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Yang X, Zhu J, Wang Q, Tang B, Shen Y, Wang B, Ji L, Liu H, Wuchty S, Zhang Z, Dong Y, Liang Z. Comparative analysis of dynamic transcriptomes reveals specific COVID-19 features and pathogenesis of immunocompromised populations. mSystems 2024; 9:e0138523. [PMID: 38752789 PMCID: PMC11237560 DOI: 10.1128/msystems.01385-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 04/10/2024] [Indexed: 06/19/2024] Open
Abstract
A dysfunction of human host genes and proteins in coronavirus infectious disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a key factor impacting clinical symptoms and outcomes. Yet, a detailed understanding of human host immune responses is still incomplete. Here, we applied RNA sequencing to 94 samples of COVID-19 patients with and without hematological tumors as well as COVID-19 uninfected non-tumor individuals to obtain a comprehensive transcriptome landscape of both hematological tumor patients and non-tumor individuals. In our analysis, we further accounted for the human-SARS-CoV-2 protein interactome, human protein interactome, and human protein complex subnetworks to understand the mechanisms of SARS-CoV-2 infection and host immune responses. Our data sets enabled us to identify important SARS-CoV-2 (non-)targeted differentially expressed genes and complexes post-SARS-CoV-2 infection in both hematological tumor and non-tumor individuals. We found several unique differentially expressed genes, complexes, and functions/pathways such as blood coagulation (APOE, SERPINE1, SERPINE2, and TFPI), lipoprotein particle remodeling (APOC2, APOE, and CETP), and pro-B cell differentiation (IGHM, VPREB1, and IGLL1) during COVID-19 infection in patients with hematological tumors. In particular, APOE, a gene that is associated with both blood coagulation and lipoprotein particle remodeling, is not only upregulated in hematological tumor patients post-SARS-CoV-2 infection but also significantly expressed in acute dead patients with hematological tumors, providing clues for the design of future therapeutic strategies specifically targeting COVID-19 in patients with hematological tumors. Our data provide a rich resource for understanding the specific pathogenesis of COVID-19 in immunocompromised patients, such as those with hematological malignancies, and developing effective therapeutics for COVID-19. IMPORTANCE A majority of previous studies focused on the characterization of coronavirus infectious disease 2019 (COVID-19) disease severity in people with normal immunity, while the characterization of COVID-19 in immunocompromised populations is still limited. Our study profiles changes in the transcriptome landscape post-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in hematological tumor patients and non-tumor individuals. Furthermore, our integrative and comparative systems biology analysis of the interactome, complexome, and transcriptome provides new insights into the tumor-specific pathogenesis of COVID-19. Our findings confirm that SARS-CoV-2 potentially tends to target more non-functional host proteins to indirectly affect host immune responses in hematological tumor patients. The identified unique genes, complexes, functions/pathways, and expression patterns post-SARS-CoV-2 infection in patients with hematological tumors increase our understanding of how SARS-CoV-2 manipulates the host molecular mechanism. Our observed differential genes/complexes and clinical indicators of normal/long infection and deceased COVID-19 patients provide clues for understanding the mechanism of COVID-19 progression in hematological tumors. Finally, our study provides an important data resource that supports the increasing value of the application of publicly accessible data sets to public health.
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Affiliation(s)
- Xiaodi Yang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Jialin Zhu
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Qingyun Wang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Bo Tang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Ye Shen
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Bingjie Wang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Li Ji
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Huihui Liu
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Miami, Florida, USA
- Department of Biology, University of Miami, Miami, Florida, USA
- Institute of Data Science and Computation, University of Miami, Miami, Florida, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, Florida, USA
| | - Ziding Zhang
- College of Biological Sciences, China Agricultural University, Beijing, China
| | - Yujun Dong
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Zeyin Liang
- Department of Hematology, Peking University First Hospital, Beijing, China
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2
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Yang X, Wuchty S, Liang Z, Ji L, Wang B, Zhu J, Zhang Z, Dong Y. Multi-modal features-based human-herpesvirus protein-protein interaction prediction by using LightGBM. Brief Bioinform 2024; 25:bbae005. [PMID: 38279649 PMCID: PMC10818167 DOI: 10.1093/bib/bbae005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 12/25/2023] [Accepted: 01/01/2021] [Indexed: 01/28/2024] Open
Abstract
The identification of human-herpesvirus protein-protein interactions (PPIs) is an essential and important entry point to understand the mechanisms of viral infection, especially in malignant tumor patients with common herpesvirus infection. While natural language processing (NLP)-based embedding techniques have emerged as powerful approaches, the application of multi-modal embedding feature fusion to predict human-herpesvirus PPIs is still limited. Here, we established a multi-modal embedding feature fusion-based LightGBM method to predict human-herpesvirus PPIs. In particular, we applied document and graph embedding approaches to represent sequence, network and function modal features of human and herpesviral proteins. Training our LightGBM models through our compiled non-rigorous and rigorous benchmarking datasets, we obtained significantly better performance compared to individual-modal features. Furthermore, our model outperformed traditional feature encodings-based machine learning methods and state-of-the-art deep learning-based methods using various benchmarking datasets. In a transfer learning step, we show that our model that was trained on human-herpesvirus PPI dataset without cytomegalovirus data can reliably predict human-cytomegalovirus PPIs, indicating that our method can comprehensively capture multi-modal fusion features of protein interactions across various herpesvirus subtypes. The implementation of our method is available at https://github.com/XiaodiYangpku/MultimodalPPI/.
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Affiliation(s)
- Xiaodi Yang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Miami FL, 33146, USA
- Department of Biology, University of Miami, Miami FL, 33146, USA
- Institute of Data Science and Computation, University of Miami, Miami, FL 33146, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Zeyin Liang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Li Ji
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Bingjie Wang
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Jialin Zhu
- Department of Hematology, Peking University First Hospital, Beijing, China
| | - Ziding Zhang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Yujun Dong
- Department of Hematology, Peking University First Hospital, Beijing, China
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3
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Nithya C, Kiran M, Nagarajaram HA. Hubs and Bottlenecks in Protein-Protein Interaction Networks. Methods Mol Biol 2024; 2719:227-248. [PMID: 37803121 DOI: 10.1007/978-1-0716-3461-5_13] [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] [Indexed: 10/08/2023]
Abstract
Protein-protein interaction networks (PPINs) represent the physical interactions among proteins in a cell. These interactions are critical in all cellular processes, including signal transduction, metabolic regulation, and gene expression. In PPINs, centrality measures are widely used to identify the most critical nodes. The two most commonly used centrality measures in networks are degree and betweenness centralities. Degree centrality is the number of connections a node has in the network, and betweenness centrality is the measure of the extent to which a node lies on the shortest paths between pairs of other nodes in the network. In PPINs, proteins with high degree and betweenness centrality are referred to as hubs and bottlenecks respectively. Hubs and bottlenecks are topologically and functionally essential proteins that play crucial roles in maintaining the network's structure and function. This article comprehensively reviews essential literature on hubs and bottlenecks, including their properties and functions.
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Affiliation(s)
- Chandramohan Nithya
- Department of Biotechnology and Bioinformatics, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
| | - Manjari Kiran
- Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, Telangana, India
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4
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Wuchty S, White AK, Olthof AM, Drake K, Hume AJ, Olejnik J, Aguiar-Pulido V, Mühlberger E, Kanadia RN. Minor intron-containing genes as an ancient backbone for viral infection? PNAS NEXUS 2024; 3:pgad479. [PMID: 38274120 PMCID: PMC10810330 DOI: 10.1093/pnasnexus/pgad479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 12/15/2023] [Indexed: 01/27/2024]
Abstract
Minor intron-containing genes (MIGs) account for <2% of all human protein-coding genes and are uniquely dependent on the minor spliceosome for proper excision. Despite their low numbers, we surprisingly found a significant enrichment of MIG-encoded proteins (MIG-Ps) in protein-protein interactomes and host factors of positive-sense RNA viruses, including SARS-CoV-1, SARS-CoV-2, MERS coronavirus, and Zika virus. Similarly, we observed a significant enrichment of MIG-Ps in the interactomes and sets of host factors of negative-sense RNA viruses such as Ebola virus, influenza A virus, and the retrovirus HIV-1. We also found an enrichment of MIG-Ps in double-stranded DNA viruses such as Epstein-Barr virus, human papillomavirus, and herpes simplex viruses. In general, MIG-Ps were highly connected and placed in central positions in a network of human-host protein interactions. Moreover, MIG-Ps that interact with viral proteins were enriched with essential genes. We also provide evidence that viral proteins interact with ancestral MIGs that date back to unicellular organisms and are mainly involved in basic cellular functions such as cell cycle, cell division, and signal transduction. Our results suggest that MIG-Ps form a stable, evolutionarily conserved backbone that viruses putatively tap to invade and propagate in human host cells.
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Affiliation(s)
- Stefan Wuchty
- Department of Computer Science, University of Miami, Coral Gables, FL 33146, USA
- Department of Biology, University of Miami, Coral Gables, FL 33146, USA
- Institute of Data Science and Computing, University of Miami, Coral Gables, FL 33146, USA
- Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33134, USA
| | - Alisa K White
- Physiology and Neurobiology Department, University of Connecticut, Storrs, CT 06269, USA
| | - Anouk M Olthof
- Physiology and Neurobiology Department, University of Connecticut, Storrs, CT 06269, USA
| | - Kyle Drake
- Physiology and Neurobiology Department, University of Connecticut, Storrs, CT 06269, USA
| | - Adam J Hume
- Department of Virology, Immunology and Microbiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA 02118, USA
- Center for Emerging Infectious Diseases Policy and Research, Boston University, Boston, MA 02118, USA
| | - Judith Olejnik
- Department of Virology, Immunology and Microbiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA 02118, USA
| | | | - Elke Mühlberger
- Department of Virology, Immunology and Microbiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118, USA
- National Emerging Infectious Diseases Laboratories, Boston University, Boston, MA 02118, USA
| | - Rahul N Kanadia
- Physiology and Neurobiology Department, University of Connecticut, Storrs, CT 06269, USA
- Institute for Systems Genomics, University of Connecticut, Storrs, CT 06269, USA
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5
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Onisiforou A, Spyrou GM. Identification of viral-mediated pathogenic mechanisms in neurodegenerative diseases using network-based approaches. Brief Bioinform 2021; 22:bbab141. [PMID: 34237135 PMCID: PMC8574625 DOI: 10.1093/bib/bbab141] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 03/01/2021] [Accepted: 03/23/2021] [Indexed: 12/18/2022] Open
Abstract
During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbations of the human interactome causing the generation of various complex diseases. Evidences suggest that viral-mediated perturbations are a possible pathogenic etiology in several neurodegenerative diseases (NDs). These diseases are characterized by chronic progressive degeneration of neurons, and current therapeutic approaches provide only mild symptomatic relief; therefore, there is unmet need for the discovery of novel therapeutic interventions. In this paper, we initially review databases and tools that can be utilized to investigate viral-mediated perturbations in complex NDs using network-based analysis by examining the interaction between the ND-related PPI disease networks and the virus-host PPI network. Afterwards, we present our theoretical-driven integrative network-based bioinformatics approach that accounts for pathogen-genes-disease-related PPIs with the aim to identify viral-mediated pathogenic mechanisms focusing in multiple sclerosis (MS) disease. We identified seven high centrality nodes that can act as disease communicator nodes and exert systemic effects in the MS-enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways network. In addition, we identified 12 KEGG pathways, 5 Reactome pathways and 52 Gene Ontology Immune System Processes by which 80 viral proteins from eight viral species might exert viral-mediated pathogenic mechanisms in MS. Finally, our analysis highlighted the Th17 differentiation pathway, a disease communicator node and part of the 12 underlined KEGG pathways, as a key viral-mediated pathogenic mechanism and a possible therapeutic target for MS disease.
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Affiliation(s)
- Anna Onisiforou
- Department of Bioinformatics, Cyprus Institute of Neurology & Genetics, and the Cyprus School of Molecular Medicine, Cyprus
| | - George M Spyrou
- Department of Bioinformatics, Cyprus Institute of Neurology & Genetics, and professor at the Cyprus School of Molecular Medicine, Cyprus
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6
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Habibi M, Taheri G. Topological network based drug repurposing for coronavirus 2019. PLoS One 2021; 16:e0255270. [PMID: 34324563 PMCID: PMC8320924 DOI: 10.1371/journal.pone.0255270] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Accepted: 07/13/2021] [Indexed: 12/14/2022] Open
Abstract
The COVID-19 pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has become the current health concern and threat to the entire world. Thus, the world needs the fast recognition of appropriate drugs to restrict the spread of this disease. The global effort started to identify the best drug compounds to treat COVID-19, but going through a series of clinical trials and our lack of information about the details of the virus's performance has slowed down the time to reach this goal. In this work, we try to select the subset of human proteins as candidate sets that can bind to approved drugs. Our method is based on the information on human-virus protein interaction and their effect on the biological processes of the host cells. We also define some informative topological and statistical features for proteins in the protein-protein interaction network. We evaluate our selected sets with two groups of drugs. The first group contains the experimental unapproved treatments for COVID-19, and we show that from 17 drugs in this group, 15 drugs are approved by our selected sets. The second group contains the external clinical trials for COVID-19, and we show that 85% of drugs in this group, target at least one protein of our selected sets. We also study COVID-19 associated protein sets and identify proteins that are essential to disease pathology. For this analysis, we use DAVID tools to show and compare disease-associated genes that are contributed between the COVID-19 comorbidities. Our results for shared genes show significant enrichment for cardiovascular-related, hypertension, diabetes type 2, kidney-related and lung-related diseases. In the last part of this work, we recommend 56 potential effective drugs for further research and investigation for COVID-19 treatment. Materials and implementations are available at: https://github.com/MahnazHabibi/Drug-repurposing.
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Affiliation(s)
- Mahnaz Habibi
- Department of Mathematics, Qazvin Branch, Islamic Azad University, Qazvin, Iran
- * E-mail:
| | - Golnaz Taheri
- Department of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
- Science for Life Laboratory, Stockholm, Sweden
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7
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Yang X, Yang S, Lian X, Wuchty S, Zhang Z. Transfer learning via multi-scale convolutional neural layers for human-virus protein-protein interaction prediction. Bioinformatics 2021; 37:4771-4778. [PMID: 34273146 PMCID: PMC8406877 DOI: 10.1093/bioinformatics/btab533] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 06/03/2021] [Accepted: 07/16/2021] [Indexed: 11/20/2022] Open
Abstract
Motivation To complement experimental efforts, machine learning-based computational methods are playing an increasingly important role to predict human–virus protein–protein interactions (PPIs). Furthermore, transfer learning can effectively apply prior knowledge obtained from a large source dataset/task to a small target dataset/task, improving prediction performance. Results To predict interactions between human and viral proteins, we combine evolutionary sequence profile features with a Siamese convolutional neural network (CNN) architecture and a multi-layer perceptron. Our architecture outperforms various feature encodings-based machine learning and state-of-the-art prediction methods. As our main contribution, we introduce two transfer learning methods (i.e. ‘frozen’ type and ‘fine-tuning’ type) that reliably predict interactions in a target human–virus domain based on training in a source human–virus domain, by retraining CNN layers. Finally, we utilize the ‘frozen’ type transfer learning approach to predict human–SARS-CoV-2 PPIs, indicating that our predictions are topologically and functionally similar to experimentally known interactions. Availability and implementation: The source codes and datasets are available at https://github.com/XiaodiYangCAU/TransPPI/. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Xiaodi Yang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Shiping Yang
- State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Xianyi Lian
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
| | - Stefan Wuchty
- Dept. of Computer Science, University of Miami, Miami, FL 33146, USA.,Dept. of Biology, University of Miami, Miami, FL 33146, USA.,Sylvester Comprehensive Cancer Center, University of Miami, Miami, FL 33136, USA
| | - Ziding Zhang
- State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, China
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8
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Abstract
Identification of HIV-1 HDFs remains a crucial step to understand the complicated relationships between human and HIV-1. To complement the experimental identification of HDFs, we have implemented an existing network-based gene discovery strategy to predict HDFs from the human genome. The core idea of the proposed method is that the rich information deposited in host gene functional networks can be effectively utilized to infer the potential HDFs. We hope the proposed prediction method could further guide hypothesis-driven experimental efforts to interrogate human–HIV-1 relationships and provide new hints for the development of antiviral drugs to combat HIV-1 infection. Human immunodeficiency virus type 1 (HIV-1) depends on a class of host proteins called host dependency factors (HDFs) to facilitate its infection. So far experimental efforts have detected a certain number of HDFs, but the gene inventory of HIV-1 HDFs remains incomplete. Here, we implemented an existing network-based gene discovery strategy to predict HIV-1 HDFs. First, an encoding scheme based on a publicly available human tissue-specific gene functional network (GIANT; http://giant.princeton.edu/) was designed to convert each human gene into a 25,825-dimensional feature vector. Then, a random forest-based predictive model was trained on a data set containing 868 known HDFs and 1,736 non-HDFs. Through 5-fold cross-validation, an independent test, and comparison with one existing method, the proposed prediction method consistently revealed accurate and competitive performance. The highlight of our method should be ascribed to the introduction of the GIANT encoding scheme, which contains rich information regarding gene interactions. By merging known HDFs and genome-wide HDF prediction results, network analysis was conducted to catch the common patterns of HDFs in the context of the GIANT network. Interestingly, HDFs reveal significantly lower betweenness than HIV-1-interacting human proteins (i.e., HIV targets). In the meantime, the functional roles of HDFs were also examined by mapping all the HDF candidates into human protein complexes. Especially, we observed the frequent co-occurrence of HDFs and HIV targets at the protein complex level. Collectively, we hope the proposed prediction method not only can accelerate the HDF identification and antiviral drug target discovery, but also can provide some mechanistic insights into human-virus relationships. IMPORTANCE Identification of HIV-1 HDFs remains a crucial step to understand the complicated relationships between human and HIV-1. To complement the experimental identification of HDFs, we have implemented an existing network-based gene discovery strategy to predict HDFs from the human genome. The core idea of the proposed method is that the rich information deposited in host gene functional networks can be effectively utilized to infer the potential HDFs. We hope the proposed prediction method could further guide hypothesis-driven experimental efforts to interrogate human–HIV-1 relationships and provide new hints for the development of antiviral drugs to combat HIV-1 infection.
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9
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Goodacre N, Devkota P, Bae E, Wuchty S, Uetz P. Protein-protein interactions of human viruses. Semin Cell Dev Biol 2018; 99:31-39. [PMID: 30031213 PMCID: PMC7102568 DOI: 10.1016/j.semcdb.2018.07.018] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 04/02/2018] [Accepted: 07/17/2018] [Indexed: 12/16/2022]
Abstract
Viruses infect their human hosts by a series of interactions between viral and host proteins, indicating that detailed knowledge of such virus-host interaction interfaces are critical for our understanding of viral infection mechanisms, disease etiology and the development of new drugs. In this review, we primarily survey human host-virus interaction data that are available from public databases following the standardized PSI-MS format. Notably, available host-virus protein interaction information is strongly biased toward a small number of virus families including herpesviridae, papillomaviridae, orthomyxoviridae and retroviridae. While we explore the reliability and relevance of these protein interactions we also survey the current knowledge about viruses functional and topological targets. Furthermore, we assess emerging frontiers of host-virus protein interaction research, focusing on protein interaction interfaces of hosts that are infected by different viruses and viruses that infect multiple hosts. Finally, we cover the current status of research that investigates the relationships of virus-targeted host proteins to other comorbidities as well as the influence of host-virus protein interactions on human metabolism.
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Affiliation(s)
- Norman Goodacre
- Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Prajwal Devkota
- Dept. of Computer Science, Univ. of Miami, Coral Gables, FL, 33146, USA
| | - Eunhae Bae
- Division of Viral Products, Office of Vaccines Research and Review, Center for Biologics Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Stefan Wuchty
- Dept. of Computer Science, Univ. of Miami, Coral Gables, FL, 33146, USA; Center for Computational Science, Univ. of Miami, Coral Gables, FL, 33146, USA; Dept. of Biology, Univ. of Miami, Coral Gables, FL, 33146, USA; Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, 33136, USA.
| | - Peter Uetz
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA, 23284, USA.
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Devkota P, Danzi MC, Wuchty S. Beyond degree and betweenness centrality: Alternative topological measures to predict viral targets. PLoS One 2018; 13:e0197595. [PMID: 29795705 PMCID: PMC5967884 DOI: 10.1371/journal.pone.0197595] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2017] [Accepted: 05/04/2018] [Indexed: 11/18/2022] Open
Abstract
The availability of large-scale screens of host-virus interaction interfaces enabled the topological analysis of viral protein targets of the host. In particular, host proteins that bind viral proteins are generally hubs and proteins with high betweenness centrality. Recently, other topological measures were introduced that a virus may tap to infect a host cell. Utilizing experimentally determined sets of human protein targets from Herpes, Hepatitis, HIV and Influenza, we pooled molecular interactions between proteins from different pathway databases. Apart from a protein's degree and betweenness centrality, we considered a protein's pathway participation, ability to topologically control a network and protein PageRank index. In particular, we found that proteins with increasing values of such measures tend to accumulate viral targets and distinguish viral targets from non-targets. Furthermore, all such topological measures strongly correlate with the occurrence of a given protein in different pathways. Building a random forest classifier that is based on such topological measures, we found that protein PageRank index had the highest impact on the classification of viral (non-)targets while proteins' ability to topologically control an interaction network played the least important role.
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Affiliation(s)
- Prajwal Devkota
- Dept. of Computer Science, Univ. of Miami, Coral Gables, FL, United States of America
| | - Matt C. Danzi
- The Miami Project to Cure Paralysis, Miller School of Medicine, University of Miami, Miami, FL, United States of America
- Center for Computational Science, Univ. of Miami, Coral Gables, FL, United States of America
| | - Stefan Wuchty
- Dept. of Computer Science, Univ. of Miami, Coral Gables, FL, United States of America
- Center for Computational Science, Univ. of Miami, Coral Gables, FL, United States of America
- Dept. of Biology, Univ. of Miami, Coral Gables, FL, United States of America
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, United States of America
- * E-mail:
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11
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Abstract
Since cell regulation and protein expression can be dramatically altered upon infection by viruses, studying the mechanisms by which viruses infect cells and the regulatory networks they disrupt is essential to understanding viral pathogenicity. This line of study can also lead to discoveries about the workings of host cells themselves. Computational methods are rapidly being developed to investigate viral-host interactions, and here we highlight recent methods and the insights that they have revealed so far, with a particular focus on methods that integrate different types of data. We also review the challenges of working with viruses compared with traditional cellular biology, and the limitations of current experimental and informatics methods.
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12
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Wuchty S, Boltz T, Küçük-McGinty H. Links between critical proteins drive the controllability of protein interaction networks. Proteomics 2017; 17:e1700056. [DOI: 10.1002/pmic.201700056] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2017] [Revised: 03/23/2017] [Accepted: 04/07/2017] [Indexed: 01/03/2023]
Affiliation(s)
- Stefan Wuchty
- Department of Computer Science; University of Miami; Coral Gables FL USA
- Center of Computational Sciences; University of Miami; Coral Gables FL USA
- Sylvester Comprehensive Cancer Center; University of Miami; Miami FL USA
| | - Toni Boltz
- Department of Computer Science; University of Miami; Coral Gables FL USA
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13
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Mariano R, Wuchty S, Vizoso-Pinto MG, Häuser R, Uetz P. The interactome of Streptococcus pneumoniae and its bacteriophages show highly specific patterns of interactions among bacteria and their phages. Sci Rep 2016; 6:24597. [PMID: 27103053 PMCID: PMC4840434 DOI: 10.1038/srep24597] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2015] [Accepted: 03/22/2016] [Indexed: 12/11/2022] Open
Abstract
Although an abundance of bacteriophages exists, little is known about interactions between their proteins and those of their bacterial hosts. Here, we experimentally determined the phage-host interactomes of the phages Dp-1 and Cp-1 and their underlying protein interaction network in the host Streptococcus pneumoniae. We compared our results to the interaction patterns of E. coli phages lambda and T7. Dp-1 and Cp-1 target highly connected host proteins, occupy central network positions, and reach many protein clusters through the interactions of their targets. In turn, lambda and T7 targets cluster to conserved and essential proteins in E. coli, while such patterns were largely absent in S. pneumoniae. Furthermore, targets in E. coli were mutually strongly intertwined, while targets of Dp-1 and Cp-1 were strongly connected through essential and orthologous proteins in their immediate network vicinity. In both phage-host systems, the impact of phages on their protein targets appears to extend from their network neighbors, since proteins that interact with phage targets were located in central network positions, have a strong topologically disruptive effect and touch complexes with high functional heterogeneity. Such observations suggest that the phages, biological impact is accomplished through a surprisingly limited topological reach of their targets.
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Affiliation(s)
- Rachelle Mariano
- Dept. of Computer Science, University of Miami, Coral Gables, FL 33146, USA
| | - Stefan Wuchty
- Dept. of Computer Science, University of Miami, Coral Gables, FL 33146, USA.,Center for Computational Science, University of Miami, Coral Gables, FL 33146, USA
| | - Maria G Vizoso-Pinto
- Max von Pettenkofer-Institute, Department of Virology, Ludwig-Maximilians-University, Munich, Germany.,Instituto Superior de Investigaciones Biológicas (INSIBIO), CONICET-UNT, and Instituto de Fisiología, Facultad de Medicina, UNT. San Miguel de Tucumán, Argentina
| | - Roman Häuser
- Genomics and Proteomics Core Facilities, German Cancer Research Center, Im Neuenheimer Feld 580, 69120 Heidelberg, Germany
| | - Peter Uetz
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284, USA
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Local Action with Global Impact: Highly Similar Infection Patterns of Human Viruses and Bacteriophages. mSystems 2016; 1:mSystems00030-15. [PMID: 27822522 PMCID: PMC5069743 DOI: 10.1128/msystems.00030-15] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2015] [Accepted: 02/16/2016] [Indexed: 11/20/2022] Open
Abstract
The investigation of host-pathogen interaction interfaces and their constituent factors is crucial for our understanding of an organism's pathogenesis. Here, we explored the interactomes of HIV, hepatitis C virus, influenza A virus, human papillomavirus, herpes simplex virus, and vaccinia virus in a human host by analyzing the combined sets of virus targets and human genes that are required for viral infection. We also considered targets and required genes of bacteriophages lambda and T7 infection in Escherichia coli. We found that targeted proteins and their immediate network neighbors significantly pool with proteins required for infection and essential for cell growth, forming large connected components in both the human and E. coli protein interaction networks. The impact of both viruses and phages on their protein targets appears to extend to their network neighbors, as these are enriched with topologically central proteins that have a significant disruptive topological effect and connect different protein complexes. Moreover, viral and phage targets and network neighbors are enriched with transcription factors, methylases, and acetylases in human viruses, while such interactions are much less prominent in bacteriophages. IMPORTANCE While host-virus interaction interfaces have been previously investigated, relatively little is known about the indirect interactions of pathogen and host proteins required for viral infection and host cell function. Therefore, we investigated the topological relationships of human and bacterial viruses and how they interact with their hosts. We focused on those host proteins that are directly targeted by viruses, those that are required for infection, and those that are essential for both human and bacterial cells (here, E. coli). Generally, we observed that targeted, required, and essential proteins in both hosts interact in a highly intertwined fashion. While there exist highly similar topological patterns, we found that human viruses target transcription factors through methylases and acetylases, proteins that played no such role in bacteriophages.
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15
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Dong Y, Kuang Q, Dai X, Li R, Wu Y, Leng W, Li Y, Li M. Improving the Understanding of Pathogenesis of Human Papillomavirus 16 via Mapping Protein-Protein Interaction Network. BIOMED RESEARCH INTERNATIONAL 2015; 2015:890381. [PMID: 25961044 PMCID: PMC4414230 DOI: 10.1155/2015/890381] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2014] [Revised: 08/27/2014] [Accepted: 09/01/2014] [Indexed: 01/09/2023]
Abstract
The human papillomavirus 16 (HPV16) has high risk to lead various cancers and afflictions, especially, the cervical cancer. Therefore, investigating the pathogenesis of HPV16 is very important for public health. Protein-protein interaction (PPI) network between HPV16 and human was used as a measure to improve our understanding of its pathogenesis. By adopting sequence and topological features, a support vector machine (SVM) model was built to predict new interactions between HPV16 and human proteins. All interactions were comprehensively investigated and analyzed. The analysis indicated that HPV16 enlarged its scope of influence by interacting with human proteins as much as possible. These interactions alter a broad array of cell cycle progression. Furthermore, not only was HPV16 highly prone to interact with hub proteins and bottleneck proteins, but also it could effectively affect a breadth of signaling pathways. In addition, we found that the HPV16 evolved into high carcinogenicity on the condition that its own reproduction had been ensured. Meanwhile, this work will contribute to providing potential new targets for antiviral therapeutics and help experimental research in the future.
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Affiliation(s)
- Yongcheng Dong
- College of Life Sciences, Sichuan University, Chengdu 610064, China
| | - Qifan Kuang
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Xu Dai
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Rong Li
- College of Computer Science, Sichuan University, Chengdu 610064, China
| | - Yiming Wu
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Weijia Leng
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Yizhou Li
- College of Chemistry, Sichuan University, Chengdu 610064, China
| | - Menglong Li
- College of Chemistry, Sichuan University, Chengdu 610064, China
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16
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Khuri S, Wuchty S. Essentiality and centrality in protein interaction networks revisited. BMC Bioinformatics 2015; 16:109. [PMID: 25880655 PMCID: PMC4411940 DOI: 10.1186/s12859-015-0536-x] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2014] [Accepted: 03/13/2015] [Indexed: 12/29/2022] Open
Abstract
Background Minimum dominating sets (MDSet) of protein interaction networks allow the control of underlying protein interaction networks through their topological placement. While essential proteins are enriched in MDSets, we hypothesize that the statistical properties of biological functions of essential genes are enhanced when we focus on essential MDSet proteins (e-MDSet). Results Here, we determined minimum dominating sets of proteins (MDSet) in interaction networks of E. coli, S. cerevisiae and H. sapiens, defined as subsets of proteins whereby each remaining protein can be reached by a single interaction. We compared several topological and functional parameters of essential, MDSet, and essential MDSet (e-MDSet) proteins. In particular, we observed that their topological placement allowed e-MDSet proteins to provide a positive correlation between degree and lethality, connect more protein complexes, and have a stronger impact on network resilience than essential proteins alone. In comparison to essential proteins we further found that interactions between e-MDSet proteins appeared more frequently within complexes, while interactions of e-MDSet proteins between complexes were depleted. Finally, these e-MDSet proteins classified into functional groupings that play a central role in survival and adaptability. Conclusions The determination of e-MDSet of an organism highlights a set of proteins that enhances the enrichment signals of biological functions of essential proteins. As a consequence, we surmise that e-MDSets may provide a new method of evaluating the core proteins of an organism.
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Affiliation(s)
- Sawsan Khuri
- Department of Computer Science, University of Miami, Coral Gables, FL, 33146, USA. .,Center for Computational Science, University of Miami, Coral Gables, FL, 33146, USA.
| | - Stefan Wuchty
- Department of Computer Science, University of Miami, Coral Gables, FL, 33146, USA. .,Center for Computational Science, University of Miami, Coral Gables, FL, 33146, USA.
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17
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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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18
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Halehalli RR, Nagarajaram HA. Molecular principles of human virus protein-protein interactions. ACTA ACUST UNITED AC 2014; 31:1025-33. [PMID: 25417202 DOI: 10.1093/bioinformatics/btu763] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2014] [Accepted: 11/12/2014] [Indexed: 01/01/2023]
Abstract
MOTIVATION Viruses, from the human protein-protein interaction network perspective, target hubs, bottlenecks and interconnected nodes enriched in certain biological pathways. However, not much is known about the general characteristic features of the human proteins interacting with viral proteins (referred to as hVIPs) as well as the motifs and domains utilized by human-virus protein-protein interactions (referred to as Hu-Vir PPIs). RESULTS Our study has revealed that hVIPs are mostly disordered proteins, whereas viral proteins are mostly ordered proteins. Protein disorder in viral proteins and hVIPs varies from one subcellular location to another. In any given viral-human PPI pair, at least one of the two proteins is structurally disordered suggesting that disorder associated conformational flexibility as one of the characteristic features of virus-host interaction. Further analyses reveal that hVIPs are (i) slowly evolving proteins, (ii) associated with high centrality scores in human-PPI network, (iii) involved in multiple pathways, (iv) enriched in eukaryotic linear motifs (ELMs) associated with protein modification, degradation and regulatory processes, (v) associated with high number of splice variants and (vi) expressed abundantly across multiple tissues. These aforementioned findings suggest that conformational flexibility, spatial diversity, abundance and slow evolution are the characteristic features of the human proteins targeted by viral proteins. Hu-Vir PPIs are mostly mediated via domain-motif interactions (DMIs) where viral proteins employ motifs that mimic host ELMs to bind to domains in human proteins. DMIs are shared among viruses belonging to different families indicating a possible convergent evolution of these motifs to help viruses to adopt common strategies to subvert host cellular pathways. AVAILABILITY AND IMPLEMENTATION Hu-Vir PPI data, DDI and DMI data for human-virus PPI can be downloaded from http://cdfd.org.in/labpages/computational_biology_datasets.html. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Rachita Ramachandra Halehalli
- Laboratory of Computational Biology, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, Telangana, 500001, India and Graduate School, Manipal University, Manipal, 576104, Karnataka, India Laboratory of Computational Biology, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, Telangana, 500001, India and Graduate School, Manipal University, Manipal, 576104, Karnataka, India
| | - Hampapathalu Adimurthy Nagarajaram
- Laboratory of Computational Biology, Centre for DNA Fingerprinting and Diagnostics, Hyderabad, Telangana, 500001, India and Graduate School, Manipal University, Manipal, 576104, Karnataka, India
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19
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Emig-Agius D, Olivieri K, Pache L, Shih HL, Pustovalova O, Bessarabova M, Young JAT, Chanda SK, Ideker T. An integrated map of HIV-human protein complexes that facilitate viral infection. PLoS One 2014; 9:e96687. [PMID: 24817247 PMCID: PMC4016004 DOI: 10.1371/journal.pone.0096687] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2013] [Accepted: 04/11/2014] [Indexed: 12/03/2022] Open
Abstract
Recent proteomic and genetic studies have aimed to identify a complete network of interactions between HIV and human proteins and genes. This HIV-human interaction network provides invaluable information as to how HIV exploits the host machinery and can be used as a starting point for further functional analyses. We integrated this network with complementary datasets of protein function and interaction to nominate human protein complexes with likely roles in viral infection. Based on our approach we identified a global map of 40 HIV-human protein complexes with putative roles in HIV infection, some of which are involved in DNA replication and repair, transcription, translation, and cytoskeletal regulation. Targeted RNAi screens were used to validate several proteins and complexes for functional impact on viral infection. Thus, our HIV-human protein complex map provides a significant resource of potential HIV-host interactions for further study.
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Affiliation(s)
- Dorothea Emig-Agius
- Departments of Medicine and Bioengineering, University of California at San Diego, La Jolla, California, United States of America
- IP&Science, Thomson Reuters Scientific Inc., Carlsbad, California, United States of America
| | - Kevin Olivieri
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Lars Pache
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Hsin Ling Shih
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Olga Pustovalova
- IP&Science, Thomson Reuters Scientific Inc., Carlsbad, California, United States of America
| | - Marina Bessarabova
- IP&Science, Thomson Reuters Scientific Inc., Carlsbad, California, United States of America
| | - John A. T. Young
- The Salk Institute for Biological Studies, La Jolla, California, United States of America
| | - Sumit K. Chanda
- Sanford-Burnham Medical Research Institute, La Jolla, California, United States of America
| | - Trey Ideker
- Departments of Medicine and Bioengineering, University of California at San Diego, La Jolla, California, United States of America
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20
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Abstract
Recently, the focus of network research shifted to network controllability, prompting us to determine proteins that are important for the control of the underlying interaction webs. In particular, we determined minimum dominating sets of proteins (MDSets) in human and yeast protein interaction networks. Such groups of proteins were defined as optimized subsets where each non-MDSet protein can be reached by an interaction from an MDSet protein. Notably, we found that MDSet proteins were enriched with essential, cancer-related, and virus-targeted genes. Their central position allowed MDSet proteins to connect protein complexes and to have a higher impact on network resilience than hub proteins. As for their involvement in regulatory functions, MDSet proteins were enriched with transcription factors and protein kinases and were significantly involved in bottleneck interactions, regulatory links, phosphorylation events, and genetic interactions.
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21
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Rachita HR, Nagarajaram HA. Viral proteins that bridge unconnected proteins and components in the human PPI network. ACTA ACUST UNITED AC 2014; 10:2448-58. [DOI: 10.1039/c4mb00219a] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Viral proteins bridging unconnected components of the Hu-PPI network play a crucial role in viral replication and hence form attractive targets for therapeutic interventions.
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Affiliation(s)
- H. R. Rachita
- Centre for DNA Fingerprinting and Diagnostics
- Gruhakalpa
- Hyderabad 500001, India
| | - H. A. Nagarajaram
- Centre for DNA Fingerprinting and Diagnostics
- Gruhakalpa
- Hyderabad 500001, India
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22
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Blasche S, Wuchty S, Rajagopala SV, Uetz P. The protein interaction network of bacteriophage lambda with its host, Escherichia coli. J Virol 2013; 87:12745-55. [PMID: 24049175 PMCID: PMC3838138 DOI: 10.1128/jvi.02495-13] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2013] [Accepted: 09/10/2013] [Indexed: 11/20/2022] Open
Abstract
Although most of the 73 open reading frames (ORFs) in bacteriophage λ have been investigated intensively, the function of many genes in host-phage interactions remains poorly understood. Using yeast two-hybrid screens of all lambda ORFs for interactions with its host Escherichia coli, we determined a raw data set of 631 host-phage interactions resulting in a set of 62 high-confidence interactions after multiple rounds of retesting. These links suggest novel regulatory interactions between the E. coli transcriptional network and lambda proteins. Targeted host proteins and genes required for lambda infection are enriched among highly connected proteins, suggesting that bacteriophages resemble interaction patterns of human viruses. Lambda tail proteins interact with both bacterial fimbrial proteins and E. coli proteins homologous to other phage proteins. Lambda appears to dramatically differ from other phages, such as T7, because of its unusually large number of modified and processed proteins, which reduces the number of host-virus interactions detectable by yeast two-hybrid screens.
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Affiliation(s)
- Sonja Blasche
- Genomics and Proteomics Core Facilities, German Cancer Research Center, Heidelberg, Germany
| | - Stefan Wuchty
- National Center of Biotechnology Information, National Institutes of Health, Bethesda, Maryland, USA
| | | | - Peter Uetz
- Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, Virginia, USA
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23
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Schleker S, Trilling M. Data-warehousing of protein-protein interactions indicates that pathogens preferentially target hub and bottleneck proteins. Front Microbiol 2013; 4:51. [PMID: 23483866 PMCID: PMC3593624 DOI: 10.3389/fmicb.2013.00051] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2013] [Accepted: 02/21/2013] [Indexed: 01/08/2023] Open
Affiliation(s)
- Sylvia Schleker
- Institute of Complex Systems, Molecular Biophysics (ICS-5), Forschungszentrum Juelich GmbH Juelich, Germany
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24
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Abstract
High-throughput methods for screening of physical and functional interactions now provide the means to study virus-host interactions on a genome scale. The limited coverage of these methods and the large size and uncertain quality of the identified interaction sets, however, require sophisticated computational approaches to obtain novel insights and hypotheses on virus infection processes from these interactions. Here, we describe the central steps of bioinformatics methods applied most commonly for this task and highlight important aspects that need to be considered and potential pitfalls that should be avoided.
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Affiliation(s)
- Susanne M. Bailer
- University of Stuttgart Institute of Interfacial Process, Stuttgart, Germany
| | - Diana Lieber
- Ulm University Medical Center Institute of Virology, Ulm, Germany
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25
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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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26
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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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27
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Wuchty S. Computational prediction of host-parasite protein interactions between P. falciparum and H. sapiens. PLoS One 2011; 6:e26960. [PMID: 22114664 PMCID: PMC3219658 DOI: 10.1371/journal.pone.0026960] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2011] [Accepted: 10/07/2011] [Indexed: 11/18/2022] Open
Abstract
To obtain candidates of interactions between proteins of the malaria parasite Plasmodium falciparum and the human host, homologous and conserved interactions were inferred from various sources of interaction data. Such candidate interactions were assessed by applying a machine learning approach and further filtered according to expression and molecular characteristics, enabling involved proteins to indeed interact. The analysis of predicted interactions indicated that parasite proteins predominantly target central proteins to take control of a human host cell. Furthermore, parasite proteins utilized their protein repertoire in a combinatorial manner, providing a broad connection to host cellular processes. In particular, several prominent pathways of signaling and regulation proteins were predicted to interact with parasite chaperones. Such a result suggests an important role of remodeling proteins in the interaction interface between the human host and the parasite. Identification of such molecular strategies that allow the parasite to take control of the host has the potential to deepen our understanding of the parasite specific remodeling processes of the host cell and illuminate new avenues of disease intervention.
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Affiliation(s)
- Stefan Wuchty
- National Center for Biotechnology Information, National Institutes of Health, Bethesda, Maryland, United States of America.
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28
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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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29
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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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