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Panditrao G, Bhowmick R, Meena C, Sarkar RR. Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects. J Biosci 2022. [PMID: 36210749 PMCID: PMC9018971 DOI: 10.1007/s12038-022-00253-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.
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Affiliation(s)
- Gauri Panditrao
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Rupa Bhowmick
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
| | - Chandrakala Meena
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
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Ackerman EE, Shoemaker JE. Network Controllability-Based Prioritization of Candidates for SARS-CoV-2 Drug Repositioning. Viruses 2020; 12:v12101087. [PMID: 32993136 PMCID: PMC7650805 DOI: 10.3390/v12101087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Revised: 09/22/2020] [Accepted: 09/23/2020] [Indexed: 12/12/2022] Open
Abstract
In a short time, the COVID-19 pandemic has left the world with over 25 million cases and staggering death tolls that are still rising. Treatments for SARS-CoV-2 infection are desperately needed as there are currently no approved drug therapies. With limited knowledge of viral mechanisms, a network controllability method of prioritizing existing drugs for repurposing efforts is optimal for quickly moving through the drug approval pipeline using limited, available, virus-specific data. Based on network topology and controllability, 16 proteins involved in translation, cellular transport, cellular stress, and host immune response are predicted as regulators of the SARS-CoV-2 infected cell. Of the 16, eight are prioritized as possible drug targets where two, PVR and SCARB1, are previously unexplored. Known compounds targeting these genes are suggested for viral inhibition study. Prioritized proteins in agreement with previous analysis and viral inhibition studies verify the ability of network controllability to predict biologically relevant candidates.
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Affiliation(s)
- Emily E. Ackerman
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA;
| | - Jason E. Shoemaker
- Department of Chemical and Petroleum Engineering, University of Pittsburgh, Pittsburgh, PA 15260, USA;
- The McGowan Institute for Regenerative Medicine (MIRM), University of Pittsburgh, Pittsburgh, PA 15260, USA
- Department of Computational and Systems Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15260, USA
- Correspondence:
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Ochsner SA, Pillich RT, McKenna NJ. Consensus transcriptional regulatory networks of coronavirus-infected human cells. Sci Data 2020; 7:314. [PMID: 32963239 PMCID: PMC7509801 DOI: 10.1038/s41597-020-00628-6] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 08/05/2020] [Indexed: 02/08/2023] Open
Abstract
Establishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of MERS, SARS1 and SARS2 infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family HCTs encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository.
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Affiliation(s)
- Scott A Ochsner
- The Signaling Pathways Project and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Rudolf T Pillich
- Department of Medicine, University of California San Diego, La Jolla, CA, 92093, USA
| | - Neil J McKenna
- The Signaling Pathways Project and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA.
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Ochsner SA, Pillich RT, McKenna NJ. Consensus transcriptional regulatory networks of coronavirus-infected human cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.04.24.059527. [PMID: 32511379 PMCID: PMC7263508 DOI: 10.1101/2020.04.24.059527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Establishing consensus around the transcriptional interface between coronavirus (CoV) infection and human cellular signaling pathways can catalyze the development of novel anti-CoV therapeutics. Here, we used publicly archived transcriptomic datasets to compute consensus regulatory signatures, or consensomes, that rank human genes based on their rates of differential expression in MERS-CoV (MERS), SARS-CoV-1 (SARS1) and SARS-CoV-2 (SARS2)-infected cells. Validating the CoV consensomes, we show that high confidence transcriptional targets (HCTs) of CoV infection intersect with HCTs of signaling pathway nodes with known roles in CoV infection. Among a series of novel use cases, we gather evidence for hypotheses that SARS2 infection efficiently represses E2F family target genes encoding key drivers of DNA replication and the cell cycle; that progesterone receptor signaling antagonizes SARS2-induced inflammatory signaling in the airway epithelium; and that SARS2 HCTs are enriched for genes involved in epithelial to mesenchymal transition. The CoV infection consensomes and HCT intersection analyses are freely accessible through the Signaling Pathways Project knowledgebase, and as Cytoscape-style networks in the Network Data Exchange repository.
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Affiliation(s)
- Scott A Ochsner
- The Signaling Pathways Project and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030
| | - Rudolf T Pillich
- Department of Medicine, University of California San Diego, La Jolla, CA 92093
| | - Neil J McKenna
- The Signaling Pathways Project and Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX 77030
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Farooq QUA, Shaukat Z, Zhou T, Aiman S, Gong W, Li C. Inferring Virus-Host relationship between HPV and its host Homo sapiens using protein interaction network. Sci Rep 2020; 10:8719. [PMID: 32457456 PMCID: PMC7251128 DOI: 10.1038/s41598-020-65837-w] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Accepted: 05/11/2020] [Indexed: 12/14/2022] Open
Abstract
Human papilloma virus (HPV) is a serious threat to human life globally with over 100 genotypes including cancer causing high risk HPVs. Study on protein interaction maps of pathogens with their host is a recent trend in ‘omics’ era and has been practiced by researchers to find novel drug targets. In current study, we construct an integrated protein interaction map of HPV with its host human in Cytoscape and analyze it further by using various bioinformatics tools. We found out 2988 interactions between 12 HPV and 2061 human proteins among which we identified MYLK, CDK7, CDK1, CDK2, JAK1 and 6 other human proteins associated with multiple viral oncoproteins. The functional enrichment analysis of these top-notch key genes is performed using KEGG pathway and Gene Ontology analysis, which reveals that the gene set is enriched in cell cycle a crucial cellular process, and the second most important pathway in which the gene set is involved is viral carcinogenesis. Among the viral proteins, E7 has the highest number of associations in the network followed by E6, E2 and E5. We found out a group of genes which is not targeted by the existing drugs available for HPV infections. It can be concluded that the molecules found in this study could be potential targets and could be used by scientists in their drug design studies.
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Affiliation(s)
- Qurat Ul Ain Farooq
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Zeeshan Shaukat
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
| | - Tong Zhou
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Sara Aiman
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Weikang Gong
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China
| | - Chunhua Li
- College of Life Science and Bioengineering, Beijing University of Technology, Beijing, 100124, China.
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Singh N, Rai S, Bhatnagar R, Bhatnagar S. Network analysis of host-pathogen protein interactions in microbe induced cardiovascular diseases. In Silico Biol 2020; 14:115-133. [PMID: 35001887 PMCID: PMC8842779 DOI: 10.3233/isb-210238] [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] [Indexed: 11/20/2022]
Abstract
Large-scale visualization and analysis of HPIs involved in microbial CVDs can provide crucial insights into the mechanisms of pathogenicity. The comparison of CVD associated HPIs with the entire set of HPIs can identify the pathways specific to CVDs. Therefore, topological properties of HPI networks in CVDs and all pathogens was studied using Cytoscape3.5.1. Ontology and pathway analysis were done using KOBAS 3.0. HPIs of Papilloma, Herpes, Influenza A virus as well as Yersinia pestis and Bacillus anthracis among bacteria were predominant in the whole (wHPI) and the CVD specific (cHPI) network. The central viral and secretory bacterial proteins were predicted virulent. The central viral proteins had higher number of interactions with host proteins in comparison with bacteria. Major fraction of central and essential host proteins interacts with central viral proteins. Alpha-synuclein, Ubiquitin ribosomal proteins, TATA-box-binding protein, and Polyubiquitin-C &B proteins were the top interacting proteins specific to CVDs. Signaling by NGF, Fc epsilon receptor, EGFR and ubiquitin mediated proteolysis were among the top enriched CVD specific pathways. DEXDc and HELICc were enriched host mimicry domains that may help in hijacking of cellular machinery by pathogens. This study provides a system level understanding of cardiac damage in microbe induced CVDs.
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Affiliation(s)
- Nirupma Singh
- Computational and Structural Biology Laboratory, Department of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, India
| | - Sneha Rai
- Computational and Structural Biology Laboratory, Department of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, India
| | | | - Sonika Bhatnagar
- Computational and Structural Biology Laboratory, Department of Biotechnology, Netaji Subhas Institute of Technology, Dwarka, New Delhi, India.,Computational and Structural Biology Laboratory, Department of Biological Sciences and Engineering, Netaji Subhas University of Technology, Dwarka, New Delhi, India
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