Virus-host Protein-protein Interactions Research Articles

Applying Reverse Genetics to Study Measles Virus Interactions with the Host.

The study of virus-host interactions is essential to achieve a comprehensive understanding of the viral replication process. The commonly used methods are yeast two-hybrid approach and transient expression of a single tagged viral protein in host cells followed by affinity purification of interacting cellular proteins and mass spectrometry analysis (AP-MS). However, by these approaches, virus-host protein-protein interactions are detected in the absence of a real infection, not always correctly compartmentalized, and for the yeast two-hybrid approach performed in a heterologous system. Thus, some of the detected protein-protein interactions may be artificial. Here we describe a new strategy based on recombinant viruses expressing tagged viral proteins to capture both direct and indirect protein partners during the infection (AP-MS in viral context). This way, virus-host protein-protein interacting co-complexes can be purified directly from infected cells for further characterization.

Identification of RVFV Host Factors Using Quantitative Interaction Proteomics.

Affinity enrichment coupled with liquid chromatography-tandem mass spectrometry (AE-LC-MS/MS) enables a comprehensive study of virus-host protein-protein interactions in cells and tissues infected with Rift Valley fever virus (RVFV) or ectopically expressing RVFV proteins. Depending on the research question, different experimental setups with carefully chosen controls are needed. Here, we describe the detailed workflow of sample preparation, processing, and cleanup, while also outlining critical points to consider when designing and performing AE-LC-MS/MS experiments.

The REEP5/TRAM1 complex binds SARS-CoV-2 NSP3 and promotes virus replication.

Generation of virus-host protein-protein interactions (PPIs) maps may provide clues to uncover SARS-CoV-2-hijacked cellular processes. However, these PPIs maps were created by expressing each viral protein singularly, which does not reflect the life situation in which certain viral proteins synergistically interact with host proteins. Our results reveal the host-viral protein-protein interactome of SARS-CoV-2 NSP3, NSP4, and NSP6 expressed individually or in combination. Furthermore, REEP5/TRAM1 complex interacts with NSP3 at ROs and promotes viral replication. The significance of our research is identifying virus-host interactions that may be targeted for therapeutic intervention.

SARS-CoV-2 variants evolve convergent strategies to remodel the host response

SARS-CoV-2 variants of concern (VOCs) emerged during the COVID-19 pandemic. Here, we used unbiased systems approaches to study the host-selective forces driving VOC evolution. We discovered that VOCs evolved convergent strategies to remodel the host by modulating viral RNA and protein levels, altering viral and host protein phosphorylation, and rewiring virus-host protein-protein interactions. Integrative computational analyses revealed that although Alpha, Beta, Gamma, and Delta ultimately converged to suppress interferon-stimulated genes (ISGs), Omicron BA.1 did not. ISG suppression correlated with the expression of viral innate immune antagonist proteins, including Orf6, N, and Orf9b, which we mapped to specific mutations. Later Omicron subvariants BA.4 and BA.5 more potently suppressed innate immunity than early subvariant BA.1, which correlated with Orf6 levels, although muted in BA.4 by a mutation that disrupts the Orf6-nuclear pore interaction. Our findings suggest that SARS-CoV-2 convergent evolution overcame human adaptive and innate immune barriers, laying the groundwork to tackle future pandemics.

Emergent information dynamics in many-body interconnected systems.

The information implicitly represented in the state of physical systems allows for their analysis using analytical techniques from statistical mechanics and information theory. This approach has been successfully applied to complex networks, including biophysical systems such as virus-host protein-protein interactions and whole-brain models in health and disease, drawing inspiration from quantum statistical physics. Here we propose a general mathematical framework for modeling information dynamics on complex networks, where the internal node states are vector valued, allowing each node to carry multiple types of information. This setup is relevant for various biophysical and sociotechnological models of complex systems, ranging from viral dynamics on networks to models of opinion dynamics and social contagion. Instead of focusing on node-node interactions, we shift our attention to the flow of information between network configurations. We uncover fundamental differences between widely used spin models on networks, such as voter and kinetic dynamics, which cannot be detected through classical node-based analysis. We illustrate the mathematical framework further through an exemplary application to epidemic spreading on a low-dimensional network. Our model provides an opportunity to adapt powerful analytical methods from quantum many-body systems to study the interplay between structure and dynamics in interconnected systems.

Functional Landscape of African Swine Fever Virus-Host and Virus-Virus Protein Interactions.

Viral replication fully relies on the host cell machinery, and physical interactions between viral and host proteins mediate key steps of the viral life cycle. Therefore, identifying virus-host protein-protein interactions (PPIs) provides insights into the molecular mechanisms governing virus infection and is crucial for designing novel antiviral strategies. In the case of the African swine fever virus (ASFV), a large DNA virus that causes a deadly panzootic disease in pigs, the limited understanding of host and viral targets hinders the development of effective vaccines and treatments. This review summarizes the current knowledge of virus-host and virus-virus PPIs by collecting and analyzing studies of individual viral proteins. We have compiled a dataset of experimentally determined host and virus protein targets, the molecular mechanisms involved, and the biological functions of the identified virus-host and virus-virus protein interactions during infection. Ultimately, this work provides a comprehensive and systematic overview of ASFV interactome, identifies knowledge gaps, and proposes future research directions.

Elucidating cellular interactome of chikungunya virus identifies host dependency factors

Chikungunya virus (CHIKV) is a re-emerging mosquito-transmitted RNA virus causing joint and muscle pain. To better understand how CHIKV rewires the host cell and usurps host cell functions, we generated a systematic CHIKV-human protein-protein interaction map and revealed several novel connections that will inform further mechanistic studies. One of these novel interactions, between the viral protein E1 and STIP1 homology and U-box containing protein 1 (STUB1), was found to mediate ubiquitination of E1 and degrade E1 through the proteasome. Capsid associated with G3BP1, G3BP2 and AAA+ ​ATPase valosin-containing protein (VCP). Furthermore, VCP inhibitors blocked CHIKV infection, suggesting VCP could serve as a therapeutic target. Further work is required to fully understand the functional consequences of these interactions. Given that CHIKV proteins are conserved across alphaviruses, many virus-host protein-protein interactions identified in this study might also exist in other alphaviruses. Construction of interactome of CHIKV provides the basis for further studying the function of alphavirus biology.

A correlation coefficient-based feature selection approach for virus-host protein-protein interaction prediction.

Prediction of virus-host protein-protein interactions (PPI) is a broad research area where various machine-learning-based classifiers are developed. Transforming biological data into machine-usable features is a preliminary step in constructing these virus-host PPI prediction tools. In this study, we have adopted a virus-host PPI dataset and a reduced amino acids alphabet to create tripeptide features and introduced a correlation coefficient-based feature selection. We applied feature selection across several correlation coefficient metrics and statistically tested their relevance in a structural context. We compared the performance of feature-selection models against that of the baseline virus-host PPI prediction models created using different classification algorithms without the feature selection. We also tested the performance of these baseline models against the previously available tools to ensure their predictive power is acceptable. Here, the Pearson coefficient provides the best performance with respect to the baseline model as measured by AUPR; a drop of 0.003 in AUPR while achieving a 73.3% (from 686 to 183) reduction in the number of tripeptides features for random forest. The results suggest our correlation coefficient-based feature selection approach, while decreasing the computation time and space complexity, has a limited impact on the prediction performance of virus-host PPI prediction tools.

In-Silico Study of Virus-Host Protein-Protein Interactions (PPIs) can Anticipate and Cure Viruses by Reducing Time-Duration of Vaccine Trials: A Review

COVID-19, a pandemic, has slowed global growth. This virus' fury and fast spread necessitated worldwide drug repurposing experiments. SARS-CoV-2 surface glycoprotein triggers host innate immunological and inflammatory responses. In-silico research characterised 6VSB B as more virulent than 6VSB A and important in pathophysiology. The worldwide assessment of SARS-CoV-2 virus sequences includes analysis from hundreds of high-coverage, high-quality genomes. Protein structure prediction predicts protein three-dimensional structures using amino acid sequences. SARS-CoV-2 was examined for isoelectric point, stability, GRAVY, amino acid, and atomic composition. Protein extinction coefficients quantify light absorption at a wavelength. Sequence data can compute protein molar extinction coefficients. The present review supports in-silico investigations using an immunological and molecular biology knowledge database to create novel vaccines and determine therapeutic targets when repurposing an existing drug.

Machine Learning Methods for Virus-Host Protein-Protein Interaction Prediction.

The attachment of a virion to a respective cellular receptor on the host organism occurring through the virus-host protein-protein interactions (PPIs) is a decisive step for viral pathogenicity and infectivity. Therefore, a vast number of wet-lab experimental techniques are used to study virus-host PPIs. Taking the great number and enormous variety of virus-host PPIs and the cost as well as labor of laboratory work, however, computational approaches toward analyzing the available interaction data and predicting previously unidentified interactions have been on the rise. Among them, machine-learning-based models are getting increasingly more attention with a great body of resources and tools proposed recently.In this chapter, we first provide the methodology with major steps toward the development of a virus-host PPI prediction tool. Next, we discuss the challenges involved and evaluate several existing machine-learning-based virus-host PPI prediction tools. Finally, we describe our experience with several ensemble techniques as utilized on available prediction results retrieved from individual PPI prediction tools. Overall, based on our experience, we recognize there is still room for the development of new individual and/or ensemble virus-host PPI prediction tools that leverage existing tools.

Systems Bioinformatics Reveals Possible Relationship between COVID-19 and the Development of Neurological Diseases and Neuropsychiatric Disorders.

Coronavirus Disease 2019 (COVID-19) is associated with increased incidence of neurological diseases and neuropsychiatric disorders after infection, but how it contributes to their development remains under investigation. Here, we investigate the possible relationship between COVID-19 and the development of ten neurological disorders and three neuropsychiatric disorders by exploring two pathological mechanisms: (i) dysregulation of host biological processes via virus–host protein–protein interactions (PPIs), and (ii) autoreactivity of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epitopes with host “self” proteins via molecular mimicry. We also identify potential genetic risk factors which in combination with SARS-CoV-2 infection might lead to disease development. Our analysis indicated that neurodegenerative diseases (NDs) have a higher number of disease-associated biological processes that can be modulated by SARS-CoV-2 via virus–host PPIs than neuropsychiatric disorders. The sequence similarity analysis indicated the presence of several matching 5-mer and/or 6-mer linear motifs between SARS-CoV-2 epitopes with autoreactive epitopes found in Alzheimer’s Disease (AD), Parkinson’s Disease (PD), Myasthenia Gravis (MG) and Multiple Sclerosis (MS). The results include autoreactive epitopes that recognize amyloid-beta precursor protein (APP), microtubule-associated protein tau (MAPT), acetylcholine receptors, glial fibrillary acidic protein (GFAP), neurofilament light polypeptide (NfL) and major myelin proteins. Altogether, our results suggest that there might be an increased risk for the development of NDs after COVID-19 both via autoreactivity and virus–host PPIs.

A comprehensive SARS-CoV-2-human protein-protein interactome reveals COVID-19 pathobiology and potential host therapeutic targets.

Studying viral-host protein-protein interactions can facilitate the discovery of therapies for viral infection. We use high-throughput yeast two-hybrid experiments and mass spectrometry to generate a comprehensive SARS-CoV-2-human protein-protein interactome network consisting of 739 high-confidence binary and co-complex interactions, validating 218 known SARS-CoV-2 host factors and revealing 361 novel ones. Our results show the highest overlap of interaction partners between published datasets and of genes differentially expressed in samples from COVID-19 patients. We identify an interaction between the viral protein ORF3a and the human transcription factor ZNF579, illustrating a direct viral impact on host transcription. We perform network-based screens of >2,900 FDA-approved or investigational drugs and identify 23 with significant network proximity to SARS-CoV-2 host factors. One of these drugs, carvedilol, shows clinical benefits for COVID-19 patients in an electronic health records analysis and antiviral properties in a human lung cell line infected with SARS-CoV-2. Our study demonstrates the value of network systems biology to understand human-virus interactions and provides hits for further research on COVID-19 therapeutics.

Arbovirus-vector protein interactomics identifies Loquacious as a co-factor for dengue virus replication in Aedes mosquitoes.

Efficient virus replication in Aedes vector mosquitoes is essential for the transmission of arboviral diseases such as dengue virus (DENV) in human populations. Like in vertebrates, virus-host protein-protein interactions are essential for viral replication and immune evasion in the mosquito vector. Here, 79 mosquito host proteins interacting with DENV non-structural proteins NS1 and NS5 were identified by label-free mass spectrometry, followed by a functional screening. We confirmed interactions with host factors previously observed in mammals, such as the oligosaccharyltransferase complex, and we identified protein-protein interactions that seem to be specific for mosquitoes. Among the interactors, the double-stranded RNA (dsRNA) binding protein Loquacious (Loqs), an RNA interference (RNAi) cofactor, was found to be essential for efficient replication of DENV and Zika virus (ZIKV) in mosquito cells. Loqs did not affect viral RNA stability or translation of a DENV replicon and its proviral activity was independent of its RNAi regulatory activity. Interestingly, Loqs colocalized with DENV dsRNA replication intermediates in infected cells and directly interacted with high affinity with DENV RNA in the 3' untranslated region in vitro (KD = 48-62 nM). Our study provides an interactome for DENV NS1 and NS5 and identifies Loqs as a key proviral host factor in mosquitoes. We propose that DENV hijacks a factor of the RNAi mechanism for replication of its own RNA.

Virus-host protein-protein interactions as molecular drug targets for arboviral infections

Arboviruses have the potential to spread quickly and cause a global health emergency. These are RNA viruses that use RNA-dependent RNA polymerase (RdRp) for their replication. RdRp lacks proofreading activity, leading to high error rates, low replicative fidelity, and more genetic variability. In addition, shorter generation time and faster evolutionary rate of these viruses lead to re-emergence and recurrence of arboviral infections due to the emergence of new variants and the development of antiviral resistance. During the replication inside the host cell through protein-protein interactions (PPIs), these viruses interact with several host factors and utilize the host cellular machinery for their benefit. Besides this, viruses employ several transmission strategies to combat host innate and adaptive immune responses by manipulating the signaling and metabolic pathways of the hosts. Hence, antiviral therapies targeting host-virus PPIs can provide an alternative broad-spectrum strategy against RNA viruses. The approach of targeting virus-specific proteins for developing antivirals is expected to solve the problem of antiviral drug resistance and combat emerging new variants of these viruses. This review focuses on host-virus PPIs of arboviral infections that directly affect the host immune signaling and metabolic pathways. Better understanding of these mechanisms will develop new therapeutic tools to treat viral infections.

Accurate prediction of virus-host protein-protein interactions via a Siamese neural network using deep protein sequence embeddings

SummaryPrediction and understanding of virus-host protein-protein interactions (PPIs) have relevance for the development of novel therapeutic interventions. In addition, virus-like particles open novel opportunities to deliver therapeutics to targeted cell types and tissues. Given our incomplete knowledge of PPIs on the one hand and the cost and time associated with experimental procedures on the other, we here propose a deep learning approach to predict virus-host PPIs. Our method (Siamese Tailored deep sequence Embedding of Proteins [STEP]) is based on recent deep protein sequence embedding techniques, which we integrate into a Siamese neural network. After showing the state-of-the-art performance of STEP on external datasets, we apply it to two use cases, severe acute respiratory syndrome coronavirus 2 and John Cunningham polyomavirus, to predict virus-host PPIs. Altogether our work highlights the potential of deep sequence embedding techniques originating from the field of NLP as well as explainable artificial intelligence methods for the analysis of biological sequences.

Proteomic and Phosphoproteomic Analyses Reveal Gibel Carp Responses to Cyprinid Herpesvirus 2 Infection.

Cyprinid herpesvirus 2 (CyHV-2) is a typical linear double-stranded DNA virus, which can induce severe herpesviral hematopoietic necrosis disease (HVHND) in gibel carp. However, the CyHV-2 infection mechanisms still remain unresolved till now. Here, we combined the isobaric tag for relative absolute quantitation (iTRAQ)-labeled quantitative proteomic and phosphoproteomic analysis enriched by Ti4+-immobilized titanium ion affinity chromatography (IMAC) to uncover the host responses to CyHV-2 infection in the kidneys of symptomatic and diseased gibel carp. We totally identified 192 differential expression proteins and 951 high-confident phosphopeptides involved in 657 proteins. After being infected with CyHV-2, the proteins involved in energy generation and ion balance were significantly downregulated in the host, and the phosphorylated proteins induced by viral infection mainly participated in the regulation for RNA processing, translation, cytoskeleton organization, immunoreaction, etc. Furthermore, 11 phosphorylated CyHV-2 viral proteins were found in the diseased group by the host proteome. The virus-host protein-protein interactions were investigated, in which the potential host kinases casein kinase II (CK-II) and cyclin-dependent kinase (CDK) that interacted with viral ORF88 or ORF89 were identified and can serve as candidate targets for disease treatment in the future. Overall, our study provides a comprehensive understanding of CyHV-2-induced perturbations at the protein and phosphorylation levels in gibel carp, forming a base for the treatment of HVHND.

The Intricacy of the Viral-Human Protein Interaction Networks: Resources, Data, and Analyses.

Viral infections are one of the major causes of human diseases that cause yearly millions of deaths and seriously threaten global health, as we have experienced with the COVID-19 pandemic. Numerous approaches have been adopted to understand viral diseases and develop pharmacological treatments. Among them, the study of virus-host protein-protein interactions is a powerful strategy to comprehend the molecular mechanisms employed by the virus to infect the host cells and to interact with their components. Experimental protein-protein interactions described in the scientific literature have been systematically captured into several molecular interaction databases. These data are organized in structured formats and can be easily downloaded by users to perform further bioinformatic and network studies. Network analysis of available virus-host interactomes allow us to understand how the host interactome is perturbed upon viral infection and what are the key host proteins targeted by the virus and the main cellular pathways that are subverted. In this review, we give an overview of publicly available viral-human protein-protein interactions resources and the community standards, curation rules and adopted ontologies. A description of the main virus-human interactome available is provided, together with the main network analyses that have been performed. We finally discuss the main limitations and future challenges to assess the quality and reliability of protein-protein interaction datasets and resources.

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.