51
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Lage-Rupprecht V, Schultz B, Dick J, Namysl M, Zaliani A, Gebel S, Pless O, Reinshagen J, Ellinger B, Ebeling C, Esser A, Jacobs M, Claussen C, Hofmann-Apitius M. A hybrid approach unveils drug repurposing candidates targeting an Alzheimer pathophysiology mechanism. PATTERNS (NEW YORK, N.Y.) 2022; 3:100433. [PMID: 35510183 PMCID: PMC9058900 DOI: 10.1016/j.patter.2021.100433] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/30/2021] [Accepted: 12/23/2021] [Indexed: 01/04/2023]
Abstract
The high number of failed pre-clinical and clinical studies for compounds targeting Alzheimer disease (AD) has demonstrated that there is a need to reassess existing strategies. Here, we pursue a holistic, mechanism-centric drug repurposing approach combining computational analytics and experimental screening data. Based on this integrative workflow, we identified 77 druggable modifiers of tau phosphorylation (pTau). One of the upstream modulators of pTau, HDAC6, was screened with 5,632 drugs in a tau-specific assay, resulting in the identification of 20 repurposing candidates. Four compounds and their known targets were found to have a link to AD-specific genes. Our approach can be applied to a variety of AD-associated pathophysiological mechanisms to identify more repurposing candidates.
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Affiliation(s)
- Vanessa Lage-Rupprecht
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Bruce Schultz
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Justus Dick
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, ScreeningPort, 22525 Hamburg, Germany
| | - Marcin Namysl
- Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, NetMedia Department, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Andrea Zaliani
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, ScreeningPort, 22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence Immune-Mediated Diseases CIMD, ScreeningPort, 22525 Hamburg, Germany
| | - Stephan Gebel
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Ole Pless
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, ScreeningPort, 22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence Immune-Mediated Diseases CIMD, ScreeningPort, 22525 Hamburg, Germany
| | - Jeanette Reinshagen
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, ScreeningPort, 22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence Immune-Mediated Diseases CIMD, ScreeningPort, 22525 Hamburg, Germany
| | - Bernhard Ellinger
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, ScreeningPort, 22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence Immune-Mediated Diseases CIMD, ScreeningPort, 22525 Hamburg, Germany
| | - Christian Ebeling
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Alexander Esser
- Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS, NetMedia Department, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Marc Jacobs
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
| | - Carsten Claussen
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, ScreeningPort, 22525 Hamburg, Germany
- Fraunhofer Cluster of Excellence Immune-Mediated Diseases CIMD, ScreeningPort, 22525 Hamburg, Germany
| | - Martin Hofmann-Apitius
- Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Department of Bioinformatics, Schloss Birlinghoven, 53757 Sankt Augustin, Germany
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52
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Kankanige D, Liyanage L, O'Connor MD. Application of Transcriptomics for Predicting Protein Interaction Networks, Drug Targets and Drug Candidates. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 4:693148. [PMID: 35356062 PMCID: PMC8959405 DOI: 10.3389/fmedt.2022.693148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Accepted: 01/05/2022] [Indexed: 11/15/2022] Open
Abstract
Protein interaction pathways and networks are critically-required for a vast range of biological processes. Improved discovery of candidate druggable proteins within specific cell, tissue and disease contexts will aid development of new treatments. Predicting protein interaction networks from gene expression data can provide valuable insights into normal and disease biology. For example, the resulting protein networks can be used to identify potentially druggable targets and drug candidates for testing in cell and animal disease models. The advent of whole-transcriptome expression profiling techniques—that catalogue protein-coding genes expressed within cells and tissues—has enabled development of individual algorithms for particular tasks. For example,: (i) gene ontology algorithms that predict gene/protein subsets involved in related cell processes; (ii) algorithms that predict intracellular protein interaction pathways; and (iii) algorithms that correlate druggable protein targets with known drugs and/or drug candidates. This review examines approaches, advantages and disadvantages of existing gene expression, gene ontology, and protein network prediction algorithms. Using this framework, we examine current efforts to combine these algorithms into pipelines to enable identification of druggable targets, and associated known drugs, using gene expression datasets. In doing so, new opportunities are identified for development of powerful algorithm pipelines, suitable for wide use by non-bioinformaticians, that can predict protein interaction networks, druggable proteins, and related drugs from user gene expression datasets.
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Affiliation(s)
- Dulshani Kankanige
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Campbelltown, NSW, Australia
| | - Liwan Liyanage
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Campbelltown, NSW, Australia
| | - Michael D. O'Connor
- Translational Health Research Institute, Western Sydney University, Campbelltown, NSW, Australia
- School of Medicine, Western Sydney University, Campbelltown, NSW, Australia
- *Correspondence: Michael D. O'Connor
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53
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Maity AK, Stone TC, Ward V, Webster AP, Yang Z, Hogan A, McBain H, Duku M, Ho KMA, Wolfson P, Graham DG, Beck S, Teschendorff AE, Lovat LB. Novel epigenetic network biomarkers for early detection of esophageal cancer. Clin Epigenetics 2022; 14:23. [PMID: 35164838 PMCID: PMC8845366 DOI: 10.1186/s13148-022-01243-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 02/04/2022] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Early detection of esophageal cancer is critical to improve survival. Whilst studies have identified biomarkers, their interpretation and validity is often confounded by cell-type heterogeneity. RESULTS Here we applied systems-epigenomic and cell-type deconvolution algorithms to a discovery set encompassing RNA-Seq and DNA methylation data from esophageal adenocarcinoma (EAC) patients and matched normal-adjacent tissue, in order to identify robust biomarkers, free from the confounding effect posed by cell-type heterogeneity. We identify 12 gene-modules that are epigenetically deregulated in EAC, and are able to validate all 12 modules in 4 independent EAC cohorts. We demonstrate that the epigenetic deregulation is present in the epithelial compartment of EAC-tissue. Using single-cell RNA-Seq data we show that one of these modules, a proto-cadherin module centered around CTNND2, is inactivated in Barrett's Esophagus, a precursor lesion to EAC. By measuring DNA methylation in saliva from EAC cases and controls, we identify a chemokine module centered around CCL20, whose methylation patterns in saliva correlate with EAC status. CONCLUSIONS Given our observations that a CCL20 chemokine network is overactivated in EAC tissue and saliva from EAC patients, and that in independent studies CCL20 has been found to be overactivated in EAC tissue infected with the bacterium F. nucleatum, a bacterium that normally inhabits the oral cavity, our results highlight the possibility of using DNAm measurements in saliva as a proxy for changes occurring in the esophageal epithelium. Both the CTNND2/CCL20 modules represent novel promising network biomarkers for EAC that merit further investigation.
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Affiliation(s)
- Alok K Maity
- CAS Key Lab of Computational Biology, Shanghai Institute for Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China
| | - Timothy C Stone
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Vanessa Ward
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Amy P Webster
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Zhen Yang
- Key Laboratory of Medical Epigenetics and Metabolism, Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, China
| | - Aine Hogan
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Hazel McBain
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Margaraet Duku
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Kai Man Alexander Ho
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - Paul Wolfson
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK
| | - David G Graham
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK.,Division of GI Services, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK
| | | | - Stephan Beck
- UCL Cancer Institute, University College London, Gower Street, London, WC1E 6BT, UK
| | - Andrew E Teschendorff
- CAS Key Lab of Computational Biology, Shanghai Institute for Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai, 200031, China.
| | - Laurence B Lovat
- Division of Surgery and Interventional Science, University College London, Gower Street, London, WC1E 6BT, UK. .,Division of GI Services, University College London Hospitals NHS Foundation Trust, 235 Euston Road, London, NW1 2BU, UK.
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54
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Raghavan PR. Metadichol®: A Novel Nanolipid Formulation That Inhibits SARS-CoV-2 and a Multitude of Pathological Viruses In Vitro. BIOMED RESEARCH INTERNATIONAL 2022; 2022:1558860. [PMID: 35039793 PMCID: PMC8760534 DOI: 10.1155/2022/1558860] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 10/20/2021] [Accepted: 12/15/2021] [Indexed: 01/08/2023]
Abstract
Increasing outbreaks of new pathogenic viruses have promoted the exploration of novel alternatives to time-consuming vaccines. Thus, it is necessary to develop a universal approach to halt the spread of new and unknown viruses as they are discovered. One such promising approach is to target lipid membranes, which are common to all viruses and bacteria. The ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has reaffirmed the importance of interactions between the virus envelope and the host cell plasma membrane as a critical mechanism of infection. Metadichol®, a nanolipid emulsion of long-chain alcohols, has been demonstrated as a strong candidate that inhibits the proliferation of SARS-CoV-2. Naturally derived substances, such as long-chain saturated lipid alcohols, reduce viral infectivity, including that of coronaviruses (such as SARS-CoV-2) by modifying their lipid-dependent attachment mechanism to human host cells. The receptor ACE2 mediates the entry of SARS-CoV-2 into the host cells, whereas the serine protease TMPRSS2 primes the viral S protein. In this study, Metadichol® was found to be 270 times more potent an inhibitor of TMPRSS2 (EC50 = 96 ng/mL) than camostat mesylate (EC50 = 26000 ng/mL). Additionally, it inhibits ACE with an EC50 of 71 ng/mL, but it is a very weak inhibitor of ACE2 at an EC50 of 31 μg/mL. Furthermore, the live viral assay performed in Caco-2 cells revealed that Metadichol® inhibits SARS-CoV-2 replication at an EC90 of 0.16 μg/mL. Moreover, Metadichol® had an EC90 of 0.00037 μM, making it 2081 and 3371 times more potent than remdesivir (EC50 = 0.77 μM) and chloroquine (EC50 = 1.14 μM), respectively.
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55
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Luna A, Siper MC, Korkut A, Durupinar F, Dogrusoz U, Aslan JE, Sander C, Demir E, Babur O. Analyzing causal relationships in proteomic profiles using CausalPath. STAR Protoc 2021; 2:100955. [PMID: 34877547 PMCID: PMC8633371 DOI: 10.1016/j.xpro.2021.100955] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021).
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Affiliation(s)
- Augustin Luna
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Corresponding author
| | - Metin Can Siper
- Computational Biology Program, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
| | - Anil Korkut
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Funda Durupinar
- Computer Science Department, University of Massachusetts Boston, 100 William T. Morrissey Blvd, Boston, MA 02125, USA
| | - Ugur Dogrusoz
- Computer Engineering Department, Bilkent University, Ankara 06800, Turkey
| | - Joseph E. Aslan
- Knight Cardiovascular Institute, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
| | - Chris Sander
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
| | - Emek Demir
- Computational Biology Program, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
- Department of Molecular and Medical Genetics, Oregon Health and Science University, 3181 SW Sam Jackson Park Rd, Portland, OR 97239, USA
- Pacific Northwest National Laboratories, 902 Battelle Blvd, Richland, WA 99354, USA
| | - Ozgun Babur
- Computer Science Department, University of Massachusetts Boston, 100 William T. Morrissey Blvd, Boston, MA 02125, USA
- Corresponding author
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56
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Gadepalli VS, Kim H, Liu Y, Han T, Cheng L. XDeathDB: a visualization platform for cell death molecular interactions. Cell Death Dis 2021; 12:1156. [PMID: 34907160 PMCID: PMC8669630 DOI: 10.1038/s41419-021-04397-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 08/07/2021] [Accepted: 11/09/2021] [Indexed: 12/23/2022]
Abstract
Lots of cell death initiator and effector molecules, signalling pathways and subcellular sites have been identified as key mediators in both cell death processes in cancer. The XDeathDB visualization platform provides a comprehensive cell death and their crosstalk resource for deciphering the signaling network organization of interactions among different cell death modes associated with 1461 cancer types and COVID-19, with an aim to understand the molecular mechanisms of physiological cell death in disease and facilitate systems-oriented novel drug discovery in inducing cell deaths properly. Apoptosis, autosis, efferocytosis, ferroptosis, immunogenic cell death, intrinsic apoptosis, lysosomal cell death, mitotic cell death, mitochondrial permeability transition, necroptosis, parthanatos, and pyroptosis related to 12 cell deaths and their crosstalk can be observed systematically by the platform. Big data for cell death gene-disease associations, gene-cell death pathway associations, pathway-cell death mode associations, and cell death-cell death associations is collected by literature review articles and public database from iRefIndex, STRING, BioGRID, Reactom, Pathway's commons, DisGeNET, DrugBank, and Therapeutic Target Database (TTD). An interactive webtool, XDeathDB, is built by web applications with R-Shiny, JavaScript (JS) and Shiny Server Iso. With this platform, users can search specific interactions from vast interdependent networks that occur in the realm of cell death. A multilayer spectral graph clustering method that performs convex layer aggregation to identify crosstalk function among cell death modes for a specific cancer. 147 hallmark genes of cell death could be observed in detail in these networks. These potential druggable targets are displayed systematically and tailoring networks to visualize specified relations is available to fulfil user-specific needs. Users can access XDeathDB for free at https://pcm2019.shinyapps.io/XDeathDB/ .
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Affiliation(s)
- Venkat Sundar Gadepalli
- Research Information Technology, College of Medicine, Ohio State University, 1585 Neil Ave, Columbus, OH, 43210, USA
| | - Hangil Kim
- 1Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Yueze Liu
- The Grainger College of Engineering, The University of Illinois-Urbana-Champaign, Urbana and Champaign, Champaign, IL, 61801, USA
| | - Tao Han
- 1Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA
| | - Lijun Cheng
- 1Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA.
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57
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Wong JV, Franz M, Siper MC, Fong D, Durupinar F, Dallago C, Luna A, Giorgi J, Rodchenkov I, Babur Ö, Bachman JA, Gyori BM, Demir E, Bader GD, Sander C. Author-sourced capture of pathway knowledge in computable form using Biofactoid. eLife 2021; 10:68292. [PMID: 34860157 PMCID: PMC8683078 DOI: 10.7554/elife.68292] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2021] [Accepted: 12/02/2021] [Indexed: 01/04/2023] Open
Abstract
Making the knowledge contained in scientific papers machine-readable and formally computable would allow researchers to take full advantage of this information by enabling integration with other knowledge sources to support data analysis and interpretation. Here we describe Biofactoid, a web-based platform that allows scientists to specify networks of interactions between genes, their products, and chemical compounds, and then translates this information into a representation suitable for computational analysis, search and discovery. We also report the results of a pilot study to encourage the wide adoption of Biofactoid by the scientific community.
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Affiliation(s)
- Jeffrey V Wong
- The Donnelly Centre, University of Toronto, Toronto, Canada
| | - Max Franz
- The Donnelly Centre, University of Toronto, Toronto, Canada
| | - Metin Can Siper
- Computational Biology Program, Oregon Health and Science University, Portland, United States
| | - Dylan Fong
- The Donnelly Centre, University of Toronto, Toronto, Canada
| | - Funda Durupinar
- Computer Science Department, University of Massachusetts Boston, Boston, United States
| | - Christian Dallago
- Department of Cell Biology, Harvard Medical School, Boston, United States.,Department of Systems Biology, Harvard Medical School, Boston, United States.,Department of Informatics, Technische Universität München, Garching, Germany
| | - Augustin Luna
- Department of Cell Biology, Harvard Medical School, Boston, United States.,Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States.,Broad Institute, Massachusetts Institute of Technology, Harvard University, Boston, United States
| | - John Giorgi
- The Donnelly Centre, University of Toronto, Toronto, Canada
| | | | - Özgün Babur
- Computer Science Department, University of Massachusetts Boston, Boston, United States
| | - John A Bachman
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, United States
| | - Benjamin M Gyori
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, United States
| | - Emek Demir
- Computational Biology Program, Oregon Health and Science University, Portland, United States
| | - Gary D Bader
- The Donnelly Centre, University of Toronto, Toronto, Canada.,Department of Computer Science, Department of Molecular Genetics, University of Toronto, Toronto, United States.,The Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada.,Princess Margaret Cancer Centre, University Health Network, Toronto, Canada
| | - Chris Sander
- Department of Data Sciences, Dana-Farber Cancer Institute, Boston, United States.,Broad Institute, Massachusetts Institute of Technology, Harvard University, Boston, United States
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58
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Arici MK, Tuncbag N. Performance Assessment of the Network Reconstruction Approaches on Various Interactomes. Front Mol Biosci 2021; 8:666705. [PMID: 34676243 PMCID: PMC8523993 DOI: 10.3389/fmolb.2021.666705] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 07/14/2021] [Indexed: 01/04/2023] Open
Abstract
Beyond the list of molecules, there is a necessity to collectively consider multiple sets of omic data and to reconstruct the connections between the molecules. Especially, pathway reconstruction is crucial to understanding disease biology because abnormal cellular signaling may be pathological. The main challenge is how to integrate the data together in an accurate way. In this study, we aim to comparatively analyze the performance of a set of network reconstruction algorithms on multiple reference interactomes. We first explored several human protein interactomes, including PathwayCommons, OmniPath, HIPPIE, iRefWeb, STRING, and ConsensusPathDB. The comparison is based on the coverage of each interactome in terms of cancer driver proteins, structural information of protein interactions, and the bias toward well-studied proteins. We next used these interactomes to evaluate the performance of network reconstruction algorithms including all-pair shortest path, heat diffusion with flux, personalized PageRank with flux, and prize-collecting Steiner forest (PCSF) approaches. Each approach has its own merits and weaknesses. Among them, PCSF had the most balanced performance in terms of precision and recall scores when 28 pathways from NetPath were reconstructed using the listed algorithms. Additionally, the reference interactome affects the performance of the network reconstruction approaches. The coverage and disease- or tissue-specificity of each interactome may vary, which may result in differences in the reconstructed networks.
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Affiliation(s)
- M Kaan Arici
- Graduate School of Informatics, Middle East Technical University, Ankara, Turkey.,Foot and Mouth Diseases Institute, Ministry of Agriculture and Forestry, Ankara, Turkey
| | - Nurcan Tuncbag
- Chemical and Biological Engineering, College of Engineering, Koc University, Istanbul, Turkey.,School of Medicine, Koc University, Istanbul, Turkey
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59
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Ostaszewski M, Niarakis A, Mazein A, Kuperstein I, Phair R, Orta‐Resendiz A, Singh V, Aghamiri SS, Acencio ML, Glaab E, Ruepp A, Fobo G, Montrone C, Brauner B, Frishman G, Monraz Gómez LC, Somers J, Hoch M, Kumar Gupta S, Scheel J, Borlinghaus H, Czauderna T, Schreiber F, Montagud A, Ponce de Leon M, Funahashi A, Hiki Y, Hiroi N, Yamada TG, Dräger A, Renz A, Naveez M, Bocskei Z, Messina F, Börnigen D, Fergusson L, Conti M, Rameil M, Nakonecnij V, Vanhoefer J, Schmiester L, Wang M, Ackerman EE, Shoemaker JE, Zucker J, Oxford K, Teuton J, Kocakaya E, Summak GY, Hanspers K, Kutmon M, Coort S, Eijssen L, Ehrhart F, Rex DAB, Slenter D, Martens M, Pham N, Haw R, Jassal B, Matthews L, Orlic‐Milacic M, Senff Ribeiro A, Rothfels K, Shamovsky V, Stephan R, Sevilla C, Varusai T, Ravel J, Fraser R, Ortseifen V, Marchesi S, Gawron P, Smula E, Heirendt L, Satagopam V, Wu G, Riutta A, Golebiewski M, Owen S, Goble C, Hu X, Overall RW, Maier D, Bauch A, Gyori BM, Bachman JA, Vega C, Grouès V, Vazquez M, Porras P, Licata L, Iannuccelli M, Sacco F, Nesterova A, Yuryev A, de Waard A, Turei D, Luna A, Babur O, Soliman S, Valdeolivas A, Esteban‐Medina M, Peña‐Chilet M, Rian K, Helikar T, Puniya BL, Modos D, Treveil A, Olbei M, De Meulder B, Ballereau S, Dugourd A, Naldi A, Noël V, Calzone L, Sander C, Demir E, Korcsmaros T, Freeman TC, Augé F, Beckmann JS, Hasenauer J, Wolkenhauer O, Wilighagen EL, Pico AR, Evelo CT, Gillespie ME, Stein LD, Hermjakob H, D'Eustachio P, Saez‐Rodriguez J, Dopazo J, Valencia A, Kitano H, Barillot E, Auffray C, Balling R, Schneider R. COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms. Mol Syst Biol 2021; 17:e10387. [PMID: 34664389 PMCID: PMC8524328 DOI: 10.15252/msb.202110387] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 08/25/2021] [Accepted: 08/26/2021] [Indexed: 12/13/2022] Open
Abstract
We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective.
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Affiliation(s)
- Marek Ostaszewski
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Anna Niarakis
- Université Paris‐SaclayLaboratoire Européen de Recherche pour la Polyarthrite rhumatoïde ‐ GenhotelUniv EvryEvryFrance
- Lifeware GroupInria Saclay‐Ile de FrancePalaiseauFrance
| | - Alexander Mazein
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Inna Kuperstein
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Robert Phair
- Integrative Bioinformatics, Inc.Mountain ViewCAUSA
| | - Aurelio Orta‐Resendiz
- Institut PasteurUniversité de Paris, Unité HIVInflammation et PersistanceParisFrance
- Bio Sorbonne Paris CitéUniversité de ParisParisFrance
| | - Vidisha Singh
- Université Paris‐SaclayLaboratoire Européen de Recherche pour la Polyarthrite rhumatoïde ‐ GenhotelUniv EvryEvryFrance
| | - Sara Sadat Aghamiri
- Inserm‐ Institut national de la santé et de la recherche médicaleParisFrance
| | - Marcio Luis Acencio
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Enrico Glaab
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Andreas Ruepp
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Gisela Fobo
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Corinna Montrone
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Barbara Brauner
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Goar Frishman
- Institute of Experimental Genetics (IEG)Helmholtz Zentrum München‐German Research Center for Environmental Health (GmbH)NeuherbergGermany
| | - Luis Cristóbal Monraz Gómez
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Julia Somers
- Department of Molecular and Medical GeneticsOregon Health & Sciences UniversityPortlandORUSA
| | - Matti Hoch
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
| | | | - Julia Scheel
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
| | - Hanna Borlinghaus
- Department of Computer and Information ScienceUniversity of KonstanzKonstanzGermany
| | - Tobias Czauderna
- Faculty of Information TechnologyDepartment of Human‐Centred ComputingMonash UniversityClaytonVic.Australia
| | - Falk Schreiber
- Department of Computer and Information ScienceUniversity of KonstanzKonstanzGermany
- Faculty of Information TechnologyDepartment of Human‐Centred ComputingMonash UniversityClaytonVic.Australia
| | | | | | - Akira Funahashi
- Department of Biosciences and InformaticsKeio UniversityYokohamaJapan
| | - Yusuke Hiki
- Department of Biosciences and InformaticsKeio UniversityYokohamaJapan
| | - Noriko Hiroi
- Graduate School of Media and GovernanceResearch Institute at SFCKeio UniversityKanagawaJapan
| | - Takahiro G Yamada
- Department of Biosciences and InformaticsKeio UniversityYokohamaJapan
| | - Andreas Dräger
- Computational Systems Biology of Infections and Antimicrobial‐Resistant PathogensInstitute for Bioinformatics and Medical Informatics (IBMI)University of TübingenTübingenGermany
- Department of Computer ScienceUniversity of TübingenTübingenGermany
- German Center for Infection Research (DZIF), partner siteTübingenGermany
| | - Alina Renz
- Computational Systems Biology of Infections and Antimicrobial‐Resistant PathogensInstitute for Bioinformatics and Medical Informatics (IBMI)University of TübingenTübingenGermany
- Department of Computer ScienceUniversity of TübingenTübingenGermany
| | - Muhammad Naveez
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
- Institute of Applied Computer SystemsRiga Technical UniversityRigaLatvia
| | - Zsolt Bocskei
- Sanofi R&DTranslational SciencesChilly‐MazarinFrance
| | - Francesco Messina
- Dipartimento di Epidemiologia Ricerca Pre‐Clinica e Diagnostica AvanzataNational Institute for Infectious Diseases 'Lazzaro Spallanzani' I.R.C.C.S.RomeItaly
- COVID‐19 INMI Network Medicine for IDs Study GroupNational Institute for Infectious Diseases 'Lazzaro Spallanzani' I.R.C.C.SRomeItaly
| | - Daniela Börnigen
- Bioinformatics Core FacilityUniversitätsklinikum Hamburg‐EppendorfHamburgGermany
| | - Liam Fergusson
- Royal (Dick) School of Veterinary MedicineThe University of EdinburghEdinburghUK
| | - Marta Conti
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Marius Rameil
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Vanessa Nakonecnij
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Jakob Vanhoefer
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
| | - Leonard Schmiester
- Faculty of Mathematics and Natural SciencesUniversity of BonnBonnGermany
- Center for MathematicsChair of Mathematical Modeling of Biological SystemsTechnische Universität MünchenGarchingGermany
| | - Muying Wang
- Department of Chemical and Petroleum EngineeringUniversity of PittsburghPittsburghPAUSA
| | - Emily E Ackerman
- Department of Chemical and Petroleum EngineeringUniversity of PittsburghPittsburghPAUSA
| | - Jason E Shoemaker
- Department of Chemical and Petroleum EngineeringUniversity of PittsburghPittsburghPAUSA
- Department of Computational and Systems BiologyUniversity of PittsburghPittsburghPAUSA
| | | | | | | | | | | | - Kristina Hanspers
- Institute of Data Science and BiotechnologyGladstone InstitutesSan FranciscoCAUSA
| | - Martina Kutmon
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht Centre for Systems Biology (MaCSBio)Maastricht UniversityMaastrichtThe Netherlands
| | - Susan Coort
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Lars Eijssen
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht University Medical CentreMaastrichtThe Netherlands
| | - Friederike Ehrhart
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht University Medical CentreMaastrichtThe Netherlands
| | | | - Denise Slenter
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Marvin Martens
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Nhung Pham
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Robin Haw
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | - Bijay Jassal
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | | | | | - Andrea Senff Ribeiro
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
- Universidade Federal do ParanáCuritibaBrasil
| | - Karen Rothfels
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | | | - Ralf Stephan
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
| | - Cristoffer Sevilla
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | - Thawfeek Varusai
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | - Jean‐Marie Ravel
- INSERM UMR_S 1256Nutrition, Genetics, and Environmental Risk Exposure (NGERE)Faculty of Medicine of NancyUniversity of LorraineNancyFrance
- Laboratoire de génétique médicaleCHRU NancyNancyFrance
| | - Rupsha Fraser
- Queen's Medical Research InstituteThe University of EdinburghEdinburghUK
| | - Vera Ortseifen
- Senior Research Group in Genome Research of Industrial MicroorganismsCenter for BiotechnologyBielefeld UniversityBielefeldGermany
| | - Silvia Marchesi
- Department of Surgical ScienceUppsala UniversityUppsalaSweden
| | - Piotr Gawron
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
- Institute of Computing SciencePoznan University of TechnologyPoznanPoland
| | - Ewa Smula
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Laurent Heirendt
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Guanming Wu
- Department of Medical Informatics and Clinical EpidemiologyOregon Health & Science UniversityPortlandORUSA
| | - Anders Riutta
- Institute of Data Science and BiotechnologyGladstone InstitutesSan FranciscoCAUSA
| | | | - Stuart Owen
- Department of Computer ScienceThe University of ManchesterManchesterUK
| | - Carole Goble
- Department of Computer ScienceThe University of ManchesterManchesterUK
| | - Xiaoming Hu
- Heidelberg Institute for Theoretical Studies (HITS)HeidelbergGermany
| | - Rupert W Overall
- German Center for Neurodegenerative Diseases (DZNE) DresdenDresdenGermany
- Center for Regenerative Therapies Dresden (CRTD)Technische Universität DresdenDresdenGermany
- Institute for BiologyHumboldt University of BerlinBerlinGermany
| | | | | | - Benjamin M Gyori
- Harvard Medical SchoolLaboratory of Systems PharmacologyBostonMAUSA
| | - John A Bachman
- Harvard Medical SchoolLaboratory of Systems PharmacologyBostonMAUSA
| | - Carlos Vega
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Valentin Grouès
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | | | - Pablo Porras
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | - Luana Licata
- Department of BiologyUniversity of Rome Tor VergataRomeItaly
| | | | - Francesca Sacco
- Department of BiologyUniversity of Rome Tor VergataRomeItaly
| | | | | | | | - Denes Turei
- Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Augustin Luna
- cBio Center, Divisions of Biostatistics and Computational BiologyDepartment of Data SciencesDana‐Farber Cancer InstituteBostonMAUSA
- Department of Cell BiologyHarvard Medical SchoolBostonMAUSA
| | - Ozgun Babur
- Computer Science DepartmentUniversity of Massachusetts BostonBostonMAUSA
| | | | - Alberto Valdeolivas
- Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Marina Esteban‐Medina
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
| | - Maria Peña‐Chilet
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
- Bioinformatics in Rare Diseases (BiER)Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER)FPS, Hospital Virgen del RocíoSevillaSpain
| | - Kinza Rian
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
| | - Tomáš Helikar
- Department of BiochemistryUniversity of Nebraska‐LincolnLincolnNEUSA
| | | | - Dezso Modos
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Agatha Treveil
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Marton Olbei
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | | | - Stephane Ballereau
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
| | - Aurélien Dugourd
- Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
- Institute of Experimental Medicine and Systems BiologyFaculty of Medicine, RWTHAachen UniversityAachenGermany
| | | | - Vincent Noël
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Laurence Calzone
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Chris Sander
- cBio Center, Divisions of Biostatistics and Computational BiologyDepartment of Data SciencesDana‐Farber Cancer InstituteBostonMAUSA
- Department of Cell BiologyHarvard Medical SchoolBostonMAUSA
| | - Emek Demir
- Department of Molecular and Medical GeneticsOregon Health & Sciences UniversityPortlandORUSA
| | | | - Tom C Freeman
- The Roslin InstituteUniversity of EdinburghEdinburghUK
| | - Franck Augé
- Sanofi R&DTranslational SciencesChilly‐MazarinFrance
| | | | - Jan Hasenauer
- Helmholtz Zentrum München – German Research Center for Environmental HealthInstitute of Computational BiologyNeuherbergGermany
- Interdisciplinary Research Unit Mathematics and Life SciencesUniversity of BonnBonnGermany
| | - Olaf Wolkenhauer
- Department of Systems Biology and BioinformaticsUniversity of RostockRostockGermany
| | - Egon L Wilighagen
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
| | - Alexander R Pico
- Institute of Data Science and BiotechnologyGladstone InstitutesSan FranciscoCAUSA
| | - Chris T Evelo
- Department of Bioinformatics ‐ BiGCaTNUTRIMMaastricht UniversityMaastrichtThe Netherlands
- Maastricht Centre for Systems Biology (MaCSBio)Maastricht UniversityMaastrichtThe Netherlands
| | - Marc E Gillespie
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
- St. John’s University College of Pharmacy and Health SciencesQueensNYUSA
| | - Lincoln D Stein
- MaRS CentreOntario Institute for Cancer ResearchTorontoONCanada
- Department of Molecular GeneticsUniversity of TorontoTorontoONCanada
| | - Henning Hermjakob
- European Bioinformatics Institute (EMBL‐EBI)European Molecular Biology LaboratoryHinxton, CambridgeshireUK
| | | | | | - Joaquin Dopazo
- Clinical Bioinformatics AreaFundación Progreso y Salud (FPS)Hospital Virgen del RocioSevillaSpain
- Computational Systems Medicine GroupInstitute of Biomedicine of Seville (IBIS)Hospital Virgen del RocioSevillaSpain
- Bioinformatics in Rare Diseases (BiER)Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER)FPS, Hospital Virgen del RocíoSevillaSpain
- FPS/ELIXIR‐esHospital Virgen del RocíoSevillaSpain
| | - Alfonso Valencia
- Barcelona Supercomputing Center (BSC)BarcelonaSpain
- Institució Catalana de Recerca i Estudis Avançats (ICREA)BarcelonaSpain
| | - Hiroaki Kitano
- Systems Biology InstituteTokyoJapan
- Okinawa Institute of Science and Technology Graduate SchoolOkinawaJapan
| | - Emmanuel Barillot
- Institut CuriePSL Research UniversityParisFrance
- INSERMParisFrance
- MINES ParisTechPSL Research UniversityParisFrance
| | - Charles Auffray
- Cancer Research UK Cambridge InstituteUniversity of CambridgeCambridgeUK
| | - Rudi Balling
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems BiomedicineUniversity of LuxembourgEsch‐sur‐AlzetteLuxembourg
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Czerwinska P, Mackiewicz AA. Low Levels of TRIM28-Interacting KRAB-ZNF Genes Associate with Cancer Stemness and Predict Poor Prognosis of Kidney Renal Clear Cell Carcinoma Patients. Cancers (Basel) 2021; 13:cancers13194835. [PMID: 34638319 PMCID: PMC8508054 DOI: 10.3390/cancers13194835] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/16/2021] [Accepted: 09/22/2021] [Indexed: 12/23/2022] Open
Abstract
Simple Summary This is the first report investigating the involvement of TRIM28-interacting KRAB-ZNFs in kidney cancer progression. We demonstrate a significant negative association between KRAB-ZNFs and cancer stemness followed by an attenuated immune-suppressive response and reveal the prognostic role for several KRAB-ZNFs. Our findings may help better understand the molecular basis of kidney cancer and ultimately pave the way to more appropriate prognostic tools and novel therapeutic strategies directly eradicating the dedifferentiated compartment of the tumor. Abstract Krüppel-associated box zinc finger (KRAB-ZNF) proteins are known to regulate diverse biological processes, such as embryonic development, tissue-specific gene expression, and cancer progression. However, their involvement in the regulation of cancer stemness-like phenotype acquisition and maintenance is scarcely explored across solid tumor types, and to date, there are no data for kidney renal clear cell cancer (KIRC). We have harnessed The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database transcriptomic data and used several bioinformatic tools (i.e., GEPIA2, GSCALite, TISIDB, GSEA, CIBERSORT) to verify the relation between the expression and genomic alterations in KRAB-ZNFs and kidney cancer, focusing primarily on tumor dedifferentiation status and antitumor immune response. Our results demonstrate a significant negative correlation between KRAB-ZNFs and kidney cancer dedifferentiation status followed by an attenuated immune-suppressive response. The transcriptomic profiles of high KRAB-ZNF-expressing kidney tumors are significantly enriched with stem cell markers and show a depletion of several inflammatory pathways known for favoring cancer stemness. Moreover, we show for the first time the prognostic role for several KRAB-ZNFs in kidney cancer. Our results provide new insight into the role of selected KRAB-ZNF proteins in kidney cancer development. We believe that our findings may help better understand the molecular basis of KIRC.
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Affiliation(s)
- Patrycja Czerwinska
- Department of Cancer Immunology, Poznan University of Medical Sciences, 15 Garbary St., 61-866 Poznan, Poland; or
- Department of Diagnostics and Cancer Immunology, Greater Poland Cancer Centre, 15 Garbary St., 61-866 Poznan, Poland
- Correspondence: or
| | - Andrzej Adam Mackiewicz
- Department of Cancer Immunology, Poznan University of Medical Sciences, 15 Garbary St., 61-866 Poznan, Poland; or
- Department of Diagnostics and Cancer Immunology, Greater Poland Cancer Centre, 15 Garbary St., 61-866 Poznan, Poland
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Hanspers K, Kutmon M, Coort SL, Digles D, Dupuis LJ, Ehrhart F, Hu F, Lopes EN, Martens M, Pham N, Shin W, Slenter DN, Waagmeester A, Willighagen EL, Winckers LA, Evelo CT, Pico AR. Ten simple rules for creating reusable pathway models for computational analysis and visualization. PLoS Comput Biol 2021; 17:e1009226. [PMID: 34411100 PMCID: PMC8375987 DOI: 10.1371/journal.pcbi.1009226] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Affiliation(s)
- Kristina Hanspers
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, California, United States of America
| | - Martina Kutmon
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Susan L. Coort
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Daniela Digles
- Department of Pharmaceutical Sciences, Division of Pharmaceutical Sciences, University of Vienna, Vienna, Austria
| | - Lauren J. Dupuis
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Friederike Ehrhart
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Finterly Hu
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Elisson N. Lopes
- Instituto de Ciencias Biologicas, Departamento de Bioquimica e Imunologia, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
| | - Marvin Martens
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Nhung Pham
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Woosub Shin
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Denise N. Slenter
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | | | - Egon L. Willighagen
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Laurent A. Winckers
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
| | - Chris T. Evelo
- Department of Bioinformatics—BiGCaT, NUTRIM, Maastricht University, Maastricht, the Netherlands
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, the Netherlands
| | - Alexander R. Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, California, United States of America
- * E-mail:
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62
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Chong LC, Gandhi G, Lee JM, Yeo WWY, Choi SB. Drug Discovery of Spinal Muscular Atrophy (SMA) from the Computational Perspective: A Comprehensive Review. Int J Mol Sci 2021; 22:8962. [PMID: 34445667 PMCID: PMC8396480 DOI: 10.3390/ijms22168962] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 01/27/2021] [Indexed: 01/02/2023] Open
Abstract
Spinal muscular atrophy (SMA), one of the leading inherited causes of child mortality, is a rare neuromuscular disease arising from loss-of-function mutations of the survival motor neuron 1 (SMN1) gene, which encodes the SMN protein. When lacking the SMN protein in neurons, patients suffer from muscle weakness and atrophy, and in the severe cases, respiratory failure and death. Several therapeutic approaches show promise with human testing and three medications have been approved by the U.S. Food and Drug Administration (FDA) to date. Despite the shown promise of these approved therapies, there are some crucial limitations, one of the most important being the cost. The FDA-approved drugs are high-priced and are shortlisted among the most expensive treatments in the world. The price is still far beyond affordable and may serve as a burden for patients. The blooming of the biomedical data and advancement of computational approaches have opened new possibilities for SMA therapeutic development. This article highlights the present status of computationally aided approaches, including in silico drug repurposing, network driven drug discovery as well as artificial intelligence (AI)-assisted drug discovery, and discusses the future prospects.
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Affiliation(s)
- Li Chuin Chong
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
| | - Gayatri Gandhi
- Perdana University Graduate School of Medicine, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (G.G.); (W.W.Y.Y.)
| | - Jian Ming Lee
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
| | - Wendy Wai Yeng Yeo
- Perdana University Graduate School of Medicine, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (G.G.); (W.W.Y.Y.)
| | - Sy-Bing Choi
- Centre for Bioinformatics, School of Data Sciences, Perdana University, Suite 9.2, 9th Floor, Wisma Chase Perdana, Changkat Semantan, Kuala Lumpur 50490, Malaysia; (L.C.C.); (J.M.L.)
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63
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Baltoumas FA, Zafeiropoulou S, Karatzas E, Koutrouli M, Thanati F, Voutsadaki K, Gkonta M, Hotova J, Kasionis I, Hatzis P, Pavlopoulos GA. Biomolecule and Bioentity Interaction Databases in Systems Biology: A Comprehensive Review. Biomolecules 2021; 11:1245. [PMID: 34439912 PMCID: PMC8391349 DOI: 10.3390/biom11081245] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Revised: 08/16/2021] [Accepted: 08/18/2021] [Indexed: 02/06/2023] Open
Abstract
Technological advances in high-throughput techniques have resulted in tremendous growth of complex biological datasets providing evidence regarding various biomolecular interactions. To cope with this data flood, computational approaches, web services, and databases have been implemented to deal with issues such as data integration, visualization, exploration, organization, scalability, and complexity. Nevertheless, as the number of such sets increases, it is becoming more and more difficult for an end user to know what the scope and focus of each repository is and how redundant the information between them is. Several repositories have a more general scope, while others focus on specialized aspects, such as specific organisms or biological systems. Unfortunately, many of these databases are self-contained or poorly documented and maintained. For a clearer view, in this article we provide a comprehensive categorization, comparison and evaluation of such repositories for different bioentity interaction types. We discuss most of the publicly available services based on their content, sources of information, data representation methods, user-friendliness, scope and interconnectivity, and we comment on their strengths and weaknesses. We aim for this review to reach a broad readership varying from biomedical beginners to experts and serve as a reference article in the field of Network Biology.
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Affiliation(s)
- Fotis A. Baltoumas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Sofia Zafeiropoulou
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Evangelos Karatzas
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Mikaela Koutrouli
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, 2200 Copenhagen, Denmark
| | - Foteini Thanati
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Kleanthi Voutsadaki
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Maria Gkonta
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Joana Hotova
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Ioannis Kasionis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
| | - Pantelis Hatzis
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
| | - Georgios A. Pavlopoulos
- Institute for Fundamental Biomedical Research, Biomedical Sciences Research Center “Alexander Fleming”, 16672 Vari, Greece; (S.Z.); (E.K.); (M.K.); (F.T.); (K.V.); (M.G.); (J.H.); (I.K.); (P.H.)
- Center for New Biotechnologies and Precision Medicine, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
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Perscheid C. Comprior: facilitating the implementation and automated benchmarking of prior knowledge-based feature selection approaches on gene expression data sets. BMC Bioinformatics 2021; 22:401. [PMID: 34384353 PMCID: PMC8361636 DOI: 10.1186/s12859-021-04308-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 07/27/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Reproducible benchmarking is important for assessing the effectiveness of novel feature selection approaches applied on gene expression data, especially for prior knowledge approaches that incorporate biological information from online knowledge bases. However, no full-fledged benchmarking system exists that is extensible, provides built-in feature selection approaches, and a comprehensive result assessment encompassing classification performance, robustness, and biological relevance. Moreover, the particular needs of prior knowledge feature selection approaches, i.e. uniform access to knowledge bases, are not addressed. As a consequence, prior knowledge approaches are not evaluated amongst each other, leaving open questions regarding their effectiveness. RESULTS We present the Comprior benchmark tool, which facilitates the rapid development and effortless benchmarking of feature selection approaches, with a special focus on prior knowledge approaches. Comprior is extensible by custom approaches, offers built-in standard feature selection approaches, enables uniform access to multiple knowledge bases, and provides a customizable evaluation infrastructure to compare multiple feature selection approaches regarding their classification performance, robustness, runtime, and biological relevance. CONCLUSION Comprior allows reproducible benchmarking especially of prior knowledge approaches, which facilitates their applicability and for the first time enables a comprehensive assessment of their effectiveness.
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Affiliation(s)
- Cindy Perscheid
- Hasso Plattner Institute, Digital Engineering Faculty, University of Potsdam, Potsdam, Germany.
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65
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Touré V, Flobak Å, Niarakis A, Vercruysse S, Kuiper M. The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling. Brief Bioinform 2021; 22:bbaa390. [PMID: 33378765 PMCID: PMC8294520 DOI: 10.1093/bib/bbaa390] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 11/26/2020] [Accepted: 11/27/2020] [Indexed: 12/16/2022] Open
Abstract
Causal molecular interactions represent key building blocks used in computational modeling, where they facilitate the assembly of regulatory networks. Logical regulatory networks can be used to predict biological and cellular behaviors by system perturbations and in silico simulations. Today, broad sets of causal interactions are available in a variety of biological knowledge resources. However, different visions, based on distinct biological interests, have led to the development of multiple ways to describe and annotate causal molecular interactions. It can therefore be challenging to efficiently explore various resources of causal interaction and maintain an overview of recorded contextual information that ensures valid use of the data. This review lists the different types of public resources with causal interactions, the different views on biological processes that they represent, the various data formats they use for data representation and storage, and the data exchange and conversion procedures that are available to extract and download these interactions. This may further raise awareness among the targeted audience, i.e. logical modelers and other scientists interested in molecular causal interactions, but also database managers and curators, about the abundance and variety of causal molecular interaction data, and the variety of tools and approaches to convert them into one interoperable resource.
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Affiliation(s)
- Vasundra Touré
- Department of Biology of the Norwegian University of Science and Technology
| | | | - Anna Niarakis
- Department of Biology, Univ Evry, University of Paris-Saclay, affiliated with the laboratory GenHotel in Genopole campus, and a delegate at the Lifeware Group, INRIA Saclay
| | - Steven Vercruysse
- Researcher in computer science and computational biology and focuses on building a bridge between human and computer understanding
| | - Martin Kuiper
- systems biology at the Department of Biology of the Norwegian University of Science and Technology
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66
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Postic G, Andreani J, Marcoux J, Reys V, Guerois R, Rey J, Mouton-Barbosa E, Vandenbrouck Y, Cianferani S, Burlet-Schiltz O, Labesse G, Tufféry P. Proteo3Dnet: a web server for the integration of structural information with interactomics data. Nucleic Acids Res 2021; 49:W567-W572. [PMID: 33963857 PMCID: PMC8262742 DOI: 10.1093/nar/gkab332] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Revised: 04/09/2021] [Accepted: 04/19/2021] [Indexed: 01/01/2023] Open
Abstract
Proteo3Dnet is a web server dedicated to the analysis of mass spectrometry interactomics experiments. Given a flat list of proteins, its aim is to organize it in terms of structural interactions to provide a clearer overview of the data. This is achieved using three means: (i) the search for interologs with resolved structure available in the protein data bank, including cross-species remote homology search, (ii) the search for possibly weaker interactions mediated through Short Linear Motifs as predicted by ELM-a unique feature of Proteo3Dnet, (iii) the search for protein-protein interactions physically validated in the BioGRID database. The server then compiles this information and returns a graph of the identified interactions and details about the different searches. The graph can be interactively explored to understand the way the core complexes identified could interact. It can also suggest undetected partners to the experimentalists, or specific cases of conditionally exclusive binding. The interest of Proteo3Dnet, previously demonstrated for the difficult cases of the proteasome and pragmin complexes data is, here, illustrated in the context of yeast precursors to the small ribosomal subunits and the smaller interactome of 14-3-3zeta frequent interactors. The Proteo3Dnet web server is accessible at http://bioserv.rpbs.univ-paris-diderot.fr/services/Proteo3Dnet/.
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Affiliation(s)
- Guillaume Postic
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France.,Institut Français de Bioinformatique (IFB), UMS 3601-CNRS, Université Paris-Saclay, Orsay, France
| | - Jessica Andreani
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Julien Marcoux
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France
| | - Victor Reys
- Centre de Biochimie Structurale (CBS), CNRS, INSERM, Univ Montpellier, Montpellier, France
| | - Raphaël Guerois
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Julien Rey
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
| | - Emmanuelle Mouton-Barbosa
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France
| | - Yves Vandenbrouck
- Univ. Grenoble Alpes, INSERM, CEA, UMR BioSanté U1292, CNRS, CEA, FR2048, 38000 Grenoble, France
| | - Sarah Cianferani
- Laboratoire de Spectrométrie de Masse BioOrganique, Université de Strasbourg, CNRS, IPHC UMR 7178, 67000 Strasbourg, France
| | - Odile Burlet-Schiltz
- Institut de Pharmacologie et de Biologie Structurale (IPBS), Université de Toulouse, CNRS, UPS, Toulouse, France
| | - Gilles Labesse
- Centre de Biochimie Structurale (CBS), CNRS, INSERM, Univ Montpellier, Montpellier, France
| | - Pierre Tufféry
- Université de Paris, CNRS UMR 8251, INSERM U1133, RPBS, Paris 75205, France
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67
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Teschendorff AE, Maity AK, Hu X, Weiyan C, Lechner M. Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-Seq data. Bioinformatics 2021; 37:1528-1534. [PMID: 33244588 PMCID: PMC8275983 DOI: 10.1093/bioinformatics/btaa987] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 10/26/2020] [Accepted: 11/13/2020] [Indexed: 01/16/2023] Open
Abstract
Motivation An important task in the analysis of single-cell RNA-Seq data is the estimation of differentiation potency, as this can help identify stem-or-multipotent cells in non-temporal studies or in tissues where differentiation hierarchies are not well established. A key challenge in the estimation of single-cell potency is the need for a fast and accurate algorithm, scalable to large scRNA-Seq studies profiling millions of cells. Results Here, we present a single-cell potency measure, called Correlation of Connectome and Transcriptome (CCAT), which can return accurate single-cell potency estimates of a million cells in minutes, a 100-fold improvement over current state-of-the-art methods. We benchmark CCAT against 8 other single-cell potency models and across 28 scRNA-Seq studies, encompassing over 2 million cells, demonstrating comparable accuracy than the current state-of-the-art, at a significantly reduced computational cost, and with increased robustness to dropouts. Availability and implementation CCAT is part of the SCENT R-package, freely available from https://github.com/aet21/SCENT. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Andrew E Teschendorff
- CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China.,UCL Cancer Institute, University College London, London WC1E 6BT, UK
| | - Alok K Maity
- CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Xue Hu
- CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Chen Weiyan
- CAS Key Lab of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China
| | - Matthias Lechner
- UCL Cancer Institute, University College London, London WC1E 6BT, UK.,Department of Otolaryngology-Head and Neck Surgery, Stanford University School of Medicine, Palo Alto, CA 94305-5739, USA
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68
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Abstract
Single-cell omics is transforming our understanding of cell biology and disease, yet the systems-level analysis and interpretation of single-cell data faces many challenges. In this Perspective, we describe the impact that fundamental concepts from statistical mechanics, notably entropy, stochastic processes and critical phenomena, are having on single-cell data analysis. We further advocate the need for more bottom-up modelling of single-cell data and to embrace a statistical mechanics analysis paradigm to help attain a deeper understanding of single-cell systems biology.
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Affiliation(s)
- Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, Shanghai Institutes for Biological Sciences, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China.
- UCL Cancer Institute, University College London, London, UK.
| | - Andrew P Feinberg
- Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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69
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Fotedar R, Sankaranarayanan K, Caldwell ME, Zeyara A, Al Malki A, Kaul R, Al Shamari H, Ali M, Al Marri M, Lawson PA. Reclassification of Facklamia ignava, Facklamia sourekii and Facklamia tabacinasalis as Falseniella ignava gen. nov., comb. nov., Hutsoniella sourekii gen. nov., comb. nov., and Ruoffia tabacinasalis gen. nov., comb. nov., and description of Ruoffia halotolerans sp. nov., isolated from hypersaline Inland Sea of Qatar. Antonie van Leeuwenhoek 2021; 114:1181-1193. [PMID: 34181136 DOI: 10.1007/s10482-021-01587-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 04/24/2021] [Indexed: 11/24/2022]
Abstract
A Gram-stain-positive, non-pigmented, coccus-shaped, facultatively anaerobic and α-hemolytic bacterium designated as INB8T was isolated from a hypersaline marine water sample collected at the Inland Sea of Qatar. The isolate was able to grow at 25-40 °C (optimum, 30 °C), at pH 5-11 and with 2-8% NaCl. Phylogenetic analysis based on 16S rRNA gene sequences indicated that strain INB8T was placed within the family Aerococcaceae with the highest sequence similarity to Facklamia tabacinasalis CCUG 30090T (99.5%), followed by Facklamia hominis CCUG 36813T (93.9%), Facklamia sourekii Y17312T (93.8%), Facklamia ignava CCUG 37419T (93.6%), Facklamia miroungae CCUG 42728T (93.5%), Suicoccus acidiformans ZY16052T (93.5%), Facklamia languida CCUG 37842T (93.2%), Ignavigranum ruoffiae (93.1%), and Dolosicoccus paucivorans DSM 15742T (90.8%). Average nucleotide identity and digital DNA-DNA hybridization values between strain INB8T and F. tabacinasalis CCUG 30090T were determined to be 94.5% and 58.9% respectively, confirming strain INB8T represents a novel species. The major fatty acids were C14:0, C16:0, C18:0 and C18:1 ω9c. The G + C content of strain INB8T determined from the genome was 36.3 mol%. Based on the phylogenetic, chemotaxonomic and phenotypic information, it is proposed that Facklamia tabacinasalis should be reclassified as Ruoffia tabacinasalis, Facklamia ignava be reclassified as Falseniella ignava, and Facklamia sourekii be reclassified Hutsoniella sourekii. It is further proposed that strain INB8T should be classified as a species of the genus Ruoffia for which the name Ruoffia halotolerans sp. nov. is proposed. The type strain is INB8T (= LMG 30291T = CCUG 70701T = QCC/B60/17T).
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Affiliation(s)
- Rashmi Fotedar
- Department of Genetic Engineering, Biotechnology Centre, Ministry of Municipality and Environment, Doha, Qatar.
| | - Krithivasan Sankaranarayanan
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA.,Laboratories of Molecular Anthropology and Microbiome Research, University of Oklahoma, Norman, OK, 73019, USA
| | - Matthew E Caldwell
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA
| | - Aisha Zeyara
- Department of Genetic Engineering, Biotechnology Centre, Ministry of Municipality and Environment, Doha, Qatar
| | - Amina Al Malki
- Department of Genetic Engineering, Biotechnology Centre, Ministry of Municipality and Environment, Doha, Qatar
| | - Ridhima Kaul
- Weill Cornell Medical College in Qatar, Doha, Qatar
| | - Hamad Al Shamari
- Department of Genetic Engineering, Biotechnology Centre, Ministry of Municipality and Environment, Doha, Qatar
| | - Mohammad Ali
- Equine Veterinary Medical Centre, A Member of Qatar Foundation, Al Rayan, Qatar
| | - Masoud Al Marri
- Department of Genetic Engineering, Biotechnology Centre, Ministry of Municipality and Environment, Doha, Qatar
| | - Paul A Lawson
- Department of Microbiology and Plant Biology, University of Oklahoma, Norman, OK, 73019, USA
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70
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Hu T, Zhao G, Liu Y, Long M. A Machine Learning Approach to Differentiate Two Specific Breast Cancer Subtypes Using Androgen Receptor Pathway Genes. Technol Cancer Res Treat 2021; 20:15330338211027900. [PMID: 34159849 PMCID: PMC8226237 DOI: 10.1177/15330338211027900] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Triple-negative breast cancer is a heterogeneous disease with different molecular
and histological subtypes. The Androgen receptor is expressed in a portion of
triple-negative breast cancer cases and the activation of the androgen receptor
pathway is thought to be a molecular subtyping signature as well as a
therapeutic target for triple-negative breast cancer. Thus, identification of
the androgen receptor pathway status is important for both molecular
characterization andclinical management. In this study, we investigate the
expression of the androgen receptor pathway in metaplastic breast cancer and
luminal androgen receptor subtypes of triple-negative breast cancer and found
that the androgen receptor pathway was downregulated in metaplastic breast
cancer compared to luminal androgen receptor subtype. Using random forest, we
found that the two subtypes of breast cancer can be molecularly classified with
the gene expression of the androgen receptor pathway.
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Affiliation(s)
- Taobo Hu
- Department of Breast Surgery, 71185Peking University People's Hospital, Beijing, China
| | - Guiyang Zhao
- Department of Oncology, Beijing Changping Hospital, Beijing, China
| | - Yiqiang Liu
- Department of Pathology, 71185Peking University Cancer Hospital, Beijing, China
| | - Mengping Long
- Department of Pathology, 71185Peking University Cancer Hospital, Beijing, China
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71
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Sinha R, Luna A, Schultz N, Sander C. A pan-cancer survey of cell line tumor similarity by feature-weighted molecular profiles. CELL REPORTS METHODS 2021; 1:100039. [PMID: 35475239 PMCID: PMC9017219 DOI: 10.1016/j.crmeth.2021.100039] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 03/31/2021] [Accepted: 05/24/2021] [Indexed: 02/07/2023]
Abstract
Patient-derived cell lines are often used in pre-clinical cancer research, but some cell lines are too different from tumors to be good models. Comparison of genomic and expression profiles can guide the choice of pre-clinical models, but typically not all features are equally relevant. We present TumorComparer, a computational method for comparing cellular profiles with higher weights on functional features of interest. In this pan-cancer application, we compare ∼600 cell lines and ∼8,000 tumor samples of 24 cancer types, using weights to emphasize known oncogenic alterations. We characterize the similarity of cell lines and tumors within and across cancers by using multiple datum types and rank cell lines by their inferred quality as representative models. Beyond the assessment of cell lines, the weighted similarity approach is adaptable to patient stratification in clinical trials and personalized medicine.
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Affiliation(s)
- Rileen Sinha
- Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Augustin Luna
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Boston, MA 02142, USA
| | - Nikolaus Schultz
- Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Chris Sander
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Boston, MA 02142, USA
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72
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TSCCA: A tensor sparse CCA method for detecting microRNA-gene patterns from multiple cancers. PLoS Comput Biol 2021; 17:e1009044. [PMID: 34061840 PMCID: PMC8195367 DOI: 10.1371/journal.pcbi.1009044] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 06/11/2021] [Accepted: 05/05/2021] [Indexed: 12/22/2022] Open
Abstract
Existing studies have demonstrated that dysregulation of microRNAs (miRNAs or miRs) is involved in the initiation and progression of cancer. Many efforts have been devoted to identify microRNAs as potential biomarkers for cancer diagnosis, prognosis and therapeutic targets. With the rapid development of miRNA sequencing technology, a vast amount of miRNA expression data for multiple cancers has been collected. These invaluable data repositories provide new paradigms to explore the relationship between miRNAs and cancer. Thus, there is an urgent need to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data in a pan-cancer paradigm. In this study, we present a tensor sparse canonical correlation analysis (TSCCA) method for identifying cancer-related miRNA-gene modules across multiple cancers. TSCCA is able to overcome the drawbacks of existing solutions and capture both the cancer-shared and specific miRNA-gene co-expressed modules with better biological interpretations. We comprehensively evaluate the performance of TSCCA using a set of simulated data and matched miRNA/gene expression data across 33 cancer types from the TCGA database. We uncover several dysfunctional miRNA-gene modules with important biological functions and statistical significance. These modules can advance our understanding of miRNA regulatory mechanisms of cancer and provide insights into miRNA-based treatments for cancer. MicroRNAs (miRNAs) are a class of small non-coding RNAs. Previous studies have revealed that miRNA-gene regulatory modules play key roles in the occurrence and development of cancer. However, little has been done to discover miRNA-gene regulatory modules from a pan-cancer view. Thus, it is urgently needed to develop new methods to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data of multi-cancers. To build the connections between miRNA-gene regulatory modules across different cancer types, we propose a tensor sparse canonical correlation analysis (TSCCA) method. Our specific contributions are two-fold: (1) We propose a sparse statistical learning model TSCCA and an efficient block-coordinate descent algorithm to solve it. (2) We apply TSCCA to a multi-omics data set of 33 cancer types from TCGA and identify some cancer-related miRNA-gene modules with important biological functions and statistical significance.
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73
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Wang F, Yuan C, Wu HZ, Liu B, Yang YF. Bioinformatics, Molecular Docking and Experiments In Vitro Analyze the Prognostic Value of CXC Chemokines in Breast Cancer. Front Oncol 2021; 11:665080. [PMID: 34123826 PMCID: PMC8189319 DOI: 10.3389/fonc.2021.665080] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 04/28/2021] [Indexed: 12/24/2022] Open
Abstract
The increasing incidence and mortality rate of Breast cancer (BC) make it a major public health problem around the world. CXC chemokines can mediate the migration of immune cells and regulate apoptosis in tumor. However, the expression and prognostic value of them in BC and their targeted drugs have not been clarified. Therefore, in this study, ONCOMINE, GEPIA2.0, UALCAN, Venny2.1.0, cBioPortal, STRING, Gene MANIA, Pathway Commons, DAVID6.8, Omicshare, Cytoscape3.6.1, TIMER2.0, Drug Bank, TCMSP, RSCBPDB, PubChem, pkCSM, Chem Draw, AutoDockTools-1.5.6 and PyMOL were utilized for analysis. The expression of CXCL1-3, CXCL9-13 between BC and normal tissues was significantly different in all the three databases. And the expression of CXCL1-2, CXCL12-13 was correlated with the stages of BC. But only CXCL1-3 were prone to mutation, and negatively correlated with survival and prognosis of BC patients. Taken together, CXCL1-2 might be therapeutic targets and biomarkers for BC patients. In addition, both of them were associated with immune infiltration. The results of molecular docking showed that Quercetin was most likely to be developed as drugs that interacted directly with CXCL1-2. And GLU29 of CXCL1, ASP-1, PRO-96, TRP-47 and LEU-45 of CXCL2 were the most potential sites, which provided valuable reference for further study of pharmacodynamics and mechanism. In addition, the inhibitory effect of Quercetin on proliferation and promoting apoptosis of BC related cell lines were confirmed in vitro. Western blot and Real-Time PCR confirmed that it increased the expression of CXCL1-2 in MDA-MB-231 and MCF-7 cells.
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Affiliation(s)
- Fei Wang
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - Chong Yuan
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - He-Zhen Wu
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China.,Key Laboratory of Traditional Chinese Medicine Resources and Chemistry of Hubei Province, Hubei University of Chinese Medicine, Wuhan, China.,Collaborative Innovation Center of Traditional Chinese Medicine of New Products for Geriatrics of Hubei Province, Research Department of Material Basis of Traditional Chinese Medicine and Prescription, Wuhan, China.,Key Laboratory of Traditional Chinese Medicine Resource and Compound Preparation Ministry of Education, Hubei University of Chinese Medicine, Wuhan, China
| | - Bo Liu
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China.,Key Laboratory of Traditional Chinese Medicine Resources and Chemistry of Hubei Province, Hubei University of Chinese Medicine, Wuhan, China.,Collaborative Innovation Center of Traditional Chinese Medicine of New Products for Geriatrics of Hubei Province, Research Department of Material Basis of Traditional Chinese Medicine and Prescription, Wuhan, China.,Key Laboratory of Traditional Chinese Medicine Resource and Compound Preparation Ministry of Education, Hubei University of Chinese Medicine, Wuhan, China
| | - Yan-Fang Yang
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China.,Key Laboratory of Traditional Chinese Medicine Resources and Chemistry of Hubei Province, Hubei University of Chinese Medicine, Wuhan, China.,Collaborative Innovation Center of Traditional Chinese Medicine of New Products for Geriatrics of Hubei Province, Research Department of Material Basis of Traditional Chinese Medicine and Prescription, Wuhan, China.,Key Laboratory of Traditional Chinese Medicine Resource and Compound Preparation Ministry of Education, Hubei University of Chinese Medicine, Wuhan, China
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74
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Liu DY, Liu JC, Liang S, Meng XH, Greenbaum J, Xiao HM, Tan LJ, Deng HW. Drug Repurposing for COVID-19 Treatment by Integrating Network Pharmacology and Transcriptomics. Pharmaceutics 2021; 13:545. [PMID: 33919660 PMCID: PMC8069812 DOI: 10.3390/pharmaceutics13040545] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/25/2021] [Accepted: 03/26/2021] [Indexed: 12/19/2022] Open
Abstract
Since coronavirus disease 2019 (COVID-19) is a serious new worldwide public health crisis with significant morbidity and mortality, effective therapeutic treatments are urgently needed. Drug repurposing is an efficient and cost-effective strategy with minimum risk for identifying novel potential treatment options by repositioning therapies that were previously approved for other clinical outcomes. Here, we used an integrated network-based pharmacologic and transcriptomic approach to screen drug candidates novel for COVID-19 treatment. Network-based proximity scores were calculated to identify the drug-disease pharmacological effect between drug-target relationship modules and COVID-19 related genes. Gene set enrichment analysis (GSEA) was then performed to determine whether drug candidates influence the expression of COVID-19 related genes and examine the sensitivity of the repurposing drug treatment to peripheral immune cell types. Moreover, we used the complementary exposure model to recommend potential synergistic drug combinations. We identified 18 individual drug candidates including nicardipine, orantinib, tipifarnib and promethazine which have not previously been proposed as possible treatments for COVID-19. Additionally, 30 synergistic drug pairs were ultimately recommended including fostamatinib plus tretinoin and orantinib plus valproic acid. Differential expression genes of most repurposing drugs were enriched significantly in B cells. The findings may potentially accelerate the discovery and establishment of an effective therapeutic treatment plan for COVID-19 patients.
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Affiliation(s)
- Dan-Yang Liu
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410081, China;
| | - Jia-Chen Liu
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China; (J.-C.L.); (S.L.); (X.-H.M.); (H.-M.X.)
| | - Shuang Liang
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China; (J.-C.L.); (S.L.); (X.-H.M.); (H.-M.X.)
| | - Xiang-He Meng
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China; (J.-C.L.); (S.L.); (X.-H.M.); (H.-M.X.)
| | - Jonathan Greenbaum
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, USA;
| | - Hong-Mei Xiao
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China; (J.-C.L.); (S.L.); (X.-H.M.); (H.-M.X.)
| | - Li-Jun Tan
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410081, China;
| | - Hong-Wen Deng
- Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha 410081, China;
- Center for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha 410013, China; (J.-C.L.); (S.L.); (X.-H.M.); (H.-M.X.)
- Tulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, LA 70112, USA;
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75
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Moya-García AA, Pino-Ángeles A, Sánchez-Jiménez F, Urdiales JL, Medina MÁ. Histamine, Metabolic Remodelling and Angiogenesis: A Systems Level Approach. Biomolecules 2021; 11:415. [PMID: 33799732 PMCID: PMC8000605 DOI: 10.3390/biom11030415] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 03/05/2021] [Accepted: 03/08/2021] [Indexed: 12/11/2022] Open
Abstract
Histamine is a highly pleiotropic biogenic amine involved in key physiological processes including neurotransmission, immune response, nutrition, and cell growth and differentiation. Its effects, sometimes contradictory, are mediated by at least four different G-protein coupled receptors, which expression and signalling pathways are tissue-specific. Histamine metabolism conforms a very complex network that connect many metabolic processes important for homeostasis, including nitrogen and energy metabolism. This review brings together and analyses the current information on the relationships of the "histamine system" with other important metabolic modules in human physiology, aiming to bridge current information gaps. In this regard, the molecular characterization of the role of histamine in the modulation of angiogenesis-mediated processes, such as cancer, makes a promising research field for future biomedical advances.
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Affiliation(s)
- Aurelio A. Moya-García
- Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, 29071 Málaga, Spain; (A.A.M.-G.); (M.Á.M.)
- Instituto de Investigación Biomédica de Málaga (IBIMA), 29010 Málaga, Spain
| | - Almudena Pino-Ángeles
- Unidad de Lípidos y Arteriosclerosis, Servicio de Medicina Interna, Hospital Universitario Reina Sofia, Instituto Maimonides de Investigación Biomédica de Córdoba (IMIBIC), Universidad de Córdoba, 14004 Córdoba, Spain;
- Centro de Investigación Biomédica en Red de Fisiopatología de la Obesidad y la Nutrición (CIBEROBN), Instituto de Salud Carlos III, 14004 Córdoba, Spain
| | - Francisca Sánchez-Jiménez
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, 29010 Málaga, Spain;
| | - José Luis Urdiales
- Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, 29071 Málaga, Spain; (A.A.M.-G.); (M.Á.M.)
- Instituto de Investigación Biomédica de Málaga (IBIMA), 29010 Málaga, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, 29010 Málaga, Spain;
| | - Miguel Ángel Medina
- Departamento de Biología Molecular y Bioquímica, Universidad de Málaga, 29071 Málaga, Spain; (A.A.M.-G.); (M.Á.M.)
- Instituto de Investigación Biomédica de Málaga (IBIMA), 29010 Málaga, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, 29010 Málaga, Spain;
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76
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Vlachavas EI, Bohn J, Ückert F, Nürnberg S. A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research. Int J Mol Sci 2021; 22:2822. [PMID: 33802234 PMCID: PMC8000236 DOI: 10.3390/ijms22062822] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/05/2021] [Accepted: 03/05/2021] [Indexed: 02/06/2023] Open
Abstract
Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.
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Affiliation(s)
- Efstathios Iason Vlachavas
- Medical Informatics for Translational Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (J.B.); (F.Ü.)
| | - Jonas Bohn
- Medical Informatics for Translational Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (J.B.); (F.Ü.)
| | - Frank Ückert
- Medical Informatics for Translational Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (J.B.); (F.Ü.)
- Applied Medical Informatics, University Hospital Hamburg-Eppendorf, 20251 Hamburg, Germany
| | - Sylvia Nürnberg
- Medical Informatics for Translational Oncology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; (J.B.); (F.Ü.)
- Applied Medical Informatics, University Hospital Hamburg-Eppendorf, 20251 Hamburg, Germany
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77
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Bhowmick P, Roome S, Borchers CH, Goodlett DR, Mohammed Y. An Update on MRMAssayDB: A Comprehensive Resource for Targeted Proteomics Assays in the Community. J Proteome Res 2021; 20:2105-2115. [PMID: 33683131 PMCID: PMC8041396 DOI: 10.1021/acs.jproteome.0c00961] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Precise multiplexed
quantification of proteins in biological samples
can be achieved by targeted proteomics using multiple or parallel
reaction monitoring (MRM/PRM). Combined with internal standards, the
method achieves very good repeatability and reproducibility enabling
excellent protein quantification and allowing longitudinal and cohort
studies. A laborious part of performing such experiments lies in the
preparation steps dedicated to the development and validation of individual
protein assays. Several public repositories host information on targeted
proteomics assays, including NCI’s Clinical Proteomic Tumor
Analysis Consortium assay portals, PeptideAtlas SRM Experiment Library,
SRMAtlas, PanoramaWeb, and PeptideTracker, with all offering varying
levels of details. We introduced MRMAssayDB in 2018 as an integrated
resource for targeted proteomics assays. The Web-based application
maps and links the assays from the repositories, includes comprehensive
up-to-date protein and sequence annotations, and provides multiple
visualization options on the peptide and protein level. We have extended
MRMAssayDB with more assays and extensive annotations. Currently it
contains >828 000 assays covering >51 000 proteins
from
94 organisms, of which >17 000 proteins are present in >2400
biological pathways, and >48 000 mapping to >21 000
Gene Ontology terms. This is an increase of about four times the number
of assays since introduction. We have expanded annotations of interaction,
biological pathways, and disease associations. A newly added visualization
module for coupled molecular structural annotation browsing allows
the user to interactively examine peptide sequence and any known PTMs
and disease mutations, and map all to available protein 3D structures.
Because of its integrative approach, MRMAssayDB enables a holistic
view of suitable proteotypic peptides and commonly used transitions
in empirical data. Availability: http://mrmassaydb.proteincentre.com.
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Affiliation(s)
- Pallab Bhowmick
- University of Victoria - Genome BC Proteomics Centre, Victoria, British Columbia V8Z 7X8, Canada.,University of Victoria, Victoria, British Columbia V8P 5C2, Canada
| | - Simon Roome
- University of Victoria - Genome BC Proteomics Centre, Victoria, British Columbia V8Z 7X8, Canada.,University of Victoria, Victoria, British Columbia V8P 5C2, Canada
| | - Christoph H Borchers
- Proteomics Centre, Segal Cancer Centre, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec H3T 1E2, Canada.,Gerald Bronfman Department of Oncology, Jewish General Hospital, Montreal, Quebec H3T 1E2, Canada.,Department of Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Nobel Street, Moscow 121205, Russia
| | - David R Goodlett
- University of Victoria - Genome BC Proteomics Centre, Victoria, British Columbia V8Z 7X8, Canada.,University of Victoria, Victoria, British Columbia V8P 5C2, Canada.,University of Gdansk, International Centre for Cancer Vaccine Science, 80-309 Gdansk, Poland
| | - Yassene Mohammed
- University of Victoria - Genome BC Proteomics Centre, Victoria, British Columbia V8Z 7X8, Canada.,University of Victoria, Victoria, British Columbia V8P 5C2, Canada.,Center for Proteomics and Metabolomics, Leiden University Medical Center, 2333 ZA Leiden, Netherlands
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78
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Kenner B, Chari ST, Kelsen D, Klimstra DS, Pandol SJ, Rosenthal M, Rustgi AK, Taylor JA, Yala A, Abul-Husn N, Andersen DK, Bernstein D, Brunak S, Canto MI, Eldar YC, Fishman EK, Fleshman J, Go VLW, Holt JM, Field B, Goldberg A, Hoos W, Iacobuzio-Donahue C, Li D, Lidgard G, Maitra A, Matrisian LM, Poblete S, Rothschild L, Sander C, Schwartz LH, Shalit U, Srivastava S, Wolpin B. Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review. Pancreas 2021; 50:251-279. [PMID: 33835956 PMCID: PMC8041569 DOI: 10.1097/mpa.0000000000001762] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
ABSTRACT Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.
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Affiliation(s)
| | - Suresh T. Chari
- Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | - David S. Klimstra
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Stephen J. Pandol
- Basic and Translational Pancreas Research Program, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA
| | | | - Anil K. Rustgi
- Division of Digestive and Liver Diseases, Department of Medicine, NewYork-Presbyterian/Columbia University Irving Medical Center, New York, NY
| | | | - Adam Yala
- Department of Electrical Engineering and Computer Science
- Jameel Clinic, Massachusetts Institute of Technology, Cambridge, MA
| | - Noura Abul-Husn
- Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine, Mount Sinai, New York, NY
| | - Dana K. Andersen
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD
| | | | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Copenhagen, Denmark
| | - Marcia Irene Canto
- Division of Gastroenterology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Yonina C. Eldar
- Department of Math and Computer Science, Weizmann Institute of Science, Rehovot, Israel
| | - Elliot K. Fishman
- Department of Radiology and Radiological Science, Johns Hopkins Medicine, Baltimore, MD
| | | | - Vay Liang W. Go
- UCLA Center for Excellence in Pancreatic Diseases, University of California, Los Angeles, Los Angeles, CA
| | | | - Bruce Field
- From the Kenner Family Research Fund, New York, NY
| | - Ann Goldberg
- From the Kenner Family Research Fund, New York, NY
| | | | - Christine Iacobuzio-Donahue
- David M. Rubenstein Center for Pancreatic Cancer Research, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Debiao Li
- Biomedical Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA
| | | | - Anirban Maitra
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX
| | | | | | | | | | - Lawrence H. Schwartz
- Department of Radiology, NewYork-Presbyterian Hospital/Columbia University Irving Medical Center, New York, NY
| | - Uri Shalit
- Faculty of Industrial Engineering and Management, Technion—Israel Institute of Technology, Haifa, Israel
| | - Sudhir Srivastava
- Division of Cancer Prevention, National Cancer Institute, Bethesda, MD
| | - Brian Wolpin
- Gastrointestinal Cancer Center, Dana-Farber Cancer Institute, Boston, MA
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79
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Magnano CS, Gitter A. Automating parameter selection to avoid implausible biological pathway models. NPJ Syst Biol Appl 2021; 7:12. [PMID: 33623016 PMCID: PMC7902638 DOI: 10.1038/s41540-020-00167-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Accepted: 12/07/2020] [Indexed: 11/28/2022] Open
Abstract
A common way to integrate and analyze large amounts of biological "omic" data is through pathway reconstruction: using condition-specific omic data to create a subnetwork of a generic background network that represents some process or cellular state. A challenge in pathway reconstruction is that adjusting pathway reconstruction algorithms' parameters produces pathways with drastically different topological properties and biological interpretations. Due to the exploratory nature of pathway reconstruction, there is no ground truth for direct evaluation, so parameter tuning methods typically used in statistics and machine learning are inapplicable. We developed the pathway parameter advising algorithm to tune pathway reconstruction algorithms to minimize biologically implausible predictions. We leverage background knowledge in pathway databases to select pathways whose high-level structure resembles that of manually curated biological pathways. At the core of this method is a graphlet decomposition metric, which measures topological similarity to curated biological pathways. In order to evaluate pathway parameter advising, we compare its performance in avoiding implausible networks and reconstructing pathways from the NetPath database with other parameter selection methods across four pathway reconstruction algorithms. We also demonstrate how pathway parameter advising can guide reconstruction of an influenza host factor network. Pathway parameter advising is method agnostic; it is applicable to any pathway reconstruction algorithm with tunable parameters.
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Affiliation(s)
- Chris S Magnano
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA
- Morgridge Institute for Research, Madison, WI, USA
| | - Anthony Gitter
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA.
- Morgridge Institute for Research, Madison, WI, USA.
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA.
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80
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Ietswaart R, Gyori BM, Bachman JA, Sorger PK, Churchman LS. GeneWalk identifies relevant gene functions for a biological context using network representation learning. Genome Biol 2021; 22:55. [PMID: 33526072 PMCID: PMC7852222 DOI: 10.1186/s13059-021-02264-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 01/05/2021] [Indexed: 12/13/2022] Open
Abstract
A bottleneck in high-throughput functional genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Gene Ontology (GO) enrichment methods provide insight at the gene set level. Here, we introduce GeneWalk ( github.com/churchmanlab/genewalk ) that identifies individual genes and their relevant functions critical for the experimental setting under examination. After the automatic assembly of an experiment-specific gene regulatory network, GeneWalk uses representation learning to quantify the similarity between vector representations of each gene and its GO annotations, yielding annotation significance scores that reflect the experimental context. By performing gene- and condition-specific functional analysis, GeneWalk converts a list of genes into data-driven hypotheses.
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Affiliation(s)
- Robert Ietswaart
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA
| | - Benjamin M Gyori
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | - John A Bachman
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | - L Stirling Churchman
- Department of Genetics, Blavatnik Institute, Harvard Medical School, Boston, MA, 02115, USA.
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81
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Luna A, Elloumi F, Varma S, Wang Y, Rajapakse VN, Aladjem MI, Robert J, Sander C, Pommier Y, Reinhold WC. CellMiner Cross-Database (CellMinerCDB) version 1.2: Exploration of patient-derived cancer cell line pharmacogenomics. Nucleic Acids Res 2021; 49:D1083-D1093. [PMID: 33196823 DOI: 10.1093/nar/gkaa968] [Citation(s) in RCA: 100] [Impact Index Per Article: 33.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 09/25/2020] [Accepted: 10/19/2020] [Indexed: 12/13/2022] Open
Abstract
CellMiner Cross-Database (CellMinerCDB, discover.nci.nih.gov/cellminercdb) allows integration and analysis of molecular and pharmacological data within and across cancer cell line datasets from the National Cancer Institute (NCI), Broad Institute, Sanger/MGH and MD Anderson Cancer Center (MDACC). We present CellMinerCDB 1.2 with updates to datasets from NCI-60, Broad Cancer Cell Line Encyclopedia and Sanger/MGH, and the addition of new datasets, including NCI-ALMANAC drug combination, MDACC Cell Line Project proteomic, NCI-SCLC DNA copy number and methylation data, and Broad methylation, genetic dependency and metabolomic datasets. CellMinerCDB (v1.2) includes several improvements over the previously published version: (i) new and updated datasets; (ii) support for pattern comparisons and multivariate analyses across data sources; (iii) updated annotations with drug mechanism of action information and biologically relevant multigene signatures; (iv) analysis speedups via caching; (v) a new dataset download feature; (vi) improved visualization of subsets of multiple tissue types; (vii) breakdown of univariate associations by tissue type; and (viii) enhanced help information. The curation and common annotations (e.g. tissues of origin and identifiers) provided here across pharmacogenomic datasets increase the utility of the individual datasets to address multiple researcher question types, including data reproducibility, biomarker discovery and multivariate analysis of drug activity.
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Affiliation(s)
- Augustin Luna
- cBio Center, Dana-Farber Cancer Institute and Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
| | - Fathi Elloumi
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA.,General Dynamics Information Technology Inc., Fairfax, VA 22042, USA
| | - Sudhir Varma
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA.,HiThru Analytics LLC, Princeton, NJ 08540, USA
| | - Yanghsin Wang
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA.,General Dynamics Information Technology Inc., Fairfax, VA 22042, USA
| | - Vinodh N Rajapakse
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Mirit I Aladjem
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - Jacques Robert
- Inserm unité 1218, Université de Bordeaux, Bordeaux 33076, France
| | - Chris Sander
- cBio Center, Dana-Farber Cancer Institute and Department of Cell Biology, Harvard Medical School, Boston, MA 02215, USA
| | - Yves Pommier
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
| | - William C Reinhold
- Developmental Therapeutics Branch, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD 20892, USA
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82
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Wörheide MA, Krumsiek J, Kastenmüller G, Arnold M. Multi-omics integration in biomedical research - A metabolomics-centric review. Anal Chim Acta 2021; 1141:144-162. [PMID: 33248648 PMCID: PMC7701361 DOI: 10.1016/j.aca.2020.10.038] [Citation(s) in RCA: 101] [Impact Index Per Article: 33.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/09/2020] [Accepted: 10/19/2020] [Indexed: 02/07/2023]
Abstract
Recent advances in high-throughput technologies have enabled the profiling of multiple layers of a biological system, including DNA sequence data (genomics), RNA expression levels (transcriptomics), and metabolite levels (metabolomics). This has led to the generation of vast amounts of biological data that can be integrated in so-called multi-omics studies to examine the complex molecular underpinnings of health and disease. Integrative analysis of such datasets is not straightforward and is particularly complicated by the high dimensionality and heterogeneity of the data and by the lack of universal analysis protocols. Previous reviews have discussed various strategies to address the challenges of data integration, elaborating on specific aspects, such as network inference or feature selection techniques. Thereby, the main focus has been on the integration of two omics layers in their relation to a phenotype of interest. In this review we provide an overview over a typical multi-omics workflow, focusing on integration methods that have the potential to combine metabolomics data with two or more omics. We discuss multiple integration concepts including data-driven, knowledge-based, simultaneous and step-wise approaches. We highlight the application of these methods in recent multi-omics studies, including large-scale integration efforts aiming at a global depiction of the complex relationships within and between different biological layers without focusing on a particular phenotype.
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Affiliation(s)
- Maria A Wörheide
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany
| | - Jan Krumsiek
- Institute for Computational Biomedicine, Englander Institute for Precision Medicine, Department of Physiology and Biophysics, Weill Cornell Medicine, New York, NY, USA
| | - Gabi Kastenmüller
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Matthias Arnold
- Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany; Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.
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83
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Rivas-Barragan D, Mubeen S, Guim Bernat F, Hofmann-Apitius M, Domingo-Fernández D. Drug2ways: Reasoning over causal paths in biological networks for drug discovery. PLoS Comput Biol 2020; 16:e1008464. [PMID: 33264280 PMCID: PMC7735677 DOI: 10.1371/journal.pcbi.1008464] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2020] [Revised: 12/14/2020] [Accepted: 10/23/2020] [Indexed: 12/24/2022] Open
Abstract
Elucidating the causal mechanisms responsible for disease can reveal potential therapeutic targets for pharmacological intervention and, accordingly, guide drug repositioning and discovery. In essence, the topology of a network can reveal the impact a drug candidate may have on a given biological state, leading the way for enhanced disease characterization and the design of advanced therapies. Network-based approaches, in particular, are highly suited for these purposes as they hold the capacity to identify the molecular mechanisms underlying disease. Here, we present drug2ways, a novel methodology that leverages multimodal causal networks for predicting drug candidates. Drug2ways implements an efficient algorithm which reasons over causal paths in large-scale biological networks to propose drug candidates for a given disease. We validate our approach using clinical trial information and demonstrate how drug2ways can be used for multiple applications to identify: i) single-target drug candidates, ii) candidates with polypharmacological properties that can optimize multiple targets, and iii) candidates for combination therapy. Finally, we make drug2ways available to the scientific community as a Python package that enables conducting these applications on multiple standard network formats.
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Affiliation(s)
- Daniel Rivas-Barragan
- Barcelona Supercomputing Center, Barcelona, Spain
- Computer Architecture Department, Universitat Politècnica de Catalunya, Barcelona, Spain
| | - Sarah Mubeen
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Fraunhofer Center for Machine Learning, Germany
| | | | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
| | - Daniel Domingo-Fernández
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Fraunhofer Center for Machine Learning, Germany
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84
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Cui H, Zhang L, Ford B, Cheng HL, Macklin JA, Reznicek A, Starr J. Measurement Recorder: developing a useful tool for making species descriptions that produces computable phenotypes. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2020; 2020:5995854. [PMID: 33216896 PMCID: PMC7678789 DOI: 10.1093/database/baaa079] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 08/24/2020] [Accepted: 08/27/2020] [Indexed: 12/31/2022]
Abstract
To use published phenotype information in computational analyses, there have been efforts to convert descriptions of phenotype characters from human languages to ontologized statements. This postpublication curation process is not only slow and costly, it is also burdened with significant intercurator variation (including curator-author variation), due to different interpretations of a character by various individuals. This problem is inherent in any human-based intellectual activity. To address this problem, making scientific publications semantically clear (i.e. computable) by the authors at the time of publication is a critical step if we are to avoid postpublication curation. To help authors efficiently produce species phenotypes while producing computable data, we are experimenting with an author-driven ontology development approach and developing and evaluating a series of ontology-aware software modules that would create publishable species descriptions that are readily useable in scientific computations. The first software module prototype called Measurement Recorder has been developed to assist authors in defining continuous measurements and reported in this paper. Two usability studies of the software were conducted with 22 undergraduate students majoring in information science and 32 in biology. Results suggest that participants can use Measurement Recorder without training and they find it easy to use after limited practice. Participants also appreciate the semantic enhancement features. Measurement Recorder's character reuse features facilitate character convergence among participants by 48% and have the potential to further reduce user errors in defining characters. A set of software design issues have also been identified and then corrected. Measurement Recorder enables authors to record measurements in a semantically clear manner and enriches phenotype ontology along the way. Future work includes representing the semantic data as Resource Description Framework (RDF) knowledge graphs and characterizing the division of work between authors as domain knowledge providers and ontology engineers as knowledge formalizers in this new author-driven ontology development approach.
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Affiliation(s)
- Hong Cui
- School of Information, University of Arizona, Tucson, AZ 85705, USA
| | - Limin Zhang
- School of Information, University of Arizona, Tucson, AZ 85705, USA
| | - Bruce Ford
- Department of Biological sciences, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
| | - Hsin-Liang Cheng
- Curtis Laws Wilson Library, Missouri University of Science and Technology, Rolla, MO 65409, USA
| | - James A Macklin
- Ottawa Research and Development Centre, Agriculture and Agri-Food Canada, Ottawa, ON K1A 0C6, Canada
| | - Anton Reznicek
- LSA Herbarium, University of Michigan, Ann Arbor, MI 48019, USA
| | - Julian Starr
- Department of Biology, University of Ottawa, Ottawa, ON K1N 6N5, Canada
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85
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Babur Ö, Melrose AR, Cunliffe JM, Klimek J, Pang J, Sepp ALI, Zilberman-Rudenko J, Tassi Yunga S, Zheng T, Parra-Izquierdo I, Minnier J, McCarty OJT, Demir E, Reddy AP, Wilmarth PA, David LL, Aslan JE. Phosphoproteomic quantitation and causal analysis reveal pathways in GPVI/ITAM-mediated platelet activation programs. Blood 2020; 136:2346-2358. [PMID: 32640021 PMCID: PMC7702475 DOI: 10.1182/blood.2020005496] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Accepted: 06/05/2020] [Indexed: 02/07/2023] Open
Abstract
Platelets engage cues of pending vascular injury through coordinated adhesion, secretion, and aggregation responses. These rapid, progressive changes in platelet form and function are orchestrated downstream of specific receptors on the platelet surface and through intracellular signaling mechanisms that remain systematically undefined. This study brings together cell physiological and phosphoproteomics methods to profile signaling mechanisms downstream of the immunotyrosine activation motif (ITAM) platelet collagen receptor GPVI. Peptide tandem mass tag (TMT) labeling, sample multiplexing, synchronous precursor selection (SPS), and triple stage tandem mass spectrometry (MS3) detected >3000 significant (false discovery rate < 0.05) phosphorylation events on >1300 proteins over conditions initiating and progressing GPVI-mediated platelet activation. With literature-guided causal inference tools, >300 site-specific signaling relations were mapped from phosphoproteomics data among key and emerging GPVI effectors (ie, FcRγ, Syk, PLCγ2, PKCδ, DAPP1). Through signaling validation studies and functional screening, other less-characterized targets were also considered within the context of GPVI/ITAM pathways, including Ras/MAPK axis proteins (ie, KSR1, SOS1, STAT1, Hsp27). Highly regulated GPVI/ITAM targets out of context of curated knowledge were also illuminated, including a system of >40 Rab GTPases and associated regulatory proteins, where GPVI-mediated Rab7 S72 phosphorylation and endolysosomal maturation were blocked by TAK1 inhibition. In addition to serving as a model for generating and testing hypotheses from omics datasets, this study puts forth a means to identify hemostatic effectors, biomarkers, and therapeutic targets relevant to thrombosis, vascular inflammation, and other platelet-associated disease states.
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Affiliation(s)
- Özgün Babur
- Department of Molecular and Medical Genetics
- Computational Biology Program
| | | | | | | | | | | | | | | | | | | | | | | | - Emek Demir
- Department of Molecular and Medical Genetics
- Computational Biology Program
| | | | | | - Larry L David
- Proteomics Shared Resource
- Department of Chemical Physiology & Biochemistry, Oregon Health & Science University, Portland, OR
| | - Joseph E Aslan
- Knight Cardiovascular Institute
- Department of Biomedical Engineering
- Department of Chemical Physiology & Biochemistry, Oregon Health & Science University, Portland, OR
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86
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Dong Z, Dai L, Zhang Y, Fang C, Shi G, Chen Y, Li J, Wang Q, Fu J, Yu Y, Wang W, Cheng L, Liu Y, Lin Y, Wang Y, Wang Q, Wang H, Zhang H, Zhang Y, Su X, Zhang S, Wang F, Qiu M, Zhou Z, Deng H. Hypomethylation of GDNF family receptor alpha 1 promotes epithelial-mesenchymal transition and predicts metastasis of colorectal cancer. PLoS Genet 2020; 16:e1009159. [PMID: 33175846 PMCID: PMC7682896 DOI: 10.1371/journal.pgen.1009159] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 11/23/2020] [Accepted: 09/28/2020] [Indexed: 02/05/2023] Open
Abstract
Tumor metastasis is the major cause of poor prognosis and mortality in colorectal cancer (CRC). However, early diagnosis of highly metastatic CRC is currently difficult. In the present study, we screened for a novel biomarker, GDNF family receptor alpha 1 (GFRA1) based on the expression and methylation data in CRC patients from The Cancer Genome Altlas (TCGA), followed by further analysis of the correlation between the GFRA1 expression, methylation, and prognosis of patients. Our results show DNA hypomethylation-mediated upregulation of GFRA1 in invasive CRC, and it was found to be correlated with poor prognosis of CRC patients. Furthermore, GFRA1 methylation-modified sequences were found to have potential as methylation diagnostic markers of highly metastatic CRC. The targeted demethylation of GFRA1 by dCas9-TET1CD and gRNA promoted CRC metastasis in vivo and in vitro. Mechanistically, demethylation of GFRA1 induces epithelial-mesenchymal transition (EMT) by promoting AKT phosphorylation and increasing c-Jun expression in CRC cells. Collectively, our findings indicate that GFRA1 hypomethylation can promote CRC invasion via inducing EMT, and thus, GFRA1 methylation can be used as a biomarker for the early diagnosis of highly metastasis CRC. Abnormal DNA methylation, one of important characteristics in tumor cells, is exploited as biomarkers for cancer diagnosis and prognosis prediction. Early diagnosis of highly metastatic CRC will be helpful for the clinical management, thus prolongs patient survival. However, it is currently difficult to make early diagnosis of highly metastatic CRC in clinical practice. Currently, we screened a novel biomarker gene, GFRA1, which associated with the invasion and poor prognosis of CRC. The targeted demethylation of GFRA1 exerted a significant promoting effect on CRC metastasis, and GFRA1 methylation-modified sequences are valuable diagnostic biomarker for CRC metastasis risk assessment. Mechanically, demethylation of GFRA1 induced epithelial-mesenchymal transition (EMT) by upregulating AKT phosphorylation and c-Jun expression in CRC cells. Our results demonstrate the promoting effect of GFRA1 demethylation on CRC invasion and GFRA1 methylation may be a potential prognostic marker for predicting metastasis of CRC.
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Affiliation(s)
- Zhexu Dong
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Lei Dai
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Yong Zhang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Chao Fang
- Department of Gastrointestinal Surgery, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, Sichuan, the People’s Republic of China
| | - Gang Shi
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Ye Chen
- Department of Medical Oncology, Cancer Center, the State Key Laboratory of Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, the People’s Republic of China
| | - Junshu Li
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Qin Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Jiamei Fu
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Yan Yu
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Wenshuang Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Lin Cheng
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Yi Liu
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Yi Lin
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Yuan Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Qingnan Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Huiling Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Hantao Zhang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Yujing Zhang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Xiaolan Su
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Shuang Zhang
- Department of biotherapy, Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
| | - Feng Wang
- Department of Medical Oncology, Cancer Center, the State Key Laboratory of Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, the People’s Republic of China
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, the People’s Republic of China
| | - Meng Qiu
- Department of Medical Oncology, Cancer Center, the State Key Laboratory of Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, Sichuan, the People’s Republic of China
| | - Zongguang Zhou
- Department of Gastrointestinal Surgery, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, Sichuan, the People’s Republic of China
| | - Hongxin Deng
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University and Collaborative Innovation Center for Biotherapy, Chengdu, Sichuan, the People’s Republic of China
- * E-mail:
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87
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Hanspers K, Riutta A, Summer-Kutmon M, Pico AR. Pathway information extracted from 25 years of pathway figures. Genome Biol 2020; 21:273. [PMID: 33168034 PMCID: PMC7649569 DOI: 10.1186/s13059-020-02181-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 10/16/2020] [Indexed: 12/16/2022] Open
Abstract
Thousands of pathway diagrams are published each year as static figures inaccessible to computational queries and analyses. Using a combination of machine learning, optical character recognition, and manual curation, we identified 64,643 pathway figures published between 1995 and 2019 and extracted 1,112,551 instances of human genes, comprising 13,464 unique NCBI genes, participating in a wide variety of biological processes. This collection represents an order of magnitude more genes than found in the text of the same papers, and thousands of genes missing from other pathway databases, thus presenting new opportunities for discovery and research.
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Affiliation(s)
- Kristina Hanspers
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA USA
| | - Anders Riutta
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA USA
| | - Martina Summer-Kutmon
- Maastricht Centre for Systems Biology (MaCSBio), Maastricht University, Maastricht, The Netherlands
- Department of Bioinformatics - BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands
| | - Alexander R. Pico
- Institute of Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA USA
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88
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Seçilmiş D, Hillerton T, Morgan D, Tjärnberg A, Nelander S, Nordling TEM, Sonnhammer ELL. Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data. NPJ Syst Biol Appl 2020; 6:37. [PMID: 33168813 PMCID: PMC7652823 DOI: 10.1038/s41540-020-00154-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Accepted: 10/15/2020] [Indexed: 01/11/2023] Open
Abstract
The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where ~1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified.
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Affiliation(s)
- Deniz Seçilmiş
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121, Solna, Sweden
| | - Thomas Hillerton
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121, Solna, Sweden
| | - Daniel Morgan
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121, Solna, Sweden
| | - Andreas Tjärnberg
- Center for Developmental Genetics, New York University, New York, NY, USA
| | - Sven Nelander
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Torbjörn E M Nordling
- Department of Mechanical Engineering, National Cheng Kung University, Tainan, Taiwan
| | - Erik L L Sonnhammer
- Department of Biochemistry and Biophysics, Stockholm University, Science for Life Laboratory, Box 1031, 17121, Solna, Sweden.
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89
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Zhang W, Qiu W. OTUB1 Recruits Tumor Infiltrating Lymphocytes and Is a Prognostic Marker in Digestive Cancers. Front Mol Biosci 2020; 7:212. [PMID: 33240928 PMCID: PMC7677501 DOI: 10.3389/fmolb.2020.00212] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 08/03/2020] [Indexed: 12/22/2022] Open
Abstract
Background The deubiquitinating enzyme (DUB) OTUB1 can regulate the process of ubiquitination, but the influence of OTUB1 on immunity, apoptosis, autophagy, and the prognosis of digestive cancers requires further exploration. Methods OTUB1 expression was analyzed with the Oncomine and TIMER database. Kaplan-Meier plotter was used to calculate the association between OTUB1 and clinical prognosis. The regulation of OTUB1 on cancer immunocyte infiltration was determined by the TIMER database. The interaction between OTUB1 and immune genes, gene expression profiling (GEP), key genes of apoptosis and autophagy were analyzed via GEPIA. Protein-protein interaction (PPI), gene expression profiling (GEP), and functional pathway enrichment were also performed with the STRING and Pathway Common databases, respectively. Results High OTUB1 expression was found in CHOL, LIHC, READ, ESCA, and COAD, which was significantly associated with the poorer OS of LIHC (HR = 2.07, 95% CI = 1.30-3.30, P = 0.002), with modifications by sex, stage, grade, and mutant burden. OTUB1 can promote the recruitment of B cells, CD8 + T cells, macrophages in ESCA, B cells, and neutrophils in LIHC. We determined a significant interaction between OTUB1 and USP8, RNF128, LRIG1, UBB, UBC, STAM2, RNF41, EGFR, RPS27A, and HGS by PPI. This functional pathway indicates the regulatory role of OTUB1on immune, apoptosis, and autophagy through its interaction with TP53 and ATG. Conclusions OTUB1 performed as a molecular indicator of poor prognosis in digestive cancers, regulated the infiltration of tumor immunocytes, and exerted a significant influence on apoptosis and autophagy. OTUB1 is a potential antitumor target for digestive tumors.
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Affiliation(s)
- Wenhao Zhang
- Savaid Medical School, University of Chinese Academy of Sciences, Beijing, China
| | - Wenlong Qiu
- Qilu Hospital, Shandong University, Jinan, China
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90
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netboxr: Automated discovery of biological process modules by network analysis in R. PLoS One 2020; 15:e0234669. [PMID: 33137091 PMCID: PMC7605689 DOI: 10.1371/journal.pone.0234669] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 10/14/2020] [Indexed: 12/20/2022] Open
Abstract
SUMMARY Large-scale sequencing projects, such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), have generated high throughput sequencing and molecular profiling data sets, but it is still challenging to identify potentially causal changes in cellular processes in cancer as well as in other diseases in an automated fashion. We developed the netboxr package written in the R programming language, which makes use of the NetBox algorithm to identify candidate cancer-related functional modules. The algorithm makes use of a data-driven, network-based approach that combines prior knowledge with a network clustering algorithm, obviating the need for and the limitation of independently curated functionally labeled gene sets. The method can combine multiple data types, such as mutations and copy number alterations, leading to more reliable identification of functional modules. We make the tool available in the Bioconductor R ecosystem for applications in cancer research and cell biology. AVAILABILITY AND IMPLEMENTATION The netboxr package is free and open-sourced under the GNU GPL-3 license R package available at https://www.bioconductor.org/packages/release/bioc/html/netboxr.html.
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91
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Functional in vivo and in vitro effects of 20q11.21 genetic aberrations on hPSC differentiation. Sci Rep 2020; 10:18582. [PMID: 33122739 PMCID: PMC7596514 DOI: 10.1038/s41598-020-75657-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2019] [Accepted: 10/15/2020] [Indexed: 01/01/2023] Open
Abstract
Human pluripotent stem cells (hPSCs) have promising therapeutic applications due to their infinite capacity for self-renewal and pluripotency. Genomic stability is imperative for the clinical use of hPSCs; however, copy number variation (CNV), especially recurrent CNV at 20q11.21, may contribute genomic instability of hPSCs. Furthermore, the effects of CNVs in hPSCs at the whole-transcriptome scale are poorly understood. This study aimed to examine the functional in vivo and in vitro effects of frequently detected CNVs at 20q11.21 during early-stage differentiation of hPSCs. Comprehensive transcriptome profiling of abnormal hPSCs revealed that the differential gene expression patterns had a negative effect on differentiation potential. Transcriptional heterogeneity identified by single-cell RNA sequencing (scRNA-seq) of embryoid bodies from two different isogenic lines of hPSCs revealed alterations in differentiated cell distributions compared with that of normal cells. RNA-seq analysis of 22 teratomas identified several differentially expressed lineage-specific markers in hPSCs with CNVs, consistent with the histological results of the altered ecto/meso/endodermal ratio due to CNVs. Our results suggest that CNV amplification contributes to cell proliferation, apoptosis, and cell fate specification. This work shows the functional consequences of recurrent genetic abnormalities and thereby provides evidence to support the development of cell-based applications.
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92
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Ghulam A, Lei X, Guo M, Bian C. A Review of Pathway Databases and Related Methods Analysis. Curr Bioinform 2020. [DOI: 10.2174/1574893614666191018162505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Pathway analysis integrates most of the computational tools for the investigation of
high-level and complex human diseases. In the field of bioinformatics research, biological pathways
analysis is an important part of systems biology. The molecular complexities of biological
pathways are difficult to understand in human diseases, which can be explored through pathway
analysis. In this review, we describe essential information related to pathway databases and their
mechanisms, algorithms and methods. In the pathway database analysis, we present a brief introduction
on how to gain knowledge from fundamental pathway data in regard to specific human
pathways and how to use pathway databases and pathway analysis to predict diseases during an
experiment. We also provide detailed information related to computational tools that are used in
complex pathway data analysis, the roles of these tools in the bioinformatics field and how to store
the pathway data. We illustrate various methodological difficulties that are faced during pathway
analysis. The main ideas and techniques for the pathway-based examination approaches are presented.
We provide the list of pathway databases and analytical tools. This review will serve as a
helpful manual for pathway analysis databases.
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Affiliation(s)
- Ali Ghulam
- School of Computer Science, Shaanxi Normal University, Xian, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xian, China
| | - Min Guo
- School of Computer Science, Shaanxi Normal University, Xian, China
| | - Chen Bian
- School of Computer Science, Shaanxi Normal University, Xian, China
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93
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Comprehensive analysis of the expression and prognostic value of CXC chemokines in colorectal cancer. Int Immunopharmacol 2020; 89:107077. [PMID: 33068862 DOI: 10.1016/j.intimp.2020.107077] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 09/06/2020] [Accepted: 10/03/2020] [Indexed: 02/08/2023]
Abstract
The C-X-C motif (CXC) chemokines play an important role in inflammatory processes and angiogenesis and are also associated with tumor development, progression and metastasis. They can be either promoting or inhibiting factors in colorectal cancers (CRC). The expression patterns and prognostic values of the CXC family still need further investigation. In this study, we investigated data related to transcription, translation, survival and tumor immune infiltration for CXC chemokines in patients with CRC from the ONCOMINE, GEPIA, cBioPortal, HPA and TIMER databases. We found that the expression levels of CXCL1-3, CXCL5, and CXCL8 were higher in CRC tissues than in colorectal tissues. Expression among stages significantly varied for CXCL1-3 and CXCL9-11. The survival analysis revealed that high transcriptional levels of CXCL4 and CXCL9-11 could serve as positive prognostic factors for patients with CRC. CXCL9-11 were highly associated with CD8+ T cells and natural killer (NK) cells in the tumor immune infiltration analysis, indicating their role in the antitumor immune response. This study implies that CXCL1-3, CXCL5, and CXCL8 are important factors during CRC oncogenesis and that CXCL9-11 could be new biomarkers for the prognosis of CRC.
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94
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Rinschen MM, Saez-Rodriguez J. The tissue proteome in the multi-omic landscape of kidney disease. Nat Rev Nephrol 2020; 17:205-219. [PMID: 33028957 DOI: 10.1038/s41581-020-00348-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/14/2020] [Indexed: 02/07/2023]
Abstract
Kidney research is entering an era of 'big data' and molecular omics data can provide comprehensive insights into the molecular footprints of cells. In contrast to transcriptomics, proteomics and metabolomics generate data that relate more directly to the pathological symptoms and clinical parameters observed in patients. Owing to its complexity, the proteome still holds many secrets, but has great potential for the identification of drug targets. Proteomics can provide information about protein synthesis, modification and degradation, as well as insight into the physical interactions between proteins, and between proteins and other biomolecules. Thus far, proteomics in nephrology has largely focused on the discovery and validation of biomarkers, but the systematic analysis of the nephroproteome can offer substantial additional insights, including the discovery of mechanisms that trigger and propagate kidney disease. Moreover, proteome acquisition might provide a diagnostic tool that complements the assessment of a kidney biopsy sample by a pathologist. Such applications are becoming increasingly feasible with the development of high-throughput and high-coverage technologies, such as versatile mass spectrometry-based techniques and protein arrays, and encourage further proteomics research in nephrology.
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Affiliation(s)
- Markus M Rinschen
- Department of Biomedicine, Aarhus University, Aarhus, Denmark. .,III. Department of Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany. .,Department II of Internal Medicine and Center for Molecular Medicine Cologne, University of Cologne, Cologne, Germany. .,Department of Chemistry, Scripps Center for Metabolomics and Mass Spectrometry, Scripps Research, La Jolla, CA, USA.
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University, and Heidelberg University Hospital, Bioquant, Heidelberg, Germany.,Joint Research Center for Computational Biomedicine, RWTH Aachen University Hospital, Aachen, Germany.,Molecular Medicine Partnership Unit, European Molecular Biology Laboratory and Heidelberg University, Heidelberg, Germany
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95
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Transcriptomic Profiling for the Autophagy Pathway in Colorectal Cancer. Int J Mol Sci 2020; 21:ijms21197101. [PMID: 32993062 PMCID: PMC7582824 DOI: 10.3390/ijms21197101] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 09/22/2020] [Accepted: 09/24/2020] [Indexed: 12/13/2022] Open
Abstract
The role of autophagy in colorectal cancer (CRC) pathogenesis appears to be crucial. Autophagy acts both as a tumor suppressor, by removing redundant cellular material, and a tumor-promoting factor, by providing access to components necessary for growth, metabolism, and proliferation. To date, little is known about the expression of genes that play a basal role in the autophagy in CRC. In this study, we aimed to compare the expression levels of 46 genes involved in the autophagy pathway between tumor-adjacent and tumor tissue, employing large RNA sequencing (RNA-seq) and microarray datasets. Additionally, we verified our results using data on 38 CRC cell lines. Gene set enrichment analysis revealed a significant deregulation of autophagy-related gene sets in CRC. The unsupervised clustering of tumors using the mRNA levels of autophagy-related genes revealed the existence of two major clusters: microsatellite instability (MSI)-enriched and -depleted. In cluster 1 (MSI-depleted), ATG9B and LAMP1 genes were the most prominently expressed, whereas cluster 2 (MSI-enriched) was characterized by DRAM1 upregulation. CRC cell lines were also clustered according to MSI-enriched/-depleted subgroups. The moderate deregulation of autophagy-related genes in cancer tissue, as compared to adjacent tissue, suggests a prominent field cancerization or early disruption of autophagy. Genes differentiating these clusters are promising candidates for CRC targeting therapy worthy of further investigation.
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96
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Iyer VS, Jiang L, Shen Y, Boddul SV, Panda SK, Kasza Z, Schmierer B, Wermeling F. Designing custom CRISPR libraries for hypothesis-driven drug target discovery. Comput Struct Biotechnol J 2020; 18:2237-2246. [PMID: 32952937 PMCID: PMC7479249 DOI: 10.1016/j.csbj.2020.08.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 07/24/2020] [Accepted: 08/09/2020] [Indexed: 12/20/2022] Open
Abstract
Over the last decade Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) has been developed into a potent molecular biology tool used to rapidly modify genes or their expression in a multitude of ways. In parallel, CRISPR-based screening approaches have been developed as powerful discovery platforms for dissecting the genetic basis of cellular behavior, as well as for drug target discovery. CRISPR screens can be designed in numerous ways. Here, we give a brief background to CRISPR screens and discuss the pros and cons of different design approaches, including unbiased genome-wide screens that target all known genes, as well as hypothesis-driven custom screens in which selected subsets of genes are targeted (Fig. 1). We provide several suggestions for how a custom screen can be designed, which could broadly serve as inspiration for any experiment that includes candidate gene selection. Finally, we discuss how results from CRISPR screens could be translated into drug development, as well as future trends we foresee in the rapidly evolving CRISPR screen field.
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Affiliation(s)
- Vaishnavi Srinivasan Iyer
- Center for Molecular Medicine, Division of Rheumatology, Department of Medicine, Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden.,School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore
| | - Long Jiang
- Center for Molecular Medicine, Division of Rheumatology, Department of Medicine, Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Yunbing Shen
- Center for Molecular Medicine, Division of Rheumatology, Department of Medicine, Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Sanjaykumar V Boddul
- Center for Molecular Medicine, Division of Rheumatology, Department of Medicine, Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Sudeepta Kumar Panda
- Center for Molecular Medicine, Division of Rheumatology, Department of Medicine, Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden.,Structural Genomics Consortium, Department of Medicine, Karolinska University Hospital and Karolinska Institutet, Stockholm, Sweden
| | - Zsolt Kasza
- Center for Molecular Medicine, Division of Rheumatology, Department of Medicine, Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Bernhard Schmierer
- High Throughput Genome Engineering, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Stockholm, Sweden.,Science for Life Laboratory, Stockholm, Sweden
| | - Fredrik Wermeling
- Center for Molecular Medicine, Division of Rheumatology, Department of Medicine, Solna, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
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97
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Dong Y, Lu S, Wang Z, Liu L. CCTs as new biomarkers for the prognosis of head and neck squamous cancer. Open Med (Wars) 2020; 15:672-688. [PMID: 33313411 PMCID: PMC7706129 DOI: 10.1515/med-2020-0114] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 05/30/2020] [Accepted: 06/18/2020] [Indexed: 12/23/2022] Open
Abstract
The chaperonin-containing T-complex protein 1 (CCT) subunits participate in diverse diseases. However, little is known about their expression and prognostic values in human head and neck squamous cancer (HNSC). This article aims to evaluate the effects of CCT subunits regarding their prognostic values for HNSC. We mined the transcriptional and survival data of CCTs in HNSC patients from online databases. A protein-protein interaction network was constructed and a functional enrichment analysis of target genes was performed. We observed that the mRNA expression levels of CCT1/2/3/4/5/6/7/8 were higher in HNSC tissues than in normal tissues. Survival analysis revealed that the high mRNA transcriptional levels of CCT3/4/5/6/7/8 were associated with a low overall survival. The expression levels of CCT4/7 were correlated with advanced tumor stage. And the overexpression of CCT4 was associated with higher N stage of patients. Validation of CCTs' differential expression and prognostic values was achieved by the Human Protein Atlas and GEO datasets. Mechanistic exploration of CCT subunits by the functional enrichment analysis suggests that these genes may influence the HNSC prognosis by regulating PI3K-Akt and other pathways. This study implies that CCT3/4/6/7/8 are promising biomarkers for the prognosis of HNSC.
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Affiliation(s)
- Yanbo Dong
- Department of Otolaryngology Head and Neck Surgery, Beijing Friendship Hospital, Capital Medical University, 95th Yong'an Road, Xicheng District, Beijing 100050, China
| | - Siyu Lu
- Department of Emergency, Aviation General Hospital, Beijing 100012, China
| | - Zhenxiao Wang
- Department of Otolaryngology Head and Neck Surgery, Beijing Friendship Hospital, Capital Medical University, 95th Yong'an Road, Xicheng District, Beijing 100050, China
| | - Liangfa Liu
- Department of Otolaryngology Head and Neck Surgery, Beijing Friendship Hospital, Capital Medical University, 95th Yong'an Road, Xicheng District, Beijing 100050, China
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98
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Waltemath D, Golebiewski M, Blinov ML, Gleeson P, Hermjakob H, Hucka M, Inau ET, Keating SM, König M, Krebs O, Malik-Sheriff RS, Nickerson D, Oberortner E, Sauro HM, Schreiber F, Smith L, Stefan MI, Wittig U, Myers CJ. The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE). J Integr Bioinform 2020; 17:jib-2020-0005. [PMID: 32598315 PMCID: PMC7756615 DOI: 10.1515/jib-2020-0005] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2020] [Accepted: 05/14/2020] [Indexed: 01/23/2023] Open
Abstract
This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.
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Affiliation(s)
- Dagmar Waltemath
- Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | - Martin Golebiewski
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | | | - Padraig Gleeson
- Department of Neuroscience, Physiology and Pharmacology, University College London, London, UK
| | | | - Michael Hucka
- Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
| | - Esther Thea Inau
- Medical Informatics, University Medicine Greifswald, Greifswald, Germany
| | | | - Matthias König
- Institute for Theoretical Biology, Humboldt-University Berlin, Berlin, Germany
| | - Olga Krebs
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | | | - David Nickerson
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Ernst Oberortner
- U.S. Department of Energy (DOE) Joint Genome Institute (JGI), Lawrence Berkeley National Labs, Berkeley, CA, USA
| | - Herbert M Sauro
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Falk Schreiber
- Department of Computer and Information Science, University ofKonstanz, Germany.,Faculty of IT, Monash University, Melbourne, VIC, Australia
| | - Lucian Smith
- Department of Bioengineering, University of Washington, Seattle, WA, USA
| | - Melanie I Stefan
- Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh, UK.,ZJU-UoE Institute, Zhejiang University, Haining, China.,University of Utah, Salt Lake City, UT, USA
| | - Ulrike Wittig
- Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
| | - Chris J Myers
- Centre for Discovery Brain Sciences, The University of Edinburgh, Edinburgh, UK
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99
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Zeng X, Zong W, Lin CW, Fang Z, Ma T, Lewis DA, Enwright JF, Tseng GC. Comparative Pathway Integrator: A Framework of Meta-Analytic Integration of Multiple Transcriptomic Studies for Consensual and Differential Pathway Analysis. Genes (Basel) 2020; 11:E696. [PMID: 32599927 PMCID: PMC7348908 DOI: 10.3390/genes11060696] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2020] [Revised: 06/15/2020] [Accepted: 06/17/2020] [Indexed: 11/16/2022] Open
Abstract
Pathway enrichment analysis provides a knowledge-driven approach to interpret differentially expressed genes associated with disease status. Many tools have been developed to analyze a single study. However, when multiple studies of different conditions are jointly analyzed, novel integrative tools are needed. In addition, pathway redundancy introduced by combining multiple public pathway databases hinders interpretation and knowledge discovery. We present a meta-analytic integration tool, Comparative Pathway Integrator (CPI), to address these issues using adaptively weighted Fisher's method to discover consensual and differential enrichment patterns, a tight clustering algorithm to reduce pathway redundancy, and a text mining algorithm to assist interpretation of the pathway clusters. We applied CPI to jointly analyze six psychiatric disorder transcriptomic studies to demonstrate its effectiveness, and found functions confirmed by previous biological studies as well as novel enrichment patterns. CPI's R package is accessible online on Github metaOmics/MetaPath.
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Affiliation(s)
- Xiangrui Zeng
- Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA;
| | - Wei Zong
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15260, USA; (W.Z.); (Z.F.)
| | - Chien-Wei Lin
- Division of Biostatistics, Medical College of Wisconsin, Wauwatosa, WI 53226, USA;
| | - Zhou Fang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15260, USA; (W.Z.); (Z.F.)
| | - Tianzhou Ma
- Department of Epidemiology and Biostatistics, University of Maryland, College Park, MD 20742, USA;
| | - David A. Lewis
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15260, USA; (D.A.L.); (J.F.E.)
| | - John F. Enwright
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15260, USA; (D.A.L.); (J.F.E.)
| | - George C. Tseng
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15260, USA; (W.Z.); (Z.F.)
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100
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Postic G, Marcoux J, Reys V, Andreani J, Vandenbrouck Y, Bousquet MP, Mouton-Barbosa E, Cianférani S, Burlet-Schiltz O, Guerois R, Labesse G, Tufféry P. Probing Protein Interaction Networks by Combining MS-Based Proteomics and Structural Data Integration. J Proteome Res 2020; 19:2807-2820. [PMID: 32338910 DOI: 10.1021/acs.jproteome.0c00066] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Protein-protein interactions play a major role in the molecular machinery of life, and various techniques such as AP-MS are dedicated to their identification. However, those techniques return lists of proteins devoid of organizational structure, not detailing which proteins interact with which others. Proposing a hierarchical view of the interactions between the members of the flat list becomes highly tedious for large data sets when done by hand. To help hierarchize this data, we introduce a new bioinformatics protocol that integrates information of the multimeric protein 3D structures available in the Protein Data Bank using remote homology detection, as well as information related to Short Linear Motifs and interaction data from the BioGRID. We illustrate on two unrelated use-cases of different complexity how our approach can be useful to decipher the network of interactions hidden in the list of input proteins, and how it provides added value compared to state-of-the-art resources such as Interactome3D or STRING. Particularly, we show the added value of using homology detection to distinguish between orthologs and paralogs, and to distinguish between core obligate and more facultative interactions. We also demonstrate the potential of considering interactions occurring through Short Linear Motifs.
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Affiliation(s)
- Guillaume Postic
- Université de Paris, BFA, UMR 8251, CNRS, ERL U1133, Inserm, RPBS, 75013 Paris, France.,Institut Français de Bioinformatique (IFB), UMS 3601-CNRS, Universite Paris-Saclay, 91400 Orsay, France
| | - Julien Marcoux
- Institut de Pharmacologie et de Biologie Structurale, IPBS, Université de Toulouse, CNRS, UPS, 31000 Toulouse, France
| | - Victor Reys
- CBS, Univ. Montpellier, CNRS, INSERM, 34095 Montpellier, France
| | - Jessica Andreani
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Yves Vandenbrouck
- Univ. Grenoble Alpes, INSERM, CEA, IRIG-BGE, U1038, 38000 Grenoble, France
| | - Marie-Pierre Bousquet
- Institut de Pharmacologie et de Biologie Structurale, IPBS, Université de Toulouse, CNRS, UPS, 31000 Toulouse, France
| | - Emmanuelle Mouton-Barbosa
- Institut de Pharmacologie et de Biologie Structurale, IPBS, Université de Toulouse, CNRS, UPS, 31000 Toulouse, France
| | - Sarah Cianférani
- Laboratoire de Spectrométrie de Masse BioOrganique, Université de Strasbourg, CNRS, IPHC UMR 7178, 67000 Strasbourg, France
| | - Odile Burlet-Schiltz
- Institut de Pharmacologie et de Biologie Structurale, IPBS, Université de Toulouse, CNRS, UPS, 31000 Toulouse, France
| | - Raphael Guerois
- Université Paris-Saclay, CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), 91198 Gif-sur-Yvette, France
| | - Gilles Labesse
- CBS, Univ. Montpellier, CNRS, INSERM, 34095 Montpellier, France
| | - Pierre Tufféry
- Université de Paris, BFA, UMR 8251, CNRS, ERL U1133, Inserm, RPBS, 75013 Paris, France
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