1
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Liu F, Yang Y, Xu XS, Yuan M. MESBC: A novel mutually exclusive spectral biclustering method for cancer subtyping. Comput Biol Chem 2024; 109:108009. [PMID: 38219419 DOI: 10.1016/j.compbiolchem.2023.108009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Revised: 12/22/2023] [Accepted: 12/24/2023] [Indexed: 01/16/2024]
Abstract
Many soft biclustering algorithms have been developed and applied to various biological and biomedical data analyses. However, few mutually exclusive (hard) biclustering algorithms have been proposed, which could better identify disease or molecular subtypes with survival significance based on genomic or transcriptomic data. In this study, we developed a novel mutually exclusive spectral biclustering (MESBC) algorithm based on spectral method to detect mutually exclusive biclusters. MESBC simultaneously detects relevant features (genes) and corresponding conditions (patients) subgroups and, therefore, automatically uses the signature features for each subtype to perform the clustering. Extensive simulations revealed that MESBC provided superior accuracy in detecting pre-specified biclusters compared with the non-negative matrix factorization (NMF) and Dhillon's algorithm, particularly in very noisy data. Further analysis of the algorithm on real datasets obtained from the TCGA database showed that MESBC provided more accurate (i.e., smaller p-value) overall survival prediction in patients with lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cancers when compared to the existing, gold-standard subtypes for lung cancers (integrative clustering). Furthermore, MESBC detected several genes with significant prognostic value in both LUAD and LUSC patients. External validation on an independent, unseen GEO dataset of LUAD showed that MESBC-derived clusters based on TCGA data still exhibited clear biclustering patterns and consistent, outstanding prognostic predictability, demonstrating robust generalizability of MESBC. Therefore, MESBC could potentially be used as a risk stratification tool to optimize the treatment for the patient, improve the selection of patients for clinical trials, and contribute to the development of novel therapeutic agents.
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Affiliation(s)
- Fengrong Liu
- Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China
| | - Yaning Yang
- Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China
| | | | - Min Yuan
- School of Public Health Administration, Anhui Medical University, Hefei 230032, China.
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2
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Hokello J, Tyagi P, Dimri S, Sharma AL, Tyagi M. Comparison of the Biological Basis for Non-HIV Transmission to HIV-Exposed Seronegative Individuals, Disease Non-Progression in HIV Long-Term Non-Progressors and Elite Controllers. Viruses 2023; 15:1362. [PMID: 37376660 DOI: 10.3390/v15061362] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 06/29/2023] Open
Abstract
HIV-exposed seronegative individuals (HESIs) are a small fraction of persons who are multiply exposed to human immunodeficiency virus (HIV), but do not exhibit serological or clinical evidence of HIV infection. In other words, they are groups of people maintaining an uninfected status for a long time, even after being exposed to HIV several times. The long-term non-progressors (LTNPs), on the other hand, are a group of HIV-infected individuals (approx. 5%) who remain clinically and immunologically stable for an extended number of years without combination antiretroviral therapy (cART). Meanwhile, elite controllers are comprise a much lower number (0.5%) of HIV-infected persons who spontaneously and durably control viremia to below levels of detection for at least 12 months, even when using the most sensitive assays, such as polymerase chain reaction (PCR) in the absence of cART. Despite the fact that there is no universal agreement regarding the mechanisms by which these groups of individuals are able to control HIV infection and/or disease progression, there is a general consensus that the mechanisms of protection are multifaceted and include genetic, immunological as well as viral factors. In this review, we analyze and compare the biological factors responsible for the control of HIV in these unique groups of individuals.
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Affiliation(s)
- Joseph Hokello
- Department of Biology, Faculty of Science and Education, Busitema University, Tororo P.O. Box 236, Uganda
| | - Priya Tyagi
- Cherry Hill East High School, 1750 Kresson Rd, Cherry Hill, NJ 08003, USA
| | - Shelly Dimri
- George C. Marshall High School, Fairfax County Public Schools, 7731 Leesburg Pike, Falls Church, VA 22043, USA
| | | | - Mudit Tyagi
- Center for Translational Medicine, Thomas Jefferson University, 1020 Locust Street, Philadelphia, PA 19107, USA
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3
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Ozdemir ES, Nussinov R. Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions. Front Oncol 2023; 13:1061595. [PMID: 36910650 PMCID: PMC9997845 DOI: 10.3389/fonc.2023.1061595] [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: 10/04/2022] [Accepted: 02/06/2023] [Indexed: 02/25/2023] Open
Abstract
Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.
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Affiliation(s)
- Emine Sila Ozdemir
- Cancer Early Detection Advanced Research Center, Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
| | - Ruth Nussinov
- Cancer Innovation Laboratory, Frederick National Laboratory for Cancer Research, National Cancer Institute at Frederick, Frederick, MD, United States.,Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
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4
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Wang Z, Chen C, Su Y, Ke N. Function and characteristics of TIM‑4 in immune regulation and disease (Review). Int J Mol Med 2022; 51:10. [PMID: 36524355 PMCID: PMC9848438 DOI: 10.3892/ijmm.2022.5213] [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: 09/12/2022] [Accepted: 11/23/2022] [Indexed: 12/14/2022] Open
Abstract
T‑cell/transmembrane immunoglobulin and mucin domain containing 4 (TIM‑4) is a phosphatidylserine receptor that is mainly expressed on antigen‑presenting cells and is involved in the recognition and efferocytosis of apoptotic cells. TIM‑4 has been found to be expressed in immune cells such as natural killer T, B and mast cells and to participate in multiple aspects of immune regulation, suggesting that TIM‑4 may be involved in a variety of immune‑related diseases. Recent studies have confirmed that TIM‑4 is also abnormally expressed in a variety of malignant tumor cells and is closely associated with the occurrence and development of tumors and the tumor immune microenvironment. The present study aimed to describe the expression and functional characteristics of TIM‑4 in detail and to comprehensively discuss its role in pathophysiological processes such as infection, allergy, metabolism, autoimmunity and tumor immunity. The current review provided a comprehensive understanding of the functions and characteristics of TIM‑4, as well as novel ideas for the diagnosis and treatment of diseases.
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Affiliation(s)
- Ziyao Wang
- Department of Pancreatic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China
| | - Chen Chen
- Department of Radiology, The First People's Hospital of Chengdu, Chengdu, Sichuan 610095, P.R. China
| | - Yingzhen Su
- Kunming University School of Medicine, Kunming University School, Kunming, Yunnan 650124, P.R. China
| | - Nengwen Ke
- Department of Pancreatic Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China,Correspondence to: Professor Nengwen Ke, Department of Pancreatic Surgery, West China Hospital, Sichuan University, 37 Guoxue Lane, Chengdu, Sichuan 610041, P.R. China, E-mail:
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5
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Chai H, Gu Q, Hughes J, Robertson DL. In silico prediction of HIV-1-host molecular interactions and their directionality. PLoS Comput Biol 2022; 18:e1009720. [PMID: 35134057 PMCID: PMC8856524 DOI: 10.1371/journal.pcbi.1009720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 02/18/2022] [Accepted: 12/03/2021] [Indexed: 11/18/2022] Open
Abstract
Human immunodeficiency virus type 1 (HIV-1) continues to be a major cause of disease and premature death. As with all viruses, HIV-1 exploits a host cell to replicate. Improving our understanding of the molecular interactions between virus and human host proteins is crucial for a mechanistic understanding of virus biology, infection and host antiviral activities. This knowledge will potentially permit the identification of host molecules for targeting by drugs with antiviral properties. Here, we propose a data-driven approach for the analysis and prediction of the HIV-1 interacting proteins (VIPs) with a focus on the directionality of the interaction: host-dependency versus antiviral factors. Using support vector machine learning models and features encompassing genetic, proteomic and network properties, our results reveal some significant differences between the VIPs and non-HIV-1 interacting human proteins (non-VIPs). As assessed by comparison with the HIV-1 infection pathway data in the Reactome database (sensitivity > 90%, threshold = 0.5), we demonstrate these models have good generalization properties. We find that the ‘direction’ of the HIV-1-host molecular interactions is also predictable due to different characteristics of ‘forward’/pro-viral versus ‘backward’/pro-host proteins. Additionally, we infer the previously unknown direction of the interactions between HIV-1 and 1351 human host proteins. A web server for performing predictions is available at http://hivpre.cvr.gla.ac.uk/.
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Affiliation(s)
- Haiting Chai
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - Quan Gu
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - Joseph Hughes
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
| | - David L. Robertson
- MRC-University of Glasgow Centre for Virus Research, Glasgow, United Kingdom
- * E-mail:
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6
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Ivanov S, Lagunin A, Filimonov D, Tarasova O. Network-Based Analysis of OMICs Data to Understand the HIV-Host Interaction. Front Microbiol 2020; 11:1314. [PMID: 32625189 PMCID: PMC7311653 DOI: 10.3389/fmicb.2020.01314] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2020] [Accepted: 05/25/2020] [Indexed: 12/22/2022] Open
Abstract
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 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 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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Affiliation(s)
- Sergey Ivanov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia.,Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Alexey Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia.,Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Dmitry Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
| | - Olga Tarasova
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow, Russia
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7
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Xie J, Ma A, Fennell A, Ma Q, Zhao J. It is time to apply biclustering: a comprehensive review of biclustering applications in biological and biomedical data. Brief Bioinform 2020; 20:1449-1464. [PMID: 29490019 DOI: 10.1093/bib/bby014] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Revised: 01/16/2018] [Indexed: 12/12/2022] Open
Abstract
Biclustering is a powerful data mining technique that allows clustering of rows and columns, simultaneously, in a matrix-format data set. It was first applied to gene expression data in 2000, aiming to identify co-expressed genes under a subset of all the conditions/samples. During the past 17 years, tens of biclustering algorithms and tools have been developed to enhance the ability to make sense out of large data sets generated in the wake of high-throughput omics technologies. These algorithms and tools have been applied to a wide variety of data types, including but not limited to, genomes, transcriptomes, exomes, epigenomes, phenomes and pharmacogenomes. However, there is still a considerable gap between biclustering methodology development and comprehensive data interpretation, mainly because of the lack of knowledge for the selection of appropriate biclustering tools and further supporting computational techniques in specific studies. Here, we first deliver a brief introduction to the existing biclustering algorithms and tools in public domain, and then systematically summarize the basic applications of biclustering for biological data and more advanced applications of biclustering for biomedical data. This review will assist researchers to effectively analyze their big data and generate valuable biological knowledge and novel insights with higher efficiency.
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8
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Csősz É, Tóth F, Mahdi M, Tsaprailis G, Emri M, Tőzsér J. Analysis of networks of host proteins in the early time points following HIV transduction. BMC Bioinformatics 2019; 20:398. [PMID: 31315557 PMCID: PMC6637640 DOI: 10.1186/s12859-019-2990-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 07/10/2019] [Indexed: 12/13/2022] Open
Abstract
Background Utilization of quantitative proteomics data on the network level is still a challenge in proteomics data analysis. Currently existing models use sophisticated, sometimes hard to implement analysis techniques. Our aim was to generate a relatively simple strategy for quantitative proteomics data analysis in order to utilize as much of the data generated in a proteomics experiment as possible. Results In this study, we applied label-free proteomics, and generated a network model utilizing both qualitative, and quantitative data, in order to examine the early host response to Human Immunodeficiency Virus type 1 (HIV-1). A weighted network model was generated based on the amount of proteins measured by mass spectrometry, and analysis of weighted networks and functional sub-networks revealed upregulation of proteins involved in translation, transcription, and DNA condensation in the early phase of the viral life-cycle. Conclusion A relatively simple strategy for network analysis was created and applied to examine the effect of HIV-1 on host cellular proteome. We believe that our model may prove beneficial in creating algorithms, allowing for both quantitative and qualitative studies of proteome change in various biological and pathological processes by quantitative mass spectrometry. Electronic supplementary material The online version of this article (10.1186/s12859-019-2990-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Éva Csősz
- Proteomics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary.
| | - Ferenc Tóth
- Laboratory of Retroviral Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary
| | - Mohamed Mahdi
- Laboratory of Retroviral Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary
| | - George Tsaprailis
- Arizona Research Labs, University of Arizona, PO Box 210066, Administration Building, Room 601, Tucson, AZ, 85721-0066, USA.,The Scripps Research Institute, 132 Scripps Way, Jupiter, FL, 33458, USA
| | - Miklós Emri
- Department of Medical Imaging, Division of Nuclear Medicine and Translational Imaging, Faculty of Medicine, University of Debrecen, Nagyerdei krt. 98., Debrecen, 4032, Hungary
| | - József Tőzsér
- Proteomics Core Facility, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary. .,Laboratory of Retroviral Biochemistry, Department of Biochemistry and Molecular Biology, Faculty of Medicine, University of Debrecen, Egyetem ter 1., Debrecen, 4032, Hungary.
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9
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Ray S, Alberuni S, Maulik U. Computational Prediction of HCV-Human Protein-Protein Interaction via Topological Analysis of HCV Infected PPI Modules. IEEE Trans Nanobioscience 2019; 17:55-61. [PMID: 29570075 DOI: 10.1109/tnb.2018.2797696] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
In this paper, we have developed a framework for detection of protein-protein interactions (PPI) between Hepatitis-C virus (HCV) and human proteins based on PPI and gene ontology based information of the HCV infected proteins. First, a bipartite interaction network is formed between HCV proteins and human host proteins. Next, we have analyzed different topological properties of the interaction network and observed that degree of HCV-interacting proteins is significantly higher than non-interacting host proteins. We have also observed that the HCV interacted protein pairs are functionally similar with each other than the non-interacting pairs. Following the observations, we have applied an inference mechanism to predict novel interactions between HCV and human protein. The inference mechanism is based on partitioning the network formed by HCV interacted human proteins and their first neighbors in dense and functionally similar groups using a PPI network clustering algorithm. The groups are then analyzed to predict PPIs. The predicted interaction pairs are validated using literature search in PUBMED. Experimental evidence of over 50% of the predicted pairs are found in existing literatures by searching PUBMED. A Gene Ontology and pathway based analysis is also carried out to validate the identified modules biologically.
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10
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Network controllability analysis of intracellular signalling reveals viruses are actively controlling molecular systems. Sci Rep 2019; 9:2066. [PMID: 30765882 PMCID: PMC6375943 DOI: 10.1038/s41598-018-38224-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Accepted: 12/21/2018] [Indexed: 12/19/2022] Open
Abstract
In recent years control theory has been applied to biological systems with the aim of identifying the minimum set of molecular interactions that can drive the network to a required state. However, in an intra-cellular network it is unclear how control can be achieved in practice. To address this limitation we use viral infection, specifically human immunodeficiency virus type 1 (HIV-1) and hepatitis C virus (HCV), as a paradigm to model control of an infected cell. Using a large human signalling network comprised of over 6000 human proteins and more than 34000 directed interactions, we compared two states: normal/uninfected and infected. Our network controllability analysis demonstrates how a virus efficiently brings the dynamically organised host system into its control by mostly targeting existing critical control nodes, requiring fewer nodes than in the uninfected network. The lower number of control nodes is presumably to optimise exploitation of specific sub-systems needed for virus replication and/or involved in the host response to infection. Viral infection of the human system also permits discrimination between available network-control models, which demonstrates that the minimum dominating set (MDS) method better accounts for how the biological information and signals are organised during infection by identifying most viral proteins as critical driver nodes compared to the maximum matching (MM) method. Furthermore, the host driver nodes identified by MDS are distributed throughout the pathways enabling effective control of the cell via the high ‘control centrality’ of the viral and targeted host nodes. Our results demonstrate that control theory gives a more complete and dynamic understanding of virus exploitation of the host system when compared with previous analyses limited to static single-state networks.
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11
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Ibrahim B, Arkhipova K, Andeweg AC, Posada-Céspedes S, Enault F, Gruber A, Koonin EV, Kupczok A, Lemey P, McHardy AC, McMahon DP, Pickett BE, Robertson DL, Scheuermann RH, Zhernakova A, Zwart MP, Schönhuth A, Dutilh BE, Marz M. Bioinformatics Meets Virology: The European Virus Bioinformatics Center's Second Annual Meeting. Viruses 2018; 10:E256. [PMID: 29757994 PMCID: PMC5977249 DOI: 10.3390/v10050256] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 05/11/2018] [Accepted: 05/11/2018] [Indexed: 11/16/2022] Open
Abstract
The Second Annual Meeting of the European Virus Bioinformatics Center (EVBC), held in Utrecht, Netherlands, focused on computational approaches in virology, with topics including (but not limited to) virus discovery, diagnostics, (meta-)genomics, modeling, epidemiology, molecular structure, evolution, and viral ecology. The goals of the Second Annual Meeting were threefold: (i) to bring together virologists and bioinformaticians from across the academic, industrial, professional, and training sectors to share best practice; (ii) to provide a meaningful and interactive scientific environment to promote discussion and collaboration between students, postdoctoral fellows, and both new and established investigators; (iii) to inspire and suggest new research directions and questions. Approximately 120 researchers from around the world attended the Second Annual Meeting of the EVBC this year, including 15 renowned international speakers. This report presents an overview of new developments and novel research findings that emerged during the meeting.
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Affiliation(s)
- Bashar Ibrahim
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, 07743 Jena, Germany.
| | - Ksenia Arkhipova
- Theoretical Biology and Bioinformatics, Utrecht University, 3508 TC Utrecht, The Netherlands.
| | - Arno C Andeweg
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Department of Viroscience, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands.
| | - Susana Posada-Céspedes
- Department of Biosystems Science and Engineering, ETH Zürich, 4058 Basel, Switzerland.
- SIB Swiss Institute of Bioinformatics, 4058 Basel, Switzerland.
| | - François Enault
- Université Clermont Auvergne, CNRS, LMGE, F-63000 Clermont-Ferrand, France.
| | - Arthur Gruber
- Department of Parasitology, Institute of Biomedical Sciences, University of São Paulo, 05508-000 São Paulo, Brazil.
| | - Eugene V Koonin
- National Center for Biotechnology Information, NLM, National Institutes of Health, Bethesda, MD 20894, USA.
| | - Anne Kupczok
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Institute of General Microbiology, Kiel University, 24118 Kiel, Germany.
| | - Philippe Lemey
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Clinical and Epidemiological Virology, Rega Institute, KU Leuven, University of Leuven, 3000 Leuven, Belgium.
| | - Alice C McHardy
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Department for Computational Biology of Infection Research, Helmholtz Center for Infection Research, 38124 Braunschweig, Germany.
| | - Dino P McMahon
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Institute of Biology, Free University Berlin, Schwendenerstr. 1, 14195 Berlin, Germany.
- Department for Materials and Environment, BAM Federal Institute for Materials Research and Testing, Unter den Eichen 87, 12205 Berlin, Germany.
| | - Brett E Pickett
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- J. Craig Venter Institute, Rockville, MD 20850, USA.
| | - David L Robertson
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- MRC-University of Glasgow Centre for Virus Research, Garscube Campus, Glasgow G61 1QH, UK.
| | - Richard H Scheuermann
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- J. Craig Venter Institute, La Jolla, CA 92037, USA.
| | - Alexandra Zhernakova
- Department of Genetics, University Medical Center Groningen, 9700 RB Groningen, The Netherlands.
| | - Mark P Zwart
- Department of Microbial Ecology, Netherlands Institute of Ecology (NIOO-KNAW), 6708 PB Wageningen, The Netherlands.
| | - Alexander Schönhuth
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Theoretical Biology and Bioinformatics, Utrecht University, 3508 TC Utrecht, The Netherlands.
- Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands.
| | - Bas E Dutilh
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Theoretical Biology and Bioinformatics, Utrecht University, 3508 TC Utrecht, The Netherlands.
| | - Manja Marz
- European Virus Bioinformatics Center, 07743 Jena, Germany.
- Faculty of Mathematics and Computer Science, Friedrich Schiller University Jena, 07743 Jena, Germany.
- Leibniz Institute for Age Research-Fritz Lipmann Institute, 07745 Jena, Germany.
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12
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Koch AS, Brites D, Stucki D, Evans JC, Seldon R, Heekes A, Mulder N, Nicol M, Oni T, Mizrahi V, Warner DF, Parkhill J, Gagneux S, Martin DP, Wilkinson RJ. The Influence of HIV on the Evolution of Mycobacterium tuberculosis. Mol Biol Evol 2017; 34:1654-1668. [PMID: 28369607 PMCID: PMC5455964 DOI: 10.1093/molbev/msx107] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
HIV significantly affects the immunological environment during tuberculosis coinfection, and therefore may influence the selective landscape upon which M. tuberculosis evolves. To test this hypothesis whole genome sequences were determined for 169 South African M. tuberculosis strains from HIV-1 coinfected and uninfected individuals and analyzed using two Bayesian codon-model based selection analysis approaches: FUBAR which was used to detect persistent positive and negative selection (selection respectively favoring and disfavoring nonsynonymous substitutions); and MEDS which was used to detect episodic directional selection specifically favoring nonsynonymous substitutions within HIV-1 infected individuals. Among the 25,251 polymorphic codon sites analyzed, FUBAR revealed that 189-fold more were detectably evolving under persistent negative selection than were evolving under persistent positive selection. Three specific codon sites within the genes celA2b, katG, and cyp138 were identified by MEDS as displaying significant evidence of evolving under directional selection influenced by HIV-1 coinfection. All three genes encode proteins that may indirectly interact with human proteins that, in turn, interact functionally with HIV proteins. Unexpectedly, epitope encoding regions were enriched for sites displaying weak evidence of directional selection influenced by HIV-1. Although the low degree of genetic diversity observed in our M. tuberculosis data set means that these results should be interpreted carefully, the effects of HIV-1 on epitope evolution in M. tuberculosis may have implications for the design of M. tuberculosis vaccines that are intended for use in populations with high HIV-1 infection rates.
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Affiliation(s)
- Anastasia S Koch
- Wellcome Centre for Infectious Disease Research in Africa, Institute of Infectious Disease and Molecular Medicine, and Department of Medicine, University of Cape Town, Cape Town, South Africa
| | - Daniela Brites
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - David Stucki
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Joanna C Evans
- Molecular Mycobacteriology Research Unit, Institute of Infectious Disease and Molecular Medicine and Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Ronnett Seldon
- Molecular Mycobacteriology Research Unit, Institute of Infectious Disease and Molecular Medicine and Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Alexa Heekes
- Department of Integrative Biomedical Sciences, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Nicola Mulder
- Department of Integrative Biomedical Sciences, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Mark Nicol
- University of Cape Town, and National Health Laboratory Service, Cape Town, South Africa
| | - Tolu Oni
- Division of Public Health Medicine, School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa.,The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Valerie Mizrahi
- Molecular Mycobacteriology Research Unit, Institute of Infectious Disease and Molecular Medicine and Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Digby F Warner
- Molecular Mycobacteriology Research Unit, Institute of Infectious Disease and Molecular Medicine and Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Julian Parkhill
- The Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge, United Kingdom
| | - Sebastien Gagneux
- Department of Medical Parasitology and Infection Biology, Swiss Tropical and Public Health Institute, Basel, Switzerland.,University of Basel, Basel, Switzerland
| | - Darren P Martin
- Division of Computational Biology, Department of Integrated Biology Sciences and Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Robert J Wilkinson
- Wellcome Centre for Infectious Disease Research in Africa, Institute of Infectious Disease and Molecular Medicine, and Department of Medicine, University of Cape Town, Cape Town, South Africa.,Department of Medicine, Imperial College, London, United Kingdom.,Francis Crick Institute, London, United Kingdom
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13
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Sims B, Farrow AL, Williams SD, Bansal A, Krendelchtchikov A, Gu L, Matthews QL. Role of TIM-4 in exosome-dependent entry of HIV-1 into human immune cells. Int J Nanomedicine 2017; 12:4823-4833. [PMID: 28740388 PMCID: PMC5505621 DOI: 10.2147/ijn.s132762] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Exosomes, 30–200 nm nanostructures secreted from donor cells and internalized by recipient cells, can play an important role in the cellular entry of some viruses. These microvesicles are actively secreted into various body fluids, including blood, urine, saliva, cerebrospinal fluid, and breast milk. We successfully isolated exosomes from human breast milk and plasma. The size and concentration of purified exosomes were measured by nanoparticle tracking, while Western blotting confirmed the presence of the exosomal-associated proteins CD9 and CD63, clathrin, and T cell immunoglobulin and mucin proteins (TIMs). Through viral infection assays, we determined that HIV-1 utilizes an exosome-dependent mechanism for entry into human immune cells. The virus contains high amounts of phosphatidylserine (PtdSer) and may bind PtdSer receptors, such as TIMs. This mechanism is supported by our findings that exosomes from multiple sources increased HIV-1 entry into T cells and macrophages, and viral entry was potently blocked with anti-TIM-4 antibodies.
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Affiliation(s)
- Brian Sims
- Division of Neonatology, Department of Pediatrics.,Department of Cell, Developmental and Integrative Biology.,Center for AIDS Research
| | | | - Sparkle D Williams
- Division of Neonatology, Department of Pediatrics.,Department of Cell, Developmental and Integrative Biology
| | | | - Alexandre Krendelchtchikov
- Division of Neonatology, Department of Pediatrics.,Department of Cell, Developmental and Integrative Biology.,Division of Infectious Diseases
| | - Linlin Gu
- Division of Pulmonary, Allergy and Critical Care Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham
| | - Qiana L Matthews
- Center for AIDS Research.,Division of Infectious Diseases.,Microbiology Program, Department of Biological Sciences, College of Science, Technology, Engineering and Mathematics, Alabama State University, Montgomery, AL, USA
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14
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Kumar B, Arora S, Ahmed S, Banerjea AC. Hyperactivation of mammalian target of rapamycin complex 1 by HIV-1 is necessary for virion production and latent viral reactivation. FASEB J 2016; 31:180-191. [PMID: 27702769 DOI: 10.1096/fj.201600813r] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Accepted: 09/16/2016] [Indexed: 01/03/2023]
Abstract
Generation of new HIV-1 virions requires the constant supply of proteins, nucleotides, and energy; however, it is not known which cellular pathways are perturbed and what molecular mechanisms are employed. We hypothesized that HIV-1 may regulate pathways that control synthesis of biomolecules in the cell. In this study, we provide evidence that HIV-1 hyperactivates mammalian target of rapamycin complex 1 (mTORC1), the central regulator of biosynthesis. Mechanistically, we identify the viral regulatory gene tat (transactivator) as being responsible for increasing mTORC1 activity in a PI3K-dependent manner. Furthermore, we show that hyperactivation of mTORC1 leads to activation of the enzyme, carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, dihydroorotase, and repression of initiation factor 4E-binding protein 1 activity. These are regulators of nucleotide biogenesis and protein translation, respectively. Moreover, we are able to replicate these results in HIV-1 latent cell line models. Finally, we show that inhibition of mTORC1 or PI3K inhibits viral replication and viral reactivation as a result of a decrease in biosynthesis. Overall, our study identifies a new avenue in HIV-1 biology that can lead to development of novel therapeutic targets.-Kumar, B., Arora, S., Ahmed, S., Banerjea, A. C. Hyperactivation of mammalian target of rapamycin complex 1 by HIV-1 is necessary for virion production and latent viral reactivation.
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Affiliation(s)
- Binod Kumar
- Laboratory of Virology, National Institute of Immunology, New Delhi, India
| | - Sakshi Arora
- Laboratory of Virology, National Institute of Immunology, New Delhi, India
| | - Shaista Ahmed
- Laboratory of Virology, National Institute of Immunology, New Delhi, India
| | - Akhil C Banerjea
- Laboratory of Virology, National Institute of Immunology, New Delhi, India
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15
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Henriques R, Madeira SC. BiC2PAM: constraint-guided biclustering for biological data analysis with domain knowledge. Algorithms Mol Biol 2016; 11:23. [PMID: 27651825 PMCID: PMC5024481 DOI: 10.1186/s13015-016-0085-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 08/16/2016] [Indexed: 11/10/2022] Open
Abstract
Background Biclustering has been largely used in biological data analysis, enabling the discovery of putative functional modules from omic and network data. Despite the recognized importance of incorporating domain knowledge to guide biclustering and guarantee a focus on relevant and non-trivial biclusters, this possibility has not yet been comprehensively addressed. This results from the fact that the majority of existing algorithms are only able to deliver sub-optimal solutions with restrictive assumptions on the structure, coherency and quality of biclustering solutions, thus preventing the up-front satisfaction of knowledge-driven constraints. Interestingly, in recent years, a clearer understanding of the synergies between pattern mining and biclustering gave rise to a new class of algorithms, termed as pattern-based biclustering algorithms. These algorithms, able to efficiently discover flexible biclustering solutions with optimality guarantees, are thus positioned as good candidates for knowledge incorporation. In this context, this work aims to bridge the current lack of solid views on the use of background knowledge to guide (pattern-based) biclustering tasks. Methods This work extends (pattern-based) biclustering algorithms to guarantee the satisfiability of constraints derived from background knowledge and to effectively explore efficiency gains from their incorporation. In this context, we first show the relevance of constraints with succinct, (anti-)monotone and convertible properties for the analysis of expression data and biological networks. We further show how pattern-based biclustering algorithms can be adapted to effectively prune of the search space in the presence of such constraints, as well as be guided in the presence of biological annotations. Relying on these contributions, we propose BiClustering with Constraints using PAttern Mining (BiC2PAM), an extension of BicPAM and BicNET biclustering algorithms. Results Experimental results on biological data demonstrate the importance of incorporating knowledge within biclustering to foster efficiency and enable the discovery of non-trivial biclusters with heightened biological relevance. Conclusions This work provides the first comprehensive view and sound algorithm for biclustering biological data with constraints derived from user expectations, knowledge repositories and/or literature.
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16
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Henriques R, Madeira SC. BicNET: Flexible module discovery in large-scale biological networks using biclustering. Algorithms Mol Biol 2016; 11:14. [PMID: 27213009 PMCID: PMC4875761 DOI: 10.1186/s13015-016-0074-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2015] [Accepted: 04/22/2016] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Despite the recognized importance of module discovery in biological networks to enhance our understanding of complex biological systems, existing methods generally suffer from two major drawbacks. First, there is a focus on modules where biological entities are strongly connected, leading to the discovery of trivial/well-known modules and to the inaccurate exclusion of biological entities with subtler yet relevant roles. Second, there is a generalized intolerance towards different forms of noise, including uncertainty associated with less-studied biological entities (in the context of literature-driven networks) and experimental noise (in the context of data-driven networks). Although state-of-the-art biclustering algorithms are able to discover modules with varying coherency and robustness to noise, their application for the discovery of non-dense modules in biological networks has been poorly explored and it is further challenged by efficiency bottlenecks. METHODS This work proposes Biclustering NETworks (BicNET), a biclustering algorithm to discover non-trivial yet coherent modules in weighted biological networks with heightened efficiency. Three major contributions are provided. First, we motivate the relevance of discovering network modules given by constant, symmetric, plaid and order-preserving biclustering models. Second, we propose an algorithm to discover these modules and to robustly handle noisy and missing interactions. Finally, we provide new searches to tackle time and memory bottlenecks by effectively exploring the inherent structural sparsity of network data. RESULTS Results in synthetic network data confirm the soundness, efficiency and superiority of BicNET. The application of BicNET on protein interaction and gene interaction networks from yeast, E. coli and Human reveals new modules with heightened biological significance. CONCLUSIONS BicNET is, to our knowledge, the first method enabling the efficient unsupervised analysis of large-scale network data for the discovery of coherent modules with parameterizable homogeneity.
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Affiliation(s)
- Rui Henriques
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
| | - Sara C. Madeira
- INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal
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17
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Dong Q, Zhu H, Zhang Y, Yang D. Bioinformatics Analysis of Proteome Changes in Calu-3 Cell Infected by Influenza A Virus (H5N1). J Mol Microbiol Biotechnol 2015; 25:311-9. [DOI: 10.1159/000437226] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
<b><i>Aim:</i></b> This paper aimed to identify the differentially expressed proteins (DEPs) in Calu-3 cells infected by influenza A virus (IAV) subtype H5N1. <b><i>Methods:</i></b> We downloaded proteome data (BTO: 0000762) from the Proteomics Identifications database and identified the DEPs in the IAV-infected Calu-3 cells. Then we constructed a protein-protein interaction network and a transcriptional regulatory network of the proteins. Finally, we performed gene ontology (GO) analysis to study the IAV infection at a functional level. <b><i>Results:</i></b> A total of 4 protein groups between the normal cells and the Calu-3 cells infected by IAV, severe acute respiratory syndrome or swine influenza were identified. In the networks, we found 5 significant proteins including FAN, CPSF2, AGO1, AGO2 and PAX5. In addition, we demonstrated those proteins were associated with GO terms such as phosphate metabolic process, calcium ion transport, cell division and regulation of cell motion. STAT1, NS2, CD5, NCKX6 and PDGFB were significant DEPs in these GO terms. <b><i>Conclusions:</i></b> By referring to the previous studies, we suggest that proteins including FAN, CPSF2, AGO1, AGO2, PAX5, STAT1 and PDGFB can be used as therapeutic targets of IAV infection.
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18
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Interactions of HIV-1 proteins as targets for developing anti-HIV-1 peptides. Future Med Chem 2015; 7:1055-77. [DOI: 10.4155/fmc.15.46] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Protein–protein interactions (PPI) are essential in every step of the HIV replication cycle. Mapping the interactions between viral and host proteins is a fundamental target for the design and development of new therapeutics. In this review, we focus on rational development of anti-HIV-1 peptides based on mapping viral–host and viral–viral protein interactions all across the HIV-1 replication cycle. We also discuss the mechanism of action, specificity and stability of these peptides, which are designed to inhibit PPI. Some of these peptides are excellent tools to study the mechanisms of PPI in HIV-1 replication cycle and for the development of anti-HIV-1 drug leads that modulate PPI.
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19
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The Road Less Traveled: HIV's Use of Alternative Routes through Cellular Pathways. J Virol 2015; 89:5204-12. [PMID: 25762730 DOI: 10.1128/jvi.03684-14] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
Pathogens such as HIV-1, with their minimalist genomes, must navigate cellular networks and rely on hijacking and manipulating the host machinery for successful replication. Limited overlap of host factors identified as vital for pathogen replication may be explained by considering that pathogens target, rather than specific cellular factors, crucial cellular pathways by targeting different, functionally equivalent, protein-protein interactions within that pathway. The ability to utilize alternative routes through cellular pathways may be essential for pathogen survival when restricted and provide flexibility depending on the viral replication stage and the environment in the infected host. In this minireview, we evaluate evidence supporting this notion, discuss specific HIV-1 examples, and consider the molecular mechanisms which allow pathogens to flexibly exploit different routes.
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20
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Amberkar SS, Kaderali L. An integrative approach for a network based meta-analysis of viral RNAi screens. Algorithms Mol Biol 2015; 10:6. [PMID: 25691914 PMCID: PMC4331137 DOI: 10.1186/s13015-015-0035-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Accepted: 01/27/2015] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND Big data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation. RNAi screening has become a workhorse of functional genomics, and has been applied, for example, to identify host factors involved in infection for a panel of different viruses. However, the analysis of data resulting from such screens is difficult, with often low overlap between hit lists, even when comparing screens targeting the same virus. This makes it a major challenge to select interesting candidates for further detailed, mechanistic experimental characterization. RESULTS To address this problem we propose an integrative bioinformatics pipeline that allows for a network based meta-analysis of viral high-throughput RNAi screens. Initially, we collate a human protein interaction network from various public repositories, which is then subjected to unsupervised clustering to determine functional modules. Modules that are significantly enriched with host dependency factors (HDFs) and/or host restriction factors (HRFs) are then filtered based on network topology and semantic similarity measures. Modules passing all these criteria are finally interpreted for their biological significance using enrichment analysis, and interesting candidate genes can be selected from the modules. CONCLUSIONS We apply our approach to seven screens targeting three different viruses, and compare results with other published meta-analyses of viral RNAi screens. We recover key hit genes, and identify additional candidates from the screens. While we demonstrate the application of the approach using viral RNAi data, the method is generally applicable to identify underlying mechanisms from hit lists derived from high-throughput experimental data, and to select a small number of most promising genes for further mechanistic studies.
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21
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Bandyopadhyay S, Ray S, Mukhopadhyay A, Maulik U. A review of in silico approaches for analysis and prediction of HIV-1-human protein-protein interactions. Brief Bioinform 2014; 16:830-51. [PMID: 25479794 DOI: 10.1093/bib/bbu041] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2014] [Indexed: 12/19/2022] Open
Abstract
The computational or in silico approaches for analysing the HIV-1-human protein-protein interaction (PPI) network, predicting different host cellular factors and PPIs and discovering several pathways are gaining popularity in the field of HIV research. Although there exist quite a few studies in this regard, no previous effort has been made to review these works in a comprehensive manner. Here we review the computational approaches that are devoted to the analysis and prediction of HIV-1-human PPIs. We have broadly categorized these studies into two fields: computational analysis of HIV-1-human PPI network and prediction of novel PPIs. We have also presented a comparative assessment of these studies and proposed some methodologies for discussing the implication of their results. We have also reviewed different computational techniques for predicting HIV-1-human PPIs and provided a comparative study of their applicability. We believe that our effort will provide helpful insights to the HIV research community.
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22
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Mukhopadhyay A, Maulik U. Network-based study reveals potential infection pathways of hepatitis-C leading to various diseases. PLoS One 2014; 9:e94029. [PMID: 24743187 PMCID: PMC3990553 DOI: 10.1371/journal.pone.0094029] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2013] [Accepted: 03/11/2014] [Indexed: 12/17/2022] Open
Abstract
Protein-protein interaction network-based study of viral pathogenesis has been gaining popularity among computational biologists in recent days. In the present study we attempt to investigate the possible pathways of hepatitis-C virus (HCV) infection by integrating the HCV-human interaction network, human protein interactome and human genetic disease association network. We have proposed quasi-biclique and quasi-clique mining algorithms to integrate these three networks to identify infection gateway host proteins and possible pathways of HCV pathogenesis leading to various diseases. Integrated study of three networks, namely HCV-human interaction network, human protein interaction network, and human proteins-disease association network reveals potential pathways of infection by the HCV that lead to various diseases including cancers. The gateway proteins have been found to be biologically coherent and have high degrees in human interactome compared to the other virus-targeted proteins. The analyses done in this study provide possible targets for more effective anti-hepatitis-C therapeutic involvement.
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Affiliation(s)
- Anirban Mukhopadhyay
- Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India
- * E-mail:
| | - Ujjwal Maulik
- Department of Computer Science and Engineering, Jadavpur University, Kolkata, West Bengal, India
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23
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Mukhopadhyay A, Ray S, Maulik U. Incorporating the type and direction information in predicting novel regulatory interactions between HIV-1 and human proteins using a biclustering approach. BMC Bioinformatics 2014; 15:26. [PMID: 24460683 PMCID: PMC3922888 DOI: 10.1186/1471-2105-15-26] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2013] [Accepted: 01/08/2014] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Discovering novel interactions between HIV-1 and human proteins would greatly contribute to different areas of HIV research. Identification of such interactions leads to a greater insight into drug target prediction. Some recent studies have been conducted for computational prediction of new interactions based on the experimentally validated information stored in a HIV-1-human protein-protein interaction database. However, these techniques do not predict any regulatory mechanism between HIV-1 and human proteins by considering interaction types and direction of regulation of interactions. RESULTS Here we present an association rule mining technique based on biclustering for discovering a set of rules among human and HIV-1 proteins using the publicly available HIV-1-human PPI database. These rules are subsequently utilized to predict some novel interactions among HIV-1 and human proteins. For prediction purpose both the interaction types and direction of regulation of interactions, (i.e., virus-to-host or host-to-virus) are considered here to provide important additional information about the regulation pattern of interactions. We have also studied the biclusters and analyzed the significant GO terms and KEGG pathways in which the human proteins of the biclusters participate. Moreover the predicted rules have also been analyzed to discover regulatory relationship between some human proteins in course of HIV-1 infection. Some experimental evidences of our predicted interactions have been found by searching the recent literatures in PUBMED. We have also highlighted some human proteins that are likely to act against the HIV-1 attack. CONCLUSIONS We pose the problem of identifying new regulatory interactions between HIV-1 and human proteins based on the existing PPI database as an association rule mining problem based on biclustering algorithm. We discover some novel regulatory interactions between HIV-1 and human proteins. Significant number of predicted interactions has been found to be supported by recent literature.
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Affiliation(s)
| | - Sumanta Ray
- Department of Computer Science and Engineering, Aliah University, Kolkata-700091, West Bengal, India.
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24
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Hao L, He Q, Wang Z, Craven M, Newton MA, Ahlquist P. Limited agreement of independent RNAi screens for virus-required host genes owes more to false-negative than false-positive factors. PLoS Comput Biol 2013; 9:e1003235. [PMID: 24068911 PMCID: PMC3777922 DOI: 10.1371/journal.pcbi.1003235] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2013] [Accepted: 08/07/2013] [Indexed: 11/19/2022] Open
Abstract
Systematic, genome-wide RNA interference (RNAi) analysis is a powerful approach to identify gene functions that support or modulate selected biological processes. An emerging challenge shared with some other genome-wide approaches is that independent RNAi studies often show limited agreement in their lists of implicated genes. To better understand this, we analyzed four genome-wide RNAi studies that identified host genes involved in influenza virus replication. These studies collectively identified and validated the roles of 614 cell genes, but pair-wise overlap among the four gene lists was only 3% to 15% (average 6.7%). However, a number of functional categories were overrepresented in multiple studies. The pair-wise overlap of these enriched-category lists was high, ∼19%, implying more agreement among studies than apparent at the gene level. Probing this further, we found that the gene lists implicated by independent studies were highly connected in interacting networks by independent functional measures such as protein-protein interactions, at rates significantly higher than predicted by chance. We also developed a general, model-based approach to gauge the effects of false-positive and false-negative factors and to estimate, from a limited number of studies, the total number of genes involved in a process. For influenza virus replication, this novel statistical approach estimates the total number of cell genes involved to be ∼2,800. This and multiple other aspects of our experimental and computational results imply that, when following good quality control practices, the low overlap between studies is primarily due to false negatives rather than false-positive gene identifications. These results and methods have implications for and applications to multiple forms of genome-wide analysis. Genome-wide RNA interference assays of gene functions offer the potential for systematic, global analysis of biological processes. A pressing challenge is to develop meta-analysis methods that effectively combine information from multiple studies. One puzzle is that implicated gene lists from independent studies of the same process often show relatively low overlap. This disagreement might arise from false-positive factors, such as imperfect gene targeting (off-target effects), or from false negatives if separate studies access different components of large, complex systems. We present new methods to examine the relations between individual genome-wide RNAi studies, using studies of host genes in influenza virus replication as a test case. We find that cross-study agreement is greater than suggested by overlap of reported gene lists. This better agreement is evidenced by the strong relation of independent gene lists in functional pathways and protein interaction networks, and by a statistical model that relates multi-study, gene-level findings to factors driving correct, false-negative, and false-positive gene identification. Our analysis of multiple genome-wide studies predicts that there are many undetected host genes important for influenza virus infection, and that false negatives are the major concerns for genome-wide studies.
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Affiliation(s)
- Linhui Hao
- Institute of Molecular Virology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Howard Hughes Medical Institute, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Qiuling He
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Zhishi Wang
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Mark Craven
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Michael A. Newton
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- * E-mail: (MAN); (PA)
| | - Paul Ahlquist
- Institute of Molecular Virology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Howard Hughes Medical Institute, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Morgridge Institute for Research, Madison, Wisconsin, United States of America
- * E-mail: (MAN); (PA)
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25
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Segura-Cabrera A, García-Pérez CA, Guo X, Rodríguez-Pérez MA. A viral-human interactome based on structural motif-domain interactions captures the human infectome. PLoS One 2013; 8:e71526. [PMID: 23951184 PMCID: PMC3738538 DOI: 10.1371/journal.pone.0071526] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2013] [Accepted: 06/28/2013] [Indexed: 11/23/2022] Open
Abstract
Protein interactions between a pathogen and its host are fundamental in the establishment of the pathogen and underline the infection mechanism. In the present work, we developed a single predictive model for building a host-viral interactome based on the identification of structural descriptors from motif-domain interactions of protein complexes deposited in the Protein Data Bank (PDB). The structural descriptors were used for searching, in a database of protein sequences of human and five clinically important viruses; therefore, viral and human proteins sharing a descriptor were predicted as interacting proteins. The analysis of the host-viral interactome allowed to identify a set of new interactions that further explain molecular mechanism associated with viral infections and showed that it was able to capture human proteins already associated to viral infections (human infectome) and non-infectious diseases (human diseasome). The analysis of human proteins targeted by viral proteins in the context of a human interactome showed that their neighbors are enriched in proteins reported with differential expression under infection and disease conditions. It is expected that the findings of this work will contribute to the development of systems biology for infectious diseases, and help guide the rational identification and prioritization of novel drug targets.
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Affiliation(s)
- Aldo Segura-Cabrera
- Laboratorio de Bioinformática, Centro de Biotecnología Genómica, Instituto Politécnico Nacional, Reynosa, México.
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26
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Jamieson DG, Roberts PM, Robertson DL, Sidders B, Nenadic G. Cataloging the biomedical world of pain through semi-automated curation of molecular interactions. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2013; 2013:bat033. [PMID: 23707966 PMCID: PMC3662864 DOI: 10.1093/database/bat033] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
The vast collection of biomedical literature and its continued expansion has presented a number of challenges to researchers who require structured findings to stay abreast of and analyze molecular mechanisms relevant to their domain of interest. By structuring literature content into topic-specific machine-readable databases, the aggregate data from multiple articles can be used to infer trends that can be compared and contrasted with similar findings from topic-independent resources. Our study presents a generalized procedure for semi-automatically creating a custom topic-specific molecular interaction database through the use of text mining to assist manual curation. We apply the procedure to capture molecular events that underlie ‘pain’, a complex phenomenon with a large societal burden and unmet medical need. We describe how existing text mining solutions are used to build a pain-specific corpus, extract molecular events from it, add context to the extracted events and assess their relevance. The pain-specific corpus contains 765 692 documents from Medline and PubMed Central, from which we extracted 356 499 unique normalized molecular events, with 261 438 single protein events and 93 271 molecular interactions supplied by BioContext. Event chains are annotated with negation, speculation, anatomy, Gene Ontology terms, mutations, pain and disease relevance, which collectively provide detailed insight into how that event chain is associated with pain. The extracted relations are visualized in a wiki platform (wiki-pain.org) that enables efficient manual curation and exploration of the molecular mechanisms that underlie pain. Curation of 1500 grouped event chains ranked by pain relevance revealed 613 accurately extracted unique molecular interactions that in the future can be used to study the underlying mechanisms involved in pain. Our approach demonstrates that combining existing text mining tools with domain-specific terms and wiki-based visualization can facilitate rapid curation of molecular interactions to create a custom database. Database URL: •••
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Affiliation(s)
- Daniel G Jamieson
- Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, UK, M13 9PL
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Amberkar S, Kiani NA, Bartenschlager R, Alvisi G, Kaderali L. High-throughput RNA interference screens integrative analysis: Towards a comprehensive understanding of the virus-host interplay. World J Virol 2013; 2:18-31. [PMID: 24175227 PMCID: PMC3785050 DOI: 10.5501/wjv.v2.i2.18] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2012] [Revised: 02/15/2013] [Accepted: 03/15/2013] [Indexed: 02/05/2023] Open
Abstract
Viruses are extremely heterogeneous entities; the size and the nature of their genetic information, as well as the strategies employed to amplify and propagate their genomes, are highly variable. However, as obligatory intracellular parasites, replication of all viruses relies on the host cell. Having co-evolved with their host for several million years, viruses have developed very sophisticated strategies to hijack cellular factors that promote virus uptake, replication, and spread. Identification of host cell factors (HCFs) required for these processes is a major challenge for researchers, but it enables the identification of new, highly selective targets for anti viral therapeutics. To this end, the establishment of platforms enabling genome-wide high-throughput RNA interference (HT-RNAi) screens has led to the identification of several key factors involved in the viral life cycle. A number of genome-wide HT-RNAi screens have been performed for major human pathogens. These studies enable first inter-viral comparisons related to HCF requirements. Although several cellular functions appear to be uniformly required for the life cycle of most viruses tested (such as the proteasome and the Golgi-mediated secretory pathways), some factors, like the lipid kinase Phosphatidylinositol 4-kinase IIIα in the case of hepatitis C virus, are selectively required for individual viruses. However, despite the amount of data available, we are still far away from a comprehensive understanding of the interplay between viruses and host factors. Major limitations towards this goal are the low sensitivity and specificity of such screens, resulting in limited overlap between different screens performed with the same virus. This review focuses on how statistical and bioinformatic analysis methods applied to HT-RNAi screens can help overcoming these issues thus increasing the reliability and impact of such studies.
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Xu WW, Han MJ, Chen D, Chen L, Guo Y, Willden A, Liu DQ, Zhang HT. Genome-wide search for the genes accountable for the induced resistance to HIV-1 infection in activated CD4+ T cells: apparent transcriptional signatures, co-expression networks and possible cellular processes. BMC Med Genomics 2013; 6:15. [PMID: 23635305 PMCID: PMC3655860 DOI: 10.1186/1755-8794-6-15] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2012] [Accepted: 04/23/2013] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Upon co-stimulation with CD3/CD28 antibodies, activated CD4 + T cells were found to lose their susceptibility to HIV-1 infection, exhibiting an induced resistant phenotype. This rather unexpected phenomenon has been repeatedly confirmed but the underlying cell and molecular mechanisms are still unknown. METHODS We first replicated the reported system using the specified Dynal beads with PHA/IL-2-stimulated and un-stimulated cells as controls. Genome-wide expression and analysis were then performed by using Agilent whole genome microarrays and established bioinformatics tools. RESULTS We showed that following CD3/CD28 co-stimulation, a homogeneous population emerged with uniform expression of activation markers CD25 and CD69 as well as a memory marker CD45RO at high levels. These cells differentially expressed 7,824 genes when compared with the controls on microarrays. Series-Cluster analysis identified 6 distinct expression profiles containing 1,345 genes as the representative signatures in the permissive and resistant cells. Of them, 245 (101 potentially permissive and 144 potentially resistant) were significant in gene ontology categories related to immune response, cell adhesion and metabolism. Co-expression networks analysis identified 137 "key regulatory" genes (84 potentially permissive and 53 potentially resistant), holding hub positions in the gene interactions. By mapping these genes on KEGG pathways, the predominance of actin cytoskeleton functions, proteasomes, and cell cycle arrest in induced resistance emerged. We also revealed an entire set of previously unreported novel genes for further mining and functional validation. CONCLUSIONS This initial microarray study will stimulate renewed interest in exploring this system and open new avenues for research into HIV-1 susceptibility and its reversal in target cells, serving as a foundation for the development of novel therapeutic and clinical treatments.
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Affiliation(s)
- Wen-Wen Xu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Jiaochang East Road 32, Kunming, Yunnan Province, 650223 China
- Graduate University of Chinese Academy of Sciences, Beijing, China
| | - Miao-Jun Han
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Jiaochang East Road 32, Kunming, Yunnan Province, 650223 China
- Graduate University of Chinese Academy of Sciences, Beijing, China
| | - Dai Chen
- Novel Bioinformatics Co., Ltd, Shanghai, China
| | - Ling Chen
- Yunnan centers for disease control and prevention, Kunming, China
| | - Yan Guo
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Jiaochang East Road 32, Kunming, Yunnan Province, 650223 China
| | - Andrew Willden
- Editorial Department, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Di-Qiu Liu
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Jiaochang East Road 32, Kunming, Yunnan Province, 650223 China
| | - Hua-Tang Zhang
- Key Laboratory of Animal Models and Human Disease Mechanisms of the Chinese Academy of Sciences & Yunnan Province, Kunming Institute of Zoology, Jiaochang East Road 32, Kunming, Yunnan Province, 650223 China
- Chongqing Center for Biomedical Research and Equipment Development, Chongqing Academy of Science and Technology, Chongqing, China
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Maulik U, Mukhopadhyay A, Bhattacharyya M, Kaderali L, Brors B, Bandyopadhyay S, Eils R. Mining quasi-bicliques from HIV-1-human protein interaction network: a multiobjective biclustering approach. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2013; 10:423-435. [PMID: 23929866 DOI: 10.1109/tcbb.2012.139] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
In this work, we model the problem of mining quasi-bicliques from weighted viral-host protein-protein interaction network as a biclustering problem for identifying strong interaction modules. In this regard, a multiobjective genetic algorithm-based biclustering technique is proposed that simultaneously optimizes three objective functions to obtain dense biclusters having high mean interaction strengths. The performance of the proposed technique has been compared with that of other existing biclustering methods on an artificial data. Subsequently, the proposed biclustering method is applied on the records of biologically validated and predicted interactions between a set of HIV-1 proteins and a set of human proteins to identify strong interaction modules. For this, the entire interaction information is realized as a bipartite graph. We have further investigated the biological significance of the obtained biclusters. The human proteins involved in the strong interaction module have been found to share common biological properties and they are identified as the gateways of viral infection leading to various diseases. These human proteins can be potential drug targets for developing anti-HIV drugs.
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Affiliation(s)
- Ujjwal Maulik
- Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, West Bengal, India.
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Franzosa EA, Garamszegi S, Xia Y. Toward a three-dimensional view of protein networks between species. Front Microbiol 2012; 3:428. [PMID: 23267356 PMCID: PMC3528071 DOI: 10.3389/fmicb.2012.00428] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2012] [Accepted: 12/06/2012] [Indexed: 01/27/2023] Open
Abstract
General principles governing biomolecular interactions between species are expected to differ significantly from known principles governing the interactions within species, yet these principles remain poorly understood at the systems level. A key reason for this knowledge gap is the lack of a detailed three-dimensional (3D), atomistic view of biomolecular interaction networks between species. Recent progress in structural biology, systems biology, and computational biology has enabled accurate and large-scale construction of 3D structural models of nodes and edges for protein–protein interaction networks within and between species. The resulting within- and between-species structural interaction networks have provided new biophysical, functional, and evolutionary insights into species interactions and infectious disease. Here, we review the nascent field of between-species structural systems biology, focusing on interactions between host and pathogens such as viruses.
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31
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Computational design of host transcription-factors sets whose misregulation mimics the transcriptomic effect of viral infections. Sci Rep 2012; 2:1006. [PMID: 23256040 PMCID: PMC3525979 DOI: 10.1038/srep01006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Accepted: 12/06/2012] [Indexed: 12/24/2022] Open
Abstract
The molecular mechanisms underlying viral pathogenesis are yet poorly understood owed to the large number of factors involved and the complexity of their interactions. Could we identify a minimal set of host transcription factors (TF) whose misregulation would result in the transcriptional profile characteristic of infected cells in absence of the virus? How many of such sets exist? Are all orthogonal or share critical TFs involved in specific biological functions? We have developed a computational methodology that uses a quantitative model of the transcriptional regulatory network (TRN) of Arabidopsis thaliana to explore the landscape of all possible re-engineered TRNs whose transcriptomic profiles mimic those observed in infected plants. We found core sets containing between six and 34 TFs, depending on the virus, whose in silico knockout or overexpression in the TRN resulted in transcriptional profiles that minimally deviate from those observed in infected plants.
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32
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Jarboui MA, Bidoia C, Woods E, Roe B, Wynne K, Elia G, Hall WW, Gautier VW. Nucleolar protein trafficking in response to HIV-1 Tat: rewiring the nucleolus. PLoS One 2012; 7:e48702. [PMID: 23166591 PMCID: PMC3499507 DOI: 10.1371/journal.pone.0048702] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2012] [Accepted: 10/03/2012] [Indexed: 12/20/2022] Open
Abstract
The trans-activator Tat protein is a viral regulatory protein essential for HIV-1 replication. Tat trafficks to the nucleoplasm and the nucleolus. The nucleolus, a highly dynamic and structured membrane-less sub-nuclear compartment, is the site of rRNA and ribosome biogenesis and is involved in numerous cellular functions including transcriptional regulation, cell cycle control and viral infection. Importantly, transient nucleolar trafficking of both Tat and HIV-1 viral transcripts are critical in HIV-1 replication, however, the role(s) of the nucleolus in HIV-1 replication remains unclear. To better understand how the interaction of Tat with the nucleolar machinery contributes to HIV-1 pathogenesis, we investigated the quantitative changes in the composition of the nucleolar proteome of Jurkat T-cells stably expressing HIV-1 Tat fused to a TAP tag. Using an organellar proteomic approach based on mass spectrometry, coupled with Stable Isotope Labelling in Cell culture (SILAC), we quantified 520 proteins, including 49 proteins showing significant changes in abundance in Jurkat T-cell nucleolus upon Tat expression. Numerous proteins exhibiting a fold change were well characterised Tat interactors and/or known to be critical for HIV-1 replication. This suggests that the spatial control and subcellular compartimentaliation of these cellular cofactors by Tat provide an additional layer of control for regulating cellular machinery involved in HIV-1 pathogenesis. Pathway analysis and network reconstruction revealed that Tat expression specifically resulted in the nucleolar enrichment of proteins collectively participating in ribosomal biogenesis, protein homeostasis, metabolic pathways including glycolytic, pentose phosphate, nucleotides and amino acids biosynthetic pathways, stress response, T-cell signaling pathways and genome integrity. We present here the first differential profiling of the nucleolar proteome of T-cells expressing HIV-1 Tat. We discuss how these proteins collectively participate in interconnected networks converging to adapt the nucleolus dynamic activities, which favor host biosynthetic activities and may contribute to create a cellular environment supporting robust HIV-1 production.
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Affiliation(s)
- Mohamed Ali Jarboui
- Centre for Research in Infectious Diseases (CRID), School of Medicine and Medical Science (SMMS), University College Dublin (UCD), Dublin, Ireland
| | - Carlo Bidoia
- Centre for Research in Infectious Diseases (CRID), School of Medicine and Medical Science (SMMS), University College Dublin (UCD), Dublin, Ireland
| | - Elena Woods
- Centre for Research in Infectious Diseases (CRID), School of Medicine and Medical Science (SMMS), University College Dublin (UCD), Dublin, Ireland
| | - Barbara Roe
- Centre for Research in Infectious Diseases (CRID), School of Medicine and Medical Science (SMMS), University College Dublin (UCD), Dublin, Ireland
| | - Kieran Wynne
- Mass Spectrometry Resource (MSR), Conway Institute for Biomolecular and Biomedical Research, University College Dublin (UCD), Dublin, Ireland
| | - Giuliano Elia
- Mass Spectrometry Resource (MSR), Conway Institute for Biomolecular and Biomedical Research, University College Dublin (UCD), Dublin, Ireland
| | - William W. Hall
- Centre for Research in Infectious Diseases (CRID), School of Medicine and Medical Science (SMMS), University College Dublin (UCD), Dublin, Ireland
| | - Virginie W. Gautier
- Centre for Research in Infectious Diseases (CRID), School of Medicine and Medical Science (SMMS), University College Dublin (UCD), Dublin, Ireland
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Arnold R, Boonen K, Sun MG, Kim PM. Computational analysis of interactomes: current and future perspectives for bioinformatics approaches to model the host-pathogen interaction space. Methods 2012; 57:508-18. [PMID: 22750305 PMCID: PMC7128575 DOI: 10.1016/j.ymeth.2012.06.011] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2012] [Revised: 06/20/2012] [Accepted: 06/21/2012] [Indexed: 11/05/2022] Open
Abstract
Bacterial and viral pathogens affect their eukaryotic host partly by interacting with proteins of the host cell. Hence, to investigate infection from a systems' perspective we need to construct complete and accurate host-pathogen protein-protein interaction networks. Because of the paucity of available data and the cost associated with experimental approaches, any construction and analysis of such a network in the near future has to rely on computational predictions. Specifically, this challenge consists of a number of sub-problems: First, prediction of possible pathogen interactors (e.g. effector proteins) is necessary for bacteria and protozoa. Second, the prospective host binding partners have to be determined and finally, the impact on the host cell analyzed. This review gives an overview of current bioinformatics approaches to obtain and understand host-pathogen interactions. As an application example of the methods covered, we predict host-pathogen interactions of Salmonella and discuss the value of these predictions as a prospective for further research.
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Affiliation(s)
- Roland Arnold
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada M5S 3E1
| | - Kurt Boonen
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada M5S 3E1
| | - Mark G.F. Sun
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada M5S 3E1
| | - Philip M. Kim
- Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada M5S 3E1
- Banting and Best Department of Medical Research, University of Toronto, Toronto, ON, Canada M5S 3E1
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada M5S 3E1
- Department of Computer Science, University of Toronto, Toronto, ON, Canada M5S 3E1
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Rodrigo G, Carrera J, Ruiz-Ferrer V, del Toro FJ, Llave C, Voinnet O, Elena SF. A meta-analysis reveals the commonalities and differences in Arabidopsis thaliana response to different viral pathogens. PLoS One 2012; 7:e40526. [PMID: 22808182 PMCID: PMC3395709 DOI: 10.1371/journal.pone.0040526] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2011] [Accepted: 06/12/2012] [Indexed: 11/19/2022] Open
Abstract
Understanding the mechanisms by which plants trigger host defenses in response to viruses has been a challenging problem owing to the multiplicity of factors and complexity of interactions involved. The advent of genomic techniques, however, has opened the possibility to grasp a global picture of the interaction. Here, we used Arabidopsis thaliana to identify and compare genes that are differentially regulated upon infection with seven distinct (+)ssRNA and one ssDNA plant viruses. In the first approach, we established lists of genes differentially affected by each virus and compared their involvement in biological functions and metabolic processes. We found that phylogenetically related viruses significantly alter the expression of similar genes and that viruses naturally infecting Brassicaceae display a greater overlap in the plant response. In the second approach, virus-regulated genes were contextualized using models of transcriptional and protein-protein interaction networks of A. thaliana. Our results confirm that host cells undergo significant reprogramming of their transcriptome during infection, which is possibly a central requirement for the mounting of host defenses. We uncovered a general mode of action in which perturbations preferentially affect genes that are highly connected, central and organized in modules.
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Affiliation(s)
- Guillermo Rodrigo
- Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas - Universidad Politécnica de Valencia, València, Spain
| | - Javier Carrera
- Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas - Universidad Politécnica de Valencia, València, Spain
- Instituto ITACA, Universidad Politécnica de Valencia, València, Spain
| | | | | | - César Llave
- Centro de Investigaciones Biológicas, CSIC, Madrid, Spain
| | - Olivier Voinnet
- Institut de Biologie Moléculaire des Plantes, CNRS, Strasbourg, France
| | - Santiago F. Elena
- Instituto de Biología Molecular y Celular de Plantas, Consejo Superior de Investigaciones Científicas - Universidad Politécnica de Valencia, València, Spain
- Santa Fe Institute, Santa Fe, New Mexico, United States of America
- * E-mail:
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35
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Navare AT, Sova P, Purdy DE, Weiss JM, Wolf-Yadlin A, Korth MJ, Chang ST, Proll SC, Jahan TA, Krasnoselsky AL, Palermo RE, Katze MG. Quantitative proteomic analysis of HIV-1 infected CD4+ T cells reveals an early host response in important biological pathways: protein synthesis, cell proliferation, and T-cell activation. Virology 2012; 429:37-46. [PMID: 22542004 DOI: 10.1016/j.virol.2012.03.026] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2012] [Revised: 02/07/2012] [Accepted: 03/30/2012] [Indexed: 10/28/2022]
Abstract
Human immunodeficiency virus (HIV-1) depends upon host-encoded proteins to facilitate its replication while at the same time inhibiting critical components of innate and/or intrinsic immune response pathways. To characterize the host cell response on protein levels in CD4+ lymphoblastoid SUP-T1 cells after infection with HIV-1 strain LAI, we used mass spectrometry (MS)-based global quantitation with iTRAQ (isobaric tag for relative and absolute quantification). We found 266, 60 and 22 proteins differentially expressed (DE) (P-value ≤ 0.05) at 4, 8, and 20 hours post-infection (hpi), respectively, compared to time-matched mock-infected samples. The majority of changes in protein abundance occurred at an early stage of infection well before the de novo production of viral proteins. Functional analyses of these DE proteins showed enrichment in several biological pathways including protein synthesis, cell proliferation, and T-cell activation. Importantly, these early changes before the time of robust viral production have not been described before.
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Affiliation(s)
- Arti T Navare
- Department of Microbiology, University of Washington, Seattle, WA 98195-8070, USA
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36
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Mukhopadhyay A, Maulik U, Bandyopadhyay S. A novel biclustering approach to association rule mining for predicting HIV-1-human protein interactions. PLoS One 2012; 7:e32289. [PMID: 22539940 PMCID: PMC3335119 DOI: 10.1371/journal.pone.0032289] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2011] [Accepted: 01/26/2012] [Indexed: 11/18/2022] Open
Abstract
Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1–human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1–human interaction network. Novel HIV-1–human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed.
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Affiliation(s)
- Anirban Mukhopadhyay
- Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India.
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37
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Jamieson DG, Gerner M, Sarafraz F, Nenadic G, Robertson DL. Towards semi-automated curation: using text mining to recreate the HIV-1, human protein interaction database. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2012; 2012:bas023. [PMID: 22529179 PMCID: PMC3332570 DOI: 10.1093/database/bas023] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Manual curation has long been used for extracting key information found within the primary literature for input into biological databases. The human immunodeficiency virus type 1 (HIV-1), human protein interaction database (HHPID), for example, contains 2589 manually extracted interactions, linked to 14,312 mentions in 3090 articles. The advancement of text-mining (TM) techniques has offered a possibility to rapidly retrieve such data from large volumes of text to a high degree of accuracy. Here, we present a recreation of the HHPID using the current state of the art in TM. To retrieve interactions, we performed gene/protein named entity recognition (NER) and applied two molecular event extraction tools on all abstracts and titles cited in the HHPID. Our best NER scores for precision, recall and F-score were 87.5%, 90.0% and 88.6%, respectively, while event extraction achieved 76.4%, 84.2% and 80.1%, respectively. We demonstrate that over 50% of the HHPID interactions can be recreated from abstracts and titles. Furthermore, from 49 available open-access full-text articles, we extracted a total of 237 unique HIV-1-human interactions, as opposed to 187 interactions recorded in the HHPID from the same articles. On average, we extracted 23 times more mentions of interactions and events from a full-text article than from an abstract and title, with a 6-fold increase in the number of unique interactions. We further demonstrated that more frequently occurring interactions extracted by TM are more likely to be true positives. Overall, the results demonstrate that TM was able to recover a large proportion of interactions, many of which were found within the HHPID, making TM a useful assistant in the manual curation process. Finally, we also retrieved other types of interactions in the context of HIV-1 that are not currently present in the HHPID, thus, expanding the scope of this data set. All data is available at http://gnode1.mib.man.ac.uk/HIV1-text-mining.
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Affiliation(s)
- Daniel G Jamieson
- Computational and Evolutionary Biology, Faculty of Life Sciences, University of Manchester, Manchester, UK
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38
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Lai YH, Li ZC, Chen LL, Dai Z, Zou XY. Identification of potential host proteins for influenza A virus based on topological and biological characteristics by proteome-wide network approach. J Proteomics 2012; 75:2500-13. [DOI: 10.1016/j.jprot.2012.02.034] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2011] [Revised: 02/21/2012] [Accepted: 02/26/2012] [Indexed: 12/31/2022]
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Expression analysis of LEDGF/p75, APOBEC3G, TRIM5alpha, and tetherin in a Senegalese cohort of HIV-1-exposed seronegative individuals. PLoS One 2012; 7:e33934. [PMID: 22479480 PMCID: PMC3313979 DOI: 10.1371/journal.pone.0033934] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2011] [Accepted: 02/23/2012] [Indexed: 11/19/2022] Open
Abstract
Background HIV-1 replication depends on a delicate balance between cellular co-factors and antiviral restriction factors. Lens epithelium-derived growth factor (LEDGF/p75) benefits HIV, whereas apolipoprotein B mRNA-editing catalytic polypeptide-like 3G (APOBEC3G), tripartite motif 5alpha (TRIM5α), and tetherin exert anti-HIV activity. Expression levels of these proteins possibly contribute to HIV-1 resistance in HIV-1-exposed populations. Methodology/Principal Findings We used real-time PCR and flow cytometry to study mRNA and protein levels respectively in PBMC and PBMC subsets. We observed significantly reduced LEDGF/p75 protein levels in CD4+ lymphocytes of HIV-1-exposed seronegative subjects relative to healthy controls, whereas we found no differences in APOBEC3G, TRIM5α, or tetherin expression. Untreated HIV-1-infected patients generally expressed higher mRNA and protein levels than healthy controls. Increased tetherin levels, in particular, correlated with markers of disease progression: directly with the viral load and T cell activation and inversely with the CD4 count. Conclusions/Significance Our data suggest that reduced LEDGF/p75 levels may play a role in resistance to HIV-1 infection, while increased tetherin levels could be a marker of advanced HIV disease. Host factors that influence HIV-1 infection and disease could be important targets for new antiviral therapies.
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Lee S, Salwinski L, Zhang C, Chu D, Sampankanpanich C, Reyes NA, Vangeloff A, Xing F, Li X, Wu TT, Sahasrabudhe S, Deng H, LaCount DJ, Sun R. An integrated approach to elucidate the intra-viral and viral-cellular protein interaction networks of a gamma-herpesvirus. PLoS Pathog 2011; 7:e1002297. [PMID: 22028648 PMCID: PMC3197595 DOI: 10.1371/journal.ppat.1002297] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2011] [Accepted: 08/17/2011] [Indexed: 12/22/2022] Open
Abstract
Genome-wide yeast two-hybrid (Y2H) screens were conducted to elucidate the molecular functions of open reading frames (ORFs) encoded by murine γ-herpesvirus 68 (MHV-68). A library of 84 MHV-68 genes and gene fragments was generated in a Gateway entry plasmid and transferred to Y2H vectors. All possible pair-wise interactions between viral proteins were tested in the Y2H assay, resulting in the identification of 23 intra-viral protein-protein interactions (PPIs). Seventy percent of the interactions between viral proteins were confirmed by co-immunoprecipitation experiments. To systematically investigate virus-cellular protein interactions, the MHV-68 Y2H constructs were screened against a cellular cDNA library, yielding 243 viral-cellular PPIs involving 197 distinct cellar proteins. Network analyses indicated that cellular proteins targeted by MHV-68 had more partners in the cellular PPI network and were located closer to each other than expected by chance. Taking advantage of this observation, we scored the cellular proteins based on their network distances from other MHV-68-interacting proteins and segregated them into high (Y2H-HP) and low priority/not-scored (Y2H-LP/NS) groups. Significantly more genes from Y2H-HP altered MHV-68 replication when their expression was inhibited with siRNAs (53% of genes from Y2H-HP, 21% of genes from Y2H-LP/NS, and 16% of genes randomly chosen from the human PPI network; p<0.05). Enriched Gene Ontology (GO) terms in the Y2H-HP group included regulation of apoptosis, protein kinase cascade, post-translational protein modification, transcription from RNA polymerase II promoter, and IκB kinase/NFκB cascade. Functional validation assays indicated that PCBP1, which interacted with MHV-68 ORF34, may be involved in regulating late virus gene expression in a manner consistent with the effects of its viral interacting partner. Our study integrated Y2H screening with multiple functional validation approaches to create γ-herpes viral-viral and viral-cellular protein interaction networks. Persistent infections by the herpesviruses Epstein Barr virus (EBV) and Kaposi's sarcoma herpesvirus (KSHV) are associated with tumor formation. To better understand how these and other related viruses interact with their host cells to promote virus replication and cause disease, we studied murine gamma-herpesvirus 68 (MHV-68). MHV-68 belongs to the same group of herpesviruses as EBV and KSHV, but has the advantage of being able to replicate efficiently in cell culture. Our study used genome-wide screens to identify 23 protein-protein interactions between the 80 MHV-68 proteins. Several of these interactions are likely to be important for assembling new viruses. We also discovered 243 interactions between MHV-68 and cellular proteins. To help prioritize cellular proteins for follow up studies, we developed a new computational tool to analyze our data. Proteins with high priority scores were more likely to affect viral replication than low priority proteins. Among the cellular proteins that had the greatest effect on MHV-68 replication was PCBP1, which negatively regulated MHV-68 late gene expression. This study identified many novel cellular proteins involved in MHV-68 replication and established a method to identify important proteins from high-throughput virus-cellular protein-protein interaction data sets.
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Affiliation(s)
- Shaoying Lee
- School of Dentistry, University of California Los Angeles, Los Angeles, California, United States of America
- Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California, United States of America
| | - Lukasz Salwinski
- UCLA DOE-Institute for Genomics and Proteomics, University of California Los Angeles, Los Angeles, California, United States of America
| | - Chaoying Zhang
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West LaFayette, Indiana, United States of America
| | - Derrick Chu
- Department of Molecular Cell and Developmental Biology, University of California Los Angeles, Los Angeles, California, United States of America
| | - Claire Sampankanpanich
- Department of Molecular Cell and Developmental Biology, University of California Los Angeles, Los Angeles, California, United States of America
| | - Nichole A. Reyes
- Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California, United States of America
| | - Abbey Vangeloff
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West LaFayette, Indiana, United States of America
| | - Fangfang Xing
- Department of Molecular Cell and Developmental Biology, University of California Los Angeles, Los Angeles, California, United States of America
| | - Xudong Li
- Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California, United States of America
| | - Ting-Ting Wu
- Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California, United States of America
| | | | - Hongyu Deng
- School of Dentistry, University of California Los Angeles, Los Angeles, California, United States of America
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Douglas J. LaCount
- Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West LaFayette, Indiana, United States of America
- * E-mail: (DJL); (RS)
| | - Ren Sun
- Department of Molecular and Medical Pharmacology, University of California Los Angeles, Los Angeles, California, United States of America
- * E-mail: (DJL); (RS)
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Acosta-Leal R, Duffy S, Xiong Z, Hammond RW, Elena SF. Advances in plant virus evolution: translating evolutionary insights into better disease management. PHYTOPATHOLOGY 2011; 101:1136-48. [PMID: 21554186 DOI: 10.1094/phyto-01-11-0017] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Recent studies in plant virus evolution are revealing that genetic structure and behavior of virus and viroid populations can explain important pathogenic properties of these agents, such as host resistance breakdown, disease severity, and host shifting, among others. Genetic variation is essential for the survival of organisms. The exploration of how these subcellular parasites generate and maintain a certain frequency of mutations at the intra- and inter-host levels is revealing novel molecular virus-plant interactions. They emphasize the role of host environment in the dynamic genetic composition of virus populations. Functional genomics has identified host factors that are transcriptionally altered after virus infections. The analyses of these data by means of systems biology approaches are uncovering critical plant genes specifically targeted by viruses during host adaptation. Also, a next-generation resequencing approach of a whole virus genome is opening new avenues to study virus recombination and the relationships between intra-host virus composition and pathogenesis. Altogether, the analyzed data indicate that systematic disruption of some specific parameters of evolving virus populations could lead to more efficient ways of disease prevention, eradication, or tolerable virus-plant coexistence.
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Murali TM, Dyer MD, Badger D, Tyler BM, Katze MG. Network-based prediction and analysis of HIV dependency factors. PLoS Comput Biol 2011; 7:e1002164. [PMID: 21966263 PMCID: PMC3178628 DOI: 10.1371/journal.pcbi.1002164] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2010] [Accepted: 06/30/2011] [Indexed: 01/27/2023] Open
Abstract
HIV Dependency Factors (HDFs) are a class of human proteins that are essential for HIV replication, but are not lethal to the host cell when silenced. Three previous genome-wide RNAi experiments identified HDF sets with little overlap. We combine data from these three studies with a human protein interaction network to predict new HDFs, using an intuitive algorithm called SinkSource and four other algorithms published in the literature. Our algorithm achieves high precision and recall upon cross validation, as do the other methods. A number of HDFs that we predict are known to interact with HIV proteins. They belong to multiple protein complexes and biological processes that are known to be manipulated by HIV. We also demonstrate that many predicted HDF genes show significantly different programs of expression in early response to SIV infection in two non-human primate species that differ in AIDS progression. Our results suggest that many HDFs are yet to be discovered and that they have potential value as prognostic markers to determine pathological outcome and the likelihood of AIDS development. More generally, if multiple genome-wide gene-level studies have been performed at independent labs to study the same biological system or phenomenon, our methodology is applicable to interpret these studies simultaneously in the context of molecular interaction networks and to ask if they reinforce or contradict each other. Medicines to cure infectious diseases usually target proteins in the pathogens. Since pathogens have short life cycles, the targeted proteins can rapidly evolve and make the medicines ineffective, especially in viruses such as HIV. However, since viruses have very small genomes, they must exploit the cellular machinery of the host to propagate. Therefore, disrupting the activity of selected host proteins may impede viruses. Three recent experiments have discovered hundreds of such proteins in human cells that HIV depends upon. Surprisingly, these three sets have very little overlap. In this work, we demonstrate that this discrepancy can be explained by considering physical interactions between the human proteins in these studies. Moreover, we exploit these interactions to predict new dependency factors for HIV. Our predictions show very significant overlaps with human proteins that are known to interact with HIV proteins and with human cellular processes that are known to be subverted by the virus. Most importantly, we show that proteins predicted by us may play a prominent role in affecting HIV-related disease progression in lymph nodes. Therefore, our predictions constitute a powerful resource for experimentalists who desire to discover new human proteins that can control the spread of HIV.
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Affiliation(s)
- T. M. Murali
- Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
- * E-mail: (TMM) (TM); (MGK) (MK)
| | - Matthew D. Dyer
- Applied Biosystems, Foster City, California, United States of America
| | - David Badger
- Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Brett M. Tyler
- Virginia Bioinformatics Institute, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, United States of America
| | - Michael G. Katze
- Department of Microbiology, University of Washington, Seattle, Washington, United States of America
- * E-mail: (TMM) (TM); (MGK) (MK)
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Friedel CC, Haas J. Virus-host interactomes and global models of virus-infected cells. Trends Microbiol 2011; 19:501-8. [PMID: 21855347 DOI: 10.1016/j.tim.2011.07.003] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2011] [Revised: 07/12/2011] [Accepted: 07/13/2011] [Indexed: 01/01/2023]
Abstract
Novel high-throughput technologies such as yeast two-hybrid and RNA interference (RNAi) screens provide the tools to study interactions between viral proteins and the host on a genomic scale. In this review, we provide an overview of studies in which these technologies were applied and of computational approaches for the analysis of the identified viral interactors in the context of the host cell. The results of these studies illustrate the advantages of integrative systems biology approaches in the investigation of viral pathogens.
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Affiliation(s)
- Caroline C Friedel
- Institut für Pharmazie und Molekulare Biotechnologie, Universität Heidelberg, 69120 Heidelberg, Germany
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Elena SF, Carrera J, Rodrigo G. A systems biology approach to the evolution of plant-virus interactions. CURRENT OPINION IN PLANT BIOLOGY 2011; 14:372-377. [PMID: 21458360 DOI: 10.1016/j.pbi.2011.03.013] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2011] [Revised: 03/01/2011] [Accepted: 03/09/2011] [Indexed: 05/30/2023]
Abstract
Omic approaches to the analysis of plant-virus interactions are becoming increasingly popular. These types of data, in combination with models of interaction networks, will aid in revealing not only host components that are important for the virus life cycle, but also general patterns about the way in which different viruses manipulate host regulation of gene expression for their own benefit and possible mechanisms by which viruses evade host defenses. Here, we review studies identifying host genes regulated by viruses and discuss how these genes integrate in host regulatory and interaction networks, with a particular focus on the physical properties of these networks.
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Affiliation(s)
- Santiago F Elena
- Instituto de Biología Molecular y Celular de Plantas, CSIC-UPV, Ingeniero Fausto Elio s/n, 46022 València, Spain.
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Fahey ME, Bennett MJ, Mahon C, Jäger S, Pache L, Kumar D, Shapiro A, Rao K, Chanda SK, Craik CS, Frankel AD, Krogan NJ. GPS-Prot: a web-based visualization platform for integrating host-pathogen interaction data. BMC Bioinformatics 2011; 12:298. [PMID: 21777475 PMCID: PMC3213248 DOI: 10.1186/1471-2105-12-298] [Citation(s) in RCA: 59] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2011] [Accepted: 07/22/2011] [Indexed: 01/07/2023] Open
Abstract
BACKGROUND The increasing availability of HIV-host interaction datasets, including both physical and genetic interactions, has created a need for software tools to integrate and visualize the data. Because these host-pathogen interactions are extensive and interactions between human proteins are found within many different databases, it is difficult to generate integrated HIV-human interaction networks. RESULTS We have developed a web-based platform, termed GPS-Prot http://www.gpsprot.org, that allows for facile integration of different HIV interaction data types as well as inclusion of interactions between human proteins derived from publicly-available databases, including MINT, BioGRID and HPRD. The software has the ability to group proteins into functional modules or protein complexes, generating more intuitive network representations and also allows for the uploading of user-generated data. CONCLUSIONS GPS-Prot is a software tool that allows users to easily create comprehensive and integrated HIV-host networks. A major advantage of this platform compared to other visualization tools is its web-based format, which requires no software installation or data downloads. GPS-Prot allows novice users to quickly generate networks that combine both genetic and protein-protein interactions between HIV and its human host into a single representation. Ultimately, the platform is extendable to other host-pathogen systems.
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Affiliation(s)
- Marie E Fahey
- Department of Cellular and Molecular Pharmacology, University of California San Francisco, 1700 4th Street, San Francisco, CA 94158, USA
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Sequence- and interactome-based prediction of viral protein hotspots targeting host proteins: a case study for HIV Nef. PLoS One 2011; 6:e20735. [PMID: 21738584 PMCID: PMC3125164 DOI: 10.1371/journal.pone.0020735] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2011] [Accepted: 05/08/2011] [Indexed: 01/03/2023] Open
Abstract
Virus proteins alter protein pathways of the host toward the synthesis of viral particles by breaking and making edges via binding to host proteins. In this study, we developed a computational approach to predict viral sequence hotspots for binding to host proteins based on sequences of viral and host proteins and literature-curated virus-host protein interactome data. We use a motif discovery algorithm repeatedly on collections of sequences of viral proteins and immediate binding partners of their host targets and choose only those motifs that are conserved on viral sequences and highly statistically enriched among binding partners of virus protein targeted host proteins. Our results match experimental data on binding sites of Nef to host proteins such as MAPK1, VAV1, LCK, HCK, HLA-A, CD4, FYN, and GNB2L1 with high statistical significance but is a poor predictor of Nef binding sites on highly flexible, hoop-like regions. Predicted hotspots recapture CD8 cell epitopes of HIV Nef highlighting their importance in modulating virus-host interactions. Host proteins potentially targeted or outcompeted by Nef appear crowding the T cell receptor, natural killer cell mediated cytotoxicity, and neurotrophin signaling pathways. Scanning of HIV Nef motifs on multiple alignments of hepatitis C protein NS5A produces results consistent with literature, indicating the potential value of the hotspot discovery in advancing our understanding of virus-host crosstalk.
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Maulik U, Bhattacharyya M, Mukhopadhyay A, Bandyopadhyay S. Identifying the immunodeficiency gateway proteins in humans and their involvement in microRNA regulation. MOLECULAR BIOSYSTEMS 2011; 7:1842-51. [PMID: 21437347 DOI: 10.1039/c1mb05026e] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Very little is known to date about the regulation protocol between transcription factors (TFs), genes and microRNAs (miRNAs) associated with diseases in various organisms. In this paper, we focus on finding the activity of miRNAs through the HIV-1 regulatory pathway in humans at the systems level. For this, we integrate and study the characteristics of the interaction information between HIV-1 and human proteins obtained from literature and prediction analysis. This information, realized in the form of a bipartite network, is subsequently mined with an exhaustive graph search technique to identify the strong significant biclusters, which are effectively the bicliques. They are unified further to form the core bipartite subnetwork. Many of the known HIV-1 associated kinase proteins (including LCK) are found in this core module. From this, the secondary significant proteins are identified by mapping these gateway proteins to the human protein-protein interaction network. Finally, these proteins are mapped onto the TF-to-miRNA and miRNA-to-gene regulatory networks derived from a couple of current studies to obtain a global view of the HIV-1 mediated TF-gene-miRNA inter-regulatory network. Interestingly, a few miRNAs participating in this pathway at the secondary level are found to have oncogenic involvement.
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Affiliation(s)
- Ujjwal Maulik
- Department of Computer Science and Engineering, Jadavpur University, Kolkata-700032, India.
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