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Dugied G, Laurent EM, Attia M, Gimeno JP, Bachiri K, Samavarchi-Tehrani P, Donati F, Rahou Y, Munier S, Amara F, Dos Santos M, Soler N, Volant S, Pietrosemoli N, Gingras AC, Pavlopoulos GA, van der Werf S, Falter-Braun P, Aloy P, Jacob Y, Komarova A, Sofianatos Y, Coyaud E, Demeret C. Multimodal SARS-CoV-2 interactome sketches the virus-host spatial organization. Commun Biol 2025; 8:501. [PMID: 40140549 PMCID: PMC11947133 DOI: 10.1038/s42003-025-07933-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2024] [Accepted: 03/12/2025] [Indexed: 03/28/2025] Open
Abstract
An accurate spatial representation of protein-protein interaction networks is needed to achieve a realistic and biologically relevant representation of interactomes. Here, we leveraged the spatial information included in Proximity-Dependent Biotin Identification (BioID) interactomes of SARS-CoV-2 proteins to calculate weighted distances and model the organization of the SARS-CoV-2-human interactome in three dimensions (3D) within a cell-like volume. Cell regions with viral occupancy were highlighted, along with the coordination of viral proteins exploiting the cellular machinery. Profiling physical intra-virus and virus-host contacts enabled us to demonstrate both the accuracy and the predictive value of our 3D map for direct interactions, meaning that proteins in closer proximity tend to interact physically. Several functionally important virus-host complexes were detected, and robust structural models were obtained, opening the way to structure-directed drug discovery screens. This PPI discovery pipeline approach brings us closer to a realistic spatial representation of interactomes, which, when applied to viruses or other pathogens, can provide significant information for infection. Thus, it represents a promising tool for coping with emerging infectious diseases.
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Affiliation(s)
- Guillaume Dugied
- Institut Pasteur, Université Paris Cité, UMR 3569, Centre National de la Recherche Scientifique, Molecular Genetics of RNA Viruses, 28 rue du Docteur Roux, F-75015, Paris, France
- Institut Pasteur, Université Paris Cité, Interactomics, RNA and Immunity, 28 rue du Docteur Roux, F-75015, Paris, France
| | - Estelle Mn Laurent
- Univ. Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000, Lille, France
| | - Mikaël Attia
- Institut Pasteur, Université Paris Cité, UMR 3569, Centre National de la Recherche Scientifique, Molecular Genetics of RNA Viruses, 28 rue du Docteur Roux, F-75015, Paris, France
- Institut Pasteur, Université Paris Cité, Interactomics, RNA and Immunity, 28 rue du Docteur Roux, F-75015, Paris, France
| | - Jean-Pascal Gimeno
- Univ. Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000, Lille, France
| | - Kamel Bachiri
- Univ. Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000, Lille, France
| | | | - Flora Donati
- Institut Pasteur, Université Paris Cité, National Reference Center for respiratory viruses, 28 rue du Docteur Roux, F-75015, Paris, France
| | - Yannis Rahou
- Institut Pasteur, Université Paris Cité, UMR 3569, Centre National de la Recherche Scientifique, Molecular Genetics of RNA Viruses, 28 rue du Docteur Roux, F-75015, Paris, France
- Institut Pasteur, Université Paris Cité, National Reference Center for respiratory viruses, 28 rue du Docteur Roux, F-75015, Paris, France
| | - Sandie Munier
- Institut Pasteur, Université Paris Cité, UMR 3569, Centre National de la Recherche Scientifique, Molecular Genetics of RNA Viruses, 28 rue du Docteur Roux, F-75015, Paris, France
| | - Faustine Amara
- Institut Pasteur, Université Paris Cité, UMR 3569, Centre National de la Recherche Scientifique, Molecular Genetics of RNA Viruses, 28 rue du Docteur Roux, F-75015, Paris, France
| | - Mélanie Dos Santos
- Institut Pasteur, Université Paris Cité, UMR 3569, Centre National de la Recherche Scientifique, Molecular Genetics of RNA Viruses, 28 rue du Docteur Roux, F-75015, Paris, France
| | - Nicolas Soler
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10 -12, 08020, Barcelona, Spain
| | - Stevenn Volant
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Université Paris Cité, F-75015, Paris, France
| | - Natalia Pietrosemoli
- Bioinformatics and Biostatistics Hub, Institut Pasteur, Université Paris Cité, F-75015, Paris, France
| | - Anne-Claude Gingras
- Lunenfeld-Tanenbaum Research Institute at Mount Sinai Hospital, Sinai Health, Toronto, ON, Canada
| | - Georgios A Pavlopoulos
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", 34 Fleming Street, 16672, Vari, Greece
| | - Sylvie van der Werf
- Institut Pasteur, Université Paris Cité, UMR 3569, Centre National de la Recherche Scientifique, Molecular Genetics of RNA Viruses, 28 rue du Docteur Roux, F-75015, Paris, France
- Institut Pasteur, Université Paris Cité, National Reference Center for respiratory viruses, 28 rue du Docteur Roux, F-75015, Paris, France
| | - Pascal Falter-Braun
- Institute of Network Biology (INET), Molecular Targets and Therapeutics Center (MTTC), Helmholtz Center Munich, German Research Center for Environmental Health, Munich-Neuherberg, Munich, Germany
- Microbe-Host Interactions, Faculty of Biology, Ludwig-Maximilians-Universität (LMU) München, Planegg-Martinsried, Munich, Germany
| | - Patrick Aloy
- Institute for Research in Biomedicine (IRB Barcelona), The Barcelona Institute of Science and Technology, Baldiri Reixac 10 -12, 08020, Barcelona, Spain
- Institució Catalana de Recerca i Estudis Avançat (ICREA), Pg. Lluís Companys, 23, 08010, Barcelona, Spain
| | - Yves Jacob
- Institut Pasteur, Université Paris Cité, UMR 3569, Centre National de la Recherche Scientifique, Molecular Genetics of RNA Viruses, 28 rue du Docteur Roux, F-75015, Paris, France
| | - Anastassia Komarova
- Institut Pasteur, Université Paris Cité, UMR 3569, Centre National de la Recherche Scientifique, Molecular Genetics of RNA Viruses, 28 rue du Docteur Roux, F-75015, Paris, France
- Institut Pasteur, Université Paris Cité, Interactomics, RNA and Immunity, 28 rue du Docteur Roux, F-75015, Paris, France
| | - Yorgos Sofianatos
- Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", 34 Fleming Street, 16672, Vari, Greece.
| | - Etienne Coyaud
- Univ. Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de Masse - PRISM, F-59000, Lille, France.
| | - Caroline Demeret
- Institut Pasteur, Université Paris Cité, UMR 3569, Centre National de la Recherche Scientifique, Molecular Genetics of RNA Viruses, 28 rue du Docteur Roux, F-75015, Paris, France.
- Institut Pasteur, Université Paris Cité, Interactomics, RNA and Immunity, 28 rue du Docteur Roux, F-75015, Paris, France.
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2
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Bottino L, Settino M, Promenzio L, Cannataro M. Scoliosis Management through Apps and Software Tools. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20085520. [PMID: 37107802 PMCID: PMC10138677 DOI: 10.3390/ijerph20085520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/10/2023] [Accepted: 04/11/2023] [Indexed: 05/11/2023]
Abstract
Background: Scoliosis is curvature of the spine, often found in adolescents, which can impact on quality of life. Generally, scoliosis is diagnosed by measuring the Cobb angle, which represents the gold standard for scoliosis grade quantification. Commonly, scoliosis evaluation is conducted in person by medical professionals using traditional methods (i.e., involving a scoliometer and/or X-ray radiographs). In recent years, as has happened in various medicine disciplines, it is possible also in orthopedics to observe the spread of Information and Communications Technology (ICT) solutions (i.e., software-based approaches). As an example, smartphone applications (apps) and web-based applications may help the doctors in screening and monitoring scoliosis, thereby reducing the number of in-person visits. Objectives: This paper aims to provide an overview of the main features of the most popular scoliosis ICT tools, i.e., apps and web-based applications for scoliosis diagnosis, screening, and monitoring. Several apps are assessed and compared with the aim of providing a valid starting point for doctors and patients in their choice of software-based tools. Benefits for the patients may be: reducing the number of visits to the doctor, self-monitoring of scoliosis. Benefits for the doctors may be: monitoring the scoliosis progression over time, managing several patients in a remote way, mining the data of several patients for evaluating different therapeutic or exercise prescriptions. Materials and Methods: We first propose a methodology for the evaluation of scoliosis apps in which five macro-categories are considered: (i) technological aspects (e.g., available sensors, how angles are measured); (ii) the type of measurements (e.g., Cobb angle, angle of trunk rotation, axial vertebral rotation); (iii) availability (e.g., app store and eventual fee to pay); (iv) the functions offered to the user (e.g., posture monitoring, exercise prescription); (v) overall evaluation (e.g., pros and cons, usability). Then, six apps and one web-based application are described and evaluated using this methodology. Results: The results for assessment of scoliosis apps are shown in a tabular format for ease of understanding and intuitive comparison, which can help the doctors, specialists, and families in their choice of scoliosis apps. Conclusions: The use of ICT solutions for spinal curvature assessment and monitoring brings several advantages to both patients and orthopedics specialists. Six scoliosis apps and one web-based application are evaluated, and a guideline for their selection is provided.
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Affiliation(s)
- Lorella Bottino
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy
| | - Marzia Settino
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy
| | - Luigi Promenzio
- Pediatric Orthopaedics Department, Villa Serena for Children, 88100 Catanzaro, Italy
| | - Mario Cannataro
- Department of Medical and Surgical Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy
- Correspondence:
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3
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Robin V, Bodein A, Scott-Boyer MP, Leclercq M, Périn O, Droit A. Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context. Front Mol Biosci 2022; 9:962799. [PMID: 36158572 PMCID: PMC9494275 DOI: 10.3389/fmolb.2022.962799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/16/2022] [Indexed: 11/26/2022] Open
Abstract
At the heart of the cellular machinery through the regulation of cellular functions, protein-protein interactions (PPIs) have a significant role. PPIs can be analyzed with network approaches. Construction of a PPI network requires prediction of the interactions. All PPIs form a network. Different biases such as lack of data, recurrence of information, and false interactions make the network unstable. Integrated strategies allow solving these different challenges. These approaches have shown encouraging results for the understanding of molecular mechanisms, drug action mechanisms, and identification of target genes. In order to give more importance to an interaction, it is evaluated by different confidence scores. These scores allow the filtration of the network and thus facilitate the representation of the network, essential steps to the identification and understanding of molecular mechanisms. In this review, we will discuss the main computational methods for predicting PPI, including ones confirming an interaction as well as the integration of PPIs into a network, and we will discuss visualization of these complex data.
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Affiliation(s)
- Vivian Robin
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Antoine Bodein
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Marie-Pier Scott-Boyer
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Mickaël Leclercq
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
| | - Olivier Périn
- Digital Sciences Department, L'Oréal Advanced Research, Aulnay-sous-bois, France
| | - Arnaud Droit
- Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada
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Milano M, Agapito G, Cannataro M. Challenges and Limitations of Biological Network Analysis. BIOTECH 2022; 11:24. [PMID: 35892929 PMCID: PMC9326688 DOI: 10.3390/biotech11030024] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/04/2022] [Accepted: 07/06/2022] [Indexed: 11/17/2022] Open
Abstract
High-Throughput technologies are producing an increasing volume of data that needs large amounts of data storage, effective data models and efficient, possibly parallel analysis algorithms. Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms' properties. For instance, in biological pathway representation, the nodes can represent proteins, RNA and fat molecules, while the edges represent the interaction between molecules. Otherwise, biological networks such as Protein-Protein Interaction (PPI) Networks, represent the biochemical interactions among proteins by using nodes that model the proteins from a given organism, and edges that model the protein-protein interactions, whereas pathway networks enable the representation of biochemical-reaction cascades that happen within the cells or tissues. In this paper, we discuss the main models for standard representation of pathways and PPI networks, the data models for the representation and exchange of pathway and protein interaction data, the main databases in which they are stored and the alignment algorithms for the comparison of pathways and PPI networks of different organisms. Finally, we discuss the challenges and the limitations of pathways and PPI network representation and analysis. We have identified that network alignment presents a lot of open problems worthy of further investigation, especially concerning pathway alignment.
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Affiliation(s)
- Marianna Milano
- Department of Medical and Clinical Surgery, University Magna Græcia, 88100 Catanzaro, Italy; (M.M.); (M.C.)
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
| | - Giuseppe Agapito
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
- Department of Law, Economics and Social Sciences, University Magna Græcia, 88100 Catanzaro, Italy
| | - Mario Cannataro
- Department of Medical and Clinical Surgery, University Magna Græcia, 88100 Catanzaro, Italy; (M.M.); (M.C.)
- Data Analytics Research Center, University Magna Græcia, 88100 Catanzaro, Italy
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5
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Rout M, Kour B, Vuree S, Lulu SS, Medicherla KM, Suravajhala P. Diabetes mellitus susceptibility with varied diseased phenotypes and its comparison with phenome interactome networks. World J Clin Cases 2022; 10:5957-5964. [PMID: 35949812 PMCID: PMC9254192 DOI: 10.12998/wjcc.v10.i18.5957] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/02/2022] [Accepted: 04/22/2022] [Indexed: 02/06/2023] Open
Abstract
An emerging area of interest in understanding disease phenotypes is systems genomics. Complex diseases such as diabetes have played an important role towards understanding the susceptible genes and mutations. A wide number of methods have been employed and strategies such as polygenic risk score and allele frequencies have been useful, but understanding the candidate genes harboring those mutations is an unmet goal. In this perspective, using systems genomic approaches, we highlight the application of phenome-interactome networks in diabetes and provide deep insights. LINC01128, which we previously described as candidate for diabetes, is shown as an example to discuss the approach.
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Affiliation(s)
- Madhusmita Rout
- Department of Pediatrics, University of Oklahoma Health Sciences Centre, Oklahoma City, OK 73104, United States
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur 302001, Rajasthan, India
| | - Bhumandeep Kour
- Department of Biotechnology, Lovely Professional University, Phagwara 144001, Punjab, India
| | - Sugunakar Vuree
- Department of Biotechnology, Lovely Professional University, Phagwara 144001, Punjab, India
| | - Sajitha S Lulu
- Department of Biotechnology, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Krishna Mohan Medicherla
- Department of Biotechnology and Bioinformatics, Birla Institute of Scientific Research, Jaipur 302001, Rajasthan, India
| | - Prashanth Suravajhala
- Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Vallikavu PO, Amritapuri, Clappana, Kollam 690525, Kerala, India
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Analysis of Multifactor-Driven Myopia Disease Modules to Guide Personalized Treatment and Drug Development. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:5262259. [PMID: 35586671 PMCID: PMC9110184 DOI: 10.1155/2022/5262259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 03/22/2022] [Accepted: 04/02/2022] [Indexed: 11/17/2022]
Abstract
Myopia is recognized as a multifactor, multicascade complex disease. However, people still know little about the pathogenesis of myopia. Therefore, we aim to guide the personalized treatment, drug research, and development of myopia. Here, based on the interaction network of myopia-related genes, this study constructed a multifactor-driven myopia disease module map. We first identified differentially expressed (DE) miRNAs in myopia. Then, we constructed a myopia-related protein interaction network targeted by these DE miRNAs. Further, we clustered the network into modules and identified module-driven factors, including ncRNAs and transcription factors. Especially, miR-16-5p and miR-34b-5p significantly differentially expressed drive the pathogenic module to influence the progression of myopia. At the same time, transcription factors were involved in myopia-related functions and pathways by regulating the expression of genes in modules, such as Ctnnb1, Myc, and Notch1. In addition, we identified 43 genes in modules that played key roles in the development and progression of myopia such as Vamp2, Egfr, and Wasl. Finally, we constructed a comprehensive multifactor-driven myopia pathogenic module landscape and predicted potential drug and drug targets for myopia. In general, our work not only provided candidates for biological experiments which laid the foundation for the in-depth study of myopia but also has a high reference value for the personalized treatment of myopia and drug development.
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7
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Redhu N, Thakur Z. Network biology and applications. Bioinformatics 2022. [DOI: 10.1016/b978-0-323-89775-4.00024-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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8
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Kataria R, Kaundal R. Deciphering the Crosstalk Mechanisms of Wheat-Stem Rust Pathosystem: Genome-Scale Prediction Unravels Novel Host Targets. FRONTIERS IN PLANT SCIENCE 2022; 13:895480. [PMID: 35800602 PMCID: PMC9253690 DOI: 10.3389/fpls.2022.895480] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2022] [Accepted: 05/31/2022] [Indexed: 05/04/2023]
Abstract
Triticum aestivum (wheat), a major staple food grain, is affected by various biotic stresses. Among these, fungal diseases cause about 15-20% of yield loss, worldwide. In this study, we performed a comparative analysis of protein-protein interactions between two Puccinia graminis races (Pgt 21-0 and Pgt Ug99) that cause stem (black) rust in wheat. The available molecular techniques to study the host-pathogen interaction mechanisms are expensive and labor-intensive. We implemented two computational approaches (interolog and domain-based) for the prediction of PPIs and performed various functional analysis to determine the significant differences between the two pathogen races. The analysis revealed that T. aestivum-Pgt 21-0 and T. aestivum-Pgt Ug99 interactomes consisted of ∼90M and ∼56M putative PPIs, respectively. In the predicted PPIs, we identified 115 Pgt 21-0 and 34 Pgt Ug99 potential effectors that were highly involved in pathogen virulence and development. Functional enrichment analysis of the host proteins revealed significant GO terms and KEGG pathways such as O-methyltransferase activity (GO:0008171), regulation of signal transduction (GO:0009966), lignin metabolic process (GO:0009808), plastid envelope (GO:0009526), plant-pathogen interaction pathway (ko04626), and MAPK pathway (ko04016) that are actively involved in plant defense and immune signaling against the biotic stresses. Subcellular localization analysis anticipated the host plastid as a primary target for pathogen attack. The highly connected host hubs in the protein interaction network belonged to protein kinase domain including Ser/Thr protein kinase, MAPK, and cyclin-dependent kinase. We also identified 5,577 transcription factors in the interactions, associated with plant defense during biotic stress conditions. Additionally, novel host targets that are resistant to stem rust disease were also identified. The present study elucidates the functional differences between Pgt 21-0 and Pgt Ug99, thus providing the researchers with strain-specific information for further experimental validation of the interactions, and the development of durable, disease-resistant crop lines.
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Affiliation(s)
- Raghav Kataria
- Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences, Utah State University, Logan, UT, United States
| | - Rakesh Kaundal
- Department of Plants, Soils, and Climate, College of Agriculture and Applied Sciences, Utah State University, Logan, UT, United States
- Bioinformatics Facility, Center for Integrated BioSystems, Utah State University, Logan, UT, United States
- Department of Computer Science, College of Science, Utah State University, Logan, UT, United States
- *Correspondence: Rakesh Kaundal,
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Identification of significant protein in protein-protein interaction of Alzheimer disease using top-k representative skyline query. JURNAL TEKNOLOGI DAN SISTEM KOMPUTER 2021. [DOI: 10.14710/jtsiskom.2021.13985] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Alzheimer's disease is the most common neurodegenerative disease. This study aims to analyze protein-protein interaction (PPI) to provide a better understanding of multifactorial neurodegenerative diseases and can be used to find proteins that have a significant role in Alzheimer's disease. PPI data were obtained from experimental and computational predictions and analyzed using centrality measures. The Top-k RSP method was applied to find significant proteins in PPI networks using the dominance rule. The method was applied to the PPI data with the interaction sources from the experimental and experiment+prediction. The results indicate that APP and PSEN1 are significant proteins for Alzheimer's disease. This study also showed that both data sources (experiment+prediction) and the Top-k RSP algorithm proved useful for PPI analysis of Alzheimer's disease.
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Zhou W, Gao M, Liang C, Lin B, Wu Q, Chen R, Xiong X, Chen X, Wang S, Wu L, Wu Y, Li H, Fu X, Hong W. Systematic Understanding of the Mechanism of Baicalin against Gastric Cancer Using Transcriptome Analysis. BIOMED RESEARCH INTERNATIONAL 2021; 2021:5521058. [PMID: 34337018 PMCID: PMC8315853 DOI: 10.1155/2021/5521058] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 06/11/2021] [Indexed: 11/18/2022]
Abstract
BACKGROUND Gastric cancer (GC) is the most common type of cancer. It is highly malignant and is characterized by rapid and uncontrolled growth. The antitumour activity of Baicalin was studied in multiple cancers. However, its mechanism of action has not been fully elucidated. We provided a systematic understanding of the mechanism of action of baicalin against GC using a transcriptome analysis of RNA-seq. METHODS Human GC cells (SGC-7901) were exposed to 200 μg/ml baicalin for 24 h. RNA-seq with a transcriptome, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to identify the antitumour effects of baicalin on SGC-7901 cells in vitro. A protein-protein interaction (PPI) network of differentially expressed genes (DEGs) was constructed. A competitive endogenous RNA (ceRNA) network was constructed and further analysed after validation using qRT-PCR. RESULTS A total of 68 lncRNAs, 20 miRNAs, and 1648 mRNAs were differentially expressed in baicalin-treated SGC-7901 GC cells. Three lncRNAs, 6 miRNAs, and 7 mRNAs were included in the ceRNA regulatory network. GO analysis revealed that the main DEGs were involved in the biological processes of the cell cycle and cell death. KEGG pathway analysis further suggested that the p53 signalling pathway was involved in the baicalin-induced antitumour effect on SGC-7901 cells. Further confirmation using qPCR indicated that baicalin induced an antitumour effect on SGC-7901 cells, which is consistent with the results of the sequencing data. CONCLUSIONS In summary, the mechanism of baicalin against GC involves multiple targets and signalling pathways. These results provide new insight into the antitumour mechanism of baicalin and help the development of new strategies to cure GC.
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Affiliation(s)
- Wenqu Zhou
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Mi Gao
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Chunxiao Liang
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Guangdong, China
- Department of Thoracic Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China
| | - Biting Lin
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Qinghua Wu
- Department of Thoracic Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China
| | - Ruikun Chen
- Department of Thoracic Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China
| | - Xiaoxiao Xiong
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Xing Chen
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Shijie Wang
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Liting Wu
- Department of Thoracic Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China
| | - Yiling Wu
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Haiqing Li
- The Third Clinical School of Guangzhou Medical University, Guangzhou Guangdong, China
| | - Xin Fu
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, Guangdong, China
| | - Wei Hong
- GMU-GIBH Joint School of Life Sciences, Guangzhou Medical University, Guangzhou, Guangdong, China
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11
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Kowalski A. A survey of human histone H1 subtypes interaction networks: Implications for histones H1 functioning. Proteins 2021; 89:792-810. [PMID: 33550666 DOI: 10.1002/prot.26059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 12/23/2020] [Accepted: 01/31/2021] [Indexed: 11/08/2022]
Abstract
To show a spectrum of histone H1 subtypes (H1.1-H1.5) activity realized through the protein-protein interactions, data selected from APID resources were processed with sequence-based bioinformatics approaches. Histone H1 subtypes participate in over half a thousand interactions with nuclear and cytosolic proteins (ComPPI database) engaged in the enzymatic activity and binding of nucleic acids and proteins (SIFTER tool). Small-scale networks of H1 subtypes (STRING network) have similar topological parameters (P > .05) which are, however, different for networks hubs between subtype H1.1 and H1.4 and subtype H1.3 and H1.5 (P < .05) (Cytoscape software). Based on enriched GO terms (g:Profiler toolset) of interacting proteins, molecular function and biological process of networks hubs is related to RNA binding and ribosome biogenesis (subtype H1.1 and H1.4), cell cycle and cell division (subtype H1.3 and H1.5) and protein ubiquitination and degradation (subtype H1.2). The residue propensity (BIPSPI predictor) and secondary structures of interacting surfaces (GOR algorithm) as well as a value of equilibrium dissociation constant (ISLAND predictor) indicate that a type of H1 subtypes interactions is transient in term of the stability and medium-strong in relation to the strength of binding. Histone H1 subtypes bind interacting partners in the intrinsic disorder-dependent mode (FoldIndex, PrDOS predictor), according to the coupled folding and binding and mutual synergistic folding mechanism. These results evidence that multifunctional H1 subtypes operate via protein interactions in the networks of crucial cellular processes and, therefore, confirm a new histone H1 paradigm relating to its functioning in the protein-protein interaction networks.
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Affiliation(s)
- Andrzej Kowalski
- Division of Medical Biology, Institute of Biology, Jan Kochanowski University in Kielce, Kielce, Poland
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12
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Poverennaya EV, Kiseleva OI, Ivanov AS, Ponomarenko EA. Methods of Computational Interactomics for Investigating Interactions of Human Proteoforms. BIOCHEMISTRY (MOSCOW) 2020; 85:68-79. [PMID: 32079518 DOI: 10.1134/s000629792001006x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Human genome contains ca. 20,000 protein-coding genes that could be translated into millions of unique protein species (proteoforms). Proteoforms coded by a single gene often have different functions, which implies different protein partners. By interacting with each other, proteoforms create a network reflecting the dynamics of cellular processes in an organism. Perturbations of protein-protein interactions change the network topology, which often triggers pathological processes. Studying proteoforms is a relatively new research area in proteomics, and this is why there are comparatively few experimental studies on the interaction of proteoforms. Bioinformatics tools can facilitate such studies by providing valuable complementary information to the experimental data and, in particular, expanding the possibilities of the studies of proteoform interactions.
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Affiliation(s)
| | - O I Kiseleva
- Institute of Biomedical Chemistry, Moscow, 119121, Russia
| | - A S Ivanov
- Institute of Biomedical Chemistry, Moscow, 119121, Russia
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13
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Struweg I. A Twitter Social Network Analysis: The South African Health Insurance Bill Case. LECTURE NOTES IN COMPUTER SCIENCE 2020. [PMCID: PMC7134296 DOI: 10.1007/978-3-030-45002-1_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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14
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Tian Y, Yu M, Sun L, Liu L, Wang J, Hui K, Nan Q, Nie X, Ren Y, Ren X. Distinct Patterns of mRNA and lncRNA Expression Differences Between Lung Squamous Cell Carcinoma and Adenocarcinoma. J Comput Biol 2019; 27:1067-1078. [PMID: 31750732 DOI: 10.1089/cmb.2019.0164] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
This study aimed to assess mRNA and lncRNA expression differences between lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). Cancer tissues were obtained from three LUSC and three LUAD patients, followed by RNA-seq. Differentially expressed mRNAs (DE-mRNAs) and lncRNAs (DE-lncRNAs) were identified between LUSC and LUAD, after which functional enrichment analysis and protein-protein interaction (PPI) network construction was performed on DEGs. Coexpression analysis of lncRNA-gene and prediction of DEG-related miRNAs as well as function enrichment analysis, and construction of competing endogenous RNAs (ceRNA) regulatory network were then conducted. Moreover, survival analysis on differentially expressed RNAs was performed based on data downloaded from The Cancer Genome Atlas (TCGA) database. In this study, 518 DEGs and 117 DE-lncRNAs were identified between LUSC and LUAD. The DEGs were mainly associated with cell adhesion, PI3K-Akt signaling pathway, and focal adhesion. PPI network analysis indicated several genes with highest connectivity, such as CCND1. DE-lncRNAs that coexpressed with DEGs were also associated with tight junction and DE-lncRNAs that had more corepressed relationships with DEGs included GSEC, NKX2-1-AS1, LINC01415, and LINC00839. Moreover, the genes and lncRNAs with higher connectivity in the ceRNA network included NEAT1, SLC5A3, LINC00839, ETV1, CMTM4, and SNX30. Several genes were significantly related to the survival of patients with LUSC and LUAD, including ETV1, RTKN2, SNX30, PAK2, and CCND1. Genes and lncRNAs associated with cell junction have specific patterns in two major histological subtypes of NSCLC. GSEC, NKX2-1-AS1, NEAT1, CCND1, and ETV1 may be potential novel biomarkers for personalized treatment strategies of NSCLC.
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Affiliation(s)
- Yingxuan Tian
- Department of Elderly Respiratory Medicine, Shaanxi Provincial People's Hospital, Xi'an, China.,Department of Elderly Medicine, the Affiliated Shaanxi Provincial People's Hospital, Xi'an Medical College, Xi'an, China
| | - Min Yu
- Department of Oncology Medicine, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Li Sun
- Department of Elderly Respiratory Medicine, Shaanxi Provincial People's Hospital, Xi'an, China.,Department of Elderly Medicine, the Affiliated Shaanxi Provincial People's Hospital, Xi'an Medical College, Xi'an, China
| | - Linghua Liu
- Department of Elderly Respiratory Medicine, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Jun Wang
- Department of Elderly Respiratory Medicine, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Ke Hui
- Department of Elderly Respiratory Medicine, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Qiaofeng Nan
- Department of Elderly Medicine, the Affiliated Shaanxi Provincial People's Hospital, Xi'an Medical College, Xi'an, China
| | - Xinyu Nie
- Graduate School of Medical College, Yan'an University, Yan'an, China
| | - Yajuan Ren
- Department of Elderly Respiratory Medicine, Shaanxi Provincial People's Hospital, Xi'an, China
| | - Xiaoping Ren
- Department of Elderly Respiratory Medicine, Shaanxi Provincial People's Hospital, Xi'an, China
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15
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Kwon D, Lee D, Kim J, Lee J, Sim M, Kim J. Using INTERSPIA to Explore the Dynamics of Protein-Protein Interactions Among Multiple Species. ACTA ACUST UNITED AC 2019; 68:e88. [PMID: 31751498 DOI: 10.1002/cpbi.88] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
INTER-Species Protein Interaction Analysis (INTERSPIA) is a web application for identifying diverse patterns of protein-protein interactions (PPIs) in different species. Given a set of proteins of interest to the user, INTERSPIA first discovers additional proteins that are functionally associated with the input proteins as well as different or common patterns of PPIs among the proteins in multiple species through a server-side pipeline. Second, it visualizes the dynamics of PPIs in multiple species via an easy-to-use web interface. This article contains a basic protocol describing how to visualize diverse patterns of PPIs of input proteins in multiple species, and how to use them for functional analysis in the web interface. INTERSPIA is freely available at http://bioinfo.konkuk.ac.kr/INTERSPIA/. © 2019 by John Wiley & Sons, Inc. Basic Protocol: Running INTERSPIA using a list of input proteins.
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Affiliation(s)
- Daehong Kwon
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Daehwan Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Juyeon Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Jongin Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Mikang Sim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
| | - Jaebum Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul, Republic of Korea
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16
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Klymkowsky MW. Filaments and phenotypes: cellular roles and orphan effects associated with mutations in cytoplasmic intermediate filament proteins. F1000Res 2019; 8. [PMID: 31602295 PMCID: PMC6774051 DOI: 10.12688/f1000research.19950.1] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 09/20/2019] [Indexed: 12/11/2022] Open
Abstract
Cytoplasmic intermediate filaments (IFs) surround the nucleus and are often anchored at membrane sites to form effectively transcellular networks. Mutations in IF proteins (IFps) have revealed mechanical roles in epidermis, muscle, liver, and neurons. At the same time, there have been phenotypic surprises, illustrated by the ability to generate viable and fertile mice null for a number of IFp-encoding genes, including vimentin. Yet in humans, the vimentin ( VIM) gene displays a high probability of intolerance to loss-of-function mutations, indicating an essential role. A number of subtle and not so subtle IF-associated phenotypes have been identified, often linked to mechanical or metabolic stresses, some of which have been found to be ameliorated by the over-expression of molecular chaperones, suggesting that such phenotypes arise from what might be termed "orphan" effects as opposed to the absence of the IF network per se, an idea originally suggested by Toivola et al. and Pekny and Lane.
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Affiliation(s)
- Michael W Klymkowsky
- Molecular, Cellular & Developmental Biology, University of Colorado, Boulder, Boulder, CO, 80303, USA
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17
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Kwon D, Lee D, Kim J, Lee J, Sim M, Kim J. INTERSPIA: a web application for exploring the dynamics of protein-protein interactions among multiple species. Nucleic Acids Res 2019; 46:W89-W94. [PMID: 29746660 PMCID: PMC6031021 DOI: 10.1093/nar/gky378] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 04/27/2018] [Indexed: 02/06/2023] Open
Abstract
Proteins perform biological functions through cascading interactions with each other by forming protein complexes. As a result, interactions among proteins, called protein-protein interactions (PPIs) are not completely free from selection constraint during evolution. Therefore, the identification and analysis of PPI changes during evolution can give us new insight into the evolution of functions. Although many algorithms, databases and websites have been developed to help the study of PPIs, most of them are limited to visualize the structure and features of PPIs in a chosen single species with limited functions in the visualization perspective. This leads to difficulties in the identification of different patterns of PPIs in different species and their functional consequences. To resolve these issues, we developed a web application, called INTER-Species Protein Interaction Analysis (INTERSPIA). Given a set of proteins of user's interest, INTERSPIA first discovers additional proteins that are functionally associated with the input proteins and searches for different patterns of PPIs in multiple species through a server-side pipeline, and second visualizes the dynamics of PPIs in multiple species using an easy-to-use web interface. INTERSPIA is freely available at http://bioinfo.konkuk.ac.kr/INTERSPIA/.
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Affiliation(s)
- Daehong Kwon
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Daehwan Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Juyeon Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Jongin Lee
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Mikang Sim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
| | - Jaebum Kim
- Department of Biomedical Science and Engineering, Konkuk University, Seoul 05029, Korea
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18
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Liu D, Huo Y, Chen S, Xu D, Yang B, Xue C, Fu L, Bu L, Song S, Mei C. Identification of Key Genes and Candidated Pathways in Human Autosomal Dominant Polycystic Kidney Disease by Bioinformatics Analysis. Kidney Blood Press Res 2019; 44:533-552. [DOI: 10.1159/000500458] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 04/04/2019] [Indexed: 11/19/2022] Open
Abstract
Background/Aims: Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic form of kidney disease. High-throughput microarray analysis has been applied for elucidating key genes and pathways associated with ADPKD. Most genetic profiling data from ADPKD patients have been uploaded to public databases but not thoroughly analyzed. This study integrated 2 human microarray profile datasets to elucidate the potential pathways and protein-protein interactions (PPIs) involved in ADPKD via bioinformatics analysis in order to identify possible therapeutic targets. Methods: The kidney tissue microarray data of ADPKD patients and normal individuals were searched and obtained from NCBI Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified, and enriched pathways and central node genes were elucidated using related websites and software according to bioinformatics analysis protocols. Seven DEGs were validated between polycystic kidney disease and control kidney samples by quantitative real-time polymerase chain reaction. Results: Two original human microarray datasets, GSE7869 and GSE35831, were integrated and thoroughly analyzed. In total, 6,422 and 1,152 DEGs were extracted from GSE7869 and GSE35831, respectively, and of these, 561 DEGs were consistent between the databases (291 upregulated genes and 270 downregulated genes). From 421 nodes, 34 central node genes were obtained from a PPI network complex of DEGs. Two significant modules were selected from the PPI network complex by using Cytotype MCODE. Most of the identified genes are involved in protein binding, extracellular region or space, platelet degranulation, mitochondrion, and metabolic pathways. Conclusions: The DEGs and related enriched pathways in ADPKD identified through this integrated bioinformatics analysis provide insights into the molecular mechanisms of ADPKD and potential therapeutic strategies. Specifically, abnormal decorin expression in different stages of ADPKD may represent a new therapeutic target in ADPKD, and regulation of metabolism and mitochondrial function in ADPKD may become a focus of future research.
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19
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de Graaf SC, Klykov O, van den Toorn H, Scheltema RA. Cross-ID: Analysis and Visualization of Complex XL-MS-Driven Protein Interaction Networks. J Proteome Res 2019; 18:642-651. [PMID: 30575379 PMCID: PMC6407916 DOI: 10.1021/acs.jproteome.8b00725] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Protein interactions enable much more complex behavior than the sum of the individual protein parts would suggest and represents a level of biological complexity requiring full understanding when unravelling cellular processes. Cross-linking mass spectrometry has emerged as an attractive approach to study these interactions, and recent advances in mass spectrometry and data analysis software have enabled the identification of thousands of cross-links from a single experiment. The resulting data complexity is, however, difficult to understand and requires interactive software tools. Even though solutions are available, these represent an agglomerate of possibilities, and each features its own input format, often forcing manual conversion. Here we present Cross-ID, a visualization platform that links directly into the output of XlinkX for Proteome Discoverer but also plays well with other platforms by supporting a user-controllable text-file importer. The platform includes features like grouping, spectral viewer, gene ontology (GO) enrichment, post-translational modification (PTM) visualization, domains and secondary structure mapping, data set comparison, previsualization overlap check, and more. Validation of detected cross-links is available for proteins and complexes with known structure or for protein complexes through the DisVis online platform ( http://milou.science.uu.nl/cgi/services/DISVIS/disvis/ ). Graphs are exportable in PDF format, and data sets can be exported in tab-separated text files for evaluation through other software.
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Affiliation(s)
- Sebastiaan C de Graaf
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research, and Utrecht Institute for Pharmaceutical Sciences , Utrecht University , Padualaan 8 , 3584 CH Utrecht , The Netherlands.,Netherlands Proteomics Centre , Padualaan 8 , 3584 CH Utrecht , The Netherlands
| | - Oleg Klykov
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research, and Utrecht Institute for Pharmaceutical Sciences , Utrecht University , Padualaan 8 , 3584 CH Utrecht , The Netherlands.,Netherlands Proteomics Centre , Padualaan 8 , 3584 CH Utrecht , The Netherlands
| | - Henk van den Toorn
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research, and Utrecht Institute for Pharmaceutical Sciences , Utrecht University , Padualaan 8 , 3584 CH Utrecht , The Netherlands.,Netherlands Proteomics Centre , Padualaan 8 , 3584 CH Utrecht , The Netherlands
| | - Richard A Scheltema
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research, and Utrecht Institute for Pharmaceutical Sciences , Utrecht University , Padualaan 8 , 3584 CH Utrecht , The Netherlands.,Netherlands Proteomics Centre , Padualaan 8 , 3584 CH Utrecht , The Netherlands
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20
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Cannataro M. Big Data Analysis in Bioinformatics. ENCYCLOPEDIA OF BIG DATA TECHNOLOGIES 2019:161-180. [DOI: 10.1007/978-3-319-77525-8_139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/02/2023]
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21
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Liu X, Chang C, Han M, Yin R, Zhan Y, Li C, Ge C, Yu M, Yang X. PPIExp: A Web-Based Platform for Integration and Visualization of Protein-Protein Interaction Data and Spatiotemporal Proteomics Data. J Proteome Res 2018; 18:633-641. [PMID: 30565464 DOI: 10.1021/acs.jproteome.8b00713] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Integrating spatiotemporal proteomics data with protein-protein interaction (PPI) data can help researchers make an in-depth exploration of their proteins of interest in a dynamic manner. However, there is still a lack of proper tools for the biologists who usually have few programming skills to construct a PPI network for a protein list, visualize active PPI subnetworks, and then select key nodes for further study. We propose a web-based platform named PPIExp that can automatically construct a PPI network, perform clustering analysis according to protein abundances, and perform functional enrichment analysis. More importantly, it provides multiple effective visualization interfaces, such as the interface to display the PPI network map, the interface to display a dendrogram and heatmap for the clustering result, and the interface to display the expression pattern of a selected protein. To visualize the active PPI subnetworks in specific space or time, it provides buttons to highlight the differentially expressed proteins under each condition on the network map. Additionally, to help researchers determine which proteins are worth further attention, PPIExp provides extensive one-click interactive operations to map node centrality measures to node size on the network and highlight three types of proteins, that is, the proteins in an enriched functional term, the coexpressed proteins selected from the dendgrogram and heatmap, and the proteins input by users. PPIExp is available at http://www.fgvis.com/expressvis/PPIExp .
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Affiliation(s)
- Xian Liu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Cheng Chang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Mingfei Han
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Ronghua Yin
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Yiqun Zhan
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Changyan Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Changhui Ge
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Miao Yu
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
| | - Xiaoming Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing) , Beijing Institute of Life Omics , Beijing 102206 , P. R. China
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Shao YC, Nie XC, Song GQ, Wei Y, Xia P, Xu XY. Prognostic value of DKK2 from the Dickkopf family in human breast cancer. Int J Oncol 2018; 53:2555-2565. [PMID: 30320375 PMCID: PMC6203157 DOI: 10.3892/ijo.2018.4588] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Accepted: 09/14/2018] [Indexed: 12/27/2022] Open
Abstract
Breast cancer is one of the most frequently diagnosed types of cancer with a high mortality and malignancy rate in women worldwide. The Dickkopf (DKK) protein family, as a canonical Wnt/β-catenin pathway antagonist, has been implicated in both physiological and pathological processes. This study aimed to comprehensively characterize the prognostic value and elucidate the mechanisms of DKKs in breast cancer and its subtypes. Firstly, DKK mRNA expression and corresponding outcome were analyzed by means of the Gene Expression-Based Outcome for Breast Cancer Online (GOBO) platform based on PAM50 intrinsic breast cancer subtypes. Subsequently, we extracted breast cancer datasets from the Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) to validate the expression profile and prognostic values from the GOBO platform. Moreover, a protein-protein network was created and functional enrichment was conducted to explore the underlying mechanisms of action of the DKKs. In addition, we uncovered the genetic and epigenetic alterations of DKK2 in breast cancer. The main finding of this study was the differential roles of DKKs in the PAM50 subtypes of breast cancer analyzed. The overall trend was that a high level of DKK2 was associated with a good survival in breast cancer, although it played an opposite role in the Normal-like subtype. We also found that DKK2 carried out its functions through multiple signaling pathways, not limited to the Wnt/β-catenin cascade in breast cancer. Finally, we used our own data to validate the bioinformatics analysis data for DKK2 by RT-qPCR. Taken together, our findings suggest that DKK2 may be a potential prognostic biomarker for the Normal-like subtype of breast cancer. However, the prognostic role of DKKs in the subtypes of breast cancer still requires validation by larger sample studies in the future.
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Affiliation(s)
- You-Cheng Shao
- Department of Pathophysiology, College of Basic Medicine Science, China Medical University, Shenyang, Liaoning 110122
| | - Xiao-Cui Nie
- Shenyang Maternity and Infant Hospital, Shenyang, Liaoning 110011
| | - Guo-Qing Song
- Department of Breast Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning 110004
| | - Yan Wei
- Department of Pathophysiology, College of Basic Medicine Science, China Medical University, Shenyang, Liaoning 110122
| | - Pu Xia
- Department of Cell Biology, College of Basic Medical Science, Jinzhou Medical University, Jinzhou, Liaoning 121001, P.R. China
| | - Xiao-Yan Xu
- Department of Pathophysiology, College of Basic Medicine Science, China Medical University, Shenyang, Liaoning 110122
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24
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Felgueiras J, Silva JV, Fardilha M. Adding biological meaning to human protein-protein interactions identified by yeast two-hybrid screenings: A guide through bioinformatics tools. J Proteomics 2018; 171:127-140. [PMID: 28526529 DOI: 10.1016/j.jprot.2017.05.012] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 04/26/2017] [Accepted: 05/13/2017] [Indexed: 02/02/2023]
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25
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Glatigny A, Gambette P, Bourand-Plantefol A, Dujardin G, Mucchielli-Giorgi MH. Development of an in silico method for the identification of subcomplexes involved in the biogenesis of multiprotein complexes in Saccharomyces cerevisiae. BMC SYSTEMS BIOLOGY 2017; 11:67. [PMID: 28693620 PMCID: PMC5504824 DOI: 10.1186/s12918-017-0442-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Accepted: 06/28/2017] [Indexed: 11/23/2022]
Abstract
Background Large sets of protein-protein interaction data coming either from biological experiments or predictive methods are available and can be combined to construct networks from which information about various cell processes can be extracted. We have developed an in silico approach based on these information to model the biogenesis of multiprotein complexes in the yeast Saccharomyces cerevisiae. Results Firstly, we have built three protein interaction networks by collecting the protein-protein interactions, which involved the subunits of three complexes, from different databases. The protein-protein interactions come from different kinds of biological experiments or are predicted. We have chosen the elongator and the mediator head complexes that are soluble and exhibit an architecture with subcomplexes that could be functional modules, and the mitochondrial bc1 complex, which is an integral membrane complex and for which a late assembly subcomplex has been described. Secondly, by applying a clustering strategy to these networks, we were able to identify subcomplexes involved in the biogenesis of the complexes as well as the proteins interacting with each subcomplex. Thirdly, in order to validate our in silico results for the cytochrome bc1 complex we have analysed the physical interactions existing between three subunits by performing immunoprecipitation experiments in several genetic context. Conclusions For the two soluble complexes (the elongator and mediator head), our model shows a strong clustering of subunits that belong to a known subcomplex or module. For the membrane bc1 complex, our approach has suggested new interactions between subunits in the early steps of the assembly pathway that were experimentally confirmed. Scripts can be downloaded from the site: http://bim.igmors.u-psud.fr/isips. Electronic supplementary material The online version of this article (doi:10.1186/s12918-017-0442-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Annie Glatigny
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Sud, Université Paris-Saclay, Avenue de la Terrasse, 91198, Gif-sur-Yvette, France
| | - Philippe Gambette
- Université Paris-Est, LIGM (UMR 8049), CNRS, ENPC, ESIEE, UPEM, 77454, Champs-sur-Marne, France
| | - Alexa Bourand-Plantefol
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Sud, Université Paris-Saclay, Avenue de la Terrasse, 91198, Gif-sur-Yvette, France
| | - Geneviève Dujardin
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Sud, Université Paris-Saclay, Avenue de la Terrasse, 91198, Gif-sur-Yvette, France
| | - Marie-Hélène Mucchielli-Giorgi
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris Sud, Université Paris-Saclay, Avenue de la Terrasse, 91198, Gif-sur-Yvette, France. .,Sorbonne Universités, UPMC Univ Paris 06, UFR927, F-75005, Paris, France.
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Malhotra AG, Jha M, Singh S, Pandey KM. Construction of a Comprehensive Protein-Protein Interaction Map for Vitiligo Disease to Identify Key Regulatory Elements: A Systemic Approach. Interdiscip Sci 2017; 10:500-514. [PMID: 28290051 DOI: 10.1007/s12539-017-0213-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2016] [Revised: 12/09/2016] [Accepted: 12/29/2016] [Indexed: 12/14/2022]
Abstract
Vitiligo is an idiopathic disorder characterized by depigmented patches on the skin due to progressive loss of melanocytes. Several genetic, immunological, and pathophysiological investigations have established vitiligo as a polygenetic disorder with multifactorial etiology. However, no definite model explaining the interplay between these causative factors has been established hitherto. Therefore, we studied the disorder at the system level to identify the key proteins involved by exploring their molecular connectivity in terms of topological parameters. The existing research data helped us in collating 215 proteins involved in vitiligo onset or progression. Interaction study of these proteins leads to a comprehensive vitiligo map with 4845 protein nodes linked with 107,416 edges. Based on centrality measures, a backbone network with 500 nodes has been derived. This has presented a clear overview of the proteins and processes involved and the crosstalk between them. Clustering backbone proteins revealed densely connected regions inferring major molecular interaction modules essential for vitiligo. Finally, a list of top order proteins that play a key role in the disease pathomechanism has been formulated. This includes SUMO2, ESR1, COPS5, MYC, SMAD3, and Cullin proteins. While this list is in fair agreement with the available literature, it also introduces new candidate proteins that can be further explored. A subnetwork of 64 vitiligo core proteins was built by analyzing the backbone and seed protein networks. Our finding suggests that the topology, along with functional clustering, provides a deep insight into the behavior of proteins. This in turn aids in the illustration of disease condition and discovery of significant proteins involved in vitiligo.
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Affiliation(s)
- Anvita Gupta Malhotra
- Department of Biological Science and Engineering, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India
| | - Mohit Jha
- Department of Biological Science and Engineering, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India
| | - Sudha Singh
- Department of Biological Science and Engineering, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India
| | - Khushhali M Pandey
- Department of Biological Science and Engineering, Maulana Azad National Institute of Technology (MANIT), Bhopal, 462003, India.
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Roy S, Guzzi PH. Biological Network Inference from Microarray Data, Current Solutions, and Assessments. Methods Mol Biol 2016; 1375:155-167. [PMID: 26507508 DOI: 10.1007/7651_2015_284] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Currently in bioinformatics and systems biology there is a growing interest for the analysis of associations among biological molecules at a network level. A main research in this area is represented by the inference of biological networks from experimental data. Biological network inference aims to reconstruct network of interactions (or associations) among biological molecules (e.g., genes or proteins) starting from experimental observations. The current scenario is characterized by a growing number of algorithms for the inference, while few attention has been posed on the determination of fair assessments and comparisons. Current assessments are usually based on the comparison of the algorithms using reference networks or gold standard datasets. Here we survey some selected inference algorithms and we compare current assessments. We also present a systematic listing of freely available inference and assessment tools for easy reference. Finally we outline some possible future directions of research, such as the use of a prior knowledge into the assessment process.
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Affiliation(s)
- Swarup Roy
- Department of Information Technology, North-Eastern Hill University, Shillong, India.
| | - Pietro Hiram Guzzi
- Department of Surgical and Medical Sciences, University of Catanzaro, Catanzaro, Italy.
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Abstract
The importance of semantic-based methods and algorithms for the analysis and management of biological data is growing for two main reasons. From a biological side, knowledge contained in ontologies is more and more accurate and complete, from a computational side, recent algorithms are using in a valuable way such knowledge. Here we focus on semantic-based management and analysis of protein interaction networks referring to all the approaches of analysis of protein-protein interaction data that uses knowledge encoded into biological ontologies. Semantic approaches for studying high-throughput data have been largely used in the past to mine genomic and expression data. Recently, the emergence of network approaches for investigating molecular machineries has stimulated in a parallel way the introduction of semantic-based techniques for analysis and management of network data. The application of these computational approaches to the study of microarray data can broad the application scenario of them and simultaneously can help the understanding of disease development and progress.
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Affiliation(s)
- Agapito Giuseppe
- Department of Surgical and Medical Sciences, University of Catanzaro, Viale Europa-Localita Germaneto, Catanzaro, 88100, Italy.
| | - Marianna Milano
- Department of Surgical and Medical Sciences, University of Catanzaro, Viale Europa-Localita Germaneto, Catanzaro, 88100, Italy.
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Thébault P, Bourqui R, Benchimol W, Gaspin C, Sirand-Pugnet P, Uricaru R, Dutour I. Advantages of mixing bioinformatics and visualization approaches for analyzing sRNA-mediated regulatory bacterial networks. Brief Bioinform 2015; 16:795-805. [PMID: 25477348 PMCID: PMC4570199 DOI: 10.1093/bib/bbu045] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2014] [Revised: 11/05/2014] [Indexed: 12/29/2022] Open
Abstract
The revolution in high-throughput sequencing technologies has enabled the acquisition of gigabytes of RNA sequences in many different conditions and has highlighted an unexpected number of small RNAs (sRNAs) in bacteria. Ongoing exploitation of these data enables numerous applications for investigating bacterial transacting sRNA-mediated regulation networks. Focusing on sRNAs that regulate mRNA translation in trans, recent works have noted several sRNA-based regulatory pathways that are essential for key cellular processes. Although the number of known bacterial sRNAs is increasing, the experimental validation of their interactions with mRNA targets remains challenging and involves expensive and time-consuming experimental strategies. Hence, bioinformatics is crucial for selecting and prioritizing candidates before designing any experimental work. However, current software for target prediction produces a prohibitive number of candidates because of the lack of biological knowledge regarding the rules governing sRNA-mRNA interactions. Therefore, there is a real need to develop new approaches to help biologists focus on the most promising predicted sRNA-mRNA interactions. In this perspective, this review aims at presenting the advantages of mixing bioinformatics and visualization approaches for analyzing predicted sRNA-mediated regulatory bacterial networks.
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Jeanquartier F, Jean-Quartier C, Holzinger A. Integrated web visualizations for protein-protein interaction databases. BMC Bioinformatics 2015; 16:195. [PMID: 26077899 PMCID: PMC4466863 DOI: 10.1186/s12859-015-0615-z] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2015] [Accepted: 05/15/2015] [Indexed: 12/27/2022] Open
Abstract
Background Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks. Results We selected M =10 out of N =53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015. Conclusions Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing.
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Affiliation(s)
- Fleur Jeanquartier
- Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz, 8036, Austria.
| | - Claire Jean-Quartier
- Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz, 8036, Austria.
| | - Andreas Holzinger
- Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and Documentation, Medical University Graz, Auenbruggerplatz 2/V, Graz, 8036, Austria. .,Institute for Information Systems & Computer Media Graz University of Technology, Inffeldgasse 16c, Graz, 8010, Austria.
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Talwar P, Silla Y, Grover S, Gupta M, Grewal GK, Kukreti R. Systems Pharmacology and Pharmacogenomics for Drug Discovery and Development. SYSTEMS AND SYNTHETIC BIOLOGY 2015. [DOI: 10.1007/978-94-017-9514-2_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Hirst JD, Glowacki DR, Baaden M. Molecular simulations and visualization: introduction and overview. Faraday Discuss 2014; 169:9-22. [DOI: 10.1039/c4fd90024c] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Salazar GA, Meintjes A, Mazandu GK, Rapanoël HA, Akinola RO, Mulder NJ. A web-based protein interaction network visualizer. BMC Bioinformatics 2014; 15:129. [PMID: 24885165 PMCID: PMC4029974 DOI: 10.1186/1471-2105-15-129] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2013] [Accepted: 04/24/2014] [Indexed: 01/18/2023] Open
Abstract
Background Interaction between proteins is one of the most important mechanisms in the execution of cellular functions. The study of these interactions has provided insight into the functioning of an organism’s processes. As of October 2013, Homo sapiens had over 170000 Protein-Protein interactions (PPI) registered in the Interologous Interaction Database, which is only one of the many public resources where protein interactions can be accessed. These numbers exemplify the volume of data that research on the topic has generated. Visualization of large data sets is a well known strategy to make sense of information, and protein interaction data is no exception. There are several tools that allow the exploration of this data, providing different methods to visualize protein network interactions. However, there is still no native web tool that allows this data to be explored interactively online. Results Given the advances that web technologies have made recently it is time to bring these interactive views to the web to provide an easily accessible forum to visualize PPI. We have created a Web-based Protein Interaction Network Visualizer: PINV, an open source, native web application that facilitates the visualization of protein interactions (http://biosual.cbio.uct.ac.za/pinv.html). We developed PINV as a set of components that follow the protocol defined in BioJS and use the D3 library to create the graphic layouts. We demonstrate the use of PINV with multi-organism interaction networks for a predicted target from Mycobacterium tuberculosis, its interacting partners and its orthologs. Conclusions The resultant tool provides an attractive view of complex, fully interactive networks with components that allow the querying, filtering and manipulation of the visible subset. Moreover, as a web resource, PINV simplifies sharing and publishing, activities which are vital in today’s research collaborative environments. The source code is freely available for download at https://github.com/4ndr01d3/biosual.
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Affiliation(s)
- Gustavo A Salazar
- Computational Biology Group, IDM, Faculty of Health Sciences, University of Cape Town, Anzio Road, Cape Town, South Africa.
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Salazar GA, Meintjes A, Mulder N. PPI layouts: BioJS components for the display of Protein-Protein Interactions. F1000Res 2014; 3:50. [PMID: 25075288 PMCID: PMC4103490 DOI: 10.12688/f1000research.3-50.v1] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 02/03/2014] [Indexed: 01/17/2023] Open
Abstract
SUMMARY We present two web-based components for the display of Protein-Protein Interaction networks using different self-organizing layout methods: force-directed and circular. These components conform to the BioJS standard and can be rendered in an HTML5-compliant browser without the need for third-party plugins. We provide examples of interaction networks and how the components can be used to visualize them, and refer to a more complex tool that uses these components. AVAILABILITY http://github.com/biojs/biojs; http://dx.doi.org/10.5281/zenodo.7753.
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Affiliation(s)
- Gustavo A Salazar
- Computational Biology Group, University of Cape Town, Cape Town, South Africa
| | - Ayton Meintjes
- Computational Biology Group, University of Cape Town, Cape Town, South Africa
| | - Nicola Mulder
- Computational Biology Group, University of Cape Town, Cape Town, South Africa
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Li W, Kinch LN, Grishin NV. Pclust: protein network visualization highlighting experimental data. ACTA ACUST UNITED AC 2013; 29:2647-8. [PMID: 23918248 DOI: 10.1093/bioinformatics/btt451] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
SUMMARY One approach to infer functions of new proteins from their homologs utilizes visualization of an all-against-all pairwise similarity network (A2ApsN) that exploits the speed of BLAST and avoids the complexity of multiple sequence alignment. However, identifying functions of the protein clusters in A2ApsN is never trivial, due to a lack of linking characterized proteins to their relevant information in current software packages. Given the database errors introduced by automatic annotation transfer, functional deduction should be made from proteins with experimental studies, i.e. 'reference proteins'. Here, we present a web server, termed Pclust, which provides a user-friendly interface to visualize the A2ApsN, placing emphasis on such 'reference proteins' and providing access to their full information in source databases, e.g. articles in PubMed. The identification of 'reference proteins' and the ease of cross-database linkage will facilitate understanding the functions of protein clusters in the network, thus promoting interpretation of proteins of interest. AVAILABILITY The Pclust server is freely available at http://prodata.swmed.edu/pclust
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Affiliation(s)
- Wenlin Li
- Departments of Biophysics and Biochemistry and Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX 75390-9050, USA
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Rother M, Münzner U, Thieme S, Krantz M. Information content and scalability in signal transduction network reconstruction formats. MOLECULAR BIOSYSTEMS 2013; 9:1993-2004. [PMID: 23636168 DOI: 10.1039/c3mb00005b] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
One of the first steps towards holistic understanding of cellular networks is the integration of the available information in a human and machine readable format. This network reconstruction process is well established for metabolic networks, and numerous genome wide metabolic reconstructions are already available. Extending these strategies to signalling networks has proven difficult, primarily due to the combinatorial nature of regulatory modifications. The combinatorial nature of possible protein-protein interactions and post translational modifications affects both network size and the correspondence between the reconstructed network and the underlying empirical data. Here, we discuss different approaches to reconstruction of signal transduction networks. We divide the current approaches into topological, specific state based and reaction-contingency based, and discuss their different information content and scalability. The discussion focusses on graphical formats but the points are in general applicable also to mathematical models and databases. While the formats have complementary strengths especially for small networks, reaction-contingency based formats have a number of advantages in the light of global network reconstruction. In particular, they minimise the need for assumptions, maximise the congruence with empirical data, and scale efficiently with network size.
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Affiliation(s)
- Magdalena Rother
- Theoretical Biophysics, Humboldt-Universität zu Berlin, Invalidenstr. 42, 10115 Berlin, Germany
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