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Gene Regulatory Network Analysis of Perivascular Adipose Tissue of Abdominal Aortic Aneurysm Identifies Master Regulators of Key Pathogenetic Pathways. Biomedicines 2020; 8:biomedicines8080288. [PMID: 32823940 PMCID: PMC7459520 DOI: 10.3390/biomedicines8080288] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 07/30/2020] [Accepted: 08/12/2020] [Indexed: 11/19/2022] Open
Abstract
The lack of medical therapy to treat abdominal aortic aneurysm (AAA) stems from our inadequate understanding of the mechanisms underlying AAA pathogenesis. To date, the only available treatment option relies on surgical intervention, which aims to prevent AAA rupture. Identifying specific regulators of pivotal pathogenetic mechanisms would allow the development of novel treatments. With this work, we sought to identify regulatory factors associated with co-expressed genes characterizing the diseased perivascular adipose tissue (PVAT) of AAA patients, which is crucially involved in AAA pathogenesis. We applied a reverse engineering approach to identify cis-regulatory elements of diseased PVAT genes, the associated transcription factors, and upstream regulators. Finally, by analyzing the topological properties of the reconstructed regulatory disease network, we prioritized putative targets for AAA interference treatment options. Overall, we identified NFKB1, SPIB, and TBP as the most relevant transcription factors, as well as MAPK1 and GSKB3 protein kinases and RXRA nuclear receptor as key upstream regulators. We showed that these factors could regulate different co-expressed gene subsets in AAA PVAT, specifically associated with both innate and antigen-driven immune response pathways. Inhibition of these factors may represent a novel option for the development of efficient immunomodulatory strategies to treat AAA.
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Sabetian S, Shamsir MS. Computer aided analysis of disease linked protein networks. Bioinformation 2019; 15:513-522. [PMID: 31485137 PMCID: PMC6704336 DOI: 10.6026/97320630015513] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2019] [Revised: 04/16/2019] [Accepted: 04/17/2019] [Indexed: 12/26/2022] Open
Abstract
Proteins can interact in various ways, ranging from direct physical relationships to indirect interactions in a formation of protein-protein interaction network. Diagnosis of the protein connections is critical to identify various cellular pathways. Today constructing and analyzing the protein interaction network is being developed as a powerful approach to create network pharmacology toward detecting unknown genes and proteins associated with diseases. Discovery drug targets regarding therapeutic decisions are exciting outcomes of studying disease networks. Protein connections may be identified by experimental and recent new computational approaches. Due to difficulties in analyzing in-vivo proteins interactions, many researchers have encouraged improving computational methods to design protein interaction network. In this review, the experimental and computational approaches and also advantages and disadvantages of these methods regarding the identification of new interactions in a molecular mechanism have been reviewed. Systematic analysis of complex biological systems including network pharmacology and disease network has also been discussed in this review.
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Affiliation(s)
- Soudabeh Sabetian
- Department of Biological and Health Sciences, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia
- Infertility Research Center, Shiraz University, Shiraz 71454, Iran, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Mohd Shahir Shamsir
- Department of Biological and Health Sciences, Faculty of Bioscience and Medical Engineering, Universiti Teknologi Malaysia, 81310 Johor, Malaysia
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Shadab M, Das S, Banerjee A, Sinha R, Asad M, Kamran M, Maji M, Jha B, Deepthi M, Kumar M, Tripathi A, Kumar B, Chakrabarti S, Ali N. RNA-Seq Revealed Expression of Many Novel Genes Associated With Leishmania donovani Persistence and Clearance in the Host Macrophage. Front Cell Infect Microbiol 2019; 9:17. [PMID: 30805314 PMCID: PMC6370631 DOI: 10.3389/fcimb.2019.00017] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 01/17/2019] [Indexed: 12/14/2022] Open
Abstract
Host- as well as parasite-specific factors are equally crucial in allowing either the Leishmania parasites to dominate, or host macrophages to resist infection. To identify such factors, we infected murine peritoneal macrophages with either the virulent (vAG83) or the non-virulent (nvAG83) parasites of L. donovani. Then, through dual RNA-seq, we simultaneously elucidated the transcriptomic changes occurring both in the host and the parasites. Through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the differentially expressed (DE) genes, we showed that the vAG83-infected macrophages exhibit biased anti-inflammatory responses compared to the macrophages infected with the nvAG83. Moreover, the vAG83-infected macrophages displayed suppression of many important cellular processes, including protein synthesis. Further, through protein-protein interaction study, we showed significant downregulation in the expression of many hubs and hub-bottleneck genes in macrophages infected with vAG83 as compared to nvAG83. Cell signaling study showed that these two parasites activated the MAPK and PI3K-AKT signaling pathways differentially in the host cells. Through gene ontology analyses of the parasite-specific genes, we discovered that the genes for virulent factors and parasite survival were significantly upregulated in the intracellular amastigotes of vAG83. In contrast, genes involved in the immune stimulations, and those involved in negative regulation of the cell cycle and transcriptional regulation, were upregulated in the nvAG83. Collectively, these results depicted a differential regulation in the host and the parasite-specific molecules during in vitro persistence and clearance of the parasites.
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Affiliation(s)
- Mohammad Shadab
- Infectious Diseases and Immunology Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Sonali Das
- Infectious Diseases and Immunology Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Anindyajit Banerjee
- Structural Biology and Bio-Informatics Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Roma Sinha
- Infectious Diseases and Immunology Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Mohammad Asad
- Infectious Diseases and Immunology Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Mohd Kamran
- Infectious Diseases and Immunology Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Mithun Maji
- Infectious Diseases and Immunology Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Baijayanti Jha
- Infectious Diseases and Immunology Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Makaraju Deepthi
- Infectious Diseases and Immunology Division, Indian Institute of Chemical Biology, Kolkata, India
| | | | | | - Bipin Kumar
- Nucleome Informatics Pvt. Ltd., Hyderabad, India
| | - Saikat Chakrabarti
- Structural Biology and Bio-Informatics Division, Indian Institute of Chemical Biology, Kolkata, India
| | - Nahid Ali
- Infectious Diseases and Immunology Division, Indian Institute of Chemical Biology, Kolkata, India
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Hao T, Wang Q, Zhao L, Wu D, Wang E, Sun J. Analyzing of Molecular Networks for Human Diseases and Drug Discovery. Curr Top Med Chem 2018; 18:1007-1014. [PMID: 30101711 PMCID: PMC6174636 DOI: 10.2174/1568026618666180813143408] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2017] [Revised: 06/22/2018] [Accepted: 07/03/2018] [Indexed: 01/11/2023]
Abstract
Molecular networks represent the interactions and relations of genes/proteins, and also encode molecular mechanisms of biological processes, development and diseases. Among the molecular networks, protein-protein Interaction Networks (PINs) have become effective platforms for uncovering the molecular mechanisms of diseases and drug discovery. PINs have been constructed for various organisms and utilized to solve many biological problems. In human, most proteins present their complex functions by interactions with other proteins, and the sum of these interactions represents the human protein interactome. Especially in the research on human disease and drugs, as an emerging tool, the PIN provides a platform to systematically explore the molecular complexities of specific diseases and the references for drug design. In this review, we summarized the commonly used approaches to aid disease research and drug discovery with PINs, including the network topological analysis, identification of novel pathways, drug targets and sub-network biomarkers for diseases. With the development of bioinformatic techniques and biological networks, PINs will play an increasingly important role in human disease research and drug discovery.
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Affiliation(s)
- Tong Hao
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Qian Wang
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Lingxuan Zhao
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Dan Wu
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Edwin Wang
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China.,University of Calgary Cumming School of Medicine, Calgary, Alberta T2N 4Z6, Canada
| | - Jinsheng Sun
- Tianjin Key Laboratory of Animal and Plant Resistance/College of Life Sciences, Tianjin Normal University, Tianjin 300387, China.,Tianjin Bohai Fisheries Research Institute, Tianjin 300221, China
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Kotlyar M, Rossos AEM, Jurisica I. Prediction of Protein-Protein Interactions. ACTA ACUST UNITED AC 2017; 60:8.2.1-8.2.14. [PMID: 29220074 DOI: 10.1002/cpbi.38] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
The authors provide an overview of physical protein-protein interaction prediction, covering the main strategies for predicting interactions, approaches for assessing predictions, and online resources for accessing predictions. This unit focuses on the main advancements in each of these areas over the last decade. The methods and resources that are presented here are not an exhaustive set, but characterize the current state of the field-highlighting key challenges and achievements. © 2017 by John Wiley & Sons, Inc.
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Affiliation(s)
- Max Kotlyar
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Andrea E M Rossos
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Igor Jurisica
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Departments of Medical Biophysics and Computer Science, University of Toronto, Ontario, Canada.,Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia
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Rahmani H, Blockeel H, Bender A. Using a Human Drug Network for generating novel hypotheses about drugs. INTELL DATA ANAL 2016. [DOI: 10.3233/ida-150800] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Hossein Rahmani
- School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
- Department of Knowledge Engineering, Universiteit Maastricht, Maastricht, The Netherlands
| | - Hendrik Blockeel
- Department of Computer Science, KU Leuven, Leuven, Belgium
- Leiden Institute of Advanced Computer Science, Leiden University, CA Leiden, The Netherlands
| | - Andreas Bender
- Unilever Centre for Molecular Science Informatics, Department of Chemistry, University of Cambridge, Cambridge, UK
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Kotlyar M, Pastrello C, Sheahan N, Jurisica I. Integrated interactions database: tissue-specific view of the human and model organism interactomes. Nucleic Acids Res 2015; 44:D536-41. [PMID: 26516188 PMCID: PMC4702811 DOI: 10.1093/nar/gkv1115] [Citation(s) in RCA: 167] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Accepted: 10/13/2015] [Indexed: 01/28/2023] Open
Abstract
IID (Integrated Interactions Database) is the first database providing tissue-specific protein–protein interactions (PPIs) for model organisms and human. IID covers six species (S. cerevisiae (yeast), C. elegans (worm), D. melonogaster (fly), R. norvegicus (rat), M. musculus (mouse) and H. sapiens (human)) and up to 30 tissues per species. Users query IID by providing a set of proteins or PPIs from any of these organisms, and specifying species and tissues where IID should search for interactions. If query proteins are not from the selected species, IID enables searches across species and tissues automatically by using their orthologs; for example, retrieving interactions in a given tissue, conserved in human and mouse. Interaction data in IID comprises three types of PPI networks: experimentally detected PPIs from major databases, orthologous PPIs and high-confidence computationally predicted PPIs. Interactions are assigned to tissues where their proteins pairs or encoding genes are expressed. IID is a major replacement of the I2D interaction database, with larger PPI networks (a total of 1,566,043 PPIs among 68,831 proteins), tissue annotations for interactions, and new query, analysis and data visualization capabilities. IID is available at http://ophid.utoronto.ca/iid.
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Affiliation(s)
- Max Kotlyar
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 1L7, Canada
| | - Chiara Pastrello
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 1L7, Canada
| | - Nicholas Sheahan
- School of Computing, Queen's University, Kingston, ON, K7L 2N8, Canada
| | - Igor Jurisica
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, M5G 1L7, Canada Departments of Medical Biophysics and Computer Science, University of Toronto, Toronto, ON, M5S 1A4, Canada
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Syafrizayanti, Betzen C, Hoheisel JD, Kastelic D. Methods for analyzing and quantifying protein–protein interaction. Expert Rev Proteomics 2014; 11:107-20. [DOI: 10.1586/14789450.2014.875857] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Csermely P, Korcsmáros T, Kiss HJM, London G, Nussinov R. Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review. Pharmacol Ther 2013; 138:333-408. [PMID: 23384594 PMCID: PMC3647006 DOI: 10.1016/j.pharmthera.2013.01.016] [Citation(s) in RCA: 506] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2013] [Accepted: 01/22/2013] [Indexed: 02/02/2023]
Abstract
Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.
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Affiliation(s)
- Peter Csermely
- Department of Medical Chemistry, Semmelweis University, P.O. Box 260, H-1444 Budapest 8, Hungary.
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Hu R, Wang X, Zhan X. Multi-parameter systematic strategies for predictive, preventive and personalised medicine in cancer. EPMA J 2013; 4:2. [PMID: 23339750 PMCID: PMC3564825 DOI: 10.1186/1878-5085-4-2] [Citation(s) in RCA: 43] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2012] [Accepted: 01/09/2013] [Indexed: 12/11/2022]
Abstract
Cancer is a complex disease that causes the alterations in the levels of gene, RNA, protein and metabolite. With the development of genomics, transcriptomics, proteomics and metabolomic techniques, the characterisation of key mutations and molecular pathways responsible for tumour progression has led to the identification of a large number of potential targets. The increasing understanding of molecular carcinogenesis has begun to change paradigms in oncology from traditional single-factor strategy to multi-parameter systematic strategy. The therapeutic model of cancer has changed from adopting the general radiotherapy and chemotherapy to personalised strategy. The development of predictive, preventive and personalised medicine (PPPM) will allow prediction of response with substantially increased accuracy, stratification of particular patient groups and eventual personalisation of medicine. The PPPM will change the approach to tumour diseases from a systematic and comprehensive point of view in the future. Patients will be treated according to the specific molecular profiles that are found in the individual tumour tissue and preferentially with targeted substances, if available.
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Affiliation(s)
- Rong Hu
- Key Laboratory of Cancer Proteomics of Chinese Ministry of Health, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha, Hunan, 410008, People's Republic of China.
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von Eichborn J, Dunkel M, Gohlke BO, Preissner SC, Hoffmann MF, Bauer JMJ, Armstrong JD, Schaefer MH, Andrade-Navarro MA, Le Novere N, Croning MDR, Grant SGN, van Nierop P, Smit AB, Preissner R. SynSysNet: integration of experimental data on synaptic protein-protein interactions with drug-target relations. Nucleic Acids Res 2012; 41:D834-40. [PMID: 23143269 PMCID: PMC3531074 DOI: 10.1093/nar/gks1040] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
We created SynSysNet, available online at http://bioinformatics.charite.de/synsysnet, to provide a platform that creates a comprehensive 4D network of synaptic interactions. Neuronal synapses are fundamental structures linking nerve cells in the brain and they are responsible for neuronal communication and information processing. These processes are dynamically regulated by a network of proteins. New developments in interaction proteomics and yeast two-hybrid methods allow unbiased detection of interactors. The consolidation of data from different resources and methods is important to understand the relation to human behaviour and disease and to identify new therapeutic approaches. To this end, we established SynSysNet from a set of ∼1000 synapse specific proteins, their structures and small-molecule interactions. For two-thirds of these, 3D structures are provided (from Protein Data Bank and homology modelling). Drug-target interactions for 750 approved drugs and 50 000 compounds, as well as 5000 experimentally validated protein–protein interactions, are included. The resulting interaction network and user-selected parts can be viewed interactively and exported in XGMML. Approximately 200 involved pathways can be explored regarding drug-target interactions. Homology-modelled structures are downloadable in Protein Data Bank format, and drugs are available as MOL-files. Protein–protein interactions and drug-target interactions can be viewed as networks; corresponding PubMed IDs or sources are given.
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Affiliation(s)
- Joachim von Eichborn
- Structural Bioinformatics Group, Institute of Physiology, Charité-University Medicine Berlin, Lindenberger Weg 80, 13125 Berlin, Germany
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