Identifying Chemicals with Potential Therapy of HIV Based on Protein-Protein and Protein-Chemical Interaction Network

Li, Biqing; Niu, Bing; Lei, Chen; Wei, Zejun; Huang, Tao; Jiang, Min; Lü, Jing; Zheng, Mingyue; Kong, Xiangyin; Cai, Yu-Dong

doi:10.1371/journal.pone.0065207

Cited by 17 publications

(7 citation statements)

References 78 publications

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“…And, results from genome-wide transcriptomes of primary monocytes from HIV-positive patients on highly active antiretroviral therapy were compared to the database for gene set enrichment analysis ( 23 ). As would be expected, the database has also provided useful information for identifying novel anti-HIV drugs ( 24 ). Finally, the database has to some extent been used to create protein-level analysis for other pathogen–host interactions ( 25 , 26 ).…”

Section: Value Of the Database To The Hiv/aids Research Communitymentioning

confidence: 76%

HIV-1, human interaction database: current status and new features

Ako-adjei

Wallin

et al. 2014

Nucleic Acids Research

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The ‘Human Immunodeficiency Virus Type 1 (HIV-1), Human Interaction Database’, available through the National Library of Medicine at http://www.ncbi.nlm.nih.gov/genome/viruses/retroviruses/hiv-1/interactions, serves the scientific community exploring the discovery of novel HIV vaccine candidates and therapeutic targets. Each HIV-1 human protein interaction can be retrieved without restriction by web-based downloads and ftp protocols and includes: Reference Sequence (RefSeq) protein accession numbers, National Center for Biotechnology Information Gene identification numbers, brief descriptions of the interactions, searchable keywords for interactions and PubMed identification numbers (PMIDs) of journal articles describing the interactions. In addition to specific HIV-1 protein–human protein interactions, included are interaction effects upon HIV-1 replication resulting when individual human gene expression is blocked using siRNA. A total of 3142 human genes are described participating in 12 786 protein–protein interactions, along with 1316 replication interactions described for each of 1250 human genes identified using small interfering RNA (siRNA). Together the data identifies 4006 human genes involved in 14 102 interactions. With the inclusion of siRNA interactions we introduce a redesigned web interface to enhance viewing, filtering and downloading of the combined data set.

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Section: Value Of the Database To The Hiv/aids Research Communitymentioning

confidence: 76%

HIV-1, human interaction database: current status and new features

Ako-adjei

Wallin

et al. 2014

Nucleic Acids Research

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“…The betweenness centrality quantifies the number of times a protein acts as a bridge along the shortest path between two other proteins in the network (Figure 3B). The degree and centrality measures can be used to identify the essential human proteins influencing HIV-human interactions (Dickerson et al, 2010;van Dijk et al, 2010;Huang et al, 2011;Li et al, 2013;Ma et al, 2013;Bandyopadhyay et al, 2015;Xie et al, 2015). For example, Huang et al (2011) compared gene expression profiles from CD4+ T cells between HIV-1-resistant and susceptible subjects using Minimum Redundancy-Maximum Relevance and Incremental Feature Selection algorithms.…”

Section: Identification Of Essential Proteins In a Protein-protein Inmentioning

confidence: 99%

Network-Based Analysis of OMICs Data to Understand the HIV–Host Interaction

et al. 2020

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The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV-host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV-host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding networkbased analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV-human protein-protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies represent a basis for developing new anti-HIV therapeutic strategies such as new drugs preventing infection of CD4+ cells and viral replication, effective vaccines, "shock and kill" and "block and lock" approaches to cure latent infection.

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“…The betweenness centrality quantifies the number of times a protein acts as a bridge along the shortest path between two other proteins in the network. The degree and centrality measures can be used to identify the essential human proteins influencing HIV-human interactions (Dickerson et al, 2010;van Dijk et al, 2010;Huang et al, 2011;Li et al, 2013;Ma et al, 2013;Bandyopadhyay et al, 2015;Xie et al, 2015). For example, Huang T. and colleagues compared gene expression profiles from CD4+ T cells between HIV-1-resistant and susceptible subjects using Minimum Redundancy-Maximum Relevance and Incremental Feature Selection algorithms.…”

Section: Identification Of Essential Proteins In a Protein-protein Inmentioning

confidence: 99%

Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction

Ivanov

Lagunin

Filimonov

et al. 2020

Preprint

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The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV-1-host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV-host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV-human protein-protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies may represent a basis for developing new anti-HIV therapeutic strategies such as new small-molecule drugs preventing infection of CD4+ cells and viral replication, effective vaccines, "shock and kill" and "block and lock" approaches to cure latent infection.

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Identifying Chemicals with Potential Therapy of HIV Based on Protein-Protein and Protein-Chemical Interaction Network

Cited by 17 publications

References 78 publications

HIV-1, human interaction database: current status and new features

HIV-1, human interaction database: current status and new features

Network-Based Analysis of OMICs Data to Understand the HIV–Host Interaction

Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction

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