metabolites, and 3 compartments, taking into account the actual stoichiometry of the reactions, including the stoichiometric role of the cofactors and the irreversibility of some reactions. In order to model oxidative phosphorylation (OXPHOS) functioning, the proton gradient through the inner mitochondrial membrane is represented by two pseudometabolites DPH (∆pH) and DPSI (∆ψ). To illustrate the interest of such a reduced and quantitative model of metabolism in mammalian cells, we used flux balance analysis (FBA) to study all the possible fates of glutamine in metabolism. Our analysis shows that glutamine can supply carbon sources for cell energy production and can be used as carbon and nitrogen sources to synthesize essential metabolites. Finally, we studied the interplay between glucose and glutamine for the formation of cell biomass according to ammonia microenvironment. We then propose a quantitative analysis of the Warburg effect.
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232
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Mazat JP, Ransac S. The Fate of Glutamine in Human Metabolism. The Interplay with Glucose in Proliferating Cells. Metabolites 2019. [PMID: 31027329 DOI: 10.1101/477224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Abstract
Genome-scale models of metabolism (GEM) are used to study how metabolism varies in different physiological conditions. However, the great number of reactions involved in GEM makes it difficult to understand these variations. In order to have a more understandable tool, we developed a reduced metabolic model of central carbon and nitrogen metabolism, C2M2N with 77 reactions, 54 internal metabolites, and 3 compartments, taking into account the actual stoichiometry of the reactions, including the stoichiometric role of the cofactors and the irreversibility of some reactions. In order to model oxidative phosphorylation (OXPHOS) functioning, the proton gradient through the inner mitochondrial membrane is represented by two pseudometabolites DPH (∆pH) and DPSI (∆ψ). To illustrate the interest of such a reduced and quantitative model of metabolism in mammalian cells, we used flux balance analysis (FBA) to study all the possible fates of glutamine in metabolism. Our analysis shows that glutamine can supply carbon sources for cell energy production and can be used as carbon and nitrogen sources to synthesize essential metabolites. Finally, we studied the interplay between glucose and glutamine for the formation of cell biomass according to ammonia microenvironment. We then propose a quantitative analysis of the Warburg effect.
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Affiliation(s)
- Jean-Pierre Mazat
- IBGC CNRS UMR 5095 & Université de Bordeaux, 1, rue Camille Saint-Saëns, 33077 Bordeaux-CEDEX, France.
| | - Stéphane Ransac
- IBGC CNRS UMR 5095 & Université de Bordeaux, 1, rue Camille Saint-Saëns, 33077 Bordeaux-CEDEX, France.
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233
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Jamialahmadi O, Hashemi-Najafabadi S, Motamedian E, Romeo S, Bagheri F. A benchmark-driven approach to reconstruct metabolic networks for studying cancer metabolism. PLoS Comput Biol 2019; 15:e1006936. [PMID: 31009458 PMCID: PMC6497301 DOI: 10.1371/journal.pcbi.1006936] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 05/02/2019] [Accepted: 03/11/2019] [Indexed: 02/07/2023] Open
Abstract
Genome-scale metabolic modeling has emerged as a promising way to study the metabolic alterations underlying cancer by identifying novel drug targets and biomarkers. To date, several computational methods have been developed to integrate high-throughput data with existing human metabolic reconstructions to generate context-specific cancer metabolic models. Despite a number of studies focusing on benchmarking the context-specific algorithms, no quantitative assessment has been made to compare the predictive performance of these methods. Here, we integrated various and different datasets used in previous works to design a quantitative platform to examine functional and consistency performance of several existing genome-scale cancer modeling approaches. Next, we used the results obtained here to develop a method for the reconstruction of context-specific metabolic models. We then compared the predictive power and consistency of networks generated by our method to other computational approaches investigated here. Our results showed a satisfactory performance of the developed method in most of the benchmarks. This benchmarking platform is of particular use in algorithm selection and assessing the performance of newly developed algorithms. More importantly, it can serve as guidelines for designing and developing new methods focusing on weaknesses and strengths of existing algorithms.
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Affiliation(s)
- Oveis Jamialahmadi
- Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - Sameereh Hashemi-Najafabadi
- Department of Biomedical Engineering, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
- * E-mail: (SHN); (EM)
| | - Ehsan Motamedian
- Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
- * E-mail: (SHN); (EM)
| | - Stefano Romeo
- Department of Molecular and Clinical Medicine, University of Gothenburg, Gothenburg, Sweden
- Clinical Nutrition Unit, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
- Cardiology Department, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Fatemeh Bagheri
- Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
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234
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Granata I, Troiano E, Sangiovanni M, Guarracino MR. Integration of transcriptomic data in a genome-scale metabolic model to investigate the link between obesity and breast cancer. BMC Bioinformatics 2019; 20:162. [PMID: 30999849 PMCID: PMC6471692 DOI: 10.1186/s12859-019-2685-9] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Obesity is a complex disorder associated with an increased risk of developing several comorbid chronic diseases, including postmenopausal breast cancer. Although many studies have investigated this issue, the link between body weight and either risk or poor outcome of breast cancer is still to characterize. Systems biology approaches, based on the integration of multiscale models and data from a wide variety of sources, are particularly suitable for investigating the underlying molecular mechanisms of complex diseases. In this scenario, GEnome-scale metabolic Models (GEMs) are a valuable tool, since they represent the metabolic structure of cells and provide a functional scaffold for simulating and quantifying metabolic fluxes in living organisms through constraint-based mathematical methods. The integration of omics data into the structural information described by GEMs allows to build more accurate descriptions of metabolic states. RESULTS In this work, we exploited gene expression data of postmenopausal breast cancer obese and lean patients to simulate a curated GEM of the human adipocyte, available in the Human Metabolic Atlas database. To this aim, we used a published algorithm which exploits a data-driven approach to overcome the limitation of defining a single objective function to simulate the model. The flux solutions were used to build condition-specific graphs to visualise and investigate the reaction networks and their properties. In particular, we performed a network topology differential analysis to search for pattern differences and identify the principal reactions associated with significant changes across the two conditions under study. CONCLUSIONS Metabolic network models represent an important source to study the metabolic phenotype of an organism in different conditions. Here we demonstrate the importance of exploiting Next Generation Sequencing data to perform condition-specific GEM analyses. In particular, we show that the qualitative and quantitative assessment of metabolic fluxes modulated by gene expression data provides a valuable method for investigating the mechanisms associated with the phenotype under study, and can foster our interpretation of biological phenomena.
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Affiliation(s)
- Ilaria Granata
- High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131 Italy
| | - Enrico Troiano
- High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131 Italy
| | - Mara Sangiovanni
- Stazione Zoologica Anton Dohrn, Villa Comunale, Napoli, 80121 Italy
| | - Mario Rosario Guarracino
- High Performance Computing and Networking Institute, National Research Council of Italy, Via P. Castellino, 111, Napoli, 80131 Italy
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235
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Pannala VR, Vinnakota KC, Rawls KD, Estes SK, O'Brien TP, Printz RL, Papin JA, Reifman J, Shiota M, Young JD, Wallqvist A. Mechanistic identification of biofluid metabolite changes as markers of acetaminophen-induced liver toxicity in rats. Toxicol Appl Pharmacol 2019; 372:19-32. [PMID: 30974156 DOI: 10.1016/j.taap.2019.04.001] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2019] [Revised: 03/22/2019] [Accepted: 04/05/2019] [Indexed: 12/12/2022]
Abstract
Acetaminophen (APAP) is the most commonly used analgesic and antipyretic drug in the world. Yet, it poses a major risk of liver injury when taken in excess of the therapeutic dose. Current clinical markers do not detect the early onset of liver injury associated with excess APAP-information that is vital to reverse injury progression through available therapeutic interventions. Hence, several studies have used transcriptomics, proteomics, and metabolomics technologies, both independently and in combination, in an attempt to discover potential early markers of liver injury. However, the casual relationship between these observations and their relation to the APAP mechanism of liver toxicity are not clearly understood. Here, we used Sprague-Dawley rats orally gavaged with a single dose of 2 g/kg of APAP to collect tissue samples from the liver and kidney for transcriptomic analysis and plasma and urine samples for metabolomic analysis. We developed and used a multi-tissue, metabolism-based modeling approach to integrate these data, characterize the effect of excess APAP levels on liver metabolism, and identify a panel of plasma and urine metabolites that are associated with APAP-induced liver toxicity. Our analyses, which indicated that pathways involved in nucleotide-, lipid-, and amino acid-related metabolism in the liver were most strongly affected within 10 h following APAP treatment, identified a list of potential metabolites in these pathways that could serve as plausible markers of APAP-induced liver injury. Our approach identifies toxicant-induced changes in endogenous metabolism, is applicable to other toxicants based on transcriptomic data, and provides a mechanistic framework for interpreting metabolite alterations.
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Affiliation(s)
- Venkat R Pannala
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA; Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.
| | - Kalyan C Vinnakota
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Bethesda, MD 20817, USA; Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA
| | - Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Box 800759, Health System, Charlottesville, Virginia 22908, USA
| | - Shanea K Estes
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Tracy P O'Brien
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Richard L Printz
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Box 800759, Health System, Charlottesville, Virginia 22908, USA
| | - Jaques Reifman
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA
| | - Masakazu Shiota
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA
| | - Jamey D Young
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, TN 37232, USA; Department of Chemical and Biomolecular Engineering, Vanderbilt University School of Engineering, Nashville, TN 37232, USA.
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD 21702, USA.
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From sugar to liver fat and public health: systems biology driven studies in understanding non-alcoholic fatty liver disease pathogenesis. Proc Nutr Soc 2019; 78:290-304. [PMID: 30924429 DOI: 10.1017/s0029665119000570] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Non-alcoholic fatty liver disease (NAFLD) is now a major public health concern with an estimated prevalence of 25-30% of adults in many countries. Strongly associated with obesity and the metabolic syndrome, the pathogenesis of NAFLD is dependent on complex interactions between genetic and environmental factors that are not completely understood. Weight loss through diet and lifestyle modification underpins clinical management; however, the roles of individual dietary nutrients (e.g. saturated and n-3 fatty acids; fructose, vitamin D, vitamin E) in the pathogenesis or treatment of NAFLD are only partially understood. Systems biology offers valuable interdisciplinary methods that are arguably ideal for application to the studying of chronic diseases such as NAFLD, and the roles of nutrition and diet in their molecular pathogenesis. Although present in silico models are incomplete, computational tools are rapidly evolving and human metabolism can now be simulated at the genome scale. This paper will review NAFLD and its pathogenesis, including the roles of genetics and nutrition in the development and progression of disease. In addition, the paper introduces the concept of systems biology and reviews recent work utilising genome-scale metabolic networks and developing multi-scale models of liver metabolism relevant to NAFLD. A future is envisioned where individual genetic, proteomic and metabolomic information can be integrated computationally with clinical data, yielding mechanistic insight into the pathogenesis of chronic diseases such as NAFLD, and informing personalised nutrition and stratified medicine approaches for improving prognosis.
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237
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Non-Alcoholic Fatty Liver Disease and Risk of Incident Type 2 Diabetes: Role of Circulating Branched-Chain Amino Acids. Nutrients 2019; 11:nu11030705. [PMID: 30917546 PMCID: PMC6471562 DOI: 10.3390/nu11030705] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 03/14/2019] [Accepted: 03/21/2019] [Indexed: 12/20/2022] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is likely to be associated with elevated plasma branched-chain amino acids (BCAAs) and may precede the development of type 2 diabetes (T2D). We hypothesized that BCAAs may be involved in the pathogenesis of T2D attributable to NAFLD and determined the extent to which plasma BCAAs influence T2D development in NAFLD. We evaluated cross-sectional associations of NAFLD with fasting plasma BCAAs (nuclear magnetic resonance spectroscopy), and prospectively determined the extent to which the influence of NAFLD on incident T2D is attributable to BCAA elevations. In the current study, 5791 Prevention of REnal and Vascular ENd-stage Disease (PREVEND) cohort participants without T2D at baseline were included. Elevated fatty liver index (FLI) ≥60, an algorithm based on triglycerides, gamma-glutamyltransferase, body mass index (BMI) and waist circumference, was used as proxy of NAFLD. Elevated FLI ≥ 60 was present in 1671 (28.9%) participants. Cross-sectionally, BCAAs were positively associated with FLI ≥ 60 (β = 0.208, p < 0.001). During a median follow-up of 7.3 years, 276 participants developed T2D, of which 194 (70.2%) had an FLI ≥ 60 (log-rank test, p < 0.001). Cox regression analyses revealed that both FLI ≥60 (hazard ratio (HR) 3.46, 95% CI 2.45⁻4.87, p < 0.001) and higher BCAA levels (HR 1.19, 95% CI 1.03⁻1.37, p = 0.01) were positively associated with incident T2D. Mediation analysis showed that the association of FLI with incident T2D was in part attributable to elevated BCAAs (proportion mediated 19.6%). In conclusion, both elevated FLI and elevated plasma BCAA levels are associated with risk of incident T2D. The association of NAFLD with T2D development seems partly mediated by elevated BCAAs.
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238
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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: 622] [Impact Index Per Article: 124.4] [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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239
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The OMICs Window into Nonalcoholic Fatty Liver Disease (NAFLD). Metabolites 2019; 9:metabo9020025. [PMID: 30717274 PMCID: PMC6409793 DOI: 10.3390/metabo9020025] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2018] [Revised: 01/26/2019] [Accepted: 01/30/2019] [Indexed: 12/17/2022] Open
Abstract
Nonalcoholic fatty liver disease (NAFLD) is a common cause of hepatic abnormalities worldwide. Nonalcoholic steatohepatitis (NASH) is part of the spectrum of NAFLD and leads to progressive liver disease, such as cirrhosis and hepatocellular carcinoma. In NASH patient, fibrosis represents the major predictor of liver-related mortality; therefore, it is important to have an early and accurate diagnosis of NASH. The current gold standard for the diagnosis of NASH is still liver biopsy. The development of biomarkers able to predict disease severity, prognosis, as well as response to therapy without the need for a biopsy is the focus of most up-to-date genomic, transcriptomic, proteomic, and metabolomic research. In the future, patients might be diagnosed and treated according to their molecular signatures. In this short review, we discuss how information from genomics, proteomics, and metabolomics contribute to the understanding of NAFLD pathogenesis.
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240
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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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241
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Imaizumi A, Adachi Y, Kawaguchi T, Higasa K, Tabara Y, Sonomura K, Sato TA, Takahashi M, Mizukoshi T, Yoshida HO, Kageyama N, Okamoto C, Takasu M, Mori M, Noguchi Y, Shimba N, Miyano H, Yamada R, Matsuda F. Genetic basis for plasma amino acid concentrations based on absolute quantification: a genome-wide association study in the Japanese population. Eur J Hum Genet 2019; 27:621-630. [PMID: 30659259 PMCID: PMC6460579 DOI: 10.1038/s41431-018-0296-y] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Revised: 10/15/2018] [Accepted: 10/25/2018] [Indexed: 12/02/2022] Open
Abstract
To assess the use of plasma free amino acids (PFAAs) as biomarkers for metabolic disorders, it is essential to identify genetic factors that influence PFAA concentrations. PFAA concentrations were absolutely quantified by liquid chromatography–mass spectrometry using plasma samples from 1338 Japanese individuals, and genome-wide quantitative trait locus (QTL) analysis was performed for the concentrations of 21 PFAAs. We next conducted a conditional QTL analysis using the concentration of each PFAA adjusted by the other 20 PFAAs as covariates to elucidate genetic determinants that influence PFAA concentrations. We identified eight genes that showed a significant association with PFAA concentrations, of which two, SLC7A2 and PKD1L2, were identified. SLC7A2 was associated with the plasma levels of arginine and ornithine, and PKD1L2 with the level of glycine. The significant associations of these two genes were revealed in the conditional QTL analysis, but a significant association between serine and the CPS1 gene disappeared when glycine was used as a covariate. We demonstrated that conditional QTL analysis is useful for determining the metabolic pathways predominantly used for PFAA metabolism. Our findings will help elucidate the physiological roles of genetic components that control the metabolism of amino acids.
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Affiliation(s)
- Akira Imaizumi
- Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Kanagawa, 210-8681, Japan.
| | - Yusuke Adachi
- Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Kanagawa, 210-8681, Japan
| | - Takahisa Kawaguchi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, 606-8507, Japan
| | - Koichiro Higasa
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, 606-8507, Japan.,Department of Genome Analysis, Institute of Biomedical Science, Kansai Medical University, Hirakata, Osaka, 573-1010, Japan
| | - Yasuharu Tabara
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, 606-8507, Japan
| | - Kazuhiro Sonomura
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, 606-8507, Japan.,Life Science Laboratories, Shimadzu Corporation, Seika, Kyoto, 619-0237, Japan
| | - Taka-Aki Sato
- Life Science Laboratories, Shimadzu Corporation, Seika, Kyoto, 619-0237, Japan
| | - Meiko Takahashi
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, 606-8507, Japan
| | - Toshimi Mizukoshi
- Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Kanagawa, 210-8681, Japan
| | - Hiro-O Yoshida
- Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Kanagawa, 210-8681, Japan
| | - Naoko Kageyama
- Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Kanagawa, 210-8681, Japan
| | - Chisato Okamoto
- Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Kanagawa, 210-8681, Japan
| | - Mariko Takasu
- Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Kanagawa, 210-8681, Japan
| | - Maiko Mori
- Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Kanagawa, 210-8681, Japan
| | - Yasushi Noguchi
- Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Kanagawa, 210-8681, Japan
| | - Nobuhisa Shimba
- R&D Planning Dept., Ajinomoto Co., Inc, Tokyo, 104-8315, Japan
| | - Hiroshi Miyano
- Institute for Innovation, Ajinomoto Co., Inc., Kawasaki, Kanagawa, 210-8681, Japan
| | - Ryo Yamada
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, 606-8507, Japan
| | - Fumihiko Matsuda
- Center for Genomic Medicine, Kyoto University Graduate School of Medicine, Kyoto, 606-8507, Japan.
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242
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Sánchez BJ, Li F, Kerkhoven EJ, Nielsen J. SLIMEr: probing flexibility of lipid metabolism in yeast with an improved constraint-based modeling framework. BMC SYSTEMS BIOLOGY 2019; 13:4. [PMID: 30634957 PMCID: PMC6330394 DOI: 10.1186/s12918-018-0673-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 12/19/2018] [Indexed: 11/18/2022]
Abstract
BACKGROUND A recurrent problem in genome-scale metabolic models (GEMs) is to correctly represent lipids as biomass requirements, due to the numerous of possible combinations of individual lipid species and the corresponding lack of fully detailed data. In this study we present SLIMEr, a formalism for correctly representing lipid requirements in GEMs using commonly available experimental data. RESULTS SLIMEr enhances a GEM with mathematical constructs where we Split Lipids Into Measurable Entities (SLIME reactions), in addition to constraints on both the lipid classes and the acyl chain distribution. By implementing SLIMEr on the consensus GEM of Saccharomyces cerevisiae, we can represent accurate amounts of lipid species, analyze the flexibility of the resulting distribution, and compute the energy costs of moving from one metabolic state to another. CONCLUSIONS The approach shows potential for better understanding lipid metabolism in yeast under different conditions. SLIMEr is freely available at https://github.com/SysBioChalmers/SLIMEr .
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Affiliation(s)
- Benjamín J. Sánchez
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | - Feiran Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | - Eduard J. Kerkhoven
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark
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243
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Lone MA, Santos T, Alecu I, Silva LC, Hornemann T. 1-Deoxysphingolipids. Biochim Biophys Acta Mol Cell Biol Lipids 2019; 1864:512-521. [PMID: 30625374 DOI: 10.1016/j.bbalip.2018.12.013] [Citation(s) in RCA: 61] [Impact Index Per Article: 12.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 12/19/2018] [Accepted: 12/20/2018] [Indexed: 12/14/2022]
Abstract
Sphingolipids (SLs) are fundamental components of eukaryotic cells. 1-Deoxysphingolipids differ structurally from canonical SLs as they lack the essential C1-OH group. Consequently, 1-deoxysphingolipids cannot be converted to complex sphingolipids and are not degraded over the canonical catabolic pathways. Pathologically elevated 1-deoxySLs are involved in several disease conditions. Within this review, we will provide an up-to-date overview on the metabolic, physiological and pathophysiological aspects of this enigmatic class of "headless" sphingolipids.
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Affiliation(s)
- M A Lone
- Institute for Clinical Chemistry, University Hospital Zurich, Switzerland; Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, Switzerland
| | - T Santos
- Institute for Clinical Chemistry, University Hospital Zurich, Switzerland; Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, Switzerland; iMed.ULisboa - Research Institute for Medicines, Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003 Lisboa, Portugal; Centro de Química-Física Molecular and IN-Institute of Nanoscience and Nanotechnology, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - I Alecu
- Neural Regeneration Laboratory, India Taylor Lipidomic Research Platform, Department of Biochemistry, Microbiology and Immunology, Ottawa Institute of Systems Biology, Ottawa Brain and Mind Research Institute, University of Ottawa, Canada
| | - L C Silva
- iMed.ULisboa - Research Institute for Medicines, Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003 Lisboa, Portugal; Centro de Química-Física Molecular and IN-Institute of Nanoscience and Nanotechnology, Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - T Hornemann
- Institute for Clinical Chemistry, University Hospital Zurich, Switzerland; Zurich Center for Integrative Human Physiology (ZIHP), University of Zurich, Switzerland.
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244
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Liu Z, Zhang C, Lee S, Kim W, Klevstig M, Harzandi AM, Sikanic N, Arif M, Ståhlman M, Nielsen J, Uhlen M, Boren J, Mardinoglu A. Pyruvate kinase L/R is a regulator of lipid metabolism and mitochondrial function. Metab Eng 2019; 52:263-272. [PMID: 30615941 DOI: 10.1016/j.ymben.2019.01.001] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Revised: 12/26/2018] [Accepted: 01/03/2019] [Indexed: 02/07/2023]
Abstract
The pathogenesis of non-alcoholic fatty liver disease (NAFLD) and hepatocellular carcinoma (HCC) has been associated with altered expression of liver-specific genes including pyruvate kinase liver and red blood cell (PKLR), patatin-like phospholipase domain containing 3 (PNPLA3) and proprotein convertase subtilisin/kexin type 9 (PCSK9). Here, we inhibited and overexpressed the expression of these three genes in HepG2 cells, generated RNA-seq data before and after perturbation and revealed the altered global biological functions with the modulation of these genes using integrated network (IN) analysis. We found that modulation of these genes effects the total triglycerides levels within the cells and viability of the cells. Next, we generated IN for HepG2 cells, identified reporter transcription factors based on IN and found that the modulation of these genes affects key metabolic pathways associated with lipid metabolism (steroid biosynthesis, PPAR signalling pathway, fatty acid synthesis and oxidation) and cancer development (DNA replication, cell cycle and p53 signalling) involved in the progression of NAFLD and HCC. Finally, we observed that inhibition of PKLR lead to decreased glucose uptake and decreased mitochondrial activity in HepG2 cells. Hence, our systems level analysis indicated that PKLR can be targeted for development efficient treatment strategy for NAFLD and HCC.
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Affiliation(s)
- Zhengtao Liu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Cheng Zhang
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Sunjae Lee
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Woonghee Kim
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Martina Klevstig
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Azadeh M Harzandi
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Natasa Sikanic
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Muhammad Arif
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Marcus Ståhlman
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Mathias Uhlen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Jan Boren
- Department of Molecular and Clinical Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, 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 SE1 9RT, United Kingdom.
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245
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Chen Y, Li G, Nielsen J. Genome-Scale Metabolic Modeling from Yeast to Human Cell Models of Complex Diseases: Latest Advances and Challenges. Methods Mol Biol 2019; 2049:329-345. [PMID: 31602620 DOI: 10.1007/978-1-4939-9736-7_19] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Genome-scale metabolic models (GEMs) are mathematical models that enable systematic analysis of metabolism. This modeling concept has been applied to study the metabolism of many organisms including the eukaryal model organism, the yeast Saccharomyces cerevisiae, that also serves as an important cell factory for production of fuels and chemicals. With the application of yeast GEMs, our knowledge of metabolism is increasing. Therefore, GEMs have also been used for modeling human cells to study metabolic diseases. Here we introduce the concept of GEMs and provide a protocol for reconstructing GEMs. Besides, we show the historic development of yeast GEMs and their applications. Also, we review human GEMs as well as their uses in the studies of complex diseases.
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Affiliation(s)
- Yu Chen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | - Gang Li
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden.
- Novo Nordisk Foundation Center for Biosustainability, Chalmers University of Technology, Gothenburg, Sweden.
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark.
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246
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Vieira V, Ferreira J, Rodrigues R, Liu F, Rocha M. A Model Integration Pipeline for the Improvement of Human Genome-Scale Metabolic Reconstructions. J Integr Bioinform 2018; 16:/j/jib.2019.16.issue-1/jib-2018-0068/jib-2018-0068.xml. [PMID: 30808160 PMCID: PMC6798860 DOI: 10.1515/jib-2018-0068] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2018] [Revised: 10/25/2018] [Accepted: 11/14/2018] [Indexed: 01/17/2023] Open
Abstract
Metabolism has been a major field of study in the last years, mainly due to its importance in understanding cell physiology and certain disease phenotypes due to its deregulation. Genome-scale metabolic models (GSMMs) have been established as important tools to help achieve a better understanding of human metabolism. Towards this aim, advances in systems biology and bioinformatics have allowed the reconstruction of several human GSMMs, although some limitations and challenges remain, such as the lack of external identifiers for both metabolites and reactions. A pipeline was developed to integrate multiple GSMMs, starting by retrieving information from the main human GSMMs and evaluating the presence of external database identifiers and annotations for both metabolites and reactions. Information from metabolites was included into a graph database with omics data repositories, allowing clustering of metabolites through their similarity regarding database cross-referencing. Metabolite annotation of several older GSMMs was enriched, allowing the identification and integration of common entities. Using this information, as well as other metrics, we successfully integrated reactions from these models. These methods can be leveraged towards the creation of a unified consensus model of human metabolism.
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Affiliation(s)
- Vítor Vieira
- Center of Biological Engineering, University of Minho – Campus de Gualtar, Braga, Portugal
| | - Jorge Ferreira
- Center of Biological Engineering, University of Minho – Campus de Gualtar, Braga, Portugal
| | - Rúben Rodrigues
- Center of Biological Engineering, University of Minho – Campus de Gualtar, Braga, Portugal
| | - Filipe Liu
- Argonne National Laboratory, Lemont, IL, USA
| | - Miguel Rocha
- Center of Biological Engineering, University of Minho – Campus de Gualtar, Braga, Portugal
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247
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Rawls KD, Dougherty BV, Blais EM, Stancliffe E, Kolling GL, Vinnakota K, Pannala VR, Wallqvist A, Papin JA. A simplified metabolic network reconstruction to promote understanding and development of flux balance analysis tools. Comput Biol Med 2018; 105:64-71. [PMID: 30584952 DOI: 10.1016/j.compbiomed.2018.12.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 12/11/2018] [Accepted: 12/13/2018] [Indexed: 11/26/2022]
Abstract
GEnome-scale Network REconstructions (GENREs) mathematically describe metabolic reactions of an organism or a specific cell type. GENREs can be used with a number of constraint-based reconstruction and analysis (COBRA) methods to make computational predictions on how a system changes in different environments. We created a simplified GENRE (referred to as iSIM) that captures central energy metabolism with nine metabolic reactions to illustrate the use of and promote the understanding of GENREs and constraint-based methods. We demonstrate the simulation of single and double gene deletions, flux variability analysis (FVA), and test a number of metabolic tasks with the GENRE. Code to perform these analyses is provided in Python, R, and MATLAB. Finally, with iSIM as a guide, we demonstrate how inaccuracies in GENREs can limit their use in the interrogation of energy metabolism.
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Affiliation(s)
- Kristopher D Rawls
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA
| | - Bonnie V Dougherty
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA
| | - Edik M Blais
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA
| | - Ethan Stancliffe
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA
| | - Glynis L Kolling
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA; Department of Medicine, Division of Infectious Diseases and International Health, University of Virginia, Charlottesville, VA, 22908, USA
| | - Kalyan Vinnakota
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, USA
| | - Venkat R Pannala
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, USA
| | - Anders Wallqvist
- Department of Defense Biotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, Maryland, USA
| | - Jason A Papin
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, 22908, USA.
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248
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Metabolic network-based stratification of hepatocellular carcinoma reveals three distinct tumor subtypes. Proc Natl Acad Sci U S A 2018; 115:E11874-E11883. [PMID: 30482855 DOI: 10.1073/pnas.1807305115] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is one of the most frequent forms of liver cancer, and effective treatment methods are limited due to tumor heterogeneity. There is a great need for comprehensive approaches to stratify HCC patients, gain biological insights into subtypes, and ultimately identify effective therapeutic targets. We stratified HCC patients and characterized each subtype using transcriptomics data, genome-scale metabolic networks and network topology/controllability analysis. This comprehensive systems-level analysis identified three distinct subtypes with substantial differences in metabolic and signaling pathways reflecting at genomic, transcriptomic, and proteomic levels. These subtypes showed large differences in clinical survival associated with altered kynurenine metabolism, WNT/β-catenin-associated lipid metabolism, and PI3K/AKT/mTOR signaling. Integrative analyses indicated that the three subtypes rely on alternative enzymes (e.g., ACSS1/ACSS2/ACSS3, PKM/PKLR, ALDOB/ALDOA, MTHFD1L/MTHFD2/MTHFD1) to catalyze the same reactions. Based on systems-level analysis, we identified 8 to 28 subtype-specific genes with pivotal roles in controlling the metabolic network and predicted that these genes may be targeted for development of treatment strategies for HCC subtypes by performing in silico analysis. To validate our predictions, we performed experiments using HepG2 cells under normoxic and hypoxic conditions and observed opposite expression patterns between genes expressed in high/moderate/low-survival tumor groups in response to hypoxia, reflecting activated hypoxic behavior in patients with poor survival. In conclusion, our analyses showed that the heterogeneous HCC tumors can be stratified using a metabolic network-driven approach, which may also be applied to other cancer types, and this stratification may have clinical implications to drive the development of precision medicine.
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Poupin N, Corlu A, Cabaton NJ, Dubois-Pot-Schneider H, Canlet C, Person E, Bruel S, Frainay C, Vinson F, Maurier F, Morel F, Robin MA, Fromenty B, Zalko D, Jourdan F. Large-Scale Modeling Approach Reveals Functional Metabolic Shifts during Hepatic Differentiation. J Proteome Res 2018; 18:204-216. [PMID: 30394098 DOI: 10.1021/acs.jproteome.8b00524] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Being able to explore the metabolism of broad metabolizing cells is of critical importance in many research fields. This article presents an original modeling solution combining metabolic network and omics data to identify modulated metabolic pathways and changes in metabolic functions occurring during differentiation of a human hepatic cell line (HepaRG). Our results confirm the activation of hepato-specific functionalities and newly evidence modulation of other metabolic pathways, which could not be evidenced from transcriptomic data alone. Our method takes advantage of the network structure to detect changes in metabolic pathways that do not have gene annotations and exploits flux analyses techniques to identify activated metabolic functions. Compared to the usual cell-specific metabolic network reconstruction approaches, it limits false predictions by considering several possible network configurations to represent one phenotype rather than one arbitrarily selected network. Our approach significantly enhances the comprehensive and functional assessment of cell metabolism, opening further perspectives to investigate metabolic shifts occurring within various biological contexts.
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Affiliation(s)
- Nathalie Poupin
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Anne Corlu
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Nicolas J Cabaton
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Hélène Dubois-Pot-Schneider
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Cécile Canlet
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Elodie Person
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Sandrine Bruel
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Clément Frainay
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Florence Vinson
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Florence Maurier
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Fabrice Morel
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Marie-Anne Robin
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Bernard Fromenty
- Université Rennes, INSERM, INRA, Institut NUMECAN (Nutrition Metabolisms and Cancer), UMR_A 1341, UMR_S 1241 , F-35000 Rennes , France
| | - Daniel Zalko
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
| | - Fabien Jourdan
- UMR1331 Toxalim (Research Centre in Food Toxicology) , Université de Toulouse, INRA, ENVT, INP-Purpan, UPS , 31027 Toulouse , France
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Aberrant Metabolism in Hepatocellular Carcinoma Provides Diagnostic and Therapeutic Opportunities. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2018; 2018:7512159. [PMID: 30524660 PMCID: PMC6247426 DOI: 10.1155/2018/7512159] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Accepted: 10/03/2018] [Indexed: 02/07/2023]
Abstract
Hepatocellular carcinoma (HCC) accounts for over 80% of liver cancer cases and is highly malignant, recurrent, drug-resistant, and often diagnosed in the advanced stage. It is clear that early diagnosis and a better understanding of molecular mechanisms contributing to HCC progression is clinically urgent. Metabolic alterations clearly characterize HCC tumors. Numerous clinical parameters currently used to assess liver functions reflect changes in both enzyme activity and metabolites. Indeed, differences in glucose and acetate utilization are used as a valid clinical tool for stratifying patients with HCC. Moreover, increased serum lactate can distinguish HCC from normal subjects, and serum lactate dehydrogenase is used as a prognostic indicator for HCC patients under therapy. Currently, the emerging field of metabolomics that allows metabolite analysis in biological fluids is a powerful method for discovering new biomarkers. Several metabolic targets have been identified by metabolomics approaches, and these could be used as biomarkers in HCC. Moreover, the integration of different omics approaches could provide useful information on the metabolic pathways at the systems level. In this review, we provided an overview of the metabolic characteristics of HCC considering also the reciprocal influences between the metabolism of cancer cells and their microenvironment. Moreover, we also highlighted the interaction between hepatic metabolite production and their serum revelations through metabolomics researches.
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