Prediction of GCRV virus-host protein interactome based on structural motif-domain interactions

How different viruses perturb host cellular machinery via short linear motifs

The virus interacts with its hosts by developing protein-protein interactions. Most viruses employ protein interactions to imitate the host protein: A viral protein with the same amino acid sequence or structure as the host protein attaches to the host protein's binding partner and interferes with the host protein's pathways. Being opportunistic, viruses have evolved to manipulate host cellular mechanisms by mimicking short linear motifs. In this review, we shed light on the current understanding of mimicry via short linear motifs and focus on viral mimicry by genetically different viral subtypes by providing recent examples of mimicry evidence and how high-throughput methods can be a reliable source to study SLiM-mediated viral mimicry.

VP4/VP56/VP35 Virus-like Particles Effectively Protect Grass Carp (Ctenopharyngodon idella) against GCRV-II Infection

Grass carp reovirus (GCRV) seriously threatens the grass carp (Ctenopharyngodon idella) industry. Prophylactic GCRV vaccines prepared by virus-like particle (VLP) assembly biotechnology can improve effectiveness and safety. The highly immunogenic candidate antigens of GCRV vaccines that have been generally considered are the outer capsid proteins VP4, VP56, and VP35. In this study, VP4, VP56, and VP35 were expressed in an Escherichia coli expression system and a Pichia pastoris expression system. The successful assembly of uniform, stable, and non-toxic VP4/VP56/VP35 VLPs was confirmed through various assays. After vaccination and GCRV infection, the survival rate in the VLPs + adjuvant Astragalus polysaccharide (APS) group was the highest (62%), 40% higher than that in control group (22%). Through the antibody levels, tissue viral load, and antioxidant immunity assays, the P. pastoris VLP vaccine effectively improved IgM levels, alleviated tissue virus load, and regulated antioxidant immune-related indicators. The treatment with P. pastoris VLPs enhanced the mRNA expression of important immune-related genes in the head kidney, as measured by qRT-PCR assay. Upon hematoxylin-eosin staining examination, relatively reduced tissue pathological damage was observed in the VLPs + APS group. The novel vaccine using P. pastoris VLPs as an effective green biological agent provides a prospective strategy for the control of fish viral diseases.

Emvirus: An embedding-based neural framework for human-virus protein-protein interactions prediction

Human-virus protein-protein interactions (PPIs) play critical roles in viral infection. For example, the spike protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) binds primarily to human angiotensin-converting enzyme 2 (ACE2) protein to infect human cells. Thus, identifying and blocking these PPIs contribute to controlling and preventing viruses. However, wet-lab experiment-based identification of human-virus PPIs is usually expensive, labor-intensive, and time-consuming, which presents the need for computational methods. Many machine-learning methods have been proposed recently and achieved good results in predicting human-virus PPIs. However, most methods are based on protein sequence features and apply manually extracted features, such as statistical characteristics, phylogenetic profiles, and physicochemical properties. In this work, we present an embedding-based neural framework with convolutional neural network (CNN) and bi-directional long short-term memory unit (Bi-LSTM) architecture, named Emvirus, to predict human-virus PPIs (including human-SARS-CoV-2 PPIs). In addition, we conduct cross-viral experiments to explore the generalization ability of Emvirus. Compared to other feature extraction methods, Emvirus achieves better prediction accuracy.

Determining human-coronavirus protein-protein interaction using machine intelligence

The Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2) virus spread the novel CoronaVirus −19 (nCoV-19) pandemic, resulting in millions of fatalities globally. Recent research demonstrated that the Protein-Protein Interaction (PPI) between SARS-CoV-2 and human proteins is accountable for viral pathogenesis. However, many of these PPIs are poorly understood and unexplored, necessitating a more in-depth investigation to find latent yet critical interactions. This article elucidates the host-viral PPI through Machine Learning (ML) lenses and validates the biological significance of the same using web-based tools. ML classifiers are designed based on comprehensive datasets with five sequence-based features of human proteins, namely Amino Acid Composition, Pseudo Amino Acid Composition, Conjoint Triad, Dipeptide Composition, and Normalized Auto Correlation. A majority voting rule-based ensemble method composed of the Random Forest Model (RFM), AdaBoost, and Bagging technique is proposed that delivers encouraging statistical performance compared to other models employed in this work. The proposed ensemble model predicted a total of 111 possible SARS-CoV-2 human target proteins with a high likelihood factor ≥70%, validated by utilizing Gene Ontology (GO) and KEGG pathway enrichment analysis. Consequently, this research can aid in a deeper understanding of the molecular mechanisms underlying viral pathogenesis and provide clues for developing more efficient anti-COVID medications.

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.

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.

Deep Learning-Powered Prediction of Human-Virus Protein-Protein Interactions

Identifying human-virus protein-protein interactions (PPIs) is an essential step for understanding viral infection mechanisms and antiviral response of the human host. Recent advances in high-throughput experimental techniques enable the significant accumulation of human-virus PPI data, which have further fueled the development of machine learning-based human-virus PPI prediction methods. Emerging as a very promising method to predict human-virus PPIs, deep learning shows the powerful ability to integrate large-scale datasets, learn complex sequence-structure relationships of proteins and convert the learned patterns into final prediction models with high accuracy. Focusing on the recent progresses of deep learning-powered human-virus PPI predictions, we review technical details of these newly developed methods, including dataset preparation, deep learning architectures, feature engineering, and performance assessment. Moreover, we discuss the current challenges and potential solutions and provide future perspectives of human-virus PPI prediction in the coming post-AlphaFold2 era.

Engagement of gcFKBP5/TRAF2 by spring viremia of carp virus to promote host cell apoptosis for supporting viral replication in grass carp

Spring viremia of carp virus (SVCV) causes severe morbidity and mortality in grass carp (Ctenopharyngodon idellus) in Europe, America and several Asian countries. We found that FKBP5 (FK506-binding protein 5) is an SVCV infection response factor; however, its role in the innate immune mechanism caused by SVCV infection remains unknown. This study cloned gcFKBP5 (grass carp FKBP5) and made its mimic protein structure for function discussion. We found that gcFKBP5 expression in the primary innate immune organs of grass carp, including intestine, liver and spleen, was highly upregulated by SVCV in 24 h, with a similar result in fish cells by poly(I:C) treatment. gcFKBP overexpression aggravates viral damage to cells and increases viral replication. Furthermore, SVCV engages gcFKBP5 interacting with TRAF2 (tumour necrosis factor receptor-associated factor 2) to promote host cell apoptosis for supporting viral replication. The enhanced viral replication seems not to be due to the repression of IFN and other antiviral factors as expected. For the first time, these data show the pivotal role of gcFKBP5 in the innate immune response of grass carp to SVCV infection.

Grass Carp Reovirus VP56 Allies VP4, Recruits, Blocks, and Degrades RIG-I to More Effectively Attenuate IFN Responses and Facilitate Viral Evasion

Grass carp reovirus (GCRV), the most virulent aquareovirus, causes epidemic hemorrhagic disease and tremendous economic loss in freshwater aquaculture industry. VP56, a putative fibrin inlaying the outer surface of GCRV-II and GCRV-III, is involved in cell attachment. In the present study, we found that VP56 localizes at the early endosome, lysosome, and endoplasmic reticulum, recruits the cytoplasmic viral RNA sensor retinoic acid-inducible gene I (RIG-I) and binds to it. The interaction between VP56 and RIG-I was detected by endogenous coimmunoprecipitation (co-IP), glutathione S-transferase (GST) pulldown, and subsequent liquid chromatography-tandem mass spectrometry (LC-MS/MS) and was then confirmed by traditional co-IPs and a novel far-red mNeptune-based bimolecular fluorescence complementation system. VP56 binds to the helicase domain of RIG-I. VP56 enhances K48-linked ubiquitination of RIG-I to degrade it by the proteasomal pathway. Thus, VP56 impedes the initial immune function of RIG-I by dual mechanisms (blockade and degradation) and attenuates signaling from RIG-I recognizing viral RNA, subsequently weakening downstream signaling transduction and interferon (IFN) responses. Accordingly, host antiviral effectors are reduced, and cytopathic effects are increased. These findings were corroborated by RNA sequencing (RNA-seq) and VP56 knockdown. Finally, we found that VP56 and the major outer capsid protein VP4 bind together in the cytosol to enhance the degradation of RIG-I and more efficiently facilitate viral replication. Collectively, the results indicated that VP56 allies VP4, recruits, blocks, and degrades RIG-I, thereby attenuating IFNs and antiviral effectors to facilitate viral evasion more effectively. This study reveals a virus attacking target and an escaping strategy from host antiviral immunity for GCRV and will help understand mechanisms of infection of reoviruses. IMPORTANCE Grass carp reovirus (GCRV) fibrin VP56 and major outer capsid protein VP4 inlay and locate on the outer surface of GCRV-II and GCRV-III, which causes tremendous loss in grass carp and black carp industries. Fibrin is involved in cell attachment and plays an important role in reovirus infection. The present study identified the interaction proteins of VP56 and found that VP56 and VP4 bind to the different domains of the viral RNA sensor retinoic acid-inducible gene I (RIG-I) in grass carp to block RIG-I sensing of viral RNA and induce RIG-I degradation by the proteasomal pathway to attenuate signaling transduction, thereby suppressing interferons (IFNs) and antiviral effectors, facilitating viral replication. VP56 and VP4 bind together in the cytosol to more efficiently facilitate viral evasion. This study reveals a virus attacking a target and an escaping strategy from host antiviral immunity for GCRV and will be helpful in understanding the mechanisms of infection of reoviruses.

Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions

The microbiome, by virtue of its interactions with the host, is implicated in various host functions including its influence on nutrition and homeostasis. Many chronic diseases such as diabetes, cancer, inflammatory bowel diseases are characterized by a disruption of microbial communities in at least one biological niche/organ system. Various molecular mechanisms between microbial and host components such as proteins, RNAs, metabolites have recently been identified, thus filling many gaps in our understanding of how the microbiome modulates host processes. Concurrently, high-throughput technologies have enabled the profiling of heterogeneous datasets capturing community level changes in the microbiome as well as the host responses. However, due to limitations in parallel sampling and analytical procedures, big gaps still exist in terms of how the microbiome mechanistically influences host functions at a system and community level. In the past decade, computational biology and machine learning methodologies have been developed with the aim of filling the existing gaps. Due to the agnostic nature of the tools, they have been applied in diverse disease contexts to analyze and infer the interactions between the microbiome and host molecular components. Some of these approaches allow the identification and analysis of affected downstream host processes. Most of the tools statistically or mechanistically integrate different types of -omic and meta -omic datasets followed by functional/biological interpretation. In this review, we provide an overview of the landscape of computational approaches for investigating mechanistic interactions between individual microbes/microbiome and the host and the opportunities for basic and clinical research. These could include but are not limited to the development of activity- and mechanism-based biomarkers, uncovering mechanisms for therapeutic interventions and generating integrated signatures to stratify patients.

The analysis on the human protein domain targets and host-like interacting motifs for the MERS-CoV and SARS-CoV/CoV-2 infers the molecular mimicry of coronavirus

The MERS-CoV, SARS-CoV, and SARS-CoV-2 are highly pathogenic viruses that can cause severe pneumonic diseases in humans. Unfortunately, there is a non-available effective treatment to combat these viruses. Domain-motif interactions (DMIs) are an essential means by which viruses mimic and hijack the biological processes of host cells. To disentangle how viruses achieve this process can help to develop new rational therapies. Data mining was performed to obtain DMIs stored as regular expressions (regexp) in 3DID and ELM databases. The mined regexp information was mapped on the coronaviruses' proteomes. Most motifs on viral protein that could interact with human proteins are shared across the coronavirus species, indicating that molecular mimicry is a common strategy for coronavirus infection. Enrichment ontology analysis for protein domains showed a shared biological process and molecular function terms related to carbon source utilization and potassium channel regulation. Some of the mapped motifs were nested on B, and T cell epitopes, suggesting that it could be as an alternative way for reverse vaccinology. The information obtained in this study could be used for further theoretic and experimental explorations on coronavirus infection mechanism and development of medicines for treatment.

HVIDB: a comprehensive database for human-virus protein-protein interactions

While leading to millions of people's deaths every year the treatment of viral infectious diseases remains a huge public health challenge.Therefore, an in-depth understanding of human-virus protein-protein interactions (PPIs) as the molecular interface between a virus and its host cell is of paramount importance to obtain new insights into the pathogenesis of viral infections and development of antiviral therapeutic treatments. However, current human-virus PPI database resources are incomplete, lack annotation and usually do not provide the opportunity to computationally predict human-virus PPIs. Here, we present the Human-Virus Interaction DataBase (HVIDB, http://zzdlab.com/hvidb/) that provides comprehensively annotated human-virus PPI data as well as seamlessly integrates online PPI prediction tools. Currently, HVIDB highlights 48 643 experimentally verified human-virus PPIs covering 35 virus families, 6633 virally targeted host complexes, 3572 host dependency/restriction factors as well as 911 experimentally verified/predicted 3D complex structures of human-virus PPIs. Furthermore, our database resource provides tissue-specific expression profiles of 6790 human genes that are targeted by viruses and 129 Gene Expression Omnibus series of differentially expressed genes post-viral infections. Based on these multifaceted and annotated data, our database allows the users to easily obtain reliable information about PPIs of various human viruses and conduct an in-depth analysis of their inherent biological significance. In particular, HVIDB also integrates well-performing machine learning models to predict interactions between the human host and viral proteins that are based on (i) sequence embedding techniques, (ii) interolog mapping and (iii) domain-domain interaction inference. We anticipate that HVIDB will serve as a one-stop knowledge base to further guide hypothesis-driven experimental efforts to investigate human-virus relationships.

Disruption of small molecule transporter systems by Transporter-Interfering Chemicals (TICs)

Small molecule transporters (SMTs) in the ABC and SLC families are important players in disposition of diverse endo- and xenobiotics. Interactions of environmental chemicals with these transporters were first postulated in the 1990s, and since validated in numerous in vitro and in vivo scenarios. Recent results on the co-crystal structure of ABCB1 with the flame-retardant BDE-100 demonstrate that a diverse range of man-made and natural toxic molecules, hereafter termed transporter-interfering chemicals (TICs), can directly bind to SMTs and interfere with their function. TIC-binding modes mimic those of substrates, inhibitors, modulators, inducers, and possibly stimulants through direct and allosteric mechanisms. Similarly, the effects could directly or indirectly agonize, antagonize or perhaps even prime the SMT system to alter transport function. Importantly, TICs are distinguished from drugs and pharmaceuticals that interact with transporters in that exposure is unintended and inherently variant. Here, we review the molecular mechanisms of environmental chemical interaction with SMTs, the methodological considerations for their evaluation, and the future directions for TIC discovery.

Study of combining virtual screening and antiviral treatments of the Sars-CoV-2 (Covid-19)

The recent epidemic outbreak of a novel human coronavirus called SARS-CoV-2 and causing the respiratory tract disease COVID-19 has reached worldwide resonance and a global effort is being undertaken to characterize the molecular features and evolutionary origins of this virus. Therefore, rapid and accurate identification of pathogenic viruses plays a vital role in selecting appropriate treatments, saving people's lives and preventing epidemics. Additionally, general treatments, coronavirus-specific treatments, and antiviral treatments useful in fighting COVID-19 are addressed. This review sets out to shed light on the SARS-CoV-2 and host receptor recognition, a crucial factor for successful virus infection and taking immune-informatics approaches to identify B- and T-cell epitopes for surface glycoprotein of SARS-CoV-2. A variety of improved or new approaches also have been developed. It is anticipated that this will assist researchers and clinicians in developing better techniques for timely and effective detection of coronavirus infection. Moreover, the genomic sequence of the virus responsible for COVID-19, as well as the experimentally determined three-dimensional structure of the Main protease (Mpro) is available. The reported structure of the target Mpro was described in this review to identify potential drugs for COVID-19 using virtual high throughput screening.

HMI-PRED: A Web Server for Structural Prediction of Host-Microbe Interactions Based on Interface Mimicry

Microbes, commensals, and pathogens, control the numerous functions in the host cells. They can alter host signaling and modulate immune surveillance by interacting with the host proteins. For shedding light on the contribution of microbes to health and disease, it is vital to discern how microbial proteins rewire host signaling and through which host proteins they do this. Host-Microbe Interaction PREDictor (HMI-PRED) is a user-friendly web server for structural prediction of protein-protein interactions (PPIs) between the host and a microbial species, including bacteria, viruses, fungi, and protozoa. HMI-PRED relies on "interface mimicry" through which the microbial proteins hijack host binding surfaces. Given the structure of a microbial protein of interest, HMI-PRED will return structural models of potential host-microbe interaction (HMI) complexes, the list of host endogenous and exogenous PPIs that can be disrupted, and tissue expression of the microbe-targeted host proteins. The server also allows users to upload homology models of microbial proteins. Broadly, it aims at large-scale, efficient identification of HMIs. The prediction results are stored in a repository for community access. HMI-PRED is free and available at https://interactome.ku.edu.tr/hmi.

Grass Carp Reovirus Major Outer Capsid Protein VP4 Interacts with RNA Sensor RIG-I to Suppress Interferon Response

Diseases caused by viruses threaten the production industry and food safety of aquaculture which is a great animal protein source. Grass carp reovirus (GCRV) has caused tremendous loss, and the molecular function of viral proteins during infection needs further research, as for most aquatic viruses. In this study, interaction between GCRV major outer capsid protein VP4 and RIG-I, a critical viral RNA sensor, was screened out by GST pull-down, endogenous immunoprecipitation and subsequent LC-MS/MS, and then verified by co-IP and an advanced far-red fluorescence complementation system. VP4 was proved to bind to the CARD and RD domains of RIG-I and promoted K48-linked ubiquitination of RIG-I to degrade RIG-I. VP4 reduced mRNA and promoter activities of key genes of RLR pathway and sequential IFN production. As a consequence, antiviral effectors were suppressed and GCRV replication increased, resulting in intensified cytopathic effect. Furthermore, results of transcriptome sequencing of VP4 stably expressed CIK (C. idella kidney) cells indicated that VP4 activated the MyD88-dependent TLR pathway. Knockdown of VP4 obtained opposite effects. These results collectively revealed that VP4 interacts with RIG-I to restrain interferon response and assist GCRV invasion. This study lays the foundation for anti-dsRNA virus molecular function research in teleost and provides a novel insight into the strategy of immune evasion for aquatic virus.

Structural Genomics of SARS-CoV-2 Indicates Evolutionary Conserved Functional Regions of Viral Proteins

During its first two and a half months, the recently emerged 2019 novel coronavirus, SARS-CoV-2, has already infected over one-hundred thousand people worldwide and has taken more than four thousand lives. However, the swiftly spreading virus also caused an unprecedentedly rapid response from the research community facing the unknown health challenge of potentially enormous proportions. Unfortunately, the experimental research to understand the molecular mechanisms behind the viral infection and to design a vaccine or antivirals is costly and takes months to develop. To expedite the advancement of our knowledge, we leveraged data about the related coronaviruses that is readily available in public databases and integrated these data into a single computational pipeline. As a result, we provide comprehensive structural genomics and interactomics roadmaps of SARS-CoV-2 and use this information to infer the possible functional differences and similarities with the related SARS coronavirus. All data are made publicly available to the research community.

Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method

The identification of human-virus protein-protein interactions (PPIs) is an essential and challenging research topic, potentially providing a mechanistic understanding of viral infection. Given that the experimental determination of human-virus PPIs is time-consuming and labor-intensive, computational methods are playing an important role in providing testable hypotheses, complementing the determination of large-scale interactome between species. In this work, we applied an unsupervised sequence embedding technique (doc2vec) to represent protein sequences as rich feature vectors of low dimensionality. Training a Random Forest (RF) classifier through a training dataset that covers known PPIs between human and all viruses, we obtained excellent predictive accuracy outperforming various combinations of machine learning algorithms and commonly-used sequence encoding schemes. Rigorous comparison with three existing human-virus PPI prediction methods, our proposed computational framework further provided very competitive and promising performance, suggesting that the doc2vec encoding scheme effectively captures context information of protein sequences, pertaining to corresponding protein-protein interactions. Our approach is freely accessible through our web server as part of our host-pathogen PPI prediction platform (http://zzdlab.com/InterSPPI/). Taken together, we hope the current work not only contributes a useful predictor to accelerate the exploration of human-virus PPIs, but also provides some meaningful insights into human-virus relationships.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Establishment of a cell line from egg of rare minnow Gobiocypris rarus for propagation of grass carp reovirus genotype II

The rare minnow, Gobiocypris rarus, is small experimental fish proven to be sensitive to Grass Carp Reovirus (GCRV) infection. In present study we established a new cell (GrE) from eggs of G. rarus. GrE cells grew well at 28 °C in M199 medium containing 10% fetal bovine serum, and has been subcultured for over 70 passages. Chromosome analysis indicated that 40% of the cells were diploid 2n = 66 while the chromosome number of the fish is 2n = 50. Viral replication in GrE cells was confirmed by transmission electron microscopy, immunofluorescence assays and virus titration experiments. GrE cells and Cyenopharyngodon idellus kidney cells were infected with two GCRV genotypes while the virus copies of GCRV II in GrE peaked at 2.25 × 10⁵ on 12th dpi. In vivo challenge experiments using GCRV I and II isolates at generations 1 and 20 indicated that GCRV II reproduce similar symptoms and histopathological changes of the disease in the rare minnow. These results indicated that GrE is permissive for GCRV genotype II propagation and can be used for pathogenesis studies and vaccine development of the predominant genotype of GCRV.

A developed subunit vaccine based on fiber protein VP56 of grass carp reovirus providing immune protection against grass carp hemorrhagic disease

Grass carp reovirus (GCRV) is the main viral pathogen that endangers grass carp seriously. Application of vaccine has been considered to be the most effective way to prevent virus infection. VP56 is a protein encoded by gene segment 7 of grass carp reovirus, and is predicted to share homology with fiber protein of mammalian reovirus (MRV). In our study, the immunogenicity of VP56 was evaluated by neutralization test. GCRV was incubated with mouse anti-VP56 antibody, and then was injected into grass carp. Results showed that disease progress and death occurrence was hindered in the experimental group compared with the control group. For further study, the recombinant VP56 protein (rVP56) expressed by pET-32a (+) vector was purified, and was used as subunit vaccine to immunize grass carp. After each fish (15 ± 1.5 g) was injected with 30 μg purified rVP56 intraperitoneally, the immune protective efficacy of recombinant VP56 protein was assessed by a series of immune parameters. The population of red blood cells in immunized fish increased significantly after 5 d post injection (dpi), and reached a peak with (2.98 ± 0.17) × 10⁹/ml at 7 dpi (p < 0.05). The numbers of white blood cells peaked with (8.42 ± 1.01) × 10⁷/ml at 7 dpi (p < 0.05). Additionally, the percentage of monocytes and neutrophils rose to a peak with (9.05 ± 0.92)% and (25.93 ± 2.60)% respectively at 5 dpi (p < 0.05 or p < 0.01), whereas lymphocytes reached the highest value of (85.81 ± 2.73) % at 14 dpi (p < 0.01). Serum antibody titer in the vaccinated fish increased significantly and reached a peak at 21 dpi (p < 0.01). The mRNA expression levels of type I interferon (IFN1), major histocompatibility complex class I (MHC I), Toll-like receptor 22 (TLR22), and immunoglobulin M (IgM) were significantly up-regulated in head kidney and spleen (p < 0.05 or p < 0.01). The GCRV challenge test showed that the relative survival rate in immunized group was 71%-75%. Collectively, the results indicated that rVP56 protein can induce immune protection in grass carp, and can be consider as a candidate vaccine against GCRV infection.

Interface-Based Structural Prediction of Novel Host-Pathogen Interactions

About 20% of the cancer incidences worldwide have been estimated to be associated with infections. However, the molecular mechanisms of exactly how they contribute to host tumorigenesis are still unknown. To evade host defense, pathogens hijack host proteins at different levels: sequence, structure, motif, and binding surface, i.e., interface. Interface similarity allows pathogen proteins to compete with host counterparts to bind to a target protein, rewire physiological signaling, and result in persistent infections, as well as cancer. Identification of host-pathogen interactions (HPIs)-along with their structural details at atomic resolution-may provide mechanistic insight into pathogen-driven cancers and innovate therapeutic intervention. HPI data including structural details is scarce and large-scale experimental detection is challenging. Therefore, there is an urgent and mounting need for efficient and robust computational approaches to predict HPIs and their complex (bound) structures. In this chapter, we review the first and currently only interface-based computational approach to identify novel HPIs. The concept of interface mimicry promises to identify more HPIs than complete sequence or structural similarity. We illustrate this concept with a case study on Kaposi's sarcoma herpesvirus (KSHV) to elucidate how it subverts host immunity and helps contribute to malignant transformation of the host cells.

Structure-based prediction of West Nile virus-human protein-protein interactions

In recent years, West Nile virus (WNV) has posed a great threat to global human health due to its explosive spread. Studying the protein-protein interactions (PPIs) between WNV and human is beneficial for understanding the pathogenesis of WNV and the immune response mechanism of human against WNV infection at the molecular level. In this study, we identified the human target proteins which interact with WNV based on protein structure similarity, and then the interacting pairs were filtered by the subcellular co-localization information. As a result, a network of 3346 interactions was constructed, involving 6 WNV proteins and 1970 human target proteins. To our knowledge, this is the first predicted interactome for WNV-human. By analyzing the topological properties and evolutionary rates of the human target proteins, it was demonstrated that these proteins tend to be the hub and bottleneck proteins in the human PPI network and are more conserved than the non-target ones. Triplet analysis showed that the target proteins are adjacent to each other in the human PPI network, suggesting that these proteins may have similar biological functions. Further, the functional enrichment analysis indicated that the target proteins are mainly involved in virus process, transcription regulation, cell adhesion, and so on. In addition, the common and specific targets were identified and compared based on the networks between WNV-human and Dengue virus II (DENV2)-human. Finally, by combining topological features and existing drug target information, we identified 30 potential anti-WNV human targets, among which 11 ones were reported to be associated with WNV infection. Communicated by Ramaswamy H. Sarma.

Inter-Species/Host-Parasite Protein Interaction Predictions Reviewed

BACKGROUND

Host-parasite protein interactions (HPPI) are those interactions occurring between a parasite and its host. Host-parasite protein interaction enhances the understanding of how parasite can infect its host. The interaction plays an important role in initiating infections, although it is not all host-parasite interactions that result in infection. Identifying the protein-protein interactions (PPIs) that allow a parasite to infect its host has a lot do in discovering possible drug targets. Such PPIs, when altered, would prevent the host from being infected by the parasite and in some cases, result in the parasite inability to complete specific stages of its life cycle and invariably lead to the death of such parasite. It therefore becomes important to understand the workings of host-parasite interactions which are the major causes of most infectious diseases.

OBJECTIVE

Many studies have been conducted in literature to predict HPPI, mostly using computational methods with few experimental methods. Computational method has proved to be faster and more efficient in manipulating and analyzing real life data. This study looks at various computational methods used in literature for host-parasite/inter-species protein-protein interaction predictions with the hope of getting a better insight into computational methods used and identify whether machine learning approaches have been extensively used for the same purpose.

METHODS

The various methods involved in host-parasite protein interactions were reviewed with their individual strengths. Tabulations of studies that carried out host-parasite/inter-species protein interaction predictions were performed, analyzing their predictive methods, filters used, potential protein-protein interactions discovered in those studies and various validation measurements used as the case may be. The commonly used measurement indexes for such studies were highlighted displaying the various formulas. Finally, future prospects of studies specific to human-plasmodium falciparum PPI predictions were proposed.

RESULT

We discovered that quite a few studies reviewed implemented machine learning approach for HPPI predictions when compared with methods such as sequence homology search and protein structure and domain-motif. The key challenge well noted in HPPI predictions is getting relevant information.

CONCLUSION

This review presents useful knowledge and future directions on the subject matter.

Proteome-wide analysis of human motif-domain interactions mapped on influenza a virus

Background

The influenza A virus (IAV) is a constant threat for humans worldwide. The understanding of motif-domain protein participation is essential to combat the pathogen.

Results

In this study, a data mining approach was employed to extract influenza-human Protein-Protein interactions (PPI) from VirusMentha,Virus MINT, IntAct, and Pfam databases, to mine motif-domain interactions (MDIs) stored as Regular Expressions (RegExp) in 3DID database. A total of 107 RegExp related to human MDIs were searched on 51,242 protein fragments from H1N1, H1N2, H2N2, H3N2 and H5N1 strains obtained from Virus Variation database. A total 46 MDIs were frequently mapped on the IAV proteins and shared between the different strains. IAV kept host-like MDIs that were associated with the virus survival, which could be related to essential biological process such as microtubule-based processes, regulation of cell cycle check point, regulation of replication and transcription of DNA, etc. in human cells. The amino acid motifs were searched for matches in the immune epitope database and it was found that some motifs are part of experimentally determined epitopes on IAV, implying that such interactions exist.

Conclusion

The directed data-mining method employed could be used to identify functional motifs in other viruses for envisioning new therapies.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2237-8) contains supplementary material, which is available to authorized users.

EGCG: Potential application as a protective agent against grass carp reovirus in aquaculture

Grass carp reovirus (GCRV) is the primary cause of grass carp haemorrhagic disease. The major catechin in green tea, (-)-epigallocatechin-3-gallate (EGCG), has been found to have anti-GCRV activity in the C. idellus kidney cell line (CIK). The aim of this study was to test the potential application of EGCG as an anti-GCRV agent in aquaculture. Here, we demonstrate that various concentrations (99%, 50% and 35%) of EGCG could inhibit GCRV infectivity. EGCG (50%) + GCRV treatment significantly reduced the number of dead fish at 1-, 2-, 3-, 4 -and 5-day post-challenge compared with the negative control (GCRV challenge without EGCG treatment). The safety of EGCG compound products on cell survival was studied using four fish cell lines; we did not detect a significant change in cell viability within 24 hours of EGCG incubation. We also evaluated toxicity and concentrations of malondialdehyde (MDA), glutathione (GSH) and lysozyme (LZM) in the grass carp, and the results showed that even a high dose of EGCG did not induce toxicity. Following EGCG compound injection, the concentration of MDA decreased and the concentration of GSH and LZM increased compared with the control groups. We also detected EGCG concentration in grass carp plasma and kidney using HPLC with electrochemical detection after intraperitoneal injection at a dose of 150 mg/kg. The concentration of EGCG in the plasma and kidney reached the highest levels (20 μg/ml and 1.5 μg/ml) about 12 hr after injection and then decreased. Overall, EGCG is a safe, effective product that could inhibit GCRV infection and improve immunoactivity in aquaculture.

Efficient resistance to grass carp reovirus infection in JAM-A knockout cells using CRISPR/Cas9

The hemorrhagic disease of grass carp (Ctenopharyngodon idellus) induced by grass carp reovirus (GCRV) leads to huge economic losses in China and currently, there are no effective methods available for prevention and treatment. The various GCRV genotypes may be one of the major obstacles in the pursuit of an effective antiviral treatment. In this study, we exploited CRISPR/Cas9 gene editing to specifically knockout the DNA sequence of the grass carp Junctional Adhesion Molecule-A (gcJAM-A) and evaluated in vitro resistance against various GCRV genotypes. Our results show that CRISPR/Cas9 effectively knocked out gcJAM-A and reduced GCRV infection for two different genotypes in permissive grass carp kidney cells (CIK), as evidenced by suppressed cytopathic effect (CPE) and GCRV progeny production in infected cells. In addition, with ectopic expression of gcJAM-A in cells, non-permissive cells derived from Chinese giant salamander (Andrias davidianus) muscle (GSM) could be highly infected by both GCRV-JX0901 and Hubei grass carp disease reovirus (HGDRV) strains that have different genotypes. Taken together, the results demonstrate that gcJAM-A is necessary for GCRV infection, implying a potential approach for viral control in aquaculture.

Novel subunit vaccine based on grass carp reovirus VP35 protein provides protective immunity against grass carp hemorrhagic disease

The grass carp (Ctenopharyngodon idella) hemorrhagic disease, caused by grass carp reovirus (GCRV), is one of the most severe infectious diseases in aquaculture. Given that antiviral therapies are currently limitedly available, vaccination remains the most effective means for the prevention of viral diseases, such as GCRV. A reovirus strain, which was temporarily named GCRV-HN14, was recently isolated from grass carp in Henan province, China. The S11 gene fragment of GCRV-HN14 was speculated to encode viral structural protein VP35, which has no equivalent gene in other aquareviruses but has antigenic epitopes. In this study, the recombinant plasmid pET-32a-vp35 was constructed to express recombinant VP35 proteins in prokaryotic cells, which was used to create a novel subunit vaccine. The immune protection of recombinant VP35 protein was evaluated by a series of experiments in grass carp. Results showed that the number of white blood cells (WBC) in the peripheral blood increased significantly to 7.92 ± 0.72 × 10⁷/ml 5 days after vaccination (P < 0.05). The number of neutrophils and monocytes in WBC were significantly higher than those of the control 3 days after vaccination (P < 0.05) and maximally got to 12.22 ± 1.28% and 18.70 ± 1.78%, respectively. Owing to the significant increase in the number of lymphocytes (92.37 ± 2.10%; P < 0.01), the percentages of neutrophils and monocytes declined significantly (14 dpi; P < 0.01). Serum antibody levels induced by recombinant VP35 protein significantly increased 7 days post immunization and continued to increase until 5 weeks post vaccination. The mRNA expression levels of type I interferon (designated as IFN1), immunoglobulin M, Toll-like receptor 22 and major histocompatibility complex class I were up-regulated significantly in the head kidneys and spleens of immunized fish (P < 0.01). Grass carp immunized by recombinant VP35 protein showed that the relative percentage of survival was about 60% after it was challenged with GCRV. Overall, the results suggested that recombinant VP35 protein can induce immunity and protect grass carp against GCRV infection. Thus, it can be used as a subunit vaccine.

Circular RNAs: A novel type of non-coding RNA and their potential implications in antiviral immunity

Circular RNAs (circRNAs), a novel type of non-coding RNAs (ncRNAs), are ubiquitously expressed in eukaryotic cells during post-transcriptional processes. Unlike linear RNAs, circRNAs form covalent-closed continuous loops without 5' to 3' polarities and poly (A) tails. With advances in high-throughput sequencing technology, numerous circRNAs have been identified in plants, animals and humans. Notably, circRNAs display cell-type, tissue-type and developmental-stage specific expression patterns in eukaryotic transcriptome, which reveals their significant regulatory functions in gene expression. More importantly, circRNAs serve as microRNA (miRNA) sponges and crucial regulators of gene expression. Additionally, circRNAs modulate pre-mRNA alternative splicing and possess protein-coding capacity. CircRNAs exhibit altered expression under pathological conditions and are strongly associated with the development of various human diseases. Interestingly, circRNAs can also induce antiviral immune responses. A recent study found that the delivery of circRNAs generated in vitro activates RIG-I-mediated innate immune responses and provides protection against viral infection. The antiviral dsRNA-binding proteins, NF90/NF110, act as key regulators in circRNA biogenesis. NF90/NF110 are also functional in inhibiting viral replication through binding to viral mRNAs. In this review, we provide a comprehensive overview on the classification, biogenesis and functions of circRNAs. We also discuss the critical role of circRNAs in eliciting antiviral immunity, providing evidence for the potential implications of circRNAs in antiviral therapies.