51
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Biomedical applications of cell- and tissue-specific metabolic network models. J Biomed Inform 2017; 68:35-49. [DOI: 10.1016/j.jbi.2017.02.014] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2016] [Revised: 02/21/2017] [Accepted: 02/23/2017] [Indexed: 12/17/2022]
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52
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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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53
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Lim H, Gray P, Xie L, Poleksic A. Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem. Sci Rep 2016; 6:38860. [PMID: 27958331 PMCID: PMC5153628 DOI: 10.1038/srep38860] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2016] [Accepted: 11/15/2016] [Indexed: 12/18/2022] Open
Abstract
Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.
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Affiliation(s)
- Hansaim Lim
- Department of Computer Science, Hunter College, The City University of New York, New York, New York 10065, United States
| | - Paul Gray
- Department of Computer Science, University of Northern Iowa, Cedar Falls, Iowa 50614, United States
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, New York 10065, United States.,Ph.D. Program in Computer Science, Biochemistry and Biology, The Graduate Center, The City University of New York, New York, New York 10065, United States
| | - Aleksandar Poleksic
- Department of Computer Science, University of Northern Iowa, Cedar Falls, Iowa 50614, United States
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Abstract
Systems pharmacology aims to holistically understand mechanisms of drug actions to support drug discovery and clinical practice. Systems pharmacology modeling (SPM) is data driven. It integrates an exponentially growing amount of data at multiple scales (genetic, molecular, cellular, organismal, and environmental). The goal of SPM is to develop mechanistic or predictive multiscale models that are interpretable and actionable. The current explosions in genomics and other omics data, as well as the tremendous advances in big data technologies, have already enabled biologists to generate novel hypotheses and gain new knowledge through computational models of genome-wide, heterogeneous, and dynamic data sets. More work is needed to interpret and predict a drug response phenotype, which is dependent on many known and unknown factors. To gain a comprehensive understanding of drug actions, SPM requires close collaborations between domain experts from diverse fields and integration of heterogeneous models from biophysics, mathematics, statistics, machine learning, and semantic webs. This creates challenges in model management, model integration, model translation, and knowledge integration. In this review, we discuss several emergent issues in SPM and potential solutions using big data technology and analytics. The concurrent development of high-throughput techniques, cloud computing, data science, and the semantic web will likely allow SPM to be findable, accessible, interoperable, reusable, reliable, interpretable, and actionable.
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Affiliation(s)
- Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, NY 10065; .,The Graduate Center, The City University of New York, New York, NY 10016
| | - Eli J Draizen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894; .,Program in Bioinformatics, Boston University, Boston, Massachusetts 02215
| | - Philip E Bourne
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894; .,Office of the Director, National Institutes of Health, Bethesda, Maryland 20894
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55
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Identification of gefitinib off-targets using a structure-based systems biology approach; their validation with reverse docking and retrospective data mining. Sci Rep 2016; 6:33949. [PMID: 27653775 PMCID: PMC5032012 DOI: 10.1038/srep33949] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2016] [Accepted: 09/06/2016] [Indexed: 01/23/2023] Open
Abstract
Gefitinib, an EGFR tyrosine kinase inhibitor, is used as FDA approved drug in breast cancer and non-small cell lung cancer treatment. However, this drug has certain side effects and complications for which the underlying molecular mechanisms are not well understood. By systems biology based in silico analysis, we identified off-targets of gefitinib that might explain side effects of this drugs. The crystal structure of EGFR-gefitinib complex was used for binding pocket similarity searches on a druggable proteome database (Sc-PDB) by using IsoMIF Finder. The top 128 hits of putative off-targets were validated by reverse docking approach. The results showed that identified off-targets have efficient binding with gefitinib. The identified human specific off-targets were confirmed and further analyzed for their links with biological process and clinical disease pathways using retrospective studies and literature mining, respectively. Noticeably, many of the identified off-targets in this study were reported in previous high-throughput screenings. Interestingly, the present study reveals that gefitinib may have positive effects in reducing brain and bone metastasis, and may be useful in defining novel gefitinib based treatment regime. We propose that a system wide approach could be useful during new drug development and to minimize side effect of the prospective drug.
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56
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Kanji R, Sharma A, Bagler G. Phenotypic side effects prediction by optimizing correlation with chemical and target profiles of drugs. MOLECULAR BIOSYSTEMS 2016; 11:2900-6. [PMID: 26252576 DOI: 10.1039/c5mb00312a] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Despite technological progresses and improved understanding of biological systems, discovery of novel drugs is an inefficient, arduous and expensive process. Research and development cost of drugs is unreasonably high, largely attributed to the high attrition rate of candidate drugs due to adverse drug reactions. Computational methods for accurate prediction of drug side effects, rooted in empirical data of drugs, have the potential to enhance the efficacy of the drug discovery process. Identification of features critical for specifying side effects would facilitate efficient computational procedures for their prediction. We devised a generalized ordinary canonical correlation model for prediction of drug side effects based on their chemical properties as well as their target profiles. While the former is based on 2D and 3D chemical features, the latter enumerates a systems-level property of drugs. We find that the model incorporating chemical features outperforms that incorporating target profiles. Furthermore we identified the 2D and 3D chemical properties that yield best results, thereby implying their relevance in specifying adverse drug reactions.
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Affiliation(s)
- Rakesh Kanji
- Indian Institute of Technology Jodhpur, Ratanada, Jodhpur, India.
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57
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Evangelista W, Weir RL, Ellingson SR, Harris JB, Kapoor K, Smith JC, Baudry J. Ensemble-based docking: From hit discovery to metabolism and toxicity predictions. Bioorg Med Chem 2016; 24:4928-4935. [PMID: 27543390 DOI: 10.1016/j.bmc.2016.07.064] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 07/27/2016] [Accepted: 07/28/2016] [Indexed: 10/21/2022]
Abstract
This paper describes and illustrates the use of ensemble-based docking, i.e., using a collection of protein structures in docking calculations for hit discovery, the exploration of biochemical pathways and toxicity prediction of drug candidates. We describe the computational engineering work necessary to enable large ensemble docking campaigns on supercomputers. We show examples where ensemble-based docking has significantly increased the number and the diversity of validated drug candidates. Finally, we illustrate how ensemble-based docking can be extended beyond hit discovery and toward providing a structural basis for the prediction of metabolism and off-target binding relevant to pre-clinical and clinical trials.
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Affiliation(s)
- Wilfredo Evangelista
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN, United States; UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Rebecca L Weir
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN, United States; UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Sally R Ellingson
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Genome Science and Technology, University of Tennessee, Knoxville, TN, United States; Division of Biomedical Informatics, College of Medicine, University of Kentucky and Cancer Research Informatics, Markey Cancer Center, Lexington, KY, United States
| | - Jason B Harris
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Genome Science and Technology, University of Tennessee, Knoxville, TN, United States
| | - Karan Kapoor
- UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Genome Science and Technology, University of Tennessee, Knoxville, TN, United States
| | - Jeremy C Smith
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN, United States; UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Genome Science and Technology, University of Tennessee, Knoxville, TN, United States.
| | - Jerome Baudry
- Department of Biochemistry and Cellular and Molecular Biology, The University of Tennessee, Knoxville, TN, United States; UT/ORNL Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Genome Science and Technology, University of Tennessee, Knoxville, TN, United States.
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58
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Mih N, Brunk E, Bordbar A, Palsson BO. A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism. PLoS Comput Biol 2016; 12:e1005039. [PMID: 27467583 PMCID: PMC4965186 DOI: 10.1371/journal.pcbi.1005039] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Accepted: 06/27/2016] [Indexed: 12/31/2022] Open
Abstract
Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.
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Affiliation(s)
- Nathan Mih
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, California, United States of America
| | - Elizabeth Brunk
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
- * E-mail: (EB); (BOP)
| | - Aarash Bordbar
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
| | - Bernhard O. Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, California, United States of America
- Department of Pediatrics, University of California, San Diego, La Jolla, California, United States of America
- * E-mail: (EB); (BOP)
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59
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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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60
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Siragusa L, Luciani R, Borsari C, Ferrari S, Costi MP, Cruciani G, Spyrakis F. Comparing Drug Images and Repurposing Drugs with BioGPS and FLAPdock: The Thymidylate Synthase Case. ChemMedChem 2016; 11:1653-66. [PMID: 27404817 DOI: 10.1002/cmdc.201600121] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 06/08/2016] [Indexed: 12/14/2022]
Abstract
Repurposing and repositioning drugs has become a frequently pursued and successful strategy in the current era, as new chemical entities are increasingly difficult to find and get approved. Herein we report an integrated BioGPS/FLAPdock pipeline for rapid and effective off-target identification and drug repurposing. Our method is based on the structural and chemical properties of protein binding sites, that is, the ligand image, encoded in the GRID molecular interaction fields (MIFs). Protein similarity is disclosed through the BioGPS algorithm by measuring the pockets' overlap according to which pockets are clustered. Co-crystallized and known ligands can be cross-docked among similar targets, selected for subsequent in vitro binding experiments, and possibly improved for inhibitory potency. We used human thymidylate synthase (TS) as a test case and searched the entire RCSB Protein Data Bank (PDB) for similar target pockets. We chose casein kinase IIα as a control and tested a series of its inhibitors against the TS template. Ellagic acid and apigenin were identified as TS inhibitors, and various flavonoids were selected and synthesized in a second-round selection. The compounds were demonstrated to be active in the low-micromolar range.
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Affiliation(s)
- Lydia Siragusa
- Molecular Discovery Limited, 215 Marsh Road, Pinner Middlesex, London, HA5 5NE, UK
| | - Rosaria Luciani
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Chiara Borsari
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Stefania Ferrari
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Maria Paola Costi
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123, Perugia, Italy
| | - Francesca Spyrakis
- Department of Life Sciences, University of Modena and Reggio Emilia, Via Campi 103, 41125, Modena, Italy. .,Department of Food Science, University of Parma, Viale delle Scienze 17A, 43124, Parma, Italy.
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61
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Yankeelov TE, An G, Saut O, Luebeck EG, Popel AS, Ribba B, Vicini P, Zhou X, Weis JA, Ye K, Genin GM. Multi-scale Modeling in Clinical Oncology: Opportunities and Barriers to Success. Ann Biomed Eng 2016; 44:2626-41. [PMID: 27384942 DOI: 10.1007/s10439-016-1691-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2015] [Accepted: 06/29/2016] [Indexed: 12/11/2022]
Abstract
Hierarchical processes spanning several orders of magnitude of both space and time underlie nearly all cancers. Multi-scale statistical, mathematical, and computational modeling methods are central to designing, implementing and assessing treatment strategies that account for these hierarchies. The basic science underlying these modeling efforts is maturing into a new discipline that is close to influencing and facilitating clinical successes. The purpose of this review is to capture the state-of-the-art as well as the key barriers to success for multi-scale modeling in clinical oncology. We begin with a summary of the long-envisioned promise of multi-scale modeling in clinical oncology, including the synthesis of disparate data types into models that reveal underlying mechanisms and allow for experimental testing of hypotheses. We then evaluate the mathematical techniques employed most widely and present several examples illustrating their application as well as the current gap between pre-clinical and clinical applications. We conclude with a discussion of what we view to be the key challenges and opportunities for multi-scale modeling in clinical oncology.
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Affiliation(s)
- Thomas E Yankeelov
- Departments of Biomedical Engineering and Internal Medicine, Institute for Computational and Engineering Sciences, Cockrell School of Engineering, The University of Texas at Austin, 107 W. Dean Keeton, BME Building, 1 University Station, C0800, Austin, TX, 78712, USA.
| | - Gary An
- Department of Surgery and Computation Institute, The University of Chicago, Chicago, IL, USA
| | - Oliver Saut
- Institut de Mathématiques de Bordeaux, Université de Bordeaux and INRIA, Bordeaux, France
| | - E Georg Luebeck
- Program in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Aleksander S Popel
- Departments of Biomedical Engineering and Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Benjamin Ribba
- Pharma Research and Early Development, Clinical Pharmacology, F. Hoffmann-La Roche Ltd, Basel, Switzerland
| | - Paolo Vicini
- Clinical Pharmacology and DMPK, MedImmune, Gaithersburg, MD, USA
| | - Xiaobo Zhou
- Center for Bioinformatics and Systems Biology, Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USA
| | - Jared A Weis
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Kaiming Ye
- Department of Biomedical Engineering, Watson School of Engineering and Applied Science, Binghamton University, State University of New York, Binghamton, NY, USA
| | - Guy M Genin
- Departments of Mechanical Engineering and Materials Science, and Neurological Surgery, Washington University in St. Louis, St. Louis, MO, USA
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62
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Zhou K, Pedersen HK, Dawed AY, Pearson ER. Pharmacogenomics in diabetes mellitus: insights into drug action and drug discovery. Nat Rev Endocrinol 2016; 12:337-46. [PMID: 27062931 DOI: 10.1038/nrendo.2016.51] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Genomic studies have greatly advanced our understanding of the multifactorial aetiology of type 2 diabetes mellitus (T2DM) as well as the multiple subtypes of monogenic diabetes mellitus. In this Review, we discuss the existing pharmacogenetic evidence in both monogenic diabetes mellitus and T2DM. We highlight mechanistic insights from the study of adverse effects and the efficacy of antidiabetic drugs. The identification of extreme sulfonylurea sensitivity in patients with diabetes mellitus owing to heterozygous mutations in HNF1A represents a clear example of how pharmacogenetics can direct patient care. However, pharmacogenomic studies of response to antidiabetic drugs in T2DM has yet to be translated into clinical practice, although some moderate genetic effects have now been described that merit follow-up in trials in which patients are selected according to genotype. We also discuss how future pharmacogenomic findings could provide insights into treatment response in diabetes mellitus that, in addition to other areas of human genetics, facilitates drug discovery and drug development for T2DM.
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Affiliation(s)
- Kaixin Zhou
- School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
| | - Helle Krogh Pedersen
- Department of Systems Biology, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark
| | - Adem Y Dawed
- School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
| | - Ewan R Pearson
- School of Medicine, University of Dundee, Dundee, DD1 9SY, UK
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63
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Ryu JY, Kim HU, Lee SY. Reconstruction of genome-scale human metabolic models using omics data. Integr Biol (Camb) 2016; 7:859-68. [PMID: 25730289 DOI: 10.1039/c5ib00002e] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The impact of genome-scale human metabolic models on human systems biology and medical sciences is becoming greater, thanks to increasing volumes of model building platforms and publicly available omics data. The genome-scale human metabolic models started with Recon 1 in 2007, and have since been used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic models. Integration of tissue/cell type-specific omics data with the generic human metabolic models is the key step, and we discuss omics data and their integration methods to achieve this task. The initial version of the tissue/cell type-specific human metabolic models can further be computationally refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model.
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Affiliation(s)
- Jae Yong Ryu
- Metabolic and Biomolecular Engineering National Research Laboratory, Department of Chemical and Biomolecular Engineering (BK21 Plus Program), Center for Systems and Synthetic Biotechnology, Institute for the BioCentury, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 305-701, Republic of Korea.
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64
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Hodos RA, Kidd BA, Khader S, Readhead BP, Dudley JT. In silico methods for drug repurposing and pharmacology. WILEY INTERDISCIPLINARY REVIEWS. SYSTEMS BIOLOGY AND MEDICINE 2016; 8:186-210. [PMID: 27080087 PMCID: PMC4845762 DOI: 10.1002/wsbm.1337] [Citation(s) in RCA: 181] [Impact Index Per Article: 22.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Revised: 02/08/2016] [Accepted: 02/11/2016] [Indexed: 12/18/2022]
Abstract
Data in the biological, chemical, and clinical domains are accumulating at ever-increasing rates and have the potential to accelerate and inform drug development in new ways. Challenges and opportunities now lie in developing analytic tools to transform these often complex and heterogeneous data into testable hypotheses and actionable insights. This is the aim of computational pharmacology, which uses in silico techniques to better understand and predict how drugs affect biological systems, which can in turn improve clinical use, avoid unwanted side effects, and guide selection and development of better treatments. One exciting application of computational pharmacology is drug repurposing-finding new uses for existing drugs. Already yielding many promising candidates, this strategy has the potential to improve the efficiency of the drug development process and reach patient populations with previously unmet needs such as those with rare diseases. While current techniques in computational pharmacology and drug repurposing often focus on just a single data modality such as gene expression or drug-target interactions, we argue that methods such as matrix factorization that can integrate data within and across diverse data types have the potential to improve predictive performance and provide a fuller picture of a drug's pharmacological action. WIREs Syst Biol Med 2016, 8:186-210. doi: 10.1002/wsbm.1337 For further resources related to this article, please visit the WIREs website.
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Affiliation(s)
- Rachel A Hodos
- New York University and Icahn School of Medicine at Mt. Sinai, New York, NY
| | - Brian A Kidd
- Icahn School of Medicine at Mt. Sinai, New York, NY
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65
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Uhlén M, Hallström BM, Lindskog C, Mardinoglu A, Pontén F, Nielsen J. Transcriptomics resources of human tissues and organs. Mol Syst Biol 2016; 12:862. [PMID: 27044256 PMCID: PMC4848759 DOI: 10.15252/msb.20155865] [Citation(s) in RCA: 102] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Quantifying the differential expression of genes in various human organs, tissues, and cell types is vital to understand human physiology and disease. Recently, several large‐scale transcriptomics studies have analyzed the expression of protein‐coding genes across tissues. These datasets provide a framework for defining the molecular constituents of the human body as well as for generating comprehensive lists of proteins expressed across tissues or in a tissue‐restricted manner. Here, we review publicly available human transcriptome resources and discuss body‐wide data from independent genome‐wide transcriptome analyses of different tissues. Gene expression measurements from these independent datasets, generated using samples from fresh frozen surgical specimens and postmortem tissues, are consistent. Overall, the different genome‐wide analyses support a distribution in which many proteins are found in all tissues and relatively few in a tissue‐restricted manner. Moreover, we discuss the applications of publicly available omics data for building genome‐scale metabolic models, used for analyzing cell and tissue functions both in physiological and in disease contexts.
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Affiliation(s)
- Mathias Uhlén
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden Department of Proteomics, KTH - Royal Institute of Technology, Stockholm, Sweden Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark
| | - Björn M Hallström
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden Department of Proteomics, KTH - Royal Institute of Technology, Stockholm, Sweden
| | - Cecilia Lindskog
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Adil Mardinoglu
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
| | - Fredrik Pontén
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Jens Nielsen
- Science for Life Laboratory, KTH - Royal Institute of Technology, Stockholm, Sweden Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Hørsholm, Denmark Department of Biology and Biological Engineering, Chalmers University of Technology, Gothenburg, Sweden
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Anderson SJ, Feye KM, Schmidt-McCormack GR, Malovic E, Mlynarczyk GSA, Izbicki P, Arnold LF, Jefferson MA, de la Rosa BM, Wehrman RF, Luna KC, Hu HZ, Kondru NC, Kleinhenz MD, Smith JS, Manne S, Putra MR, Choudhary S, Massey N, Luo D, Berg CA, Acharya S, Sharma S, Kanuri SH, Lange JK, Carlson SA. Off-Target drug effects resulting in altered gene expression events with epigenetic and "Quasi-Epigenetic" origins. Pharmacol Res 2016; 107:229-233. [PMID: 27025785 DOI: 10.1016/j.phrs.2016.03.028] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2016] [Revised: 03/23/2016] [Accepted: 03/24/2016] [Indexed: 12/16/2022]
Abstract
This review synthesizes examples of pharmacological agents who have off-target effects of an epigenetic nature. We expand upon the paradigm of epigenetics to include "quasi-epigenetic" mechanisms. Quasi-epigenetics includes mechanisms of drugs acting upstream of epigenetic machinery or may themselves impact transcription factor regulation on a more global scale. We explore these avenues with four examples of conventional pharmaceuticals and their unintended, but not necessarily adverse, biological effects. The quasi-epigenetic drugs identified in this review include the use of beta-lactam antibiotics to alter glutamate receptor activity and the action of cyclosporine on multiple transcription factors. In addition, we report on more canonical epigenome changes associated with pharmacological agents such as lithium impacting autophagy of aberrant proteins, and opioid drugs whose chronic use increases the expression of genes associated with addictive phenotypes. By expanding our appreciation of transcriptomic regulation and the effects these drugs have on the epigenome, it is possible to enhance therapeutic applications by exploiting off-target effects and even repurposing established pharmaceuticals. That is, exploration of "pharmacoepigenetic" mechanisms can expand the breadth of the useful activity of a drug beyond the traditional drug targets such as receptors and enzymes.
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Affiliation(s)
- Stephen J Anderson
- Department of Psychology, Iowa State University College of Liberal Arts and Sciences, Ames, IA 50011, United States; Neuroscience Interdepartmental Program, Iowa State University, Ames, IA 50011, United States
| | - Kristina M Feye
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Garrett R Schmidt-McCormack
- Neuroscience Interdepartmental Program, Iowa State University, Ames, IA 50011, United States; Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Emir Malovic
- Neuroscience Interdepartmental Program, Iowa State University, Ames, IA 50011, United States
| | - Gregory S A Mlynarczyk
- Neuroscience Interdepartmental Program, Iowa State University, Ames, IA 50011, United States; Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Patricia Izbicki
- Neuroscience Interdepartmental Program, Iowa State University, Ames, IA 50011, United States
| | - Larissa F Arnold
- Department of Psychology, Iowa State University College of Liberal Arts and Sciences, Ames, IA 50011, United States; Neuroscience Interdepartmental Program, Iowa State University, Ames, IA 50011, United States
| | - Matthew A Jefferson
- Department of Kinesiology, Iowa State University College of Liberal Arts and Sciences, Ames, IA 50011, United States
| | - Bierlein M de la Rosa
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Rita F Wehrman
- Department of Veterinary Clinical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - K C Luna
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Hilary Z Hu
- Neuroscience Interdepartmental Program, Iowa State University, Ames, IA 50011, United States
| | - Naveen C Kondru
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Michael D Kleinhenz
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Joe S Smith
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Sireesha Manne
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Marson R Putra
- Neuroscience Interdepartmental Program, Iowa State University, Ames, IA 50011, United States
| | - Shivani Choudhary
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Nyzil Massey
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Diou Luo
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Carrie A Berg
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Sreemoyee Acharya
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Shaunik Sharma
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Sri Harsha Kanuri
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States
| | - Jennifer K Lange
- Department of Kinesiology, Iowa State University College of Liberal Arts and Sciences, Ames, IA 50011, United States
| | - Steve A Carlson
- Department of Biomedical Sciences, Iowa State University College of Veterinary Medicine, Ames, IA 50011, United States.
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Zhao Z, Xie L, Xie L, Bourne PE. Delineation of Polypharmacology across the Human Structural Kinome Using a Functional Site Interaction Fingerprint Approach. J Med Chem 2016; 59:4326-41. [PMID: 26929980 DOI: 10.1021/acs.jmedchem.5b02041] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Targeted polypharmacology of kinases has emerged as a promising strategy to design efficient and safe therapeutics. Here, we perform a systematic study of kinase-ligand binding modes for the human structural kinome at scale (208 kinases, 1777 unique ligands, and their complexes) by integrating chemical genomics and structural genomics data and by introducing a functional site interaction fingerprint (Fs-IFP) method. New insights into kinase-ligand binding modes were obtained. We establish relationships between the features of binding modes, the ligands, and the binding pockets, respectively. We also drive the intrinsic binding specificity and which correlation with amino acid conservation. Third, we explore the landscape of the binding modes and highlight the regions of "selectivity pocket" and "selectivity entrance". Finally, we demonstrate that Fs-IFP similarity is directly correlated to the experimentally determined profile. These improve our understanding of kinase-ligand interactions and contribute to the design of novel polypharmacological therapies targeting kinases.
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Affiliation(s)
- Zheng Zhao
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health , Bethesda, Maryland 20894, United States
| | - Li Xie
- Scripps Ranch , San Diego, California 92131, United States
| | - Lei Xie
- Department of Computer Science, Hunter College, The City University of New York , New York, New York 10065, United States.,The Graduate Center, The City University of New York , New York, New York 10016, United States
| | - Philip E Bourne
- Office of the Director, National Institutes of Health, Bethesda, Maryland 20892, United States
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68
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Brunk E, Mih N, Monk J, Zhang Z, O’Brien EJ, Bliven SE, Chen K, Chang RL, Bourne PE, Palsson BO. Systems biology of the structural proteome. BMC SYSTEMS BIOLOGY 2016; 10:26. [PMID: 26969117 PMCID: PMC4787049 DOI: 10.1186/s12918-016-0271-6] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2015] [Accepted: 02/16/2016] [Indexed: 12/19/2022]
Abstract
BACKGROUND The success of genome-scale models (GEMs) can be attributed to the high-quality, bottom-up reconstructions of metabolic, protein synthesis, and transcriptional regulatory networks on an organism-specific basis. Such reconstructions are biochemically, genetically, and genomically structured knowledge bases that can be converted into a mathematical format to enable a myriad of computational biological studies. In recent years, genome-scale reconstructions have been extended to include protein structural information, which has opened up new vistas in systems biology research and empowered applications in structural systems biology and systems pharmacology. RESULTS Here, we present the generation, application, and dissemination of genome-scale models with protein structures (GEM-PRO) for Escherichia coli and Thermotoga maritima. We show the utility of integrating molecular scale analyses with systems biology approaches by discussing several comparative analyses on the temperature dependence of growth, the distribution of protein fold families, substrate specificity, and characteristic features of whole cell proteomes. Finally, to aid in the grand challenge of big data to knowledge, we provide several explicit tutorials of how protein-related information can be linked to genome-scale models in a public GitHub repository ( https://github.com/SBRG/GEMPro/tree/master/GEMPro_recon/). CONCLUSIONS Translating genome-scale, protein-related information to structured data in the format of a GEM provides a direct mapping of gene to gene-product to protein structure to biochemical reaction to network states to phenotypic function. Integration of molecular-level details of individual proteins, such as their physical, chemical, and structural properties, further expands the description of biochemical network-level properties, and can ultimately influence how to model and predict whole cell phenotypes as well as perform comparative systems biology approaches to study differences between organisms. GEM-PRO offers insight into the physical embodiment of an organism's genotype, and its use in this comparative framework enables exploration of adaptive strategies for these organisms, opening the door to many new lines of research. With these provided tools, tutorials, and background, the reader will be in a position to run GEM-PRO for their own purposes.
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Affiliation(s)
- Elizabeth Brunk
- />Department of Bioengineering, University of California, La Jolla, San Diego, CA 92093 USA
- />Joint BioEnergy Institute, Emeryville, CA 94608 USA
| | - Nathan Mih
- />Bioinformatics and Systems Biology Program, University of California, La Jolla, San Diego, CA 92093 USA
| | - Jonathan Monk
- />Department of Bioengineering, University of California, La Jolla, San Diego, CA 92093 USA
| | - Zhen Zhang
- />Department of Bioengineering, University of California, La Jolla, San Diego, CA 92093 USA
| | - Edward J. O’Brien
- />Department of Bioengineering, University of California, La Jolla, San Diego, CA 92093 USA
| | - Spencer E. Bliven
- />Bioinformatics and Systems Biology Program, University of California, La Jolla, San Diego, CA 92093 USA
- />National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894 USA
| | - Ke Chen
- />Department of Bioengineering, University of California, La Jolla, San Diego, CA 92093 USA
| | - Roger L. Chang
- />Department of Systems Biology, Harvard Medical School, Boston, MA 02115 USA
| | - Philip E. Bourne
- />Office of the Director, National Institutes of Health, Bethesda, MD 20894 USA
| | - Bernhard O. Palsson
- />Department of Bioengineering, University of California, La Jolla, San Diego, CA 92093 USA
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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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70
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Rios FJ, Lopes RA, Neves KB, Camargo LL, Montezano AC, Touyz RM. Off-Target Vascular Effects of Cholesteryl Ester Transfer Protein Inhibitors Involve Redox-Sensitive and Signal Transducer and Activator of Transcription 3-Dependent Pathways. ACTA ACUST UNITED AC 2016; 357:415-22. [DOI: 10.1124/jpet.115.230748] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2015] [Accepted: 03/02/2016] [Indexed: 01/20/2023]
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71
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Sohrabi-Jahromi S, Marashi SA, Kalantari S. A kidney-specific genome-scale metabolic network model for analyzing focal segmental glomerulosclerosis. Mamm Genome 2016; 27:158-67. [PMID: 26923795 DOI: 10.1007/s00335-016-9622-2] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Accepted: 01/31/2016] [Indexed: 01/02/2023]
Abstract
Focal Segmental Glomerulosclerosis (FSGS) is a type of nephrotic syndrome which accounts for 20 and 40 % of such cases in children and adults, respectively. The high prevalence of FSGS makes it the most common primary glomerular disorder causing end-stage renal disease. Although the pathogenesis of this disorder has been widely investigated, the exact mechanism underlying this disease is still to be discovered. Current therapies seek to stop the progression of FSGS and often fail to cure the patients since progression to end-stage renal failure is usually inevitable. In the present work, we use a kidney-specific metabolic network model to study FSGS. The model was obtained by merging two previously published kidney-specific metabolic network models. The validity of the new model was checked by comparing the inactivating reaction genes identified in silico to the list of kidney disease implicated genes. To model the disease state, we used a complete list of FSGS metabolic biomarkers extracted from transcriptome and proteome profiling of patients as well as genetic deficiencies known to cause FSGS. We observed that some specific pathways including chondroitin sulfate degradation, eicosanoid metabolism, keratan sulfate biosynthesis, vitamin B6 metabolism, and amino acid metabolism tend to show variations in FSGS model compared to healthy kidney. Furthermore, we computationally searched for the potential drug targets that can revert the diseased metabolic state to the healthy state. Interestingly, only one drug target, N-acetylgalactosaminidase, was found whose inhibition could alter cellular metabolism towards healthy state.
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Affiliation(s)
| | - Sayed-Amir Marashi
- Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran.
| | - Shiva Kalantari
- Chronic Kidney Disease Research Center (CKDRC), Shahid Beheshti University of Medical Sciences, Tehran, Iran.
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72
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Zhao Z, Martin C, Fan R, Bourne PE, Xie L. Drug repurposing to target Ebola virus replication and virulence using structural systems pharmacology. BMC Bioinformatics 2016; 17:90. [PMID: 26887654 PMCID: PMC4757998 DOI: 10.1186/s12859-016-0941-9] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2015] [Accepted: 02/10/2016] [Indexed: 01/09/2023] Open
Abstract
Background The recent outbreak of Ebola has been cited as the largest in history. Despite this global health crisis, few drugs are available to efficiently treat Ebola infections. Drug repurposing provides a potentially efficient solution to accelerating the development of therapeutic approaches in response to Ebola outbreak. To identify such candidates, we use an integrated structural systems pharmacology pipeline which combines proteome-scale ligand binding site comparison, protein-ligand docking, and Molecular Dynamics (MD) simulation. Results One thousand seven hundred and sixty-six FDA-approved drugs and 259 experimental drugs were screened to identify those with the potential to inhibit the replication and virulence of Ebola, and to determine the binding modes with their respective targets. Initial screening has identified a number of promising hits. Notably, Indinavir; an HIV protease inhibitor, may be effective in reducing the virulence of Ebola. Additionally, an antifungal (Sinefungin) and several anti-viral drugs (e.g. Maraviroc, Abacavir, Telbivudine, and Cidofovir) may inhibit Ebola RNA-directed RNA polymerase through targeting the MTase domain. Conclusions Identification of safe drug candidates is a crucial first step toward the determination of timely and effective therapeutic approaches to address and mitigate the impact of the Ebola global crisis and future outbreaks of pathogenic diseases. Further in vitro and in vivo testing to evaluate the anti-Ebola activity of these drugs is warranted. Electronic supplementary material The online version of this article (doi:10.1186/s12859-016-0941-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Zheng Zhao
- High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei, P. R. China.,National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Che Martin
- The Graduate Center, The City University of New York, New York, USA
| | - Raymond Fan
- Department of Chemistry, Hunter College, The City University of New York, New York, USA
| | - Philip E Bourne
- Office of the Director, National Institutes of Health, Bethesda, MD, USA
| | - Lei Xie
- The Graduate Center, The City University of New York, New York, USA. .,Department of Computer Science, Hunter College, The City University of New York, New York, USA.
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73
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Hart T, Dider S, Han W, Xu H, Zhao Z, Xie L. Toward Repurposing Metformin as a Precision Anti-Cancer Therapy Using Structural Systems Pharmacology. Sci Rep 2016; 6:20441. [PMID: 26841718 PMCID: PMC4740793 DOI: 10.1038/srep20441] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2015] [Accepted: 01/04/2016] [Indexed: 01/12/2023] Open
Abstract
Metformin, a drug prescribed to treat type-2 diabetes, exhibits anti-cancer effects in a portion of patients, but the direct molecular and genetic interactions leading to this pleiotropic effect have not yet been fully explored. To repurpose metformin as a precision anti-cancer therapy, we have developed a novel structural systems pharmacology approach to elucidate metformin's molecular basis and genetic biomarkers of action. We integrated structural proteome-scale drug target identification with network biology analysis by combining structural genomic, functional genomic, and interactomic data. Through searching the human structural proteome, we identified twenty putative metformin binding targets and their interaction models. We experimentally verified the interactions between metformin and our top-ranked kinase targets. Notably, kinases, particularly SGK1 and EGFR were identified as key molecular targets of metformin. Subsequently, we linked these putative binding targets to genes that do not directly bind to metformin but whose expressions are altered by metformin through protein-protein interactions, and identified network biomarkers of phenotypic response of metformin. The molecular targets and the key nodes in genetic networks are largely consistent with the existing experimental evidence. Their interactions can be affected by the observed cancer mutations. This study will shed new light into repurposing metformin for safe, effective, personalized therapies.
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Affiliation(s)
- Thomas Hart
- The Rockefeller University, New York, New York, United States of America
- Department of Biological Sciences, Hunter College, The City University of New York, New York, New York, United States of America
| | - Shihab Dider
- Department of Biological Sciences, Hunter College, The City University of New York, New York, New York, United States of America
| | - Weiwei Han
- The Key Laboratory for Molecular Enzymology and Engineering, Ministry of Education Jilin University, Changchun, P. R. China
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Zhongming Zhao
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, Tennessee, United States of America
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, United States of America
| | - Lei Xie
- Ph.D. Program in Computer Science, Biology, and Biochemistry, The Graduate Center, The City University of New York, New York, New York, United States of America
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
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Hart T, Xie L. Providing data science support for systems pharmacology and its implications to drug discovery. Expert Opin Drug Discov 2016; 11:241-56. [PMID: 26689499 DOI: 10.1517/17460441.2016.1135126] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION The conventional one-drug-one-target-one-disease drug discovery process has been less successful in tracking multi-genic, multi-faceted complex diseases. Systems pharmacology has emerged as a new discipline to tackle the current challenges in drug discovery. The goal of systems pharmacology is to transform huge, heterogeneous, and dynamic biological and clinical data into interpretable and actionable mechanistic models for decision making in drug discovery and patient treatment. Thus, big data technology and data science will play an essential role in systems pharmacology. AREAS COVERED This paper critically reviews the impact of three fundamental concepts of data science on systems pharmacology: similarity inference, overfitting avoidance, and disentangling causality from correlation. The authors then discuss recent advances and future directions in applying the three concepts of data science to drug discovery, with a focus on proteome-wide context-specific quantitative drug target deconvolution and personalized adverse drug reaction prediction. EXPERT OPINION Data science will facilitate reducing the complexity of systems pharmacology modeling, detecting hidden correlations between complex data sets, and distinguishing causation from correlation. The power of data science can only be fully realized when integrated with mechanism-based multi-scale modeling that explicitly takes into account the hierarchical organization of biological systems from nucleic acid to proteins, to molecular interaction networks, to cells, to tissues, to patients, and to populations.
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Affiliation(s)
- Thomas Hart
- a The Rockefeller University , New York , NY , USA.,b Department of Biological Sciences, Hunter College , The City University of New York , New York , NY , USA
| | - Lei Xie
- c Department of Computer Science, Hunter College , The City University of New York , New York , NY , USA.,d The Graduate Center , The City University of New York , New York , NY , USA
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75
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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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Aziz RK, Khaw VL, Monk JM, Brunk E, Lewis R, Loh SI, Mishra A, Nagle AA, Satyanarayana C, Dhakshinamoorthy S, Luche M, Kitchen DB, Andrews KA, Palsson BØ, Charusanti P. Model-driven discovery of synergistic inhibitors against E. coli and S. enterica serovar Typhimurium targeting a novel synthetic lethal pair, aldA and prpC. Front Microbiol 2015; 6:958. [PMID: 26441892 PMCID: PMC4585216 DOI: 10.3389/fmicb.2015.00958] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2015] [Accepted: 08/28/2015] [Indexed: 11/13/2022] Open
Abstract
Mathematical models of biochemical networks form a cornerstone of bacterial systems biology. Inconsistencies between simulation output and experimental data point to gaps in knowledge about the fundamental biology of the organism. One such inconsistency centers on the gene aldA in Escherichia coli: it is essential in a computational model of E. coli metabolism, but experimentally it is not. Here, we reconcile this disparity by providing evidence that aldA and prpC form a synthetic lethal pair, as the double knockout could only be created through complementation with a plasmid-borne copy of aldA. Moreover, virtual and biological screening against the two proteins led to a set of compounds that inhibited the growth of E. coli and Salmonella enterica serovar Typhimurium synergistically at 100-200 μM individual concentrations. These results highlight the power of metabolic models to drive basic biological discovery and their potential use to discover new combination antibiotics.
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Affiliation(s)
- Ramy K Aziz
- Department of Microbiology and Immunology, Faculty of Pharmacy, Cairo University Cairo, Egypt ; Department of Bioengineering, University of California, San Diego La Jolla, CA, USA
| | - Valerie L Khaw
- Department of Bioengineering, University of California, San Diego La Jolla, CA, USA
| | - Jonathan M Monk
- Department of Bioengineering, University of California, San Diego La Jolla, CA, USA
| | - Elizabeth Brunk
- Department of Bioengineering, University of California, San Diego La Jolla, CA, USA
| | - Robert Lewis
- Computer-Aided Drug Discovery, Albany Molecular Research, Inc., Albany NY, USA
| | - Suh I Loh
- Biology and Pharmacology, Albany Molecular Research Singapore Research Centre, Pte. Ltd., Singapore Singapore
| | - Arti Mishra
- Biology and Pharmacology, Albany Molecular Research Singapore Research Centre, Pte. Ltd., Singapore Singapore
| | - Amrita A Nagle
- Biology and Pharmacology, Albany Molecular Research Singapore Research Centre, Pte. Ltd., Singapore Singapore
| | - Chitkala Satyanarayana
- Biology and Pharmacology, Albany Molecular Research Singapore Research Centre, Pte. Ltd., Singapore Singapore
| | | | - Michele Luche
- Computer-Aided Drug Discovery, Albany Molecular Research, Inc., Albany NY, USA
| | - Douglas B Kitchen
- Computer-Aided Drug Discovery, Albany Molecular Research, Inc., Albany NY, USA
| | - Kathleen A Andrews
- Department of Bioengineering, University of California, San Diego La Jolla, CA, USA
| | - Bernhard Ø Palsson
- Department of Bioengineering, University of California, San Diego La Jolla, CA, USA
| | - Pep Charusanti
- Department of Bioengineering, University of California, San Diego La Jolla, CA, USA ; The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark Hørsholm, Denmark
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Naveed H, Hameed US, Harrus D, Bourguet W, Arold ST, Gao X. An integrated structure- and system-based framework to identify new targets of metabolites and known drugs. Bioinformatics 2015; 31:3922-9. [PMID: 26286808 PMCID: PMC4673972 DOI: 10.1093/bioinformatics/btv477] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2015] [Accepted: 08/08/2015] [Indexed: 02/07/2023] Open
Abstract
Motivation: The inherent promiscuity of small molecules towards protein targets impedes our understanding of healthy versus diseased metabolism. This promiscuity also poses a challenge for the pharmaceutical industry as identifying all protein targets is important to assess (side) effects and repositioning opportunities for a drug. Results: Here, we present a novel integrated structure- and system-based approach of drug-target prediction (iDTP) to enable the large-scale discovery of new targets for small molecules, such as pharmaceutical drugs, co-factors and metabolites (collectively called ‘drugs’). For a given drug, our method uses sequence order–independent structure alignment, hierarchical clustering and probabilistic sequence similarity to construct a probabilistic pocket ensemble (PPE) that captures promiscuous structural features of different binding sites on known targets. A drug’s PPE is combined with an approximation of its delivery profile to reduce false positives. In our cross-validation study, we use iDTP to predict the known targets of 11 drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the peroxisome proliferator-activated receptor gamma and the oncogene B-cell lymphoma 2, were successfully validated through in vitro binding experiments. Our method is broadly applicable for the prediction of protein-small molecule interactions with several novel applications to biological research and drug development. Availability and implementation: The program, datasets and results are freely available to academic users at http://sfb.kaust.edu.sa/Pages/Software.aspx. Contact:xin.gao@kaust.edu.sa and stefan.arold@kaust.edu.sa Supplementary information:Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Hammad Naveed
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center
| | - Umar S Hameed
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Deborah Harrus
- Inserm U1054, Centre de Biochimie Structurale and CNRS UMR5048, Universités Montpellier 1 & 2, Montpellier, France
| | - William Bourguet
- Inserm U1054, Centre de Biochimie Structurale and CNRS UMR5048, Universités Montpellier 1 & 2, Montpellier, France
| | - Stefan T Arold
- Computational Bioscience Research Center, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Xin Gao
- Computer, Electrical and Mathematical Sciences and Engineering Division, Computational Bioscience Research Center
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78
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Sperisen P, Cominetti O, Martin FPJ. Longitudinal omics modeling and integration in clinical metabonomics research: challenges in childhood metabolic health research. Front Mol Biosci 2015; 2:44. [PMID: 26301225 PMCID: PMC4525019 DOI: 10.3389/fmolb.2015.00044] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Accepted: 07/20/2015] [Indexed: 12/14/2022] Open
Abstract
Systems biology is an important approach for deciphering the complex processes in health maintenance and the etiology of metabolic diseases. Such integrative methodologies will help better understand the molecular mechanisms involved in growth and development throughout childhood, and consequently will result in new insights about metabolic and nutritional requirements of infants, children and adults. To achieve this, a better understanding of the physiological processes at anthropometric, cellular and molecular level for any given individual is needed. In this respect, novel omics technologies in combination with sophisticated data modeling techniques are key. Due to the highly complex network of influential factors determining individual trajectories, it becomes imperative to develop proper tools and solutions that will comprehensively model biological information related to growth and maturation of our body functions. The aim of this review and perspective is to evaluate, succinctly, promising data analysis approaches to enable data integration for clinical research, with an emphasis on the longitudinal component. Approaches based on empirical and mechanistic modeling of omics data are essential to leverage findings from high dimensional omics datasets and enable biological interpretation and clinical translation. On the one hand, empirical methods, which provide quantitative descriptions of patterns in the data, are mostly used for exploring and mining datasets. On the other hand, mechanistic models are based on an understanding of the behavior of a system's components and condense information about the known functions, allowing robust and reliable analyses to be performed by bioinformatics pipelines and similar tools. Herein, we will illustrate current examples, challenges and perspectives in the applications of empirical and mechanistic modeling in the context of childhood metabolic health research.
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Affiliation(s)
- Peter Sperisen
- GI Health and Microbiome Department, Nestle Institute of Health Sciences Lausanne, Switzerland
| | - Ornella Cominetti
- Molecular Biomarkers Department, Nestle Institute of Health Sciences Lausanne, Switzerland
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79
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O'Brien EJ, Monk JM, Palsson BO. Using Genome-scale Models to Predict Biological Capabilities. Cell 2015; 161:971-987. [PMID: 26000478 DOI: 10.1016/j.cell.2015.05.019] [Citation(s) in RCA: 439] [Impact Index Per Article: 48.8] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2014] [Indexed: 11/29/2022]
Abstract
Constraint-based reconstruction and analysis (COBRA) methods at the genome scale have been under development since the first whole-genome sequences appeared in the mid-1990s. A few years ago, this approach began to demonstrate the ability to predict a range of cellular functions, including cellular growth capabilities on various substrates and the effect of gene knockouts at the genome scale. Thus, much interest has developed in understanding and applying these methods to areas such as metabolic engineering, antibiotic design, and organismal and enzyme evolution. This Primer will get you started.
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Affiliation(s)
- Edward J O'Brien
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Jonathan M Monk
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of NanoEngineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bernhard O Palsson
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby 2800, Denmark.
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80
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Liu T, Altman RB. Relating Essential Proteins to Drug Side-Effects Using Canonical Component Analysis: A Structure-Based Approach. J Chem Inf Model 2015; 55:1483-94. [PMID: 26121262 DOI: 10.1021/acs.jcim.5b00030] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
The molecular mechanism of many drug side-effects is unknown and difficult to predict. Previous methods for explaining side-effects have focused on known drug targets and their pathways. However, low affinity binding to proteins that are not usually considered drug targets may also drive side-effects. In order to assess these alternative targets, we used the 3D structures of 563 essential human proteins systematically to predict binding to 216 drugs. We first benchmarked our affinity predictions with available experimental data. We then combined singular value decomposition and canonical component analysis (SVD-CCA) to predict side-effects based on these novel target profiles. Our method predicts side-effects with good accuracy (average AUC: 0.82 for side effects present in <50% of drug labels). We also noted that side-effect frequency is the most important feature for prediction and can confound efforts at elucidating mechanism; our method allows us to remove the contribution of frequency and isolate novel biological signals. In particular, our analysis produces 2768 triplet associations between 50 essential proteins, 99 drugs, and 77 side-effects. Although experimental validation is difficult because many of our essential proteins do not have validated assays, we nevertheless attempted to validate a subset of these associations using experimental assay data. Our focus on essential proteins allows us to find potential associations that would likely be missed if we used recognized drug targets. Our associations provide novel insights about the molecular mechanisms of drug side-effects and highlight the need for expanded experimental efforts to investigate drug binding to proteins more broadly.
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Affiliation(s)
- Tianyun Liu
- †Department of Genetics, Stanford University, Stanford, California 94305, United States
| | - Russ B Altman
- ‡Department of Genetics and Department of Bioengineering, Stanford University, Stanford, California 94305, United States
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81
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Belle A, Thiagarajan R, Soroushmehr SMR, Navidi F, Beard DA, Najarian K. Big Data Analytics in Healthcare. BIOMED RESEARCH INTERNATIONAL 2015; 2015:370194. [PMID: 26229957 PMCID: PMC4503556 DOI: 10.1155/2015/370194] [Citation(s) in RCA: 119] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2015] [Revised: 05/26/2015] [Accepted: 06/16/2015] [Indexed: 02/06/2023]
Abstract
The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.
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Affiliation(s)
- Ashwin Belle
- Emergency Medicine Department, University of Michigan, Ann Arbor, MI 48109, USA
- University of Michigan Center for Integrative Research in Critical Care (MCIRCC), Ann Arbor, MI 48109, USA
| | - Raghuram Thiagarajan
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - S. M. Reza Soroushmehr
- Emergency Medicine Department, University of Michigan, Ann Arbor, MI 48109, USA
- University of Michigan Center for Integrative Research in Critical Care (MCIRCC), Ann Arbor, MI 48109, USA
| | - Fatemeh Navidi
- Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Daniel A. Beard
- University of Michigan Center for Integrative Research in Critical Care (MCIRCC), Ann Arbor, MI 48109, USA
- Department of Molecular and Integrative Physiology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Kayvan Najarian
- Emergency Medicine Department, University of Michigan, Ann Arbor, MI 48109, USA
- University of Michigan Center for Integrative Research in Critical Care (MCIRCC), Ann Arbor, MI 48109, USA
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82
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Brunk E, Rothlisberger U. Mixed Quantum Mechanical/Molecular Mechanical Molecular Dynamics Simulations of Biological Systems in Ground and Electronically Excited States. Chem Rev 2015; 115:6217-63. [PMID: 25880693 DOI: 10.1021/cr500628b] [Citation(s) in RCA: 301] [Impact Index Per Article: 33.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Elizabeth Brunk
- †Laboratory of Computational Chemistry and Biochemistry, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.,‡Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, California 94618, United States
| | - Ursula Rothlisberger
- †Laboratory of Computational Chemistry and Biochemistry, Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland.,§National Competence Center of Research (NCCR) MARVEL-Materials' Revolution: Computational Design and Discovery of Novel Materials, 1015 Lausanne, Switzerland
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83
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Network-based inference methods for drug repositioning. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:130620. [PMID: 25969690 PMCID: PMC4410541 DOI: 10.1155/2015/130620] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/08/2014] [Revised: 03/18/2015] [Accepted: 03/24/2015] [Indexed: 12/31/2022]
Abstract
Mining potential drug-disease associations can speed up drug repositioning for pharmaceutical companies. Previous computational strategies focused on prior biological information for association inference. However, such information may not be comprehensively available and may contain errors. Different from previous research, two inference methods, ProbS and HeatS, were introduced in this paper to predict direct drug-disease associations based only on the basic network topology measure. Bipartite network topology was used to prioritize the potentially indicated diseases for a drug. Experimental results showed that both methods can receive reliable prediction performance and achieve AUC values of 0.9192 and 0.9079, respectively. Case studies on real drugs indicated that some of the strongly predicted associations were confirmed by results in the Comparative Toxicogenomics Database (CTD). Finally, a comprehensive prediction of drug-disease associations enables us to suggest many new drug indications for further studies.
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84
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Ravikrishnan A, Raman K. Critical assessment of genome-scale metabolic networks: the need for a unified standard. Brief Bioinform 2015; 16:1057-68. [PMID: 25725218 DOI: 10.1093/bib/bbv003] [Citation(s) in RCA: 47] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2014] [Indexed: 12/17/2022] Open
Abstract
Genome-scale metabolic networks have been reconstructed for several organisms. These metabolic networks provide detailed information about the metabolism inside the cells, coupled with the genomic, proteomic and thermodynamic information. These networks are widely simulated using 'constraint-based' modelling techniques and find applications ranging from strain improvement for metabolic engineering to prediction of drug targets in pathogenic organisms. Components of these metabolic networks are represented in multiple file formats and also using different markup languages, with varying levels of annotations; this leads to inconsistencies and increases the complexities in comparing and analysing reconstructions on multiple platforms. In this work, we critically examine nearly 100 published genome-scale metabolic networks and their corresponding constraint-based models and discuss various issues with respect to model quality. One of the major concerns is the lack of annotations using standard identifiers that can uniquely describe several components such as metabolites, genes, proteins and reactions. We also find that many models do not have complete information regarding constraints on reactions fluxes and objective functions for carrying out simulations. Overall, our analysis highlights the need for a widely acceptable standard for representing constraint-based models. A rigorous standard can help in streamlining the process of reconstruction and improve the quality of reconstructed metabolic models.
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85
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Rios FJ, Neves KB, Nguyen Dinh Cat A, Even S, Palacios R, Montezano AC, Touyz RM. Cholesteryl ester-transfer protein inhibitors stimulate aldosterone biosynthesis in adipocytes through Nox-dependent processes. J Pharmacol Exp Ther 2015; 353:27-34. [PMID: 25617244 DOI: 10.1124/jpet.114.221002] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Hyperaldosteronism and hypertension were unexpected side effects observed in trials of torcetrapib, a cholesteryl ester-transfer protein (CETP) inhibitor that increases high-density lipoprotein. Given that CETP inhibitors are lipid soluble, accumulate in adipose tissue, and have binding sites for proteins involved in adipogenesis, and that adipocytes are a source of aldosterone, we questioned whether CETP inhibitors (torcetrapib, dalcetrapib, and anacetrapib) influence aldosterone production by adipocytes. Studies were performed using human adipocytes (SW872), which express CETP, and mouse adipocytes (3T3-L1), which lack the CETP gene. Torcetrapib, dalcetrapib, and anacetrapib increased expression of CYP11B2, CYP11B1, and steroidogenic acute regulatory protein, enzymes involved in mineralocorticoid and glucocorticoid generation. These effects were associated with increased reactive oxygen species formation. Torcetrapib, dalcetrapib, and anacetrapib upregulated signal transducer and activator of transcription 3 (STAT3) and peroxisome proliferation-activated receptor-γ, important in adipogenesis, but only torcetrapib stimulated production of chemerin, a proinflammatory adipokine. To determine mechanisms whereby CETP inhibitors mediate effects, cells were pretreated with inhibitors of Nox1/Nox4 [GKT137831; 2-(2-chlorophenyl)-4-[3-(dimethylamino)phenyl]-5-methyl-1H-pyrazolo[4,3-c]pyridine-3,6(2H,5H)-dione], Nox1 (ML171 [2-acetylphenothiazine]), mitochondria (rotenone), and STAT3 (S3I-201 [2-hydroxy-4-(((4-methylphenyl)sulfonyloxy)acetyl)amino)-benzoic acid]). In torcetrapib-stimulated cells, Nox inhibitors, rotenone, and S3I-201 downregulated CYP11B2 and steroidogenic acute regulatory protein and reduced aldosterone. Dalcetrapib and anacetrapib effects on aldosterone were variably blocked by GKT137831, ML171, rotenone, and S3I-201. In adipocytes, torcetrapib, dalcetrapib, and anacetrapib inhibit enzymatic pathways responsible for aldosterone production through Nox1/Nox4- and mitochondrial-generated reactive oxygen species and STAT3. CETP inhibitors also influence adipokine production. These processes may be CETP independent. Our findings identify novel adipocyte-related mechanisms whereby CETP inhibitors increase aldosterone production. Such phenomena may contribute to hyperaldosteronism observed in CETP inhibitor clinical trials.
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Affiliation(s)
- Francisco J Rios
- Institute of Cardiovascular and Medical Sciences, British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, United Kingdom (F.J.R., A.N.D.C., S.E., A.C.M., R.M.T.); Faculty of Pharmaceutical Sciences of Ribeirao Preto, University of Sao Paulo, Ribeirao Preto, Brazil (K.B.N.); and Departamento de Bioquímica, Fisiología y Genética Molecular Facultad de CC. de la Salud, Universidad Rey Juan Carlos, Madrid, Spain (R.P.)
| | - Karla B Neves
- Institute of Cardiovascular and Medical Sciences, British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, United Kingdom (F.J.R., A.N.D.C., S.E., A.C.M., R.M.T.); Faculty of Pharmaceutical Sciences of Ribeirao Preto, University of Sao Paulo, Ribeirao Preto, Brazil (K.B.N.); and Departamento de Bioquímica, Fisiología y Genética Molecular Facultad de CC. de la Salud, Universidad Rey Juan Carlos, Madrid, Spain (R.P.)
| | - Aurelie Nguyen Dinh Cat
- Institute of Cardiovascular and Medical Sciences, British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, United Kingdom (F.J.R., A.N.D.C., S.E., A.C.M., R.M.T.); Faculty of Pharmaceutical Sciences of Ribeirao Preto, University of Sao Paulo, Ribeirao Preto, Brazil (K.B.N.); and Departamento de Bioquímica, Fisiología y Genética Molecular Facultad de CC. de la Salud, Universidad Rey Juan Carlos, Madrid, Spain (R.P.)
| | - Sarah Even
- Institute of Cardiovascular and Medical Sciences, British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, United Kingdom (F.J.R., A.N.D.C., S.E., A.C.M., R.M.T.); Faculty of Pharmaceutical Sciences of Ribeirao Preto, University of Sao Paulo, Ribeirao Preto, Brazil (K.B.N.); and Departamento de Bioquímica, Fisiología y Genética Molecular Facultad de CC. de la Salud, Universidad Rey Juan Carlos, Madrid, Spain (R.P.)
| | - Roberto Palacios
- Institute of Cardiovascular and Medical Sciences, British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, United Kingdom (F.J.R., A.N.D.C., S.E., A.C.M., R.M.T.); Faculty of Pharmaceutical Sciences of Ribeirao Preto, University of Sao Paulo, Ribeirao Preto, Brazil (K.B.N.); and Departamento de Bioquímica, Fisiología y Genética Molecular Facultad de CC. de la Salud, Universidad Rey Juan Carlos, Madrid, Spain (R.P.)
| | - Augusto C Montezano
- Institute of Cardiovascular and Medical Sciences, British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, United Kingdom (F.J.R., A.N.D.C., S.E., A.C.M., R.M.T.); Faculty of Pharmaceutical Sciences of Ribeirao Preto, University of Sao Paulo, Ribeirao Preto, Brazil (K.B.N.); and Departamento de Bioquímica, Fisiología y Genética Molecular Facultad de CC. de la Salud, Universidad Rey Juan Carlos, Madrid, Spain (R.P.)
| | - Rhian M Touyz
- Institute of Cardiovascular and Medical Sciences, British Heart Foundation Glasgow Cardiovascular Research Centre, University of Glasgow, Glasgow, Scotland, United Kingdom (F.J.R., A.N.D.C., S.E., A.C.M., R.M.T.); Faculty of Pharmaceutical Sciences of Ribeirao Preto, University of Sao Paulo, Ribeirao Preto, Brazil (K.B.N.); and Departamento de Bioquímica, Fisiología y Genética Molecular Facultad de CC. de la Salud, Universidad Rey Juan Carlos, Madrid, Spain (R.P.)
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86
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Samish I, Bourne PE, Najmanovich RJ. Achievements and challenges in structural bioinformatics and computational biophysics. Bioinformatics 2014; 31:146-50. [PMID: 25488929 PMCID: PMC4271151 DOI: 10.1093/bioinformatics/btu769] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Motivation: The field of structural bioinformatics and computational biophysics has undergone a revolution in the last 10 years. Developments that are captured annually through the 3DSIG meeting, upon which this article reflects. Results: An increase in the accessible data, computational resources and methodology has resulted in an increase in the size and resolution of studied systems and the complexity of the questions amenable to research. Concomitantly, the parameterization and efficiency of the methods have markedly improved along with their cross-validation with other computational and experimental results. Conclusion: The field exhibits an ever-increasing integration with biochemistry, biophysics and other disciplines. In this article, we discuss recent achievements along with current challenges within the field. Contact:Rafael.Najmanovich@USherbrooke.ca
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Affiliation(s)
- Ilan Samish
- Department of Plant Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel, Ort Braude College, Karmiel, 2161002, Israel, Office of the Director, National Institutes of Health, Bethesda, MD 20814, USA and Department of Biochemistry, University of Sherbrooke, Sherbrooke, J1H 5N4, Canada Department of Plant Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel, Ort Braude College, Karmiel, 2161002, Israel, Office of the Director, National Institutes of Health, Bethesda, MD 20814, USA and Department of Biochemistry, University of Sherbrooke, Sherbrooke, J1H 5N4, Canada
| | - Philip E Bourne
- Department of Plant Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel, Ort Braude College, Karmiel, 2161002, Israel, Office of the Director, National Institutes of Health, Bethesda, MD 20814, USA and Department of Biochemistry, University of Sherbrooke, Sherbrooke, J1H 5N4, Canada
| | - Rafael J Najmanovich
- Department of Plant Sciences, Weizmann Institute of Science, Rehovot, 76100, Israel, Ort Braude College, Karmiel, 2161002, Israel, Office of the Director, National Institutes of Health, Bethesda, MD 20814, USA and Department of Biochemistry, University of Sherbrooke, Sherbrooke, J1H 5N4, Canada
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87
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Khare R, Li J, Lu Z. LabeledIn: cataloging labeled indications for human drugs. J Biomed Inform 2014; 52:448-56. [PMID: 25220766 PMCID: PMC4260997 DOI: 10.1016/j.jbi.2014.08.004] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2014] [Revised: 07/16/2014] [Accepted: 08/16/2014] [Indexed: 11/19/2022]
Abstract
Drug-disease treatment relationships, i.e., which drug(s) are indicated to treat which disease(s), are among the most frequently sought information in PubMed®. Such information is useful for feeding the Google Knowledge Graph, designing computational methods to predict novel drug indications, and validating clinical information in EMRs. Given the importance and utility of this information, there have been several efforts to create repositories of drugs and their indications. However, existing resources are incomplete. Furthermore, they neither label indications in a structured way nor differentiate them by drug-specific properties such as dosage form, and thus do not support computer processing or semantic interoperability. More recently, several studies have proposed automatic methods to extract structured indications from drug descriptions; however, their performance is limited by natural language challenges in disease named entity recognition and indication selection. In response, we report LabeledIn: a human-reviewed, machine-readable and source-linked catalog of labeled indications for human drugs. More specifically, we describe our semi-automatic approach to derive LabeledIn from drug descriptions through human annotations with aids from automatic methods. As the data source, we use the drug labels (or package inserts) submitted to the FDA by drug manufacturers and made available in DailyMed. Our machine-assisted human annotation workflow comprises: (i) a grouping method to remove redundancy and identify representative drug labels to be used for human annotation, (ii) an automatic method to recognize and normalize mentions of diseases in drug labels as candidate indications, and (iii) a two-round annotation workflow for human experts to judge the pre-computed candidates and deliver the final gold standard. In this study, we focused on 250 highly accessed drugs in PubMed Health, a newly developed public web resource for consumers and clinicians on prevention and treatment of diseases. These 250 drugs corresponded to more than 8000 drug labels (500 unique) in DailyMed in which 2950 candidate indications were pre-tagged by an automatic tool. After being reviewed independently by two experts, 1618 indications were selected, and additional 97 (missed by computer) were manually added, with an inter-annotator agreement of 88.35% as measured by the Kappa coefficient. Our final annotation results in LabeledIn consist of 7805 drug-disease treatment relationships where drugs are represented as a triplet of ingredient, dose form, and strength. A systematic comparison of LabeledIn with an existing computer-derived resource revealed significant discrepancies, confirming the need to involve humans in the creation of such a resource. In addition, LabeledIn is unique in that it contains detailed textual context of the selected indications in drug labels, making it suitable for the development of advanced computational methods for the automatic extraction of indications from free text. Finally, motivated by the studies on drug nomenclature and medication errors in EMRs, we adopted a fine-grained drug representation scheme, which enables the automatic identification of drugs with indications specific to certain dose forms or strengths. Future work includes expanding our coverage to more drugs and integration with other resources. The LabeledIn dataset and the annotation guidelines are available at http://ftp.ncbi.nlm.nih.gov/pub/lu/LabeledIn/.
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Affiliation(s)
- Ritu Khare
- National Center for Biotechnology Information (NCBI), U.S. National Institutes of Health, 8600 Rockville Pike, Bethesda, USA.
| | - Jiao Li
- Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China.
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), U.S. National Institutes of Health, 8600 Rockville Pike, Bethesda, USA.
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88
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Yizhak K, Gaude E, Le Dévédec S, Waldman YY, Stein GY, van de Water B, Frezza C, Ruppin E. Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer. eLife 2014; 3. [PMID: 25415239 PMCID: PMC4238051 DOI: 10.7554/elife.03641] [Citation(s) in RCA: 103] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2014] [Accepted: 10/28/2014] [Indexed: 12/11/2022] Open
Abstract
Utilizing molecular data to derive functional physiological models tailored for specific cancer cells can facilitate the use of individually tailored therapies. To this end we present an approach termed PRIME for generating cell-specific genome-scale metabolic models (GSMMs) based on molecular and phenotypic data. We build >280 models of normal and cancer cell-lines that successfully predict metabolic phenotypes in an individual manner. We utilize this set of cell-specific models to predict drug targets that selectively inhibit cancerous but not normal cell proliferation. The top predicted target, MLYCD, is experimentally validated and the metabolic effects of MLYCD depletion investigated. Furthermore, we tested cell-specific predicted responses to the inhibition of metabolic enzymes, and successfully inferred the prognosis of cancer patients based on their PRIME-derived individual GSMMs. These results lay a computational basis and a counterpart experimental proof of concept for future personalized metabolic modeling applications, enhancing the search for novel selective anticancer therapies.
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Affiliation(s)
- Keren Yizhak
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
| | - Edoardo Gaude
- MRC Cancer Unit, University of Cambridge, Cambridge, United Kingdom
| | - Sylvia Le Dévédec
- Division of Toxicology, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands
| | - Yedael Y Waldman
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
| | - Gideon Y Stein
- Department of Internal Medicine 'B', Beilinson Hospital, Rabin Medical Center, Petah-Tikva, Israel
| | - Bob van de Water
- Division of Toxicology, Leiden Academic Center for Drug Research, Leiden University, Leiden, Netherlands
| | - Christian Frezza
- MRC Cancer Unit, University of Cambridge, Cambridge, United Kingdom
| | - Eytan Ruppin
- Blavatnik School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel
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89
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Sahoo S, Haraldsdóttir HS, Fleming RMT, Thiele I. Modeling the effects of commonly used drugs on human metabolism. FEBS J 2014; 282:297-317. [PMID: 25345908 DOI: 10.1111/febs.13128] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2014] [Revised: 09/21/2014] [Accepted: 10/13/2014] [Indexed: 02/02/2023]
Abstract
Metabolism contributes significantly to the pharmacokinetics and pharmacodynamics of a drug. In addition, diet and genetics have a profound effect on cellular metabolism with respect to both health and disease. In the present study, we assembled a comprehensive, literature-based drug metabolic reconstruction of the 18 most highly prescribed drug groups, including statins, anti-hypertensives, immunosuppressants and analgesics. This reconstruction captures in detail our current understanding of their absorption, intracellular distribution, metabolism and elimination. We combined this drug module with the most comprehensive reconstruction of human metabolism, Recon 2, yielding Recon2_DM1796, which accounts for 2803 metabolites and 8161 reactions. By defining 50 specific drug objectives that captured the overall drug metabolism of these compounds, we investigated the effects of dietary composition and inherited metabolic disorders on drug metabolism and drug-drug interactions. Our main findings include: (a) a shift in dietary patterns significantly affects statins and acetaminophen metabolism; (b) disturbed statin metabolism contributes to the clinical phenotype of mitochondrial energy disorders; and (c) the interaction between statins and cyclosporine can be explained by several common metabolic and transport pathways other than the previously established CYP3A4 connection. This work holds the potential for studying adverse drug reactions and designing patient-specific therapies.
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Affiliation(s)
- Swagatika Sahoo
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval, Luxembourg
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90
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Khare R, Wei CH, Lu Z. Automatic extraction of drug indications from FDA drug labels. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2014; 2014:787-794. [PMID: 25954385 PMCID: PMC4419914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Extracting computable indications, i.e. drug-disease treatment relationships, from narrative drug resources is the key for building a gold standard drug indication repository. The two steps to the extraction problem are disease named-entity recognition (NER) to identify disease mentions from a free-text description and disease classification to distinguish indications from other disease mentions in the description. While there exist many tools for disease NER, disease classification is mostly achieved through human annotations. For example, we recently resorted to human annotations to prepare a corpus, LabeledIn, capturing structured indications from the drug labels submitted to FDA by pharmaceutical companies. In this study, we present an automatic end-to-end framework to extract structured and normalized indications from FDA drug labels. In addition to automatic disease NER, a key component of our framework is a machine learning method that is trained on the LabeledIn corpus to classify the NER-computed disease mentions as "indication vs. non-indication." Through experiments with 500 drug labels, our end-to-end system delivered 86.3% F1-measure in drug indication extraction, with 17% improvement over baseline. Further analysis shows that the indication classifier delivers a performance comparable to human experts and that the remaining errors are mostly due to disease NER (more than 50%). Given its performance, we conclude that our end-to-end approach has the potential to significantly reduce human annotation costs.
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Affiliation(s)
- Ritu Khare
- National Center for Biotechnology Information (NCBI), NIH, Bethesda, MD 20894
| | - Chih-Hsuan Wei
- National Center for Biotechnology Information (NCBI), NIH, Bethesda, MD 20894
| | - Zhiyong Lu
- National Center for Biotechnology Information (NCBI), NIH, Bethesda, MD 20894
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91
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Fisher CP, Kierzek AM, Plant NJ, Moore JB. Systems biology approaches for studying the pathogenesis of non-alcoholic fatty liver disease. World J Gastroenterol 2014; 20:15070-15078. [PMID: 25386055 PMCID: PMC4223240 DOI: 10.3748/wjg.v20.i41.15070] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2013] [Accepted: 03/13/2014] [Indexed: 02/06/2023] Open
Abstract
Non-alcoholic fatty liver disease (NAFLD) is a progressive disease of increasing public health concern. In western populations the disease has an estimated prevalence of 20%-40%, rising to 70%-90% in obese and type II diabetic individuals. Simplistically, NAFLD is the macroscopic accumulation of lipid in the liver, and is viewed as the hepatic manifestation of the metabolic syndrome. However, the molecular mechanisms mediating both the initial development of steatosis and its progression through non-alcoholic steatohepatitis to debilitating and potentially fatal fibrosis and cirrhosis are only partially understood. Despite increased research in this field, the development of non-invasive clinical diagnostic tools and the discovery of novel therapeutic targets has been frustratingly slow. We note that, to date, NAFLD research has been dominated by in vivo experiments in animal models and human clinical studies. Systems biology tools and novel computational simulation techniques allow the study of large-scale metabolic networks and the impact of their dysregulation on health. Here we review current systems biology tools and discuss the benefits to their application to the study of NAFLD. We propose that a systems approach utilising novel in silico modelling and simulation techniques is key to a more comprehensive, better targeted NAFLD research strategy. Such an approach will accelerate the progress of research and vital translation into clinic.
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92
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Abstract
HYPOTHESIS Different pharmacotherapies for sensorineural hearing loss (SNHL) are interconnected in metabolic networks with molecular hubs. BACKGROUND Sensorineural hearing loss is the most common sensory deficit worldwide. Dozens of drugs have shown efficacy against SNHL in animal studies and a few in human studies. Analyzing metabolic networks that interconnect these drugs will point to and prioritize development of new pharmacotherapies for human SNHL. METHODS Drugs that have shown efficacy in treating mammalian SNHL were identified through PubMed literature searches. The drugs were analyzed using the metabolomic analysis and the "grow-tool function" in ingenuity pathway analysis (IPA). The top 3 most interconnected molecules and drugs (i.e., the hubs) within the generated networks were considered important targets for the treatment of SNHL. RESULTS A total of 70 drugs were investigated with IPA. The metabolomic analysis revealed 2 statistically significant networks (Networks 1 and 2). A network analysis using the "grow-tool function" generated one statistically significant network (Network 3). Hubs of these networks were as follows: P38 mitogen-activated protein kinases (P38 MAPK), p42/p44 MAP kinase (ERK1/2) and glutathione for Network 1; protein kinase B (Akt), nuclear factor kappa B (NFkB) and ERK for Network 2; and dexamethasone, tretinoin, and cyclosporin A for Network 3. CONCLUSION Metabolomic and network analysis of the existing pharmacotherapies for SNHL has pointed to and prioritized a number of potential novel targets for treatment of SNHL.
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93
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Xie L, Ge X, Tan H, Xie L, Zhang Y, Hart T, Yang X, Bourne PE. Towards structural systems pharmacology to study complex diseases and personalized medicine. PLoS Comput Biol 2014; 10:e1003554. [PMID: 24830652 PMCID: PMC4022462 DOI: 10.1371/journal.pcbi.1003554] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Genome-Wide Association Studies (GWAS), whole genome sequencing, and high-throughput omics techniques have generated vast amounts of genotypic and molecular phenotypic data. However, these data have not yet been fully explored to improve the effectiveness and efficiency of drug discovery, which continues along a one-drug-one-target-one-disease paradigm. As a partial consequence, both the cost to launch a new drug and the attrition rate are increasing. Systems pharmacology and pharmacogenomics are emerging to exploit the available data and potentially reverse this trend, but, as we argue here, more is needed. To understand the impact of genetic, epigenetic, and environmental factors on drug action, we must study the structural energetics and dynamics of molecular interactions in the context of the whole human genome and interactome. Such an approach requires an integrative modeling framework for drug action that leverages advances in data-driven statistical modeling and mechanism-based multiscale modeling and transforms heterogeneous data from GWAS, high-throughput sequencing, structural genomics, functional genomics, and chemical genomics into unified knowledge. This is not a small task, but, as reviewed here, progress is being made towards the final goal of personalized medicines for the treatment of complex diseases.
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Affiliation(s)
- Lei Xie
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
- Ph.D. Program in Computer Science, Biology, and Biochemistry, The Graduate Center, The City University of New York, New York, New York, United States of America
- * E-mail:
| | - Xiaoxia Ge
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
| | - Hepan Tan
- Department of Computer Science, Hunter College, The City University of New York, New York, New York, United States of America
| | - Li Xie
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
| | - Yinliang Zhang
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
| | - Thomas Hart
- Department of Biological Sciences, Hunter College, The City University of New York, New York, New York, United States of America
| | - Xiaowei Yang
- School of Public Health, Hunter College, The City University of New York, New York, New York, United States of America
| | - Philip E. Bourne
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, California, United States of America
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94
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Sahoo S, Aurich MK, Jonsson JJ, Thiele I. Membrane transporters in a human genome-scale metabolic knowledgebase and their implications for disease. Front Physiol 2014; 5:91. [PMID: 24653705 PMCID: PMC3949408 DOI: 10.3389/fphys.2014.00091] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2013] [Accepted: 02/17/2014] [Indexed: 01/18/2023] Open
Abstract
Membrane transporters enable efficient cellular metabolism, aid in nutrient sensing, and have been associated with various diseases, such as obesity and cancer. Genome-scale metabolic network reconstructions capture genomic, physiological, and biochemical knowledge of a target organism, along with a detailed representation of the cellular metabolite transport mechanisms. Since the first reconstruction of human metabolism, Recon 1, published in 2007, progress has been made in the field of metabolite transport. Recently, we published an updated reconstruction, Recon 2, which significantly improved the metabolic coverage and functionality. Human metabolic reconstructions have been used to investigate the role of metabolism in disease and to predict biomarkers and drug targets. Given the importance of cellular transport systems in understanding human metabolism in health and disease, we analyzed the coverage of transport systems for various metabolite classes in Recon 2. We will review the current knowledge on transporters (i.e., their preferred substrates, transport mechanisms, metabolic relevance, and disease association for each metabolite class). We will assess missing coverage and propose modifications and additions through a transport module that is functional when combined with Recon 2. This information will be valuable for further refinements. These data will also provide starting points for further experiments by highlighting areas of incomplete knowledge. This review represents the first comprehensive overview of the transporters involved in central metabolism and their transport mechanisms, thus serving as a compendium of metabolite transporters specific for human metabolic reconstructions.
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Affiliation(s)
- Swagatika Sahoo
- Center for Systems Biology, University of Iceland Reykjavik, Iceland ; Molecular Systems Physiology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg Belval, Luxembourg
| | - Maike K Aurich
- Center for Systems Biology, University of Iceland Reykjavik, Iceland ; Molecular Systems Physiology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg Belval, Luxembourg
| | - Jon J Jonsson
- Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Iceland Reykjavik, Iceland ; Department of Genetics and Molecular Medicine, Landspitali, National University Hospital of Iceland Reykjavik, Iceland
| | - Ines Thiele
- Center for Systems Biology, University of Iceland Reykjavik, Iceland ; Molecular Systems Physiology Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg Belval, Luxembourg
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95
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Abstract
Traditionally, scientific research has focused on studying individual events, such as single mutations, gene function, or the effect that mutating one protein has on a biological phenotype. A range of technologies is beginning to provide information that will enable a holistic view of how genomic and epigenetic aberrations in cancer cells can alter the homeostasis of signalling networks within these cells, between cancer cells and the local microenvironment, and at the organ and organism level. This process, termed Systems Biology, needs to be integrated with an iterative approach wherein hypotheses and predictions that arise from modelling are refined and constrained by experimental evaluation. Systems biology approaches will be vital for developing and implementing effective strategies to deliver personalized cancer therapy. Specifically, these approaches will be important to select those patients who are most likely to benefit from targeted therapies and for the development and implementation of rational combinatorial therapies. Systems biology can help to increase therapy efficacy or bypass the emergence of resistance, thus converting the current-often short term-effects of targeted therapies into durable responses, ultimately to improve patient quality of life and provide a cure.
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96
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Masoudi-Nejad A, Asgari Y. Metabolic cancer biology: structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment. Semin Cancer Biol 2014; 30:21-9. [PMID: 24495661 DOI: 10.1016/j.semcancer.2014.01.007] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2013] [Revised: 01/15/2014] [Accepted: 01/18/2014] [Indexed: 12/21/2022]
Abstract
The cancer cell metabolism or the Warburg effect discovery goes back to 1924 when, for the first time Otto Warburg observed, in contrast to the normal cells, cancer cells have different metabolism. With the initiation of high throughput technologies and computational systems biology, cancer cell metabolism renaissances and many attempts were performed to revise the Warburg effect. The development of experimental and analytical tools which generate high-throughput biological data including lots of information could lead to application of computational models in biological discovery and clinical medicine especially for cancer. Due to the recent availability of tissue-specific reconstructed models, new opportunities in studying metabolic alteration in various kinds of cancers open up. Structural approaches at genome-scale levels seem to be suitable for developing diagnostic and prognostic molecular signatures, as well as in identifying new drug targets. In this review, we have considered these recent advances in structural-based analysis of cancer as a metabolic disease view. Two different structural approaches have been described here: topological and constraint-based methods. The ultimate goal of this type of systems analysis is not only the discovery of novel drug targets but also the development of new systems-based therapy strategies.
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Affiliation(s)
- Ali Masoudi-Nejad
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran.
| | - Yazdan Asgari
- Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, Iran
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97
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Kell DB, Goodacre R. Metabolomics and systems pharmacology: why and how to model the human metabolic network for drug discovery. Drug Discov Today 2014; 19:171-82. [PMID: 23892182 PMCID: PMC3989035 DOI: 10.1016/j.drudis.2013.07.014] [Citation(s) in RCA: 111] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2013] [Revised: 07/03/2013] [Accepted: 07/16/2013] [Indexed: 02/06/2023]
Abstract
Metabolism represents the 'sharp end' of systems biology, because changes in metabolite concentrations are necessarily amplified relative to changes in the transcriptome, proteome and enzyme activities, which can be modulated by drugs. To understand such behaviour, we therefore need (and increasingly have) reliable consensus (community) models of the human metabolic network that include the important transporters. Small molecule 'drug' transporters are in fact metabolite transporters, because drugs bear structural similarities to metabolites known from the network reconstructions and from measurements of the metabolome. Recon2 represents the present state-of-the-art human metabolic network reconstruction; it can predict inter alia: (i) the effects of inborn errors of metabolism; (ii) which metabolites are exometabolites, and (iii) how metabolism varies between tissues and cellular compartments. However, even these qualitative network models are not yet complete. As our understanding improves so do we recognise more clearly the need for a systems (poly)pharmacology.
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Affiliation(s)
- Douglas B Kell
- School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK.
| | - Royston Goodacre
- School of Chemistry and Manchester Institute of Biotechnology, The University of Manchester, 131 Princess Street, Manchester M1 7DN, UK
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98
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Thomas A, Rahmanian S, Bordbar A, Palsson BØ, Jamshidi N. Network reconstruction of platelet metabolism identifies metabolic signature for aspirin resistance. Sci Rep 2014; 4:3925. [PMID: 24473230 PMCID: PMC3905279 DOI: 10.1038/srep03925] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2013] [Accepted: 01/13/2014] [Indexed: 12/11/2022] Open
Abstract
Recently there has not been a systematic, objective assessment of the metabolic capabilities of the human platelet. A manually curated, functionally tested, and validated biochemical reaction network of platelet metabolism, iAT-PLT-636, was reconstructed using 33 proteomic datasets and 354 literature references. The network contains enzymes mapping to 403 diseases and 231 FDA approved drugs, alluding to an expansive scope of biochemical transformations that may affect or be affected by disease processes in multiple organ systems. The effect of aspirin (ASA) resistance on platelet metabolism was evaluated using constraint-based modeling, which revealed a redirection of glycolytic, fatty acid, and nucleotide metabolism reaction fluxes in order to accommodate eicosanoid synthesis and reactive oxygen species stress. These results were confirmed with independent proteomic data. The construction and availability of iAT-PLT-636 should stimulate further data-driven, systems analysis of platelet metabolism towards the understanding of pathophysiological conditions including, but not strictly limited to, coagulopathies.
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Affiliation(s)
- Alex Thomas
- 1] Department of Bioinformatics and Systems Biology, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [2] University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412
| | - Sorena Rahmanian
- 1] Department of Bioengineering, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [2] University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412
| | - Aarash Bordbar
- 1] Department of Bioengineering, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [2] University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412
| | - Bernhard Ø Palsson
- 1] Department of Bioengineering, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [2] Institute of Engineering and Medicine, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [3] University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412
| | - Neema Jamshidi
- 1] Institute of Engineering and Medicine, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [2] University of California, San Diego, 9500 Gilman Drive, Mail Code 0412, La Jolla, CA 92093-0412 [3] Department of Radiological Sciences, University of California, Los Angeles, BOX 951721, Los Angeles, CA 90095-1721
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99
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D’Alessandro A, Zolla L. Proteomics and metabolomics in cancer drug development. Expert Rev Proteomics 2014; 10:473-88. [DOI: 10.1586/14789450.2013.840440] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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100
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Wu M, Chan C. Prediction of therapeutic microRNA based on the human metabolic network. ACTA ACUST UNITED AC 2014; 30:1163-1171. [PMID: 24403541 DOI: 10.1093/bioinformatics/btt751] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2013] [Accepted: 12/22/2013] [Indexed: 12/17/2022]
Abstract
MOTIVATION MicroRNA (miRNA) expression has been found to be deregulated in human cancer, contributing, in part, to the interest of the research community in using miRNAs as alternative therapeutic targets. Although miRNAs could be potential targets, identifying which miRNAs to target for a particular type of cancer has been difficult due to the limited knowledge on their regulatory roles in cancer. We address this challenge by integrating miRNA-target prediction, metabolic modeling and context-specific gene expression data to predict therapeutic miRNAs that could reduce the growth of cancer. RESULTS We developed a novel approach to simulate a condition-specific metabolic system for human hepatocellular carcinoma (HCC) wherein overexpression of each miRNA was simulated to predict their ability to reduce cancer cell growth. Our approach achieved >80% accuracy in predicting the miRNAs that could suppress metastasis and progression of liver cancer based on various experimental evidences in the literature. This condition-specific metabolic system provides a framework to explore the mechanisms by which miRNAs modulate metabolic functions to affect cancer growth. To the best of our knowledge, this is the first computational approach implemented to predict therapeutic miRNAs for human cancer based on their functional role in cancer metabolism. Analyzing the metabolic functions altered by the miRNA-identified metabolic genes essential for cell growth and proliferation that are targeted by the miRNAs. AVAILABILITY AND IMPLEMENTATION See supplementary protocols and http://www.egr.msu.edu/changroup/Protocols%20Index.html CONTACT: krischan@egr.msu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ming Wu
- Department of Computer Science and Engineering, Department of Chemical Engineering and Materials Science and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
| | - Christina Chan
- Department of Computer Science and Engineering, Department of Chemical Engineering and Materials Science and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA Department of Computer Science and Engineering, Department of Chemical Engineering and Materials Science and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA Department of Computer Science and Engineering, Department of Chemical Engineering and Materials Science and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA
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