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Meng W, Pan H, Sha Y, Zhai X, Xing A, Lingampelly SS, Sripathi SR, Wang Y, Li K. Metabolic Connectome and Its Role in the Prediction, Diagnosis, and Treatment of Complex Diseases. Metabolites 2024; 14:93. [PMID: 38392985 PMCID: PMC10890086 DOI: 10.3390/metabo14020093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 01/17/2024] [Accepted: 01/25/2024] [Indexed: 02/25/2024] Open
Abstract
The interconnectivity of advanced biological systems is essential for their proper functioning. In modern connectomics, biological entities such as proteins, genes, RNA, DNA, and metabolites are often represented as nodes, while the physical, biochemical, or functional interactions between them are represented as edges. Among these entities, metabolites are particularly significant as they exhibit a closer relationship to an organism's phenotype compared to genes or proteins. Moreover, the metabolome has the ability to amplify small proteomic and transcriptomic changes, even those from minor genomic changes. Metabolic networks, which consist of complex systems comprising hundreds of metabolites and their interactions, play a critical role in biological research by mediating energy conversion and chemical reactions within cells. This review provides an introduction to common metabolic network models and their construction methods. It also explores the diverse applications of metabolic networks in elucidating disease mechanisms, predicting and diagnosing diseases, and facilitating drug development. Additionally, it discusses potential future directions for research in metabolic networks. Ultimately, this review serves as a valuable reference for researchers interested in metabolic network modeling, analysis, and their applications.
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Affiliation(s)
- Weiyu Meng
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Hongxin Pan
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Yuyang Sha
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Xiaobing Zhai
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | - Abao Xing
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
| | | | - Srinivasa R Sripathi
- Henderson Ocular Stem Cell Laboratory, Retina Foundation of the Southwest, Dallas, TX 75231, USA
| | - Yuefei Wang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin 301617, China
| | - Kefeng Li
- Center for Artificial Intelligence Driven Drug Discovery, Faculty of Applied Sciences, Macao Polytechnic University, Macau SAR 999078, China
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2
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Abdik E, Çakır T. Transcriptome-based biomarker prediction for Parkinson's disease using genome-scale metabolic modeling. Sci Rep 2024; 14:585. [PMID: 38182712 PMCID: PMC10770157 DOI: 10.1038/s41598-023-51034-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 12/29/2023] [Indexed: 01/07/2024] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a computational approach for the prediction of metabolite biomarkers. Here, we applied the TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) algorithm and two modified versions of TIMBR to investigate potential metabolite biomarkers for PD. To this end, we mapped thirteen post-mortem PD transcriptome datasets from the substantia nigra region onto Human-GEM. We considered a metabolite as a candidate biomarker if its production was predicted to be more efficient by a TIMBR-family algorithm in control or PD case for the majority of the datasets. Different metrics based on well-known PD-related metabolite alterations, PD-associated pathways, and a list of 25 high-confidence PD metabolite biomarkers compiled from the literature were used to compare the prediction performance of the three algorithms tested. The modified algorithm with the highest prediction power based on the metrics was called TAMBOOR, TrAnscriptome-based Metabolite Biomarkers by On-Off Reactions, which was introduced for the first time in this study. TAMBOOR performed better in terms of capturing well-known pathway alterations and metabolite secretion changes in PD. Therefore, our tool has a strong potential to be used for the prediction of novel diagnostic biomarkers for human diseases.
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Affiliation(s)
- Ecehan Abdik
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey.
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3
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Jamshidi N, Nigam KB, Nigam SK. Loss of the Kidney Urate Transporter, Urat1, Leads to Disrupted Redox Homeostasis in Mice. Antioxidants (Basel) 2023; 12:antiox12030780. [PMID: 36979028 PMCID: PMC10045411 DOI: 10.3390/antiox12030780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 02/28/2023] [Accepted: 03/15/2023] [Indexed: 03/29/2023] Open
Abstract
High uric acid is associated with gout, hypertension, metabolic syndrome, cardiovascular disease, and kidney disease. URAT1 (SLC22A12), originally discovered in mice as Rst, is generally considered a very selective uric acid transporter compared to other closely-related kidney uric acid transporters such as OAT1 (SLC22A6, NKT) and OAT3 (SLC22A8). While the role of URAT1 in regulating human uric acid is well-established, in recent studies the gene has been linked to redox regulation in flies as well as progression of renal cell carcinoma. We have now identified over twenty metabolites in the Urat1 knockout that are generally distinct from metabolites accumulating in the Oat1 and Oat3 knockout mice, with distinct molecular properties as revealed by chemoinformatics and machine learning analysis. These metabolites are involved in seemingly disparate aspects of cellular metabolism, including pyrimidine, fatty acid, and amino acid metabolism. However, through integrative systems metabolic analysis of the transcriptomic and metabolomic data using a human metabolic reconstruction to build metabolic genome-scale models (GEMs), the cellular response to loss of Urat1/Rst revealed compensatory processes related to reactive oxygen species handling and maintaining redox state balances via Vitamin C metabolism and cofactor charging reactions. These observations are consistent with the increasingly appreciated role of the antioxidant properties of uric acid. Collectively, the results highlight the role of Urat1/Rst as a transporter strongly tied to maintaining redox homeostasis, with implications for metabolic side effects from drugs that block its function.
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Affiliation(s)
- Neema Jamshidi
- Department of Radiological Sciences, University of California, Los Angeles, CA 90095, USA
- Institute of Engineering in Medicine, University of California, San Diego, La Jolla, CA 92093, USA;
- Correspondence:
| | - Kabir B. Nigam
- Department of Psychiatry, Brigham and Women’s Hospital, Boston, MA 02130, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02130, USA
| | - Sanjay K. Nigam
- Institute of Engineering in Medicine, University of California, San Diego, La Jolla, CA 92093, USA;
- Departments of Pediatrics and Medicine (Nephrology), University of California, San Diego, La Jolla, CA 92093, USA
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4
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Ng RH, Lee JW, Baloni P, Diener C, Heath JR, Su Y. Constraint-Based Reconstruction and Analyses of Metabolic Models: Open-Source Python Tools and Applications to Cancer. Front Oncol 2022; 12:914594. [PMID: 35875150 PMCID: PMC9303011 DOI: 10.3389/fonc.2022.914594] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 05/30/2022] [Indexed: 11/13/2022] Open
Abstract
The influence of metabolism on signaling, epigenetic markers, and transcription is highly complex yet important for understanding cancer physiology. Despite the development of high-resolution multi-omics technologies, it is difficult to infer metabolic activity from these indirect measurements. Fortunately, genome-scale metabolic models and constraint-based modeling provide a systems biology framework to investigate the metabolic states and define the genotype-phenotype associations by integrations of multi-omics data. Constraint-Based Reconstruction and Analysis (COBRA) methods are used to build and simulate metabolic networks using mathematical representations of biochemical reactions, gene-protein reaction associations, and physiological and biochemical constraints. These methods have led to advancements in metabolic reconstruction, network analysis, perturbation studies as well as prediction of metabolic state. Most computational tools for performing these analyses are written for MATLAB, a proprietary software. In order to increase accessibility and handle more complex datasets and models, community efforts have started to develop similar open-source tools in Python. To date there is a comprehensive set of tools in Python to perform various flux analyses and visualizations; however, there are still missing algorithms in some key areas. This review summarizes the availability of Python software for several components of COBRA methods and their applications in cancer metabolism. These tools are evolving rapidly and should offer a readily accessible, versatile way to model the intricacies of cancer metabolism for identifying cancer-specific metabolic features that constitute potential drug targets.
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Affiliation(s)
- Rachel H. Ng
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
| | - Jihoon W. Lee
- Medical Scientist Training Program, University of Washington, Seattle, WA, United States
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
| | | | | | - James R. Heath
- Institute for Systems Biology, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
- *Correspondence: James R. Heath, ; Yapeng Su,
| | - Yapeng Su
- Program in Immunology, Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
- Herbold Computational Biology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, WA, United States
- *Correspondence: James R. Heath, ; Yapeng Su,
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5
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Hartmann FSF, Udugama IA, Seibold GM, Sugiyama H, Gernaey KV. Digital models in biotechnology: Towards multi-scale integration and implementation. Biotechnol Adv 2022; 60:108015. [PMID: 35781047 DOI: 10.1016/j.biotechadv.2022.108015] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 06/03/2022] [Accepted: 06/27/2022] [Indexed: 12/28/2022]
Abstract
Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.
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Affiliation(s)
- Fabian S F Hartmann
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800 Kgs. Lyngby, Denmark
| | - Isuru A Udugama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan; Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 228 A, 2800 Kgs. Lyngby, Denmark.
| | - Gerd M Seibold
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Søltofts Plads, Building 223, 2800 Kgs. Lyngby, Denmark
| | - Hirokazu Sugiyama
- Department of Chemical System Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, 113-8656 Tokyo, Japan
| | - Krist V Gernaey
- Department of Chemical and Biochemical Engineering, Technical University of Denmark, Søltofts Plads, Building 228 A, 2800 Kgs. Lyngby, Denmark.
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6
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Vijayakumar S, Magazzù G, Moon P, Occhipinti A, Angione C. A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling. Methods Mol Biol 2022; 2399:87-122. [PMID: 35604554 DOI: 10.1007/978-1-0716-1831-8_5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://github.com/Angione-Lab/Tutorials_Combining_ML_and_GSMM .
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Affiliation(s)
- Supreeta Vijayakumar
- Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK
| | - Giuseppe Magazzù
- Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK
| | - Pradip Moon
- Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK
| | - Annalisa Occhipinti
- Computational Systems Biology and Data Analytics Research Group, Middlebrough, UK
- Centre for Digital Innovation, Teesside University, Middlesbrough, UK
| | - Claudio Angione
- Computational Systems Biology and Data Analytics Research Group, Teesside University, Middlebrough, UK.
- Centre for Digital Innovation, Teesside University, Middlesbrough, UK.
- Healthcare Innovation Centre, Teesside University, Middlesbrough, UK.
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7
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Schinn SM, Morrison C, Wei W, Zhang L, Lewis NE. Systematic evaluation of parameters for genome-scale metabolic models of cultured mammalian cells. Metab Eng 2021; 66:21-30. [PMID: 33771719 DOI: 10.1016/j.ymben.2021.03.013] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 11/25/2020] [Accepted: 03/17/2021] [Indexed: 10/21/2022]
Abstract
Genome-scale metabolic models describe cellular metabolism with mechanistic detail. Given their high complexity, such models need to be parameterized correctly to yield accurate predictions and avoid overfitting. Effective parameterization has been well-studied for microbial models, but it remains unclear for higher eukaryotes, including mammalian cells. To address this, we enumerated model parameters that describe key features of cultured mammalian cells - including cellular composition, bioprocess performance metrics, mammalian-specific pathways, and biological assumptions behind model formulation approaches. We tested these parameters by building thousands of metabolic models and evaluating their ability to predict the growth rates of a panel of phenotypically diverse Chinese Hamster Ovary cell clones. We found the following considerations to be most critical for accurate parameterization: (1) cells limit metabolic activity to maintain homeostasis, (2) cell morphology and viability change dynamically during a growth curve, and (3) cellular biomass has a particular macromolecular composition. Depending on parameterization, models predicted different metabolic phenotypes, including contrasting mechanisms of nutrient utilization and energy generation, leading to varying accuracies of growth rate predictions. Notably, accurate parameter values broadly agreed with experimental measurements. These insights will guide future investigations of mammalian metabolism.
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Affiliation(s)
- Song-Min Schinn
- Department of Pediatrics, University of California, San Diego, USA
| | - Carly Morrison
- Pfizer, Biotherapeutics Pharmaceutical Sciences, Andover, MA, USA
| | - Wei Wei
- Pfizer, Biotherapeutics Pharmaceutical Sciences, Andover, MA, USA
| | - Lin Zhang
- Pfizer, Biotherapeutics Pharmaceutical Sciences, Andover, MA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, USA; Department of Bioengineering, University of California, San Diego, USA; Novo Nordisk Foundation Center for Biosustainability at UC, San Diego, USA.
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8
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Abdik E, Çakır T. Systematic investigation of mouse models of Parkinson's disease by transcriptome mapping on a brain-specific genome-scale metabolic network. Mol Omics 2021; 17:492-502. [PMID: 34370801 DOI: 10.1039/d0mo00135j] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Genome-scale metabolic networks enable systemic investigation of metabolic alterations caused by diseases by providing interpretation of omics data. Although Mus musculus (mouse) is one of the most commonly used model organisms for neurodegenerative diseases, a brain-specific metabolic network model of mice has not yet been reconstructed. Here we reconstructed the first brain-specific metabolic network model of mice, iBrain674-Mm, by a homology-based approach, which consisted of 992 reactions controlled by 674 genes and distributed over 48 pathways. We validated the newly reconstructed network model by showing that it predicts healthy resting-state metabolic phenotypes of mouse brain compatible with the literature. We later used iBrain674-Mm to interpret various experimental mouse models of Parkinson's Disease (PD) at the transcriptome level. To this end, we applied a constraint-based modelling based biomarker prediction method called TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) to predict altered metabolite production from transcriptomic data. Systemic analysis of seven different PD mouse models by TIMBR showed that the neuronal levels of glutamate, lactate, creatine phosphate, neuronal acetylcholine, bilirubin and formate increased in most of the PD mouse models, whereas the levels of melatonin, epinephrine, astrocytic formate and astrocytic bilirubin decreased. Although most of the predictions were consistent with the literature, there were some inconsistencies among different PD mouse models, signifying that there is no perfect experimental model to reflect PD metabolism. The newly reconstructed brain-specific genome-scale metabolic network model of mice can make important contributions to the interpretation and development of experimental mouse models of PD and other neurodegenerative diseases.
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Affiliation(s)
- Ecehan Abdik
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey.
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9
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Kenner B, Chari ST, Kelsen D, Klimstra DS, Pandol SJ, Rosenthal M, Rustgi AK, Taylor JA, Yala A, Abul-Husn N, Andersen DK, Bernstein D, Brunak S, Canto MI, Eldar YC, Fishman EK, Fleshman J, Go VLW, Holt JM, Field B, Goldberg A, Hoos W, Iacobuzio-Donahue C, Li D, Lidgard G, Maitra A, Matrisian LM, Poblete S, Rothschild L, Sander C, Schwartz LH, Shalit U, Srivastava S, Wolpin B. Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review. Pancreas 2021; 50:251-279. [PMID: 33835956 PMCID: PMC8041569 DOI: 10.1097/mpa.0000000000001762] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
ABSTRACT Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.
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Affiliation(s)
| | - Suresh T. Chari
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - David S. Klimstra
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Stephen J. Pandol
- Basic and Translational Pancreas Research Program, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA
| | | | - Anil K. Rustgi
- Division of Digestive and Liver Diseases, Department of Medicine, NewYork-Presbyterian/Columbia University Irving Medical Center, New York, NY
| | | | - Adam Yala
- Department of Electrical Engineering and Computer Science
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Noura Abul-Husn
- Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine, Mount Sinai, New York, NY
| | - Dana K. Andersen
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD
| | | | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Marcia Irene Canto
- Division of Gastroenterology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Yonina C. Eldar
- Department of Math and Computer Science, Weizmann Institute of Science, Rehovot, Israel
| | - Elliot K. Fishman
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, MD
| | | | - Vay Liang W. Go
- UCLA Center for Excellence in Pancreatic Diseases, University of California, Los Angeles, Los Angeles, CA
| | | | - Bruce Field
- From the Kenner Family Research Fund, New York, NY
| | - Ann Goldberg
- From the Kenner Family Research Fund, New York, NY
| | | | - Christine Iacobuzio-Donahue
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Debiao Li
- Biomedical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | | | - Anirban Maitra
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | | | | | | | - Lawrence H. Schwartz
- Department of Radiology, NewYork-Presbyterian Hospital/Columbia University Irving Medical Center, New York, NY
| | - Uri Shalit
- Faculty of Industrial Engineering and Management, Technion—Israel Institute of Technology, Haifa, Israel
| | - Sudhir Srivastava
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD
| | - Brian Wolpin
- Gastrointestinal Cancer Center, Dana-Farber Cancer Institute, Boston, MA
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10
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Haiman ZB, Zielinski DC, Koike Y, Yurkovich JT, Palsson BO. MASSpy: Building, simulating, and visualizing dynamic biological models in Python using mass action kinetics. PLoS Comput Biol 2021; 17:e1008208. [PMID: 33507922 PMCID: PMC7872247 DOI: 10.1371/journal.pcbi.1008208] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 02/09/2021] [Accepted: 12/21/2020] [Indexed: 01/01/2023] Open
Abstract
Mathematical models of metabolic networks utilize simulation to study system-level mechanisms and functions. Various approaches have been used to model the steady state behavior of metabolic networks using genome-scale reconstructions, but formulating dynamic models from such reconstructions continues to be a key challenge. Here, we present the Mass Action Stoichiometric Simulation Python (MASSpy) package, an open-source computational framework for dynamic modeling of metabolism. MASSpy utilizes mass action kinetics and detailed chemical mechanisms to build dynamic models of complex biological processes. MASSpy adds dynamic modeling tools to the COnstraint-Based Reconstruction and Analysis Python (COBRApy) package to provide an unified framework for constraint-based and kinetic modeling of metabolic networks. MASSpy supports high-performance dynamic simulation through its implementation of libRoadRunner: the Systems Biology Markup Language (SBML) simulation engine. Three examples are provided to demonstrate how to use MASSpy: (1) a validation of the MASSpy modeling tool through dynamic simulation of detailed mechanisms of enzyme regulation; (2) a feature demonstration using a workflow for generating ensemble of kinetic models using Monte Carlo sampling to approximate missing numerical values of parameters and to quantify biological uncertainty, and (3) a case study in which MASSpy is utilized to overcome issues that arise when integrating experimental data with the computation of functional states of detailed biological mechanisms. MASSpy represents a powerful tool to address challenges that arise in dynamic modeling of metabolic networks, both at small and large scales.
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Affiliation(s)
- Zachary B. Haiman
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Daniel C. Zielinski
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
| | - Yuko Koike
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - James T. Yurkovich
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
- Institute for Systems Biology, Seattle, Washington, United States of America
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California San Diego, La Jolla, California, United States of America
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
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11
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A review of methods for the reconstruction and analysis of integrated genome-scale models of metabolism and regulation. Biochem Soc Trans 2020; 48:1889-1903. [DOI: 10.1042/bst20190840] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Revised: 07/16/2020] [Accepted: 08/21/2020] [Indexed: 02/07/2023]
Abstract
The current survey aims to describe the main methodologies for extending the reconstruction and analysis of genome-scale metabolic models and phenotype simulation with Flux Balance Analysis mathematical frameworks, via the integration of Transcriptional Regulatory Networks and/or gene expression data. Although the surveyed methods are aimed at improving phenotype simulations obtained from these models, the perspective of reconstructing integrated genome-scale models of metabolism and gene expression for diverse prokaryotes is still an open challenge.
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12
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Masid M, Ataman M, Hatzimanikatis V. Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN. Nat Commun 2020; 11:2821. [PMID: 32499584 PMCID: PMC7272419 DOI: 10.1038/s41467-020-16549-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Accepted: 05/07/2020] [Indexed: 01/31/2023] Open
Abstract
Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells.
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Affiliation(s)
- Maria Masid
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Meric Ataman
- Computational and Systems Biology, Biozentrum, University of Basel, Basel, Switzerland
| | - Vassily Hatzimanikatis
- Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
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13
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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] [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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14
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Akay A, Hess H. Deep Learning: Current and Emerging Applications in Medicine and Technology. IEEE J Biomed Health Inform 2019; 23:906-920. [PMID: 30676989 DOI: 10.1109/jbhi.2019.2894713] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Machine learning is enabling researchers to analyze and understand increasingly complex physical and biological phenomena in traditional fields such as biology, medicine, and engineering and emerging fields like synthetic biology, automated chemical synthesis, and biomanufacturing. These fields require new paradigms toward understanding increasingly complex data and converting such data into medical products and services for patients. The move toward deep learning and complex modeling is an attempt to bridge the gap between acquiring massive quantities of complex data, and converting such data into practical insights. Here, we provide an overview of the field of machine learning, its current applications and needs in traditional and emerging fields, and discuss an illustrative attempt at using deep learning to understand swarm behavior of molecular shuttles.
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15
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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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16
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Sahoo S, Ravi Kumar RK, Nicolay B, Mohite O, Sivaraman K, Khetan V, Rishi P, Ganesan S, Subramanyan K, Raman K, Miles W, Elchuri SV. Metabolite systems profiling identifies exploitable weaknesses in retinoblastoma. FEBS Lett 2018; 593:23-41. [PMID: 30417337 DOI: 10.1002/1873-3468.13294] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Revised: 09/25/2018] [Accepted: 11/06/2018] [Indexed: 11/06/2022]
Abstract
Retinoblastoma (RB) is a childhood eye cancer. Currently, chemotherapy, local therapy, and enucleation are the main ways in which these tumors are managed. The present work is the first study that uses constraint-based reconstruction and analysis approaches to identify and explain RB-specific survival strategies, which are RB tumor specific. Importantly, our model-specific secretion profile is also found in RB1-depleted human retinal cells in vitro and suggests that novel biomarkers involved in lipid metabolism may be important. Finally, RB-specific synthetic lethals have been predicted as lipid and nucleoside transport proteins that can aid in novel drug target development.
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Affiliation(s)
- Swagatika Sahoo
- Department of Chemical Engineering, Indian Institute of Technology Madras, Chennai, India.,Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India
| | | | - Brandon Nicolay
- Department of Molecular Oncology, Massachusetts General Hospital Cancer Center and Harvard Medical School, Charlestown, MA, USA.,Agios Pharmaceutical, 88 Sidney Street, Cambridge, MA, USA
| | - Omkar Mohite
- Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India.,Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.,The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Vikas Khetan
- Shri Bhagwan Mahavir Vitreoretinal Services and Ocular Oncology Services, Sankara Nethralaya, Chennai, India
| | - Pukhraj Rishi
- Shri Bhagwan Mahavir Vitreoretinal Services and Ocular Oncology Services, Sankara Nethralaya, Chennai, India
| | - Suganeswari Ganesan
- Department of Histopathology, Vision Research Foundation, Sankara Nethralaya, Chennai, India
| | | | - Karthik Raman
- Initiative for Biological Systems Engineering, Indian Institute of Technology Madras, Chennai, India.,Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai, India.,Robert Bosch Centre for Data Science and Artificial Intelligence (RBC-DSAI), Indian Institute of Technology Madras, Chennai, India
| | - Wayne Miles
- Department of Molecular Oncology, Massachusetts General Hospital Cancer Center and Harvard Medical School, Charlestown, MA, USA.,Department of Molecular Genetics, The Ohio State University Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - Sailaja V Elchuri
- Department of Nanotechnology, Vision Research Foundation, Sankara Nethralaya, Chennai, India
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17
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Smith RL, Soeters MR, Wüst RCI, Houtkooper RH. Metabolic Flexibility as an Adaptation to Energy Resources and Requirements in Health and Disease. Endocr Rev 2018; 39:489-517. [PMID: 29697773 PMCID: PMC6093334 DOI: 10.1210/er.2017-00211] [Citation(s) in RCA: 313] [Impact Index Per Article: 52.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Accepted: 04/19/2018] [Indexed: 12/15/2022]
Abstract
The ability to efficiently adapt metabolism by substrate sensing, trafficking, storage, and utilization, dependent on availability and requirement, is known as metabolic flexibility. In this review, we discuss the breadth and depth of metabolic flexibility and its impact on health and disease. Metabolic flexibility is essential to maintain energy homeostasis in times of either caloric excess or caloric restriction, and in times of either low or high energy demand, such as during exercise. The liver, adipose tissue, and muscle govern systemic metabolic flexibility and manage nutrient sensing, uptake, transport, storage, and expenditure by communication via endocrine cues. At a molecular level, metabolic flexibility relies on the configuration of metabolic pathways, which are regulated by key metabolic enzymes and transcription factors, many of which interact closely with the mitochondria. Disrupted metabolic flexibility, or metabolic inflexibility, however, is associated with many pathological conditions including metabolic syndrome, type 2 diabetes mellitus, and cancer. Multiple factors such as dietary composition and feeding frequency, exercise training, and use of pharmacological compounds, influence metabolic flexibility and will be discussed here. Last, we outline important advances in metabolic flexibility research and discuss medical horizons and translational aspects.
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Affiliation(s)
- Reuben L Smith
- Laboratory of Genetic Metabolic Diseases, Academic Medical Center, AZ Amsterdam, Netherlands.,Amsterdam Gastroenterology and Metabolism, Academic Medical Center, AZ Amsterdam, Netherlands
| | - Maarten R Soeters
- Amsterdam Gastroenterology and Metabolism, Academic Medical Center, AZ Amsterdam, Netherlands.,Department of Endocrinology and Metabolism, Internal Medicine, Academic Medical Center, AZ Amsterdam, Netherlands
| | - Rob C I Wüst
- Laboratory of Genetic Metabolic Diseases, Academic Medical Center, AZ Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Academic Medical Center, AZ Amsterdam, Netherlands.,Amsterdam Movement Sciences, Academic Medical Center, AZ Amsterdam, Netherlands
| | - Riekelt H Houtkooper
- Laboratory of Genetic Metabolic Diseases, Academic Medical Center, AZ Amsterdam, Netherlands.,Amsterdam Gastroenterology and Metabolism, Academic Medical Center, AZ Amsterdam, Netherlands.,Amsterdam Cardiovascular Sciences, Academic Medical Center, AZ Amsterdam, Netherlands
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18
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Systems analysis of metabolism in platelet concentrates during storage in platelet additive solution. Biochem J 2018; 475:2225-2240. [DOI: 10.1042/bcj20170921] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 05/25/2018] [Accepted: 06/18/2018] [Indexed: 02/06/2023]
Abstract
Platelets (PLTs) deteriorate over time when stored within blood banks through a biological process known as PLT storage lesion (PSL). Here, we describe the refinement of the biochemical model of PLT metabolism, iAT-PLT-636, and its application to describe and investigate changes in metabolism during PLT storage. Changes in extracellular acetate and citrate were measured in buffy coat and apheresis PLT units over 10 days of storage in the PLT additive solution T-Sol. Metabolic network analysis of these data was performed alongside our prior metabolomics data to describe the metabolism of fresh (days 1–3), intermediate (days 4–6), and expired (days 7–10) PLTs. Changes in metabolism were studied by comparing metabolic model flux predictions of iAT-PLT-636 between stages and between collection methods. Extracellular acetate and glucose contribute most to central carbon metabolism in PLTs. The anticoagulant citrate is metabolized in apheresis-stored PLTs and is converted into aconitate and, to a lesser degree, malate. The consumption of nutrients changes during storage and reflects altered PLT activation profiles following their collection. Irrespective of the collection method, a slowdown in oxidative phosphorylation takes place, consistent with mitochondrial dysfunction during PSL. Finally, the main contributors to intracellular ammonium and NADPH are highlighted. Future optimization of flux through these pathways provides opportunities to address intracellular pH changes and reactive oxygen species, which are both of importance to PSL. The metabolic models provide descriptions of PLT metabolism at steady state and represent a platform for future PLT metabolic research.
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19
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Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ. Next-Generation Machine Learning for Biological Networks. Cell 2018; 173:1581-1592. [PMID: 29887378 DOI: 10.1016/j.cell.2018.05.015] [Citation(s) in RCA: 444] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2017] [Revised: 03/10/2018] [Accepted: 05/07/2018] [Indexed: 02/07/2023]
Abstract
Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.
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Affiliation(s)
- Diogo M Camacho
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA
| | - Katherine M Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Brain & Cognitive Sciences and Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Rani K Powers
- Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - James C Costello
- Computational Bioscience Program, Department of Pharmacology, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.
| | - James J Collins
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115, USA; Department of Biological Engineering and Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
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20
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Constraint-based modeling in microbial food biotechnology. Biochem Soc Trans 2018; 46:249-260. [PMID: 29588387 PMCID: PMC5906707 DOI: 10.1042/bst20170268] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 03/01/2018] [Accepted: 03/02/2018] [Indexed: 12/19/2022]
Abstract
Genome-scale metabolic network reconstruction offers a means to leverage the value of the exponentially growing genomics data and integrate it with other biological knowledge in a structured format. Constraint-based modeling (CBM) enables both the qualitative and quantitative analyses of the reconstructed networks. The rapid advancements in these areas can benefit both the industrial production of microbial food cultures and their application in food processing. CBM provides several avenues for improving our mechanistic understanding of physiology and genotype–phenotype relationships. This is essential for the rational improvement of industrial strains, which can further be facilitated through various model-guided strain design approaches. CBM of microbial communities offers a valuable tool for the rational design of defined food cultures, where it can catalyze hypothesis generation and provide unintuitive rationales for the development of enhanced community phenotypes and, consequently, novel or improved food products. In the industrial-scale production of microorganisms for food cultures, CBM may enable a knowledge-driven bioprocess optimization by rationally identifying strategies for growth and stability improvement. Through these applications, we believe that CBM can become a powerful tool for guiding the areas of strain development, culture development and process optimization in the production of food cultures. Nevertheless, in order to make the correct choice of the modeling framework for a particular application and to interpret model predictions in a biologically meaningful manner, one should be aware of the current limitations of CBM.
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21
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Abstract
The science and art of Genome scale metabolic network reconstructions have been explicitly documented in the literature for organisms across all the three kingdoms of life. Constraints-based models derived from such reconstructions have been used to assess metabolic phenotypes of their complex connections to genotype accurately. The problem of infectious disease is complex due to the multifactorial response of the host to the pathogen. Systems biology approaches and modeling allow one to study, understand, and predict emergent properties of such complex responses. The integration of the host and pathogen metabolic networks and the subsequent merger of their stoichiometric matrices is nontrivial and requires understanding of both pathogen and host metabolism and physiologies. The protocol here describes the detailed process of network and stoichiometric matrix merger using a salmonella-mouse macrophage model. The protocol also discusses the interfacial and objective functions required to actually embark on the analysis of host-pathogen interaction models.
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22
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Eelen G, de Zeeuw P, Treps L, Harjes U, Wong BW, Carmeliet P. Endothelial Cell Metabolism. Physiol Rev 2018; 98:3-58. [PMID: 29167330 PMCID: PMC5866357 DOI: 10.1152/physrev.00001.2017] [Citation(s) in RCA: 304] [Impact Index Per Article: 50.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 06/19/2017] [Accepted: 06/22/2017] [Indexed: 02/06/2023] Open
Abstract
Endothelial cells (ECs) are more than inert blood vessel lining material. Instead, they are active players in the formation of new blood vessels (angiogenesis) both in health and (life-threatening) diseases. Recently, a new concept arose by which EC metabolism drives angiogenesis in parallel to well-established angiogenic growth factors (e.g., vascular endothelial growth factor). 6-Phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3-driven glycolysis generates energy to sustain competitive behavior of the ECs at the tip of a growing vessel sprout, whereas carnitine palmitoyltransferase 1a-controlled fatty acid oxidation regulates nucleotide synthesis and proliferation of ECs in the stalk of the sprout. To maintain vascular homeostasis, ECs rely on an intricate metabolic wiring characterized by intracellular compartmentalization, use metabolites for epigenetic regulation of EC subtype differentiation, crosstalk through metabolite release with other cell types, and exhibit EC subtype-specific metabolic traits. Importantly, maladaptation of EC metabolism contributes to vascular disorders, through EC dysfunction or excess angiogenesis, and presents new opportunities for anti-angiogenic strategies. Here we provide a comprehensive overview of established as well as newly uncovered aspects of EC metabolism.
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Affiliation(s)
- Guy Eelen
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium; and Laboratory of Angiogenesis and Vascular Metabolism, Center for Cancer Biology, VIB, Leuven, Belgium
| | - Pauline de Zeeuw
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium; and Laboratory of Angiogenesis and Vascular Metabolism, Center for Cancer Biology, VIB, Leuven, Belgium
| | - Lucas Treps
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium; and Laboratory of Angiogenesis and Vascular Metabolism, Center for Cancer Biology, VIB, Leuven, Belgium
| | - Ulrike Harjes
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium; and Laboratory of Angiogenesis and Vascular Metabolism, Center for Cancer Biology, VIB, Leuven, Belgium
| | - Brian W Wong
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium; and Laboratory of Angiogenesis and Vascular Metabolism, Center for Cancer Biology, VIB, Leuven, Belgium
| | - Peter Carmeliet
- Laboratory of Angiogenesis and Vascular Metabolism, Department of Oncology, KU Leuven, Leuven, Belgium; and Laboratory of Angiogenesis and Vascular Metabolism, Center for Cancer Biology, VIB, Leuven, Belgium
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23
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Informatics for Nutritional Genetics and Genomics. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2017; 1005:143-166. [PMID: 28916932 DOI: 10.1007/978-981-10-5717-5_7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
While traditional nutrition science is focusing on nourishing population, modern nutrition is aiming at benefiting individual people. The goal of modern nutritional research is to promote health, prevent diseases, and improve performance. With the development of modern technologies like bioinformatics, metabolomics, and molecular genetics, this goal is becoming more attainable. In this chapter, we will discuss the new concepts and technologies especially in informatics and molecular genetics and genomics, and how they have been implemented to change the nutrition science and lead to the emergence of new branches like nutrigenomics, nutrigenetics, and nutritional metabolomics.
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24
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Motamedian E, Taheri E, Bagheri F. Proliferation inhibition of cisplatin-resistant ovarian cancer cells using drugs screened by integrating a metabolic model and transcriptomic data. Cell Prolif 2017; 50. [PMID: 28868622 DOI: 10.1111/cpr.12370] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Accepted: 07/15/2017] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES If screening to find effective drugs is possible, the inhibition of proliferation using existing drugs can be a practical strategy to control the drug resistance of cancer. Development of a system-oriented strategy to find effective drugs was the main aim of this research. MATERIALS AND METHODS An algorithm (transcriptional regulated flux balance analysis [TRFBA]) integrating a generic human metabolic model with transcriptomic data was used to identify genes affecting the growth of drug-resistant cancer cells. Drugs that inhibit activation of the target genes were found and their effect on the proliferation was experimentally evaluated. RESULTS Experimental assessments demonstrated that TRFBA improves the prediction of cancer cell growth in comparison with previous algorithms. The algorithm was then used to propose the system-oriented strategy to search drugs effective in limiting the growth rate of the cisplatin-resistant A2780 epithelial ovarian cancer cell. Experimental evaluations resulted in the selection of azathioprine, terbinafine, hydralazine and sodium valproate that appropriately inhibit the proliferation of resistant cancer cells while minimally affecting normal cells. Furthermore, experimental data indicate that the selected drugs are synergistic and can be used in combination therapies. CONCLUSIONS The proposed strategy was successful to identify drugs effective on the viability of resistant cancer cells. This strategy can enhance the potency of treatments for drug-resistant cancer cells and provides the possibility of using existing drugs.
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Affiliation(s)
- E Motamedian
- Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - E Taheri
- Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
| | - F Bagheri
- Department of Biotechnology, Faculty of Chemical Engineering, Tarbiat Modares University, Tehran, Iran
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25
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In silico analysis of human metabolism: Reconstruction, contextualization and application of genome-scale models. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.01.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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26
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Mathematical and Computational Modeling in Complex Biological Systems. BIOMED RESEARCH INTERNATIONAL 2017; 2017:5958321. [PMID: 28386558 PMCID: PMC5366773 DOI: 10.1155/2017/5958321] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Revised: 12/20/2016] [Accepted: 01/16/2017] [Indexed: 12/22/2022]
Abstract
The biological process and molecular functions involved in the cancer progression remain difficult to understand for biologists and clinical doctors. Recent developments in high-throughput technologies urge the systems biology to achieve more precise models for complex diseases. Computational and mathematical models are gradually being used to help us understand the omics data produced by high-throughput experimental techniques. The use of computational models in systems biology allows us to explore the pathogenesis of complex diseases, improve our understanding of the latent molecular mechanisms, and promote treatment strategy optimization and new drug discovery. Currently, it is urgent to bridge the gap between the developments of high-throughput technologies and systemic modeling of the biological process in cancer research. In this review, we firstly studied several typical mathematical modeling approaches of biological systems in different scales and deeply analyzed their characteristics, advantages, applications, and limitations. Next, three potential research directions in systems modeling were summarized. To conclude, this review provides an update of important solutions using computational modeling approaches in systems biology.
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27
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Martín-Jiménez CA, Salazar-Barreto D, Barreto GE, González J. Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network. Front Aging Neurosci 2017; 9:23. [PMID: 28243200 PMCID: PMC5303712 DOI: 10.3389/fnagi.2017.00023] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2016] [Accepted: 01/27/2017] [Indexed: 12/22/2022] Open
Abstract
Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states.
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Affiliation(s)
- Cynthia A Martín-Jiménez
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana Bogotá, Colombia
| | - Diego Salazar-Barreto
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana Bogotá, Colombia
| | - George E Barreto
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad JaverianaBogotá, Colombia; Instituto de Ciencias Biomédicas, Universidad Autónoma de ChileSantiago, Chile
| | - Janneth González
- Departamento de Nutrición y Bioquímica, Facultad de Ciencias, Pontificia Universidad Javeriana Bogotá, Colombia
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28
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Abstract
Network reconstruction procedures based on meta-"omics" data are an invaluable tool for inferring total and active set of reactions mediated by different members in a microbial community. Within them, network-based methods for automatic analysis of catabolic capacities in metagenomes are currently limited. Here, we describe the complete workflow, scripts, and commands allowing the automatic reconstruction of biodegradation networks using as an input meta-sequences generated by direct DNA sequencing.
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Affiliation(s)
- Rafael Bargiela
- Institute of Catalysis, Consejo Superior de Investigaciones Científicas (CSIC), Marie Curie 2, 28049, Madrid, Spain
| | - Manuel Ferrer
- Institute of Catalysis, Consejo Superior de Investigaciones Científicas (CSIC), Marie Curie 2, 28049, Madrid, Spain.
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29
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Schultz A, Mehta S, Hu C, Hoff F, Horton T, Kornblau S, Qutub A. IDENTIFYING CANCER SPECIFIC METABOLIC SIGNATURES USING CONSTRAINT-BASED MODELS. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2017; 22:485-496. [PMID: 27897000 PMCID: PMC5173378 DOI: 10.1142/9789813207813_0045] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2023]
Abstract
Cancer metabolism differs remarkably from the metabolism of healthy surrounding tissues, and it is extremely heterogeneous across cancer types. While these metabolic differences provide promising avenues for cancer treatments, much work remains to be done in understanding how metabolism is rewired in malignant tissues. To that end, constraint-based models provide a powerful computational tool for the study of metabolism at the genome scale. To generate meaningful predictions, however, these generalized human models must first be tailored for specific cell or tissue sub-types. Here we first present two improved algorithms for (1) the generation of these context-specific metabolic models based on omics data, and (2) Monte-Carlo sampling of the metabolic model ux space. By applying these methods to generate and analyze context-specific metabolic models of diverse solid cancer cell line data, and primary leukemia pediatric patient biopsies, we demonstrate how the methodology presented in this study can generate insights into the rewiring differences across solid tumors and blood cancers.
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Affiliation(s)
- A. Schultz
- Department of Bioengineering, Rice University, Houston, Texas 77005, U.S.A
| | - S. Mehta
- Department of Bioengineering, Rice University, Houston, Texas 77005, U.S.A
| | - C.W. Hu
- Department of Bioengineering, Rice University, Houston, Texas 77005, U.S.A
| | - F.W. Hoff
- Department of Leukemia, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, U.S.A
| | - T.M. Horton
- Department of Pediatrics, Baylor College of Medicine and Texas Children’s Hospital, Houston, Texas 77030, U.S.A
| | - S.M. Kornblau
- Department of Leukemia, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, U.S.A
| | - A.A. Qutub
- Department of Bioengineering, Rice University, Houston, Texas 77005, U.S.A
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30
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Nilsson A, Nielsen J. Genome scale metabolic modeling of cancer. Metab Eng 2016; 43:103-112. [PMID: 27825806 DOI: 10.1016/j.ymben.2016.10.022] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 10/19/2016] [Accepted: 10/31/2016] [Indexed: 10/25/2022]
Abstract
Cancer cells reprogram metabolism to support rapid proliferation and survival. Energy metabolism is particularly important for growth and genes encoding enzymes involved in energy metabolism are frequently altered in cancer cells. A genome scale metabolic model (GEM) is a mathematical formalization of metabolism which allows simulation and hypotheses testing of metabolic strategies. It has successfully been applied to many microorganisms and is now used to study cancer metabolism. Generic models of human metabolism have been reconstructed based on the existence of metabolic genes in the human genome. Cancer specific models of metabolism have also been generated by reducing the number of reactions in the generic model based on high throughput expression data, e.g. transcriptomics and proteomics. Targets for drugs and bio markers for diagnostics have been identified using these models. They have also been used as scaffolds for analysis of high throughput data to allow mechanistic interpretation of changes in expression. Finally, GEMs allow quantitative flux predictions using flux balance analysis (FBA). Here we critically review the requirements for successful FBA simulations of cancer cells and discuss the symmetry between the methods used for modeling of microbial and cancer metabolism. GEMs have great potential for translational research on cancer and will therefore become of increasing importance in the future.
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Affiliation(s)
- Avlant Nilsson
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296 Gothenburg, Sweden
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, SE41296 Gothenburg, Sweden; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, DK2970 Hørsholm, Denmark.
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31
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Loor JJ, Vailati-Riboni M, McCann JC, Zhou Z, Bionaz M. TRIENNIAL LACTATION SYMPOSIUM: Nutrigenomics in livestock: Systems biology meets nutrition. J Anim Sci 2016; 93:5554-74. [PMID: 26641165 DOI: 10.2527/jas.2015-9225] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The advent of high-throughput technologies to study an animal's genome, proteome, and metabolome (i.e., "omics" tools) constituted a setback to the use of reductionism in livestock research. More recent development of "next-generation sequencing" tools was instrumental in allowing in-depth studies of the microbiome in the rumen and other sections of the gastrointestinal tract. Omics, along with bioinformatics, constitutes the foundation of modern systems biology, a field of study widely used in model organisms (e.g., rodents, yeast, humans) to enhance understanding of the complex biological interactions occurring within cells and tissues at the gene, protein, and metabolite level. Application of systems biology concepts is ideal for the study of interactions between nutrition and physiological state with tissue and cell metabolism and function during key life stages of livestock species, including the transition from pregnancy to lactation, in utero development, or postnatal growth. Modern bioinformatic tools capable of discerning functional outcomes and biologically meaningful networks complement the ever-increasing ability to generate large molecular, microbial, and metabolite data sets. Simultaneous visualization of the complex intertissue adaptations to physiological state and nutrition can now be discerned. Studies to understand the linkages between the microbiome and the absorptive epithelium using the integrative approach are emerging. We present examples of new knowledge generated through the application of functional analyses of transcriptomic, proteomic, and metabolomic data sets encompassing nutritional management of dairy cows, pigs, and poultry. Published work to date underscores that the integrative approach across and within tissues may prove useful for fine-tuning nutritional management of livestock. An important goal during this process is to uncover key molecular players involved in the organismal adaptations to nutrition.
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32
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Robinson JL, Nielsen J. Integrative analysis of human omics data using biomolecular networks. MOLECULAR BIOSYSTEMS 2016; 12:2953-64. [PMID: 27510223 DOI: 10.1039/c6mb00476h] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
High-throughput '-omics' technologies have given rise to an increasing abundance of genome-scale data detailing human biology at the molecular level. Although these datasets have already made substantial contributions to a more comprehensive understanding of human physiology and diseases, their interpretation becomes increasingly cryptic and nontrivial as they continue to expand in size and complexity. Systems biology networks offer a scaffold upon which omics data can be integrated, facilitating the extraction of new and physiologically relevant information from the data. Two of the most prevalent networks that have been used for such integrative analyses of omics data are genome-scale metabolic models (GEMs) and protein-protein interaction (PPI) networks, both of which have demonstrated success among many different omics and sample types. This integrative approach seeks to unite 'top-down' omics data with 'bottom-up' biological networks in a synergistic fashion that draws on the strengths of both strategies. As the volume and resolution of high-throughput omics data continue to grow, integrative network-based analyses are expected to play an increasingly important role in their interpretation.
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Affiliation(s)
- Jonathan L Robinson
- Department of Biology and Biological Engineering, Chalmers University of Technology, Kemivägen 10, SE412 96 Gothenburg, Sweden.
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33
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Aurich MK, Fleming RMT, Thiele I. MetaboTools: A Comprehensive Toolbox for Analysis of Genome-Scale Metabolic Models. Front Physiol 2016; 7:327. [PMID: 27536246 PMCID: PMC4971542 DOI: 10.3389/fphys.2016.00327] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 07/18/2016] [Indexed: 11/13/2022] Open
Abstract
Metabolomic data sets provide a direct read-out of cellular phenotypes and are increasingly generated to study biological questions. Previous work, by us and others, revealed the potential of analyzing extracellular metabolomic data in the context of the metabolic model using constraint-based modeling. With the MetaboTools, we make our methods available to the broader scientific community. The MetaboTools consist of a protocol, a toolbox, and tutorials of two use cases. The protocol describes, in a step-wise manner, the workflow of data integration, and computational analysis. The MetaboTools comprise the Matlab code required to complete the workflow described in the protocol. Tutorials explain the computational steps for integration of two different data sets and demonstrate a comprehensive set of methods for the computational analysis of metabolic models and stratification thereof into different phenotypes. The presented workflow supports integrative analysis of multiple omics data sets. Importantly, all analysis tools can be applied to metabolic models without performing the entire workflow. Taken together, the MetaboTools constitute a comprehensive guide to the intra-model analysis of extracellular metabolomic data from microbial, plant, or human cells. This computational modeling resource offers a broad set of computational analysis tools for a wide biomedical and non-biomedical research community.
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Affiliation(s)
- Maike K Aurich
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg Esch-sur-Alzette, Luxembourg
| | - Ronan M T Fleming
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg Esch-sur-Alzette, Luxembourg
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg Esch-sur-Alzette, Luxembourg
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34
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Liu HC, Jamshidi N, Chen Y, Eraly SA, Cho SY, Bhatnagar V, Wu W, Bush KT, Abagyan R, Palsson BO, Nigam SK. An Organic Anion Transporter 1 (OAT1)-centered Metabolic Network. J Biol Chem 2016; 291:19474-86. [PMID: 27440044 DOI: 10.1074/jbc.m116.745216] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Indexed: 01/06/2023] Open
Abstract
There has been a recent interest in the broader physiological importance of multispecific "drug" transporters of the SLC and ABC transporter families. Here, a novel multi-tiered systems biology approach was used to predict metabolites and signaling molecules potentially affected by the in vivo deletion of organic anion transporter 1 (Oat1, Slc22a6, originally NKT), a major kidney-expressed drug transporter. Validation of some predictions in wet-lab assays, together with re-evaluation of existing transport and knock-out metabolomics data, generated an experimentally validated, confidence ranked set of OAT1-interacting endogenous compounds enabling construction of an "OAT1-centered metabolic interaction network." Pathway and enrichment analysis indicated an important role for OAT1 in metabolism involving: the TCA cycle, tryptophan and other amino acids, fatty acids, prostaglandins, cyclic nucleotides, odorants, polyamines, and vitamins. The partly validated reconstructed network is also consistent with a major role for OAT1 in modulating metabolic and signaling pathways involving uric acid, gut microbiome products, and so-called uremic toxins accumulating in chronic kidney disease. Together, the findings are compatible with the hypothesized role of drug transporters in remote inter-organ and inter-organismal communication: The Remote Sensing and Signaling Hypothesis (Nigam, S. K. (2015) Nat. Rev. Drug Disc. 14, 29). The fact that OAT1 can affect many systemic biological pathways suggests that drug-metabolite interactions need to be considered beyond simple competition for the drug transporter itself and may explain aspects of drug-induced metabolic syndrome. Our approach should provide novel mechanistic insights into the role of OAT1 and other drug transporters implicated in metabolic diseases like gout, diabetes, and chronic kidney disease.
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Affiliation(s)
| | | | - Yuchen Chen
- Bioinformatics and Systems Biology Graduate Program
| | | | | | | | | | | | - Ruben Abagyan
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, La Jolla, California 92093
| | | | - Sanjay K Nigam
- Medicine, Pediatrics, and Cellular and Molecular Medicine,
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35
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Patumcharoenpol P, Doungpan N, Meechai A, Shen B, Chan JH, Vongsangnak W. An integrated text mining framework for metabolic interaction network reconstruction. PeerJ 2016; 4:e1811. [PMID: 27019783 PMCID: PMC4806637 DOI: 10.7717/peerj.1811] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2015] [Accepted: 02/24/2016] [Indexed: 11/20/2022] Open
Abstract
Text mining (TM) in the field of biology is fast becoming a routine analysis for the extraction and curation of biological entities (e.g., genes, proteins, simple chemicals) as well as their relationships. Due to the wide applicability of TM in situations involving complex relationships, it is valuable to apply TM to the extraction of metabolic interactions (i.e., enzyme and metabolite interactions) through metabolic events. Here we present an integrated TM framework containing two modules for the extraction of metabolic events (Metabolic Event Extraction module—MEE) and for the construction of a metabolic interaction network (Metabolic Interaction Network Reconstruction module—MINR). The proposed integrated TM framework performed well based on standard measures of recall, precision and F-score. Evaluation of the MEE module using the constructed Metabolic Entities (ME) corpus yielded F-scores of 59.15% and 48.59% for the detection of metabolic events for production and consumption, respectively. As for the testing of the entity tagger for Gene and Protein (GP) and metabolite with the test corpus, the obtained F-score was greater than 80% for the Superpathway of leucine, valine, and isoleucine biosynthesis. Mapping of enzyme and metabolite interactions through network reconstruction showed a fair performance for the MINR module on the test corpus with F-score >70%. Finally, an application of our integrated TM framework on a big-scale data (i.e., EcoCyc extraction data) for reconstructing a metabolic interaction network showed reasonable precisions at 69.93%, 70.63% and 46.71% for enzyme, metabolite and enzyme–metabolite interaction, respectively. This study presents the first open-source integrated TM framework for reconstructing a metabolic interaction network. This framework can be a powerful tool that helps biologists to extract metabolic events for further reconstruction of a metabolic interaction network. The ME corpus, test corpus, source code, and virtual machine image with pre-configured software are available at www.sbi.kmutt.ac.th/ preecha/metrecon.
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Affiliation(s)
- Preecha Patumcharoenpol
- Systems Biology and Bioinformatics Laboratory, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.,Center for Systems Biology, Soochow University, Suzhou, China
| | - Narumol Doungpan
- School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
| | - Asawin Meechai
- Systems Biology and Bioinformatics Laboratory, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.,Department of Chemical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
| | - Bairong Shen
- Center for Systems Biology, Soochow University, Suzhou, China
| | - Jonathan H Chan
- Systems Biology and Bioinformatics Laboratory, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.,School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
| | - Wanwipa Vongsangnak
- Center for Systems Biology, Soochow University, Suzhou, China.,Department of Zoology, Faculty of Science, Kasetsart University, Bangkok, Thailand
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36
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Schultz A, Qutub AA. Reconstruction of Tissue-Specific Metabolic Networks Using CORDA. PLoS Comput Biol 2016; 12:e1004808. [PMID: 26942765 PMCID: PMC4778931 DOI: 10.1371/journal.pcbi.1004808] [Citation(s) in RCA: 83] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2015] [Accepted: 02/13/2016] [Indexed: 01/07/2023] Open
Abstract
Human metabolism involves thousands of reactions and metabolites. To interpret this complexity, computational modeling becomes an essential experimental tool. One of the most popular techniques to study human metabolism as a whole is genome scale modeling. A key challenge to applying genome scale modeling is identifying critical metabolic reactions across diverse human tissues. Here we introduce a novel algorithm called Cost Optimization Reaction Dependency Assessment (CORDA) to build genome scale models in a tissue-specific manner. CORDA performs more efficiently computationally, shows better agreement to experimental data, and displays better model functionality and capacity when compared to previous algorithms. CORDA also returns reaction associations that can greatly assist in any manual curation to be performed following the automated reconstruction process. Using CORDA, we developed a library of 76 healthy and 20 cancer tissue-specific reconstructions. These reconstructions identified which metabolic pathways are shared across diverse human tissues. Moreover, we identified changes in reactions and pathways that are differentially included and present different capacity profiles in cancer compared to healthy tissues, including up-regulation of folate metabolism, the down-regulation of thiamine metabolism, and tight regulation of oxidative phosphorylation.
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Affiliation(s)
- André Schultz
- Department of Bioengineering, Rice University, Houston, Texas, United States of America
| | - Amina A. Qutub
- Department of Bioengineering, Rice University, Houston, Texas, United States of America
- * E-mail:
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37
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Shaked I, Oberhardt M, Atias N, Sharan R, Ruppin E. Metabolic Network Prediction of Drug Side Effects. Cell Syst 2016; 2:209-13. [DOI: 10.1016/j.cels.2016.03.001] [Citation(s) in RCA: 57] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 12/11/2015] [Accepted: 03/01/2016] [Indexed: 11/17/2022]
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38
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Aurich MK, Thiele I. Computational Modeling of Human Metabolism and Its Application to Systems Biomedicine. Methods Mol Biol 2016; 1386:253-81. [PMID: 26677187 DOI: 10.1007/978-1-4939-3283-2_12] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Modern high-throughput techniques offer immense opportunities to investigate whole-systems behavior, such as those underlying human diseases. However, the complexity of the data presents challenges in interpretation, and new avenues are needed to address the complexity of both diseases and data. Constraint-based modeling is one formalism applied in systems biology. It relies on a genome-scale reconstruction that captures extensive biochemical knowledge regarding an organism. The human genome-scale metabolic reconstruction is increasingly used to understand normal cellular and disease states because metabolism is an important factor in many human diseases. The application of human genome-scale reconstruction ranges from mere querying of the model as a knowledge base to studies that take advantage of the model's topology and, most notably, to functional predictions based on cell- and condition-specific metabolic models built based on omics data.An increasing number and diversity of biomedical questions are being addressed using constraint-based modeling and metabolic models. One of the most successful biomedical applications to date is cancer metabolism, but constraint-based modeling also holds great potential for inborn errors of metabolism or obesity. In addition, it offers great prospects for individualized approaches to diagnostics and the design of disease prevention and intervention strategies. Metabolic models support this endeavor by providing easy access to complex high-throughput datasets. Personalized metabolic models have been introduced. Finally, constraint-based modeling can be used to model whole-body metabolism, which will enable the elucidation of metabolic interactions between organs and disturbances of these interactions as either causes or consequence of metabolic diseases. This chapter introduces constraint-based modeling and describes some of its contributions to systems biomedicine.
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Affiliation(s)
- Maike K Aurich
- Luxembourg Center for Systems Biomedicine, University of Luxembourg, Campus Belval, 7, Avenue des Hauts-Fourneaux, Esch-sur-alzette, L-4362, Luxembourg
| | - Ines Thiele
- Luxembourg Center for Systems Biomedicine, University of Luxembourg, Campus Belval, 7, Avenue des Hauts-Fourneaux, Esch-sur-alzette, L-4362, Luxembourg.
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39
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Abstract
Metabolomics data are used to parameterize individual-specific kinetic models of metabolism to predict medically relevant parameters underpinning disease states or outcomes.
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Affiliation(s)
- Anupam Chowdhury
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA 16802, USA.
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40
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Moreno-Sánchez R, Saavedra E, Gallardo-Pérez JC, Rumjanek FD, Rodríguez-Enríquez S. Understanding the cancer cell phenotype beyond the limitations of current omics analyses. FEBS J 2015; 283:54-73. [DOI: 10.1111/febs.13535] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Revised: 08/24/2015] [Accepted: 09/25/2015] [Indexed: 12/27/2022]
Affiliation(s)
- Rafael Moreno-Sánchez
- Departamento de Bioquímica; Instituto Nacional de Cardiología Ignacio Chávez; Tlalpan Mexico
| | - Emma Saavedra
- Departamento de Bioquímica; Instituto Nacional de Cardiología Ignacio Chávez; Tlalpan Mexico
| | | | | | - Sara Rodríguez-Enríquez
- Departamento de Bioquímica; Instituto Nacional de Cardiología Ignacio Chávez; Tlalpan Mexico
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41
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Abstract
Type 2 diabetes (T2D) has become an increasingly challenging health burden due to its high morbidity, mortality, and heightened prevalence worldwide. Although dietary and nutritional imbalances have long been recognized as key risk factors for T2D, the underlying mechanisms remain unclear. The advent of nutritional systems biology, a field that aims to elucidate the interactions between dietary nutrients and endogenous molecular entities in disease-related tissues, offers unique opportunities to unravel the complex mechanisms underlying the health-modifying capacities of nutritional molecules. The recent revolutionary advances in omics technologies have particularly empowered this incipient field. In this review, we discuss the applications of multi-omics approaches toward a systems-level understanding of how dietary patterns and particular nutrients modulate the risk of T2D. We focus on nutritional studies utilizing transcriptomics, epigenomomics, proteomics, metabolomics, and microbiomics, and integration of diverse omics technologies. We also summarize the potential molecular mechanisms through which nutritional imbalances contribute to T2D pathogenesis based on these studies. Finally, we discuss the remaining challenges of nutritional systems biology and how the field can be optimized to further our understanding of T2D and guide disease management via nutritional interventions.
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Affiliation(s)
- Yuqi Zhao
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Rio Elizabeth Barrere-Cain
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095 USA
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA 90095 USA
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42
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Fouladiha H, Marashi SA, Shokrgozar MA. Reconstruction and validation of a constraint-based metabolic network model for bone marrow-derived mesenchymal stem cells. Cell Prolif 2015; 48:475-85. [PMID: 26132591 DOI: 10.1111/cpr.12197] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2015] [Accepted: 04/14/2015] [Indexed: 12/28/2022] Open
Abstract
OBJECTIVES Over recent years, constraint-based modelling of metabolic networks has become increasingly popular; the models are suitable for system-level modelling of cell physiology. The goal of the present work was to reconstruct a constraint-based metabolic network model of bone marrow-derived mesenchymal stem cells (BMMSCs). MATERIALS AND METHODS To reconstruct a BMMSC-specific metabolic model, transcriptomic data of BMMSCs, and additionally, the human generic metabolic network model (Recon1) were used. Then, using the mCADRE algorithm, a draft metabolic network was reconstructed. Literature and proteomic data were subsequently used to refine and improve the draft. From this, iMSC1255 was derived to be the metabolic network model of BMMSCs. RESULTS iMSC1255 has 1255 genes, 1850 metabolites and 2288 reactions. After including additional constraints based on previously reported experimental results, our model successfully predicted BMMSC growth rate and metabolic phenotypes. CONCLUSIONS Here, iMSC1255 is introduced to be the metabolic network model of bone marrow-derived mesenchymal stem cells. Based on current knowledge, this is the first report on genome-scale reconstruction and validation of a stem cell metabolic network model.
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Affiliation(s)
- H Fouladiha
- Department of Biotechnology, College of Science, University of Tehran, Tehran, 1417614411, Iran
| | - S-A Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, 1417614411, Iran
| | - M A Shokrgozar
- National Cell Bank of Iran, Pasteur Institute of Iran, Tehran, 1316943551, Iran
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43
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Bailey LB, Stover PJ, McNulty H, Fenech MF, Gregory JF, Mills JL, Pfeiffer CM, Fazili Z, Zhang M, Ueland PM, Molloy AM, Caudill MA, Shane B, Berry RJ, Bailey RL, Hausman DB, Raghavan R, Raiten DJ. Biomarkers of Nutrition for Development-Folate Review. J Nutr 2015; 145:1636S-1680S. [PMID: 26451605 PMCID: PMC4478945 DOI: 10.3945/jn.114.206599] [Citation(s) in RCA: 296] [Impact Index Per Article: 32.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2014] [Revised: 12/11/2014] [Accepted: 04/14/2015] [Indexed: 12/13/2022] Open
Abstract
The Biomarkers of Nutrition for Development (BOND) project is designed to provide evidence-based advice to anyone with an interest in the role of nutrition in health. Specifically, the BOND program provides state-of-the-art information and service with regard to selection, use, and interpretation of biomarkers of nutrient exposure, status, function, and effect. To accomplish this objective, expert panels are recruited to evaluate the literature and to draft comprehensive reports on the current state of the art with regard to specific nutrient biology and available biomarkers for assessing nutrients in body tissues at the individual and population level. Phase I of the BOND project includes the evaluation of biomarkers for 6 nutrients: iodine, iron, zinc, folate, vitamin A, and vitamin B-12. This review represents the second in the series of reviews and covers all relevant aspects of folate biology and biomarkers. The article is organized to provide the reader with a full appreciation of folate's history as a public health issue, its biology, and an overview of available biomarkers (serum folate, RBC folate, and plasma homocysteine concentrations) and their interpretation across a range of clinical and population-based uses. The article also includes a list of priority research needs for advancing the area of folate biomarkers related to nutritional health status and development.
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Affiliation(s)
- Lynn B Bailey
- Department of Foods and Nutrition, University of Georgia, Athens, GA;
| | - Patrick J Stover
- Division of Nutritional Sciences, Cornell University, Ithaca, NY
| | - Helene McNulty
- Northern Ireland Centre for Food and Health, Biomedical Sciences Research Institute, University of Ulster, Londonderry, United Kingdom
| | - Michael F Fenech
- Genome Health Nutrigenomics Laboratory, Food, Nutrition, and Bioproducts Flagship, Commonwealth Scientific and Industrial Research Organization, Adelaide, Australia
| | - Jesse F Gregory
- Food Science and Human Nutrition Department, University of Florida, Gainesville, FL
| | - James L Mills
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD
| | | | - Zia Fazili
- National Center for Environmental Health, CDC, Atlanta, GA
| | - Mindy Zhang
- National Center for Environmental Health, CDC, Atlanta, GA
| | - Per M Ueland
- Department of Clinical Science, Univeristy of Bergen, Bergen, Norway
| | - Anne M Molloy
- Institute of Molecular Medicine, Trinity College, Dublin, Ireland
| | - Marie A Caudill
- Division of Nutritional Sciences, Cornell University, Ithaca, NY
| | - Barry Shane
- Department of Nutritional Sciences and Toxicology, University of California-Berkeley, Berkeley, CA
| | - Robert J Berry
- National Center on Birth Defects and Developmental Disabilities, CDC, Atlanta, GA; and
| | | | - Dorothy B Hausman
- Department of Foods and Nutrition, University of Georgia, Athens, GA
| | - Ramkripa Raghavan
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD
| | - Daniel J Raiten
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD;
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44
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Yizhak K, Chaneton B, Gottlieb E, Ruppin E. Modeling cancer metabolism on a genome scale. Mol Syst Biol 2015; 11:817. [PMID: 26130389 PMCID: PMC4501850 DOI: 10.15252/msb.20145307] [Citation(s) in RCA: 136] [Impact Index Per Article: 15.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2014] [Revised: 04/04/2015] [Accepted: 05/26/2015] [Indexed: 12/16/2022] Open
Abstract
Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field.
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Affiliation(s)
- Keren Yizhak
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel
| | | | | | - Eytan Ruppin
- The Blavatnik School of Computer Science, Tel Aviv University, Tel Aviv, Israel The Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
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45
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Zielinski DC, Filipp FV, Bordbar A, Jensen K, Smith JW, Herrgard MJ, Mo ML, Palsson BO. Pharmacogenomic and clinical data link non-pharmacokinetic metabolic dysregulation to drug side effect pathogenesis. Nat Commun 2015; 6:7101. [PMID: 26055627 PMCID: PMC4468904 DOI: 10.1038/ncomms8101] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2015] [Accepted: 04/07/2015] [Indexed: 01/24/2023] Open
Abstract
Drug side effects cause a significant clinical and economic burden. However, mechanisms of drug action underlying side effect pathogenesis remain largely unknown. Here, we integrate pharmacogenomic and clinical data with a human metabolic network and find that non-pharmacokinetic metabolic pathways dysregulated by drugs are linked to the development of side effects. We show such dysregulated metabolic pathways contain genes with sequence variants affecting side effect incidence, play established roles in pathophysiology, have significantly altered activity in corresponding diseases, are susceptible to metabolic inhibitors and are effective targets for therapeutic nutrient supplementation. Our results indicate that metabolic dysregulation represents a common mechanism underlying side effect pathogenesis that is distinct from the role of metabolism in drug clearance. We suggest that elucidating the relationships between the cellular response to drugs, genetic variation of patients and cell metabolism may help managing side effects by personalizing drug prescriptions and nutritional intervention strategies.
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Affiliation(s)
- Daniel C Zielinski
- Department of Bioengineering, University of California, San Diego, La Jolla, California 92093-0412, USA
| | - Fabian V Filipp
- 1] Cancer Research Center, Sanford-Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, California 92037, USA [2] UC Merced, Quantitative and Systems Biology, University of California Merced, 5200 North Lake Road, Merced, California 95343, USA
| | - Aarash Bordbar
- 1] Department of Bioengineering, University of California, San Diego, La Jolla, California 92093-0412, USA [2] Sinopia Biosciences, 600 W Broadway Suite 700, San Diego, CA 92101, USA
| | - Kasper Jensen
- Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark, Kemitorvet, Building 208, Lyngby DK-2800, Denmark
| | - Jeffrey W Smith
- Cancer Research Center, Sanford-Burnham Institute for Medical Research, 10901 North Torrey Pines Road, La Jolla, California 92037, USA
| | - Markus J Herrgard
- 1] Department of Bioengineering, University of California, San Diego, La Jolla, California 92093-0412, USA [2] Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kogle Alle 6, Horshølm 2970, Denmark
| | - Monica L Mo
- Department of Bioengineering, University of California, San Diego, La Jolla, California 92093-0412, USA
| | - Bernhard O Palsson
- 1] Department of Bioengineering, University of California, San Diego, La Jolla, California 92093-0412, USA [2] Department of Pediatrics, University of California, San Diego, La Jolla, California 92093-0412, USA
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Heinken A, Thiele I. Systems biology of host-microbe metabolomics. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 7:195-219. [PMID: 25929487 PMCID: PMC5029777 DOI: 10.1002/wsbm.1301] [Citation(s) in RCA: 69] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/23/2015] [Revised: 03/25/2015] [Accepted: 04/01/2015] [Indexed: 12/15/2022]
Abstract
The human gut microbiota performs essential functions for host and well‐being, but has also been linked to a variety of disease states, e.g., obesity and type 2 diabetes. The mammalian body fluid and tissue metabolomes are greatly influenced by the microbiota, with many health‐relevant metabolites being considered ‘mammalian–microbial co‐metabolites’. To systematically investigate this complex host–microbial co‐metabolism, a systems biology approach integrating high‐throughput data and computational network models is required. Here, we review established top‐down and bottom‐up systems biology approaches that have successfully elucidated relationships between gut microbiota‐derived metabolites and host health and disease. We focus particularly on the constraint‐based modeling and analysis approach, which enables the prediction of mechanisms behind metabolic host–microbe interactions on the molecular level. We illustrate that constraint‐based models are a useful tool for the contextualization of metabolomic measurements and can further our insight into host–microbe interactions, yielding, e.g., in potential novel drugs and biomarkers. WIREs Syst Biol Med 2015, 7:195–219. doi: 10.1002/wsbm.1301 For further resources related to this article, please visit the WIREs website. Conflict of interest: The authors have declared no conflicts of interest for this article.
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Affiliation(s)
- Almut Heinken
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
| | - Ines Thiele
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
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Durmuş S, Çakır T, Özgür A, Guthke R. A review on computational systems biology of pathogen-host interactions. Front Microbiol 2015; 6:235. [PMID: 25914674 PMCID: PMC4391036 DOI: 10.3389/fmicb.2015.00235] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2014] [Accepted: 03/10/2015] [Indexed: 12/27/2022] Open
Abstract
Pathogens manipulate the cellular mechanisms of host organisms via pathogen-host interactions (PHIs) in order to take advantage of the capabilities of host cells, leading to infections. The crucial role of these interspecies molecular interactions in initiating and sustaining infections necessitates a thorough understanding of the corresponding mechanisms. Unlike the traditional approach of considering the host or pathogen separately, a systems-level approach, considering the PHI system as a whole is indispensable to elucidate the mechanisms of infection. Following the technological advances in the post-genomic era, PHI data have been produced in large-scale within the last decade. Systems biology-based methods for the inference and analysis of PHI regulatory, metabolic, and protein-protein networks to shed light on infection mechanisms are gaining increasing demand thanks to the availability of omics data. The knowledge derived from the PHIs may largely contribute to the identification of new and more efficient therapeutics to prevent or cure infections. There are recent efforts for the detailed documentation of these experimentally verified PHI data through Web-based databases. Despite these advances in data archiving, there are still large amounts of PHI data in the biomedical literature yet to be discovered, and novel text mining methods are in development to unearth such hidden data. Here, we review a collection of recent studies on computational systems biology of PHIs with a special focus on the methods for the inference and analysis of PHI networks, covering also the Web-based databases and text-mining efforts to unravel the data hidden in the literature.
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Affiliation(s)
- Saliha Durmuş
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, KocaeliTurkey
| | - Tunahan Çakır
- Computational Systems Biology Group, Department of Bioengineering, Gebze Technical University, KocaeliTurkey
| | - Arzucan Özgür
- Department of Computer Engineering, Boǧaziçi University, IstanbulTurkey
| | - Reinhard Guthke
- Leibniz Institute for Natural Product Research and Infection Biology – Hans-Knoell-Institute, JenaGermany
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48
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Motamedian E, Ghavami G, Sardari S. Investigation on metabolism of cisplatin resistant ovarian cancer using a genome scale metabolic model and microarray data. IRANIAN JOURNAL OF BASIC MEDICAL SCIENCES 2015; 18:267-76. [PMID: 25945240 PMCID: PMC4414993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2014] [Accepted: 02/18/2015] [Indexed: 11/13/2022]
Abstract
OBJECTIVES Many cancer cells show significant resistance to drugs that kill drug sensitive cancer cells and non-tumor cells and such resistance might be a consequence of the difference in metabolism. Therefore, studying the metabolism of drug resistant cancer cells and comparison with drug sensitive and normal cell lines is the objective of this research. MATERIAL AND METHODS Metabolism of cisplatin resistant and sensitive A2780 epithelial ovarian cancer cells and normal ovarian epithelium has been studied using a generic human genome-scale metabolic model and transcription data. RESULT The results demonstrate that the most different metabolisms belong to resistant and normal models, and the different reactions are involved in various metabolic pathways. However, large portion of distinct reactions are related to extracellular transport for three cell lines. Capability of metabolic models to secrete lactate was investigated to find the origin of Warburg effect. Computational results introduced SLC25A10 gene, which encodes mitochondrial dicarboxylate transporter involved in exchanging of small metabolites across the mitochondrial membrane that may play key role in high growing capacity of sensitive and resistant cancer cells. The metabolic models were also used to find single and combinatorial targets that reduce the cancer cells growth. Effect of proposed target genes on growth and oxidative phosphorylation of normal cells were determined to estimate drug side-effects. CONCLUSION The deletion results showed that although the cisplatin did not cause resistant cancer cells death, but it shifts the cancer cells to a more vulnerable metabolism.
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Affiliation(s)
- Ehsan Motamedian
- Biotechnology Group, Department of Chemical Engineering, Tarbiat Modares University, Tehran, Iran,Drug Design and Bioinformatics Group, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran
| | - Ghazaleh Ghavami
- Drug Design and Bioinformatics Group, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran,Eastern Mediterranean Health Genomics and Biotechnology Network (EMGEN), Tehran, Iran
| | - Soroush Sardari
- Drug Design and Bioinformatics Group, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran,*Corresponding author: Soroush Sardari. Drug Design and Bioinformatics Group, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, #69, Pasteur Avenue, Tehran, Iran and Eastern Mediterranean Health Genomics and Biotechnology Network (EMGEN), #69, Pasteur Avenue, Tehran 13164, Iran; Tel/Fax: +98-21-66954324;
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Tobalina L, Bargiela R, Pey J, Herbst FA, Lores I, Rojo D, Barbas C, Peláez AI, Sánchez J, von Bergen M, Seifert J, Ferrer M, Planes FJ. Context-specific metabolic network reconstruction of a naphthalene-degrading bacterial community guided by metaproteomic data. ACTA ACUST UNITED AC 2015; 31:1771-9. [PMID: 25618865 DOI: 10.1093/bioinformatics/btv036] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2014] [Accepted: 01/18/2015] [Indexed: 12/21/2022]
Abstract
MOTIVATION With the advent of meta-'omics' data, the use of metabolic networks for the functional analysis of microbial communities became possible. However, while network-based methods are widely developed for single organisms, their application to bacterial communities is currently limited. RESULTS Herein, we provide a novel, context-specific reconstruction procedure based on metaproteomic and taxonomic data. Without previous knowledge of a high-quality, genome-scale metabolic networks for each different member in a bacterial community, we propose a meta-network approach, where the expression levels and taxonomic assignments of proteins are used as the most relevant clues for inferring an active set of reactions. Our approach was applied to draft the context-specific metabolic networks of two different naphthalene-enriched communities derived from an anthropogenically influenced, polyaromatic hydrocarbon contaminated soil, with (CN2) or without (CN1) bio-stimulation. We were able to capture the overall functional differences between the two conditions at the metabolic level and predict an important activity for the fluorobenzoate degradation pathway in CN1 and for geraniol metabolism in CN2. Experimental validation was conducted, and good agreement with our computational predictions was observed. We also hypothesize different pathway organizations at the organismal level, which is relevant to disentangle the role of each member in the communities. The approach presented here can be easily transferred to the analysis of genomic, transcriptomic and metabolomic data.
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Affiliation(s)
- Luis Tobalina
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Rafael Bargiela
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Jon Pey
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Florian-Alexander Herbst
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Iván Lores
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - David Rojo
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Coral Barbas
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Ana I Peláez
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Jesús Sánchez
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Martin von Bergen
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Jana Seifert
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Manuel Ferrer
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Francisco J Planes
- CEIT and Tecnun (University of Navarra), San Sebastián, Spain, CSIC, Institute of Catalysis, Madrid, Spain, Helmholtz Centre for Environmental Research, Department of Proteomics, Leipzig, Germany, Área de Microbiología, IUBA, Universidad de Oviedo, Oviedo, Spain, Centro de Metabolómica y Bioanálisis (CEMBIO), Facultad de Farmacia, Universidad CEU San Pablo, Campus Monteprincipe, Boadilla del Monte, Madrid, Spain, Department of Metabolomics, UFZ-Helmholtz-Zentrum für Umweltforschung GmbH, Leipzig, Germany and Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
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Aurich MK, Paglia G, Rolfsson Ó, Hrafnsdóttir S, Magnúsdóttir M, Stefaniak MM, Palsson BØ, Fleming RMT, Thiele I. Prediction of intracellular metabolic states from extracellular metabolomic data. Metabolomics 2015; 11:603-619. [PMID: 25972769 PMCID: PMC4419158 DOI: 10.1007/s11306-014-0721-3] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2014] [Accepted: 07/31/2014] [Indexed: 11/03/2022]
Abstract
Metabolic models can provide a mechanistic framework to analyze information-rich omics data sets, and are increasingly being used to investigate metabolic alternations in human diseases. An expression of the altered metabolic pathway utilization is the selection of metabolites consumed and released by cells. However, methods for the inference of intracellular metabolic states from extracellular measurements in the context of metabolic models remain underdeveloped compared to methods for other omics data. Herein, we describe a workflow for such an integrative analysis emphasizing on extracellular metabolomics data. We demonstrate, using the lymphoblastic leukemia cell lines Molt-4 and CCRF-CEM, how our methods can reveal differences in cell metabolism. Our models explain metabolite uptake and secretion by predicting a more glycolytic phenotype for the CCRF-CEM model and a more oxidative phenotype for the Molt-4 model, which was supported by our experimental data. Gene expression analysis revealed altered expression of gene products at key regulatory steps in those central metabolic pathways, and literature query emphasized the role of these genes in cancer metabolism. Moreover, in silico gene knock-outs identified unique control points for each cell line model, e.g., phosphoglycerate dehydrogenase for the Molt-4 model. Thus, our workflow is well-suited to the characterization of cellular metabolic traits based on extracellular metabolomic data, and it allows the integration of multiple omics data sets into a cohesive picture based on a defined model context.
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Affiliation(s)
- Maike K. Aurich
- Center for Systems Biology, University of Iceland, Reykjavik, Iceland
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Giuseppe Paglia
- Center for Systems Biology, University of Iceland, Reykjavik, Iceland
| | - Óttar Rolfsson
- Center for Systems Biology, University of Iceland, Reykjavik, Iceland
| | | | | | - Magdalena M. Stefaniak
- School of Health Science, Faculty of Food Science and Nutrition, University of Iceland, Reykjavik, Iceland
| | - Bernhard Ø. Palsson
- Center for Systems Biology, University of Iceland, Reykjavik, Iceland
- Department of Bioengineering, University of California San Diego, La Jolla, CA USA
| | - Ronan M. T. Fleming
- Center for Systems Biology, University of Iceland, Reykjavik, Iceland
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
| | - Ines Thiele
- Center for Systems Biology, University of Iceland, Reykjavik, Iceland
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Campus Belval, Esch-Sur-Alzette, Luxembourg
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