Virus-host Protein-protein Interactions Research Articles

ChikvInt: a Chikungunya virus-host protein-protein interaction database.

Chikungunya is a fast-mutating virus causing Chikungunya virus disease (ChikvD) with a significant load of disability-adjusted life years (DALY) around the world. The outbreak of this virus is significantly higher in the tropical countries. Several experiments have identified crucial viral-host protein-protein interactions (PPIs) between Chikungunya Virus (Chikv) and the human host. However, no standard database that catalogs this PPI information exists. Here we develop a Chikv-Human PPI database, ChikvInt, to facilitate understanding ChikvD disease pathogenesis and the progress of vaccine studies. ChikvInt consists of 109 interactions and is available at www.chikvint.com.

A systematic approach to identify host targets and rapidly deliver broad-spectrum antivirals

Early vaccine development for coronavirus disease 2019 (COVID-19) was possible thanks to the prior knowledge that the main immunogenic protein of coronaviruses is the spike protein. Indeed, once the spike sequence of the initial severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant was determined, vaccines could be developed quickly. In parallel, if virus-host protein-protein interactions (PPIs) are mapped for newly emerging or re-emerging pathogens, key interacting domains can be predicted, and validation of such interactions can help deliver small-molecule inhibitors in a relatively short period of time.

Bioinformatics-Based Approaches to Study Virus-Host Interactions During SARS-CoV-2 Infection.

As the knowledge of biomolecules is increasing from the last decades, it is helping the researchers to understand the unsolved issues regarding virology. Recent technologies in high-throughput sequencing are providing the swift generation of SARS-CoV-2 genomic data with the basic inside of viral infection. Owing to various virus-host protein interactions, high-throughput technologies are unable to provide complete details of viral pathogenesis. Identifying the virus-host protein interactions using bioinformatics approaches can assist in understanding the mechanism of SARS-CoV-2 infection and pathogenesis. In this chapter, recent integrative bioinformatics approaches are discussed to help the virologists and computational biologists in the identification of structurally similar proteins of human and SARS-CoV-2 virus, and to predict the potential of virus-host interactions. Considering experimental and time limitations for effective viral drug development, computational aided drug design (CADD) can reduce the gap between drug prediction and development. More research with respect to evolutionary solutions could be helpful to make a new pipeline for virus-host protein-protein interactions and provide more understanding to disclose the cases of host switch, and also expand the virulence of the pathogen and host range in developing viral infections.

Molecular signatures of silencing suppression degeneracy from a complex RNA virus.

As genomic architectures become more complex, they begin to accumulate degenerate and redundant elements. However, analyses of the molecular mechanisms underlying these genetic architecture features remain scarce, especially in compact but sufficiently complex genomes. In the present study, we followed a proteomic approach together with a computational network analysis to reveal molecular signatures of protein function degeneracy from a plant virus (as virus-host protein-protein interactions). We employed affinity purification coupled to mass spectrometry to detect several host factors interacting with two proteins of Citrus tristeza virus (p20 and p25) that are known to function as RNA silencing suppressors, using an experimental system of transient expression in a model plant. The study was expanded by considering two different isolates of the virus, and some key interactions were confirmed by bimolecular fluorescence complementation assays. We found that p20 and p25 target a common set of plant proteins including chloroplastic proteins and translation factors. Moreover, we noted that even specific targets of each viral protein overlap in function. Notably, we identified argonaute proteins (key players in RNA silencing) as reliable targets of p20. Furthermore, we found that these viral proteins preferentially do not target hubs in the host protein interactome, but elements that can transfer information by bridging different parts of the interactome. Overall, our results demonstrate that two distinct proteins encoded in the same viral genome that overlap in function also overlap in their interactions with the cell proteome, thereby highlighting an overlooked connection from a degenerate viral system.

Alignment of virus-host protein-protein interaction networks by integer linear programming: SARS-CoV-2.

MotivationBeside socio-economic issues, coronavirus pandemic COVID-19, the infectious disease caused by the newly discovered coronavirus SARS-CoV-2, has caused a deep impact in the scientific community, that has considerably increased its effort to discover the infection strategies of the new virus. Among the extensive and crucial research that has been carried out in the last months, the analysis of the virus-host relationship plays an important role in drug discovery. Virus-host protein-protein interactions are the active agents in virus replication, and the analysis of virus-host protein-protein interaction networks is fundamental to the study of the virus-host relationship.ResultsWe have adapted and implemented a recent integer linear programming model for protein-protein interaction network alignment to virus-host networks, and obtained a consensus alignment of the SARS-CoV-1 and SARS-CoV-2 virus-host protein-protein interaction networks. Despite the lack of shared human proteins in these virus-host networks, and the low number of preserved virus-host interactions, the consensus alignment revealed aligned human proteins that share a function related to viral infection, as well as human proteins of high functional similarity that interact with SARS-CoV-1 and SARS-CoV-2 proteins, whose alignment would preserve these virus-host interactions.

Alignment of biological networks by integer linear programming: virus-host protein-protein interaction networks

BackgroundThe alignment of protein-protein interaction networks was recently formulated as an integer quadratic programming problem, along with a linearization that can be solved by integer linear programming software tools. However, the resulting integer linear program has a huge number of variables and constraints, rendering it of no practical use.ResultsWe present a compact integer linear programming reformulation of the protein-protein interaction network alignment problem, which can be solved using state-of-the-art mathematical modeling and integer linear programming software tools, along with empirical results showing that small biological networks, such as virus-host protein-protein interaction networks, can be aligned in a reasonable amount of time on a personal computer and the resulting alignments are structurally coherent and biologically meaningful.ConclusionsThe implementation of the integer linear programming reformulation using current mathematical modeling and integer linear programming software tools provided biologically meaningful alignments of virus-host protein-protein interaction networks.

Abstract IA03: Systems approaches reveal shared pathways affected in SARS-CoV-2 infection and cancer

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of the coronavirus disease 2019 (COVID-19) pandemic, has infected millions and killed hundreds of thousands of people worldwide. Clinicians are fighting the battle against the virus with a limited arsenal of drugs that have been shown to be safe and efficacious in treating SARS-CoV-2 infection. We thus rely on gaining a deeper understanding of the molecular mechanisms of this virus, and desperately need new strategies to get drugs to patients in need quickly. To combine both of these aims, my lab has focused much of our recent work on applying systems biology approaches to identify the cellular pathways hijacked by SARS-CoV-2, with the aim of pinpointing promising clinically available drugs for rapid repurposing to treat COVID-19. Identifying which cellular pathways and mechanisms are affected across different diseases will open the existing toolbox of drug treatment for COVID-19. Cancer drugs are a particularly promising set of drugs in this regard: During the process of virus replication, viruses rely on a multitude of interactions with their host cell, and hijack similar pathways that are affected in cancer cells. For example, viruses manipulate the cell cycle for their own benefit, recruit host DNA-damage machinery to viral replication sites, rely on the host translation machinery for viral protein production, and interfere with several signaling pathways, for example, to suppress cellular antiviral defenses. This list, while not comprehensive, makes apparent the commonalities between the exploitation of molecular mechanisms by cancer and virus infection. To get a full picture of cellular pathways targeted by SARS-CoV-2, we recently generated two virus-host interaction networks using systems biology approaches. First, we used affinity-purification mass spectrometry to create a virus-host protein-protein interaction (PPI) map. We cloned, tagged, and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins and complexes that physically associate with the individual viral proteins. Our map revealed 332 high-confidence PPIs with human proteins involved in a wide spectrum of cell biology, highlighting several oncogenic pathways. We identified 66 druggable human proteins at the virus-host interface, targeted by 69 compounds (of which 29 drugs are approved by the US Food and Drug Administration, 12 are in clinical trials, and 28 are preclinical compounds). We screened a subset of these in multiple viral assays and found that, for example, inhibitors of mRNA translation displayed antiviral activity. In a separate study building on these results, we created a quantitative mass spectrometry-based phosphoproteomics survey of SARS-CoV-2 infection in cell culture. Focusing on phosphorylation events, which are also highly misregulated in cancer, showed stark changes for both host and viral proteins and revealed dramatic rewiring of a number of signaling pathways. For example, SARS-CoV 2 infection promoted casein kinase II (CK2) and p38 MAP kinase activation and shutdown of mitotic kinases. We identified 87 drugs and compounds by mapping global phosphorylation profiles to dysregulated kinases and pathways. Pharmacologic inhibition of p38, CK2, CDKs, AXL, and PIKFYVE kinases possessed antiviral efficacy, representing potential COVID-19 therapies. Clinical trials with some drugs and compounds implicated by our studies are currently being discussed or are already under way. To prepare for future outbreaks, we need to further increase our knowledge of cellular pathways targeted during virus infection. To this end, in addition to a number of infectious agents my lab has studied previously, we are currently performing similar studies on the closely related coronaviruses responsible for outbreaks of SARS and Middle East respiratory syndrome (MERS) in 2003 and 2012, respectively. Combined with the results discussed above, we hope to position the health care community to successfully fight this and potential future outbreaks of novel infectious diseases. Importantly, the input of physicians and experts from other fields will be crucial to leverage the knowledge gained from our studies about the pathways targeted across diseases. Citation Format: Nevan Krogan. Systems approaches reveal shared pathways affected in SARS-CoV-2 infection and cancer [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr IA03.

Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis.

SummaryCOVID-19 (coronavirus disease 2019) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although the pathophysiology of this virus is complex and largely unknown, we employed a network-biology-fueled approach and integrated transcriptome data pertaining to lung epithelial cells with human interactome to generate Calu-3-specific human-SARS-CoV-2 interactome (CSI). Topological clustering and pathway enrichment analysis show that SARS-CoV-2 targets central nodes of the host-viral network, which participate in core functional pathways. Network centrality analyses discover 33 high-value SARS-CoV-2 targets, which are possibly involved in viral entry, proliferation, and survival to establish infection and facilitate disease progression. Our probabilistic modeling framework elucidates critical regulatory circuitry and molecular events pertinent to COVID-19, particularly the host-modifying responses and cytokine storm. Overall, our network-centric analyses reveal novel molecular components, uncover structural and functional modules, and provide molecular insights into the pathogenicity of SARS-CoV-2 that may help foster effective therapeutic design.

Temporal dynamics of protein complex formation and dissociation during human cytomegalovirus infection

The co-evolution and co-existence of viral pathogens with their hosts for millions of years is reflected in dynamic virus-host protein-protein interactions (PPIs) that are intrinsic to the spread of infections. Here, we investigate the system-wide dynamics of protein complexes throughout infection with the herpesvirus, human cytomegalovirus (HCMV). Integrating thermal shift assays and mass spectrometry quantification with virology and microscopy, we monitor the temporal formation and dissociation of hundreds of functional protein complexes and the dynamics of host-host, virus-host, and virus-virus PPIs. We establish pro-viral roles for cellular protein complexes and translocating proteins. We show the HCMV receptor integrin beta 1 dissociates from extracellular matrix proteins, becoming internalized with CD63, which is necessary for virus production. Moreover, this approach facilitates characterization of essential viral proteins, such as pUL52. This study of temporal protein complex dynamics provides insights into mechanisms of HCMV infection and a resource for biological and therapeutic studies.

SARS-CoV-2-human protein-protein interaction network.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the novel coronavirus which caused the coronavirus disease 2019 pandemic and infected more than 12 million victims and resulted in over 560,000 deaths in 213 countries around the world. Having no symptoms in the first week of infection increases the rate of spreading the virus. The increasing rate of the number of infected individuals and its high mortality necessitates an immediate development of proper diagnostic methods and effective treatments. SARS-CoV-2, similar to other viruses, needs to interact with the host proteins to reach the host cells and replicate its genome. Consequently, virus-host protein-protein interaction (PPI) identification could be useful in predicting the behavior of the virus and the design of antiviral drugs. Identification of virus-host PPIs using experimental approaches are very time consuming and expensive. Computational approaches could be acceptable alternatives for many preliminary investigations. In this study, we developed a new method to predict SARS-CoV-2-human PPIs. Our model is a three-layer network in which the first layer contains the most similar Alphainfluenzavirus proteins to SARS-CoV-2 proteins. The second layer contains protein-protein interactions between Alphainfluenzavirus proteins and human proteins. The last layer reveals protein-protein interactions between SARS-CoV-2 proteins and human proteins by using the clustering coefficient network property on the first two layers. To further analyze the results of our prediction network, we investigated human proteins targeted by SARS-CoV-2 proteins and reported the most central human proteins in human PPI network. Moreover, differentially expressed genes of previous researches were investigated and PPIs of SARS-CoV-2-human network, the human proteins of which were related to upregulated genes, were reported.

Preparing students for the data-driven life science era through a real-world viral infection case

ABSTRACT While computational biology and bioinformatics became an inherent part of most life science degrees, it remains challenging to encourage students to employ these diverse skills in a practical research context. In this education paper, we present a method to enable students to acquire hands-on skills by the data-driven study of complex biomolecular datasets. The course starts with a series of introductory lectures, including a practical session on Cytoscape and associated computational network biology techniques. As we believe that enabling students to work on their own research questions will increase their motivation and interest, we introduced a recent hallmark virus-host protein-protein interaction article from which the database served as a starting point for the students to develop their research goal, which was presented and discussed in a first informal meeting. Over the next two months, about four interactive sessions were held, where students presented their problems, progress and challenges and exchanged ideas to help each other reach their goals. The role of the lecturers was to assist the students with independently collecting data and exploring new software tools for their research question. We present this concept and methods, and we share our experiences from an evaluation of the student learning outcomes.

A dual controllability analysis of influenza virus-host protein-protein interaction networks for antiviral drug target discovery

BackgroundHost factors of influenza virus replication are often found in key topological positions within protein-protein interaction networks. This work explores how protein states can be manipulated through controllability analysis: the determination of the minimum manipulation needed to drive the cell system to any desired state. Here, we complete a two-part controllability analysis of two protein networks: a host network representing the healthy cell state and an influenza A virus-host network representing the infected cell state. In this context, controllability analyses aim to identify key regulating host factors of the infected cell’s progression. This knowledge can be utilized in further biological analysis to understand disease dynamics and isolate proteins for study as drug target candidates.ResultsBoth topological and controllability analyses provide evidence of wide-reaching network effects stemming from the addition of viral-host protein interactions. Virus interacting and driver host proteins are significant both topologically and in controllability, therefore playing important roles in cell behavior during infection. Functional analysis finds overlap of results with previous siRNA studies of host factors involved in influenza replication, NF-kB pathway and infection relevance, and roles as interferon regulating genes. 24 proteins are identified as holding regulatory roles specific to the infected cell by measures of topology, controllability, and functional role. These proteins are recommended for further study as potential antiviral drug targets.ConclusionsSeasonal outbreaks of influenza A virus are a major cause of illness and death around the world each year with a constant threat of pandemic infection. This research aims to increase the efficiency of antiviral drug target discovery using existing protein-protein interaction data and network analysis methods. These results are beneficial to future studies of influenza virus, both experimental and computational, and provide evidence that the combination of topology and controllability analyses may be valuable for future efforts in drug target discovery.

Targeting Virus-host Protein Interactions: Feature Extraction and Machine Learning Approaches.

Targeting critical viral-host Protein-Protein Interactions (PPIs) has enormous application prospects for therapeutics. Using experimental methods to evaluate all possible virus-host PPIs is labor-intensive and time-consuming. Recent growth in computational identification of virus-host PPIs provides new opportunities for gaining biological insights, including applications in disease control. We provide an overview of recent computational approaches for studying virus-host PPI interactions. In this review, a variety of computational methods for virus-host PPIs prediction have been surveyed. These methods are categorized based on the features they utilize and different machine learning algorithms including classical and novel methods. We describe the pivotal and representative features extracted from relevant sources of biological data, mainly include sequence signatures, known domain interactions, protein motifs and protein structure information. We focus on state-of-the-art machine learning algorithms that are used to build binary prediction models for the classification of virus-host protein pairs and discuss their abilities, weakness and future directions. The findings of this review confirm the importance of computational methods for finding the potential protein-protein interactions between virus and host. Although there has been significant progress in the prediction of virus-host PPIs in recent years, there is a lot of room for improvement in virus-host PPI prediction.

Comparative Flavivirus-Host Protein Interaction Mapping Reveals Mechanisms of Dengue and Zika Virus Pathogenesis

Mosquito-borne flaviviruses, including dengue virus (DENV) and Zika virus (ZIKV), are a growing public health concern. Systems-level analysis of how flaviviruses hijack cellular processes through virus-host protein-protein interactions (PPIs) provides information about their replication and pathogenic mechanisms. We used affinity purification-mass spectrometry (AP-MS) to compare flavivirus-host interactions for two viruses (DENV and ZIKV) in two hosts (human and mosquito). Conserved virus-host PPIs revealed that the flavivirus NS5 protein suppresses interferon stimulated genes by inhibiting recruitment of the transcription complex PAF1C and that chemical modulation of SEC61 inhibits DENV and ZIKV replication in human and mosquito cells. Finally, we identified a ZIKV-specific interaction between NS4A and ANKLE2, a gene linked to hereditary microcephaly, and showed that ZIKV NS4A causes microcephaly in Drosophila in an ANKLE2-dependent manner. Thus, comparative flavivirus-host PPI mapping provides biological insights and, when coupled with invivo models, can be used to unravel pathogenic mechanisms.

Prediction of virus-host protein-protein interactions mediated by short linear motifs

BackgroundShort linear motifs in host organisms proteins can be mimicked by viruses to create protein-protein interactions that disable or control metabolic pathways. Given that viral linear motif instances of host motif regular expressions can be found by chance, it is necessary to develop filtering methods of functional linear motifs. We conduct a systematic comparison of linear motifs filtering methods to develop a computational approach for predicting motif-mediated protein-protein interactions between human and the human immunodeficiency virus 1 (HIV-1).ResultsWe implemented three filtering methods to obtain linear motif sets: 1) conserved in viral proteins (C), 2) located in disordered regions (D) and 3) rare or scarce in a set of randomized viral sequences (R). The sets C,D,R are united and intersected. The resulting sets are compared by the number of protein-protein interactions correctly inferred with them – with experimental validation. The comparison is done with HIV-1 sequences and interactions from the National Institute of Allergy and Infectious Diseases (NIAID).The number of correctly inferred interactions allows to rank the interactions by the sets used to deduce them: D∪R and C. The ordering of the sets is descending on the probability of capturing functional interactions.With respect to HIV-1, the sets C∪R, D∪R, C∪D∪R infer all known interactions between HIV1 and human proteins mediated by linear motifs. We found that the majority of conserved linear motifs in the virus are located in disordered regions.ConclusionWe have developed a method for predicting protein-protein interactions mediated by linear motifs between HIV-1 and human proteins. The method only use protein sequences as inputs. We can extend the software developed to any other eukaryotic virus and host in order to find and rank candidate interactions. In future works we will use it to explore possible viral attack mechanisms based on linear motif mimicry.

A Docking Based Approach to Analyze Interaction Surfaces of Virus-Host Protein-Protein Interactions

Core elements of cell regulation are made up of protein-protein interaction (PPI) networks. Predicting relevant interacting partners from their tertiary structure is a challenging topic where computer science methods have potential to contribute to biology. Protein-protein rigid docking has been applied for this purpose by several projects. Docking-based approaches have advantages such that (i) they can suggest binding poses of predicted binding partners that would help understanding the interaction mechanisms, (ii) comparing docking results of both non-binders and binders can lead to understanding the specificity of PPI from structural viewpoints. However, the prediction power is limited mainly because of poor correlation between docking score and actual protein-protein binding affinity. To improve state of the art we propose a machine learning approach to improve docking-based PPI predictions by using residue profiles obtained by rigid docking. A possible application of our method is a problem in which we have one ‘receptor’ protein and pickup proteins that have potential to interact with it from a pool of candidate ‘ligand’ proteins. We define an interface fingerprint of a pair of proteins as a collection of residues of a ‘receptor’ protein, with information of how often each residue are included in binding sites of high scoring docking models. We applied multiple methods of machine learning to discriminate binders and non-binders using those profiles as input. In this poster we present an evaluation of our method by applying it to proteins of Protein Docking Benchmark ver. 5.0, bacterial chemotaxis, and host-virus protein interactions.We further applied our method to analyze proposed host-virus PPI interaction surfaces by post-docking analysis to compare surfaces of PPIs between the host proteins.

A complete review of computational methods for human and HIV-1 protein interaction prediction

Human Immunodeficiency Virus Type 1 HIV-1 has grabbed the attention of virologists in recent times owing to its life-threatening nature and epidemic spread throughout the globe. The virus exploits a complex interaction network of HIV-1 and human proteins for replication, and causes destruction to the human immunity power. Antiviral drugs are designed to utilise the information on viral-host Protein-Protein Interactions PPIs, so that the viral replication and infection can be prevented. Therefore, the prediction of novel interactions based on experimentally validated interactions, curated in the public PPI database, could help in discovering new therapeutic targets. This article gives an overview of HIV-1 proteins and their role in virus-replication followed by a discussion on different types of antiretroviral drugs and HIV-1-human PPI database. Thereafter, we have presented a brief explanation of different computational approaches adopted to predict new HIV-1-human PPIs along with a comparative study among them.

Prediction of interactions between viral and host proteins using supervised machine learning methods.

BackgroundViral-host protein-protein interaction plays a vital role in pathogenesis, since it defines viral infection of the host and regulation of the host proteins. Identification of key viral-host protein-protein interactions (PPIs) has great implication for therapeutics.MethodsIn this study, a systematic attempt has been made to predict viral-host PPIs by integrating different features, including domain-domain association, network topology and sequence information using viral-host PPIs from VirusMINT. The three well-known supervised machine learning methods, such as SVM, Naïve Bayes and Random Forest, which are commonly used in the prediction of PPIs, were employed to evaluate the performance measure based on five-fold cross validation techniques.ResultsOut of 44 descriptors, best features were found to be domain-domain association and methionine, serine and valine amino acid composition of viral proteins. In this study, SVM-based method achieved better sensitivity of 67% over Naïve Bayes (37.49%) and Random Forest (55.66%). However the specificity of Naïve Bayes was the highest (99.52%) as compared with SVM (74%) and Random Forest (89.08%). Overall, the SVM and Random Forest achieved accuracy of 71% and 72.41%, respectively. The proposed SVM-based method was evaluated on blind dataset and attained a sensitivity of 64%, specificity of 83%, and accuracy of 74%. In addition, unknown potential targets of hepatitis B virus-human and hepatitis E virus-human PPIs have been predicted through proposed SVM model and validated by gene ontology enrichment analysis. Our proposed model shows that, hepatitis B virus “C protein” binds to membrane docking protein, while “X protein” and “P protein” interacts with cell-killing and metabolic process proteins, respectively.ConclusionThe proposed method can predict large scale interspecies viral-human PPIs. The nature and function of unknown viral proteins (HBV and HEV), interacting partners of host protein were identified using optimised SVM model.

Exploration of Binary Virus–Host Interactions Using an Infectious Protein Complementation Assay

A precise mapping of pathogen-host interactions is essential for comprehensive understanding of the processes of infection and pathogenesis. The most frequently used techniques for interactomics are the yeast two-hybrid binary methodologies, which do not recapitulate the pathogen life cycle, and the tandem affinity purification mass spectrometry co-complex methodologies, which cannot distinguish direct from indirect interactions. New technologies are thus needed to improve the mapping of pathogen-host interactions. In the current study, we detected binary interactions between influenza A virus polymerase and host proteins during the course of an actual viral infection, using a new strategy based on trans-complementation of the Gluc1 and Gluc2 fragments of Gaussia princeps luciferase. Infectious recombinant influenza viruses that encode a Gluc1-tagged polymerase subunit were engineered to infect cultured cells transiently expressing a selected set of Gluc2-tagged cellular proteins involved in nucleocytoplasmic trafficking pathways. A random set and a literature-curated set of Gluc2-tagged cellular proteins were tested in parallel. Our assay allowed the sensitive and accurate recovery of previously described interactions, and it revealed 30% of positive, novel viral-host protein-protein interactions within the exploratory set. In addition to cellular proteins involved in the nuclear import pathway, components of the nuclear pore complex such as NUP62 and mRNA export factors such as NXF1, RMB15B, and DDX19B were identified for the first time as interactors of the viral polymerase. Gene silencing experiments further showed that NUP62 is required for efficient viral replication. Our findings give new insights regarding the subversion of host nucleocytoplasmic trafficking pathways by influenza A viruses. They also demonstrate the potential of our infectious protein complementation assay for high-throughput exploration of influenza virus interactomics in infected cells. With more infectious reverse genetics systems becoming available, this strategy should be widely applicable to numerous pathogens.

Identification and Targeting of an Interaction between a Tyrosine Motif within Hepatitis C Virus Core Protein and AP2M1 Essential for Viral Assembly

Novel therapies are urgently needed against hepatitis C virus infection (HCV), a major global health problem. The current model of infectious virus production suggests that HCV virions are assembled on or near the surface of lipid droplets, acquire their envelope at the ER, and egress through the secretory pathway. The mechanisms of HCV assembly and particularly the role of viral-host protein-protein interactions in mediating this process are, however, poorly understood. We identified a conserved heretofore unrecognized YXXΦ motif (Φ is a bulky hydrophobic residue) within the core protein. This motif is homologous to sorting signals within host cargo proteins known to mediate binding of AP2M1, the μ subunit of clathrin adaptor protein complex 2 (AP-2), and intracellular trafficking. Using microfluidics affinity analysis, protein-fragment complementation assays, and co-immunoprecipitations in infected cells, we show that this motif mediates core binding to AP2M1. YXXΦ mutations, silencing AP2M1 expression or overexpressing a dominant negative AP2M1 mutant had no effect on HCV RNA replication, however, they dramatically inhibited intra- and extracellular infectivity, consistent with a defect in viral assembly. Quantitative confocal immunofluorescence analysis revealed that core's YXXΦ motif mediates recruitment of AP2M1 to lipid droplets and that the observed defect in HCV assembly following disruption of core-AP2M1 binding correlates with accumulation of core on lipid droplets, reduced core colocalization with E2 and reduced core localization to trans-Golgi network (TGN), the presumed site of viral particles maturation. Furthermore, AAK1 and GAK, serine/threonine kinases known to stimulate binding of AP2M1 to host cargo proteins, regulate core-AP2M1 binding and are essential for HCV assembly. Last, approved anti-cancer drugs that inhibit AAK1 or GAK not only disrupt core-AP2M1 binding, but also significantly inhibit HCV assembly and infectious virus production. These results validate viral-host interactions essential for HCV assembly and yield compounds for pharmaceutical development.