VirHostNet: a knowledge base for the management and the analysis of proteome-wide virus-host interaction networks

Temporal insights into molecular and cellular responses during rAAV production in HEK293T cells

The gene therapy field seeks cost-effective, large-scale production of recombinant adeno-associated virus (rAAV) vectors for high-dosage therapeutic applications. Although strategies like suspension cell culture and transfection optimization have shown moderate success, challenges persist for large-scale applications. To unravel molecular and cellular mechanisms influencing rAAV production, we conducted an SWATH-MS proteomic analysis of HEK293T cells transfected using standard, sub-optimal, and optimal conditions. Gene Ontology and pathway analysis revealed significant protein expression variations, particularly in processes related to cellular homeostasis, metabolic regulation, vesicular transport, ribosomal biogenesis, and cellular proliferation under optimal transfection conditions. This resulted in a 50% increase in rAAV titer compared with the standard protocol. Additionally, we identified modifications in host cell proteins crucial for AAV mRNA stability and gene translation, particularly regarding AAV capsid transcripts under optimal transfection conditions. Our study identified 124 host proteins associated with AAV replication and assembly, each exhibiting distinct expression pattern throughout rAAV production stages in optimal transfection condition. This investigation sheds light on the cellular mechanisms involved in rAAV production in HEK293T cells and proposes promising avenues for further enhancing rAAV titer during production.

Navigating the Landscape: A Comprehensive Review of Current Virus Databases

Viruses are abundant and diverse entities that have important roles in public health, ecology, and agriculture. The identification and surveillance of viruses rely on an understanding of their genome organization, sequences, and replication strategy. Despite technological advancements in sequencing methods, our current understanding of virus diversity remains incomplete, highlighting the need to explore undiscovered viruses. Virus databases play a crucial role in providing access to sequences, annotations and other metadata, and analysis tools for studying viruses. However, there has not been a comprehensive review of virus databases in the last five years. This study aimed to fill this gap by identifying 24 active virus databases and included an extensive evaluation of their content, functionality and compliance with the FAIR principles. In this study, we thoroughly assessed the search capabilities of five database catalogs, which serve as comprehensive repositories housing a diverse array of databases and offering essential metadata. Moreover, we conducted a comprehensive review of different types of errors, encompassing taxonomy, names, missing information, sequences, sequence orientation, and chimeric sequences, with the intention of empowering users to effectively tackle these challenges. We expect this review to aid users in selecting suitable virus databases and other resources, and to help databases in error management and improve their adherence to the FAIR principles. The databases listed here represent the current knowledge of viruses and will help aid users find databases of interest based on content, functionality, and scope. The use of virus databases is integral to gaining new insights into the biology, evolution, and transmission of viruses, and developing new strategies to manage virus outbreaks and preserve global health.

Pathogen-driven cancers from a structural perspective: Targeting host-pathogen protein-protein interactions

Host-pathogen interactions (HPIs) affect and involve multiple mechanisms in both the pathogen and the host. Pathogen interactions disrupt homeostasis in host cells, with their toxins interfering with host mechanisms, resulting in infections, diseases, and disorders, extending from AIDS and COVID-19, to cancer. Studies of the three-dimensional (3D) structures of host-pathogen complexes aim to understand how pathogens interact with their hosts. They also aim to contribute to the development of rational therapeutics, as well as preventive measures. However, structural studies are fraught with challenges toward these aims. This review describes the state-of-the-art in protein-protein interactions (PPIs) between the host and pathogens from the structural standpoint. It discusses computational aspects of predicting these PPIs, including machine learning (ML) and artificial intelligence (AI)-driven, and overviews available computational methods and their challenges. It concludes with examples of how theoretical computational approaches can result in a therapeutic agent with a potential of being used in the clinics, as well as future directions.

Viral protein engagement of GBF1 induces host cell vulnerability through synthetic lethality

Viruses co-opt host proteins to carry out their lifecycle. Repurposed host proteins may thus become functionally compromised; a situation analogous to a loss-of-function mutation. We term such host proteins as viral-induced hypomorphs. Cells bearing cancer driver loss-of-function mutations have successfully been targeted with drugs perturbing proteins encoded by the synthetic lethal (SL) partners of cancer-specific mutations. Similarly, SL interactions of viral-induced hypomorphs can potentially be targeted as host-based antiviral therapeutics. Here, we use GBF1, which supports the infection of many RNA viruses, as a proof-of-concept. GBF1 becomes a hypomorph upon interaction with the poliovirus protein 3A. Screening for SL partners of GBF1 revealed ARF1 as the top hit, disruption of which selectively killed cells that synthesize 3A alone or in the context of a poliovirus replicon. Thus, viral protein interactions can induce hypomorphs that render host cells selectively vulnerable to perturbations that leave uninfected cells otherwise unscathed. Exploiting viral-induced vulnerabilities could lead to broad-spectrum antivirals for many viruses, including SARS-CoV-2.

The Landscape of Virus-Host Protein–Protein Interaction Databases

Knowledge of virus-host interactomes has advanced exponentially in the last decade by the use of high-throughput screening technologies to obtain a more comprehensive landscape of virus-host protein–protein interactions. In this article, we present a systematic review of the available virus-host protein–protein interaction database resources. The resources covered in this review are both generic virus-host protein–protein interaction databases and databases of protein–protein interactions for a specific virus or for those viruses that infect a particular host. The databases are reviewed on the basis of the specificity for a particular virus or host, the number of virus-host protein–protein interactions included, and the functionality in terms of browse, search, visualization, and download. Further, we also analyze the overlap of the databases, that is, the number of virus-host protein–protein interactions shared by the various databases, as well as the structure of the virus-host protein–protein interaction network, across viruses and hosts.

Systematic optimization of host-directed therapeutic targets and preclinical validation of repositioned antiviral drugs

Inhibition of host protein functions using established drugs produces a promising antiviral effect with excellent safety profiles, decreased incidence of resistant variants and favorable balance of costs and risks. Genomic methods have produced a large number of robust host factors, providing candidates for identification of antiviral drug targets. However, there is a lack of global perspectives and systematic prioritization of known virus-targeted host proteins (VTHPs) and drug targets. There is also a need for host-directed repositioned antivirals. Here, we integrated 6140 VTHPs and grouped viral infection modes from a new perspective of enriched pathways of VTHPs. Clarifying the superiority of nonessential membrane and hub VTHPs as potential ideal targets for repositioned antivirals, we proposed 543 candidate VTHPs. We then presented a large-scale drug-virus network (DVN) based on matching these VTHPs and drug targets. We predicted possible indications for 703 approved drugs against 35 viruses and explored their potential as broad-spectrum antivirals. In vitro and in vivo tests validated the efficacy of bosutinib, maraviroc and dextromethorphan against human herpesvirus 1 (HHV-1), hepatitis B virus (HBV) and influenza A virus (IAV). Their drug synergy with clinically used antivirals was evaluated and confirmed. The results proved that low-dose dextromethorphan is better than high-dose in both single and combined treatments. This study provides a comprehensive landscape and optimization strategy for druggable VTHPs, constructing an innovative and potent pipeline to discover novel antiviral host proteins and repositioned drugs, which may facilitate their delivery to clinical application in translational medicine to combat fatal and spreading viral infections.

ImitateDB: A database for domain and motif mimicry incorporating host and pathogen protein interactions

Molecular mimicry of host proteins by pathogens constitutes a strategy to hijack the host pathways. At present, there is no dedicated resource for mimicked domains and motifs in the host-pathogen interactome. In this work, the experimental host-pathogen (HP) and host-host (HH) protein-protein interactions (PPIs) were collated. The domains and motifs of these proteins were annotated using CD Search and ScanProsite, respectively. Host and pathogen proteins with a shared host interactor and similar domain/motif constitute a mimicry pair exhibiting global structural similarity (domain mimicry pair; DMP) or local sequence motif similarity (motif mimicry pair; MMP). Mimicry pairs are likely to be co-expressed and co-localized. 1,97,607 DMPs and 32,67,568 MMPs were identified in 49,265 experimental HP-PPIs and organized in a web-based resource, ImitateDB ( http://imitatedb.sblab-nsit.net ) that can be easily queried. The results are externally integrated using hyperlinked domain PSSM ID, motif ID, protein ID and PubMed ID. Kinase, UL36, Smc and DEXDc were frequent DMP domains whereas protein kinase C phosphorylation, casein kinase 2 phosphorylation, glycosylation and myristoylation sites were frequent MMP motifs. Novel DMP domains SANT, Tudor, PhoX and MMP motif microbody C-terminal targeting signal, cornichon signature and lipocalin signature were proposed. ImitateDB is a novel resource for identifying mimicry in interacting host and pathogen proteins.

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.

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.

Proteomics approaches for the identification of protease substrates during virus infection

Proteases precisely and irreversibly catalyze the hydrolysis of peptide bonds, regulating the fate, localization, and activity of many proteins. Consequently, proteolytic activity plays an important role in fundamental cellular processes such as differentiation and migration, immunological and inflammatory reactions, apoptosis and survival. During virus infection, host proteases are involved in several processes, from cell entry to initiation, progression and resolution of inflammation. On the other hand, many viruses encode their own highly specific proteases, responsible for the proteolytic processing of viral proteins, but, at the same time, to cleave host proteins to corrupt antiviral host responses and adjust protein activity to favor viral replication. Traditionally, protease substrate identification has been addressed by means of hypothesis-driven approaches, but recent advances in proteomics have made a toolkit available to uncover the extensive repertoire of host proteins cleaved during infection, either by viral or host proteases. Here, we review the currently available proteomics-based methods that can and have contributed to the systematic and unbiased identification of new protease substrates in the context of virus-host interactions. The role of specific proteases during the course of virus infections will also be highlighted.

The chemical biology of coronavirus host-cell interactions

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the current coronavirus disease 2019 (COVID-19) pandemic that has led to a global economic disruption and collapse. With several ongoing efforts to develop vaccines and treatments for COVID-19, understanding the molecular interaction between the coronavirus, host cells, and the immune system is critical for effective therapeutic interventions. Greater insight into these mechanisms will require the contribution and combination of multiple scientific disciplines including the techniques and strategies that have been successfully deployed by chemical biology to tease apart complex biological pathways. We highlight in this review well-established strategies and methods to study coronavirus-host biophysical interactions and discuss the impact chemical biology will have on understanding these interactions at the molecular level.

Lessons from the COVID-19 pandemic for advancing computational drug repurposing strategies

Responding quickly to unknown pathogens is crucial to stop uncontrolled spread of diseases that lead to epidemics, such as the novel coronavirus, and to keep protective measures at a level that causes as little social and economic harm as possible. This can be achieved through computational approaches that significantly speed up drug discovery. A powerful approach is to restrict the search to existing drugs through drug repurposing, which can vastly accelerate the usually long approval process. In this Review, we examine a representative set of currently used computational approaches to identify repurposable drugs for COVID-19, as well as their underlying data resources. Furthermore, we compare drug candidates predicted by computational methods to drugs being assessed by clinical trials. Finally, we discuss lessons learned from the reviewed research efforts, including how to successfully connect computational approaches with experimental studies, and propose a unified drug repurposing strategy for better preparedness in the case of future outbreaks.

Viral protein engagement of GBF1 induces host cell vulnerability through synthetic lethality

Viruses co-opt host proteins to carry out their lifecycle. Repurposed host proteins may thus become functionally compromised; a situation analogous to a loss-of-function mutation. We term such host proteins viral-induced hypomorphs. Cells bearing cancer driver loss-of-function mutations have successfully been targeted with drugs perturbing proteins encoded by the synthetic lethal partners of cancer-specific mutations. Synthetic lethal interactions of viral-induced hypomorphs have the potential to be similarly targeted for the development of host-based antiviral therapeutics. Here, we use GBF1, which supports the infection of many RNA viruses, as a proof-of-concept. GBF1 becomes a hypomorph upon interaction with the poliovirus protein 3A. Screening for synthetic lethal partners of GBF1 revealed ARF1 as the top hit, disruption of which, selectively killed cells that synthesize poliovirus 3A. Thus, viral protein interactions can induce hypomorphs that render host cells vulnerable to perturbations that leave uninfected cells intact. Exploiting viral-induced vulnerabilities could lead to broad-spectrum antivirals for many viruses, including SARS-CoV-2.

SUMMARY

Using a viral-induced hypomorph of GBF1, Navare et al., demonstrate that the principle of synthetic lethality is a mechanism to selectively kill virus-infected cells.

Network analysis, sequence and structure dynamics of key proteins of coronavirus and human host, and molecular docking of selected phytochemicals of nine medicinal plants

The novel coronavirus of 2019 (nCoV-19) has become a pandemic, affecting over 205 nations with over 7,410,000 confirmed cases which has resulted to over 418,000 deaths worldwide. This study aimed to identify potential therapeutic compounds and phytochemicals of medicinal plants that have potential to modulate the expression network of genes that are involve in SARS-CoV-2 pathology in human host and to understand the dynamics key proteins involved in the virus-host interactions. The method used include gene network analysis, molecular docking, and sequence and structure dynamics simulations. The results identified DNA-dependent protein kinase (DNA-PK) and Protein kinase CK2 as key players in SARS-CoV-2 lifecycle. Among the predicted drugs compounds, clemizole, monorden, spironolactone and tanespimycin showed high binding energies; among the studied repurposing compounds, remdesivir, simeprevir and valinomycin showed high binding energies; among the predicted acidic compounds, acetylursolic acid and hardwickiic acid gave high binding energies; while among the studied anthraquinones and glycosides compounds, ellagitannin and friedelanone showed high binding energies against 3-Chymotrypsin-like protease (3CL^pro), Papain-like protease (PL^pro), helicase (nsp13), RNA-dependent RNA polymerase (nsp12), 2'-O-ribose methyltransferase (nsp16) of SARS-CoV-2 and DNA-PK and CK2alpha in human. The order of affinity for CoV proteins is 5Y3E > 6NUS > 6JYT > 2XYR > 3VB6. Finally, medicinal plants with phytochemicals such as caffeine, ellagic acid, quercetin and their derivatives could possibly remediate COVID-19.Communicated by Ramaswamy H. Sarma.

Network analysis of host-pathogen protein interactions in microbe induced cardiovascular diseases

Large-scale visualization and analysis of HPIs involved in microbial CVDs can provide crucial insights into the mechanisms of pathogenicity. The comparison of CVD associated HPIs with the entire set of HPIs can identify the pathways specific to CVDs. Therefore, topological properties of HPI networks in CVDs and all pathogens was studied using Cytoscape3.5.1. Ontology and pathway analysis were done using KOBAS 3.0. HPIs of Papilloma, Herpes, Influenza A virus as well as Yersinia pestis and Bacillus anthracis among bacteria were predominant in the whole (wHPI) and the CVD specific (cHPI) network. The central viral and secretory bacterial proteins were predicted virulent. The central viral proteins had higher number of interactions with host proteins in comparison with bacteria. Major fraction of central and essential host proteins interacts with central viral proteins. Alpha-synuclein, Ubiquitin ribosomal proteins, TATA-box-binding protein, and Polyubiquitin-C &B proteins were the top interacting proteins specific to CVDs. Signaling by NGF, Fc epsilon receptor, EGFR and ubiquitin mediated proteolysis were among the top enriched CVD specific pathways. DEXDc and HELICc were enriched host mimicry domains that may help in hijacking of cellular machinery by pathogens. This study provides a system level understanding of cardiac damage in microbe induced CVDs.

Harnessing host-virus evolution in antiviral therapy and immunotherapy

Pathogen resistance and development costs are major challenges in current approaches to antiviral therapy. The high error rate of RNA synthesis and reverse‐transcription confers genome plasticity, enabling the remarkable adaptability of RNA viruses to antiviral intervention. However, this property is coupled to fundamental constraints including limits on the size of information available to manipulate complex hosts into supporting viral replication. Accordingly, RNA viruses employ various means to extract maximum utility from their informationally limited genomes that, correspondingly, may be leveraged for effective host‐oriented therapies. Host‐oriented approaches are becoming increasingly feasible because of increased availability of bioactive compounds and recent advances in immunotherapy and precision medicine, particularly genome editing, targeted delivery methods and RNAi. In turn, one driving force behind these innovations is the increasingly detailed understanding of evolutionarily diverse host–virus interactions, which is the key concern of an emerging field, neo‐virology. This review examines biotechnological solutions to disease and other sustainability issues of our time that leverage the properties of RNA and DNA viruses as developed through co‐evolution with their hosts.

PathoPhenoDB, linking human pathogens to their phenotypes in support of infectious disease research

Understanding the relationship between the pathophysiology of infectious disease, the biology of the causative agent and the development of therapeutic and diagnostic approaches is dependent on the synthesis of a wide range of types of information. Provision of a comprehensive and integrated disease phenotype knowledgebase has the potential to provide novel and orthogonal sources of information for the understanding of infectious agent pathogenesis, and support for research on disease mechanisms. We have developed PathoPhenoDB, a database containing pathogen-to-phenotype associations. PathoPhenoDB relies on manual curation of pathogen-disease relations, on ontology-based text mining as well as manual curation to associate host disease phenotypes with infectious agents. Using Semantic Web technologies, PathoPhenoDB also links to knowledge about drug resistance mechanisms and drugs used in the treatment of infectious diseases. PathoPhenoDB is accessible at http://patho.phenomebrowser.net/ , and the data are freely available through a public SPARQL endpoint.

MorCVD: A Unified Database for Host-Pathogen Protein-Protein Interactions of Cardiovascular Diseases Related to Microbes

Microbe induced cardiovascular diseases (CVDs) are less studied at present. Host-pathogen interactions (HPIs) between human proteins and microbial proteins associated with CVD can be found dispersed in existing molecular interaction databases. MorCVD database is a curated resource that combines 23,377 protein interactions between human host and 432 unique pathogens involved in CVDs in a single intuitive web application. It covers endocarditis, myocarditis, pericarditis and 16 other microbe induced CVDs. The HPI information has been compiled, curated, and presented in a freely accessible web interface (http://morcvd.sblab-nsit.net/About). Apart from organization, enrichment of the HPI data was done by adding hyperlinked protein ID, PubMed, gene ontology records. For each protein in the database, drug target and interactors (same as well as different species) information has been provided. The database can be searched by disease, protein ID, pathogen name or interaction detection method. Interactions detected by more than one method can also be listed. The information can be presented in tabular form or downloaded. A comprehensive help file has been developed to explain the various options available. Hence, MorCVD acts as a unified resource for retrieval of HPI data for researchers in CVD and microbiology.

Host Proteins Identified in Extracellular Viral Particles as Targets for Broad-Spectrum Antiviral Inhibitors

Liquid chromatography mass spectrometry (LCMS) proteomic analyses have revealed that host proteins are often captured in extracellular virions. These proteins may play a role in viral replication or infectivity and can represent targets for broad-spectrum antiviral agent development. We utilized LCMS to determine the host protein composition of Lassa virus-like particles (LASV VLPs). Multiple host proteins incorporated in LASV VLPs are also incorporated in unrelated viruses, notably ribosomal proteins. We assembled a data set of host proteins incorporated into extracellular viral particles. The frequent incorporation of specific host proteins into viruses of diverse families suggests that interactions of these proteins with viral factors may be important for effective viral replication. Drugs that target virion-associated host proteins could affect the protein in the extracellular virion or the host cell. Compounds that target proteins incorporated into virions with high frequency, but with no known antiviral activity, were assayed in a scalable viral screening platform, and hits were tested in competent viral systems. One of these molecules, GAPDH modulating small molecule CGP 3466B maleate (Omigapil), exhibited a dose-dependent inhibition of HIV, dengue virus, and Zika virus.

Using genomic analysis to identify tomato Tm-2 resistance-breaking mutations and their underlying evolutionary path in a new and emerging tobamovirus

In September 2014, a new tobamovirus was discovered in Israel that was able to break Tm-2-mediated resistance in tomato that had lasted 55 years. The virus was isolated, and sequencing of its genome showed it to be tomato brown rugose fruit virus (ToBRFV), a new tobamovirus recently identified in Jordan. Previous studies on mutant viruses that cause resistance breaking, including Tm-2-mediated resistance, demonstrated that this phenotype had resulted from only a few mutations. Identification of important residues in resistance breakers is hindered by significant background variation, with 9-15% variability in the genomic sequences of known isolates. To understand the evolutionary path leading to the emergence of this resistance breaker, we performed a comprehensive phylogenetic analysis and genomic comparison of different tobamoviruses, followed by molecular modeling of the viral helicase. The phylogenetic location of the resistance-breaking genes was found to be among host-shifting clades, and this, together with the observation of a relatively low mutation rate, suggests that a host shift contributed to the emergence of this new virus. Our comparative genomic analysis identified twelve potential resistance-breaking mutations in the viral movement protein (MP), the primary target of the related Tm-2 resistance, and nine in its replicase. Finally, molecular modeling of the helicase enabled the identification of three additional potential resistance-breaking mutations.

Systems Biology Modeling to Study Pathogen-Host Interactions

Pathogen-host interactions (PHIs) underlie the process of infection. The systems biology view of the whole PHI system is superior to the investigation of the pathogen or host separately in understanding the infection mechanisms. Especially, the identification of host-oriented drug targets for the next-generation anti-infection therapeutics requires the properties of the host factors targeted by pathogens. Here, we provide an outline of computational analysis of PHI networks, focusing on the properties of the pathogen-targeted host proteins. We also provide information about the available PHI data and the related Web-based resources.

Protein-Protein Interactions in Virus-Host Systems

To study virus–host protein interactions, knowledge about viral and host protein architectures and repertoires, their particular evolutionary mechanisms, and information on relevant sources of biological data is essential. The purpose of this review article is to provide a thorough overview about these aspects. Protein domains are basic units defining protein interactions, and the uniqueness of viral domain repertoires, their mode of evolution, and their roles during viral infection make viruses interesting models of study. Mutations at protein interfaces can reduce or increase their binding affinities by changing protein electrostatics and structural properties. During the course of a viral infection, both pathogen and cellular proteins are constantly competing for binding partners. Endogenous interfaces mediating intraspecific interactions—viral–viral or host–host interactions—are constantly targeted and inhibited by exogenous interfaces mediating viral–host interactions. From a biomedical perspective, blocking such interactions is the main mechanism underlying antiviral therapies. Some proteins are able to bind multiple partners, and their modes of interaction define how fast these “hub proteins” evolve. “Party hubs” have multiple interfaces; they establish simultaneous/stable (domain–domain) interactions, and tend to evolve slowly. On the other hand, “date hubs” have few interfaces; they establish transient/weak (domain–motif) interactions by means of short linear peptides (15 or fewer residues), and can evolve faster. Viral infections are mediated by several protein–protein interactions (PPIs), which can be represented as networks (protein interaction networks, PINs), with proteins being depicted as nodes, and their interactions as edges. It has been suggested that viral proteins tend to establish interactions with more central and highly connected host proteins. In an evolutionary arms race, viral and host proteins are constantly changing their interface residues, either to evade or to optimize their binding capabilities. Apart from gaining and losing interactions via rewiring mechanisms, virus–host PINs also evolve via gene duplication (paralogy); conservation (orthology); horizontal gene transfer (HGT) (xenology); and molecular mimicry (convergence). The last sections of this review focus on PPI experimental approaches and their limitations, and provide an overview of sources of biomolecular data for studying virus–host protein interactions.

Perturbed human sub-networks by Fusobacterium nucleatum candidate virulence proteins

BACKGROUND

Fusobacterium nucleatum is a gram-negative anaerobic species residing in the oral cavity and implicated in several inflammatory processes in the human body. Although F. nucleatum abundance is increased in inflammatory bowel disease subjects and is prevalent in colorectal cancer patients, the causal role of the bacterium in gastrointestinal disorders and the mechanistic details of host cell functions subversion are not fully understood.

RESULTS

We devised a computational strategy to identify putative secreted F. nucleatum proteins (FusoSecretome) and to infer their interactions with human proteins based on the presence of host molecular mimicry elements. FusoSecretome proteins share similar features with known bacterial virulence factors thereby highlighting their pathogenic potential. We show that they interact with human proteins that participate in infection-related cellular processes and localize in established cellular districts of the host-pathogen interface. Our network-based analysis identified 31 functional modules in the human interactome preferentially targeted by 138 FusoSecretome proteins, among which we selected 26 as main candidate virulence proteins, representing both putative and known virulence proteins. Finally, six of the preferentially targeted functional modules are implicated in the onset and progression of inflammatory bowel diseases and colorectal cancer.

CONCLUSIONS

Overall, our computational analysis identified candidate virulence proteins potentially involved in the F. nucleatum-human cross-talk in the context of gastrointestinal diseases.

Reconstructing the temporal progression of HIV-1 immune response pathways

Motivation: Most methods for reconstructing response networks from high throughput data generate static models which cannot distinguish between early and late response stages.

Results: We present TimePath, a new method that integrates time series and static datasets to reconstruct dynamic models of host response to stimulus. TimePath uses an Integer Programming formulation to select a subset of pathways that, together, explain the observed dynamic responses. Applying TimePath to study human response to HIV-1 led to accurate reconstruction of several known regulatory and signaling pathways and to novel mechanistic insights. We experimentally validated several of TimePaths’ predictions highlighting the usefulness of temporal models.

Availability and Implementation: Data, Supplementary text and the TimePath software are available from http://sb.cs.cmu.edu/timepath

Contact:zivbj@cs.cmu.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Computational prediction of virus-human protein-protein interactions using embedding kernelized heterogeneous data

Pathogenic microorganisms exploit host cellular mechanisms and evade host defense mechanisms through molecular pathogen-host interactions (PHIs). Therefore, comprehensive analysis of these PHI networks should be an initial step for developing effective therapeutics against infectious diseases. Computational prediction of PHI data is gaining increasing demand because of scarcity of experimental data. Prediction of protein-protein interactions (PPIs) within PHI systems can be formulated as a classification problem, which requires the knowledge of non-interacting protein pairs. This is a restricting requirement since we lack datasets that report non-interacting protein pairs. In this study, we formulated the "computational prediction of PHI data" problem using kernel embedding of heterogeneous data. This eliminates the abovementioned requirement and enables us to predict new interactions without randomly labeling protein pairs as non-interacting. Domain-domain associations are used to filter the predicted results leading to 175 novel PHIs between 170 human proteins and 105 viral proteins. To compare our results with the state-of-the-art studies that use a binary classification formulation, we modified our settings to consider the same formulation. Detailed evaluations are conducted and our results provide more than 10 percent improvements for accuracy and AUC (area under the receiving operating curve) results in comparison with state-of-the-art methods.

Transcriptome analyses identify key cellular factors associated with HIV-1-associated neuropathogenesis in infected men

OBJECTIVE

HIV-1 viral proteins and host inflammatory factors have a direct role in neuronal toxicity in vitro; however, the contribution of these factors in vivo in HIV-1-associated neurocognitive disorder (HAND) is not fully understood. We applied novel Systems Biology approaches to identify specific cellular and viral factors and their related pathways that are associated with different stages of HAND.

DESIGN

A cross-sectional study of individuals enrolled in the Multicenter AIDS Cohort Study including HIV-1-seronegative (N = 36) and HIV-1-seropositive individuals without neurocognitive symptoms (N = 16) or with mild neurocognitive disorder (MND) (N = 8) or HIV-associated dementia (HAD) (N = 16).

METHODS

A systematic evaluation of global transcriptome of peripheral blood mononuclear cells (PBMCs) obtained from HIV-1-seronegative individuals and from HIV-1-positive men without neurocognitive symptoms, or MND or HAD was performed.

RESULTS

MND and HAD were associated with specific changes in mRNA transcripts and microRNAs in PBMCs. Comparison of upstream regulators and TimePath analyses identified specific cellular factors associated with MND and HAD, whereas HIV-1 viral proteins played a greater role in HAD. In addition, expression of specific microRNAs - miR-let-7a, miR-124, miR-15a and others - were found to correlate with mRNA gene expression and may have a potential protective role in asymptomatic HIV-1-seropositive individuals by regulating cellular signal transduction pathways downstream of chemokines and cytokines.

CONCLUSION

These results identify signature transcriptome changes in PBMCs associated with stages of HAND and shed light on the potential contribution of host cellular factors and viral proteins in HAND development.

Toxic effect of acrylamide on the development of hippocampal neurons of weaning rats

Although numerous studies have examined the neurotoxicity of acrylamide in adult animals, the effects on neuronal development in the embryonic and lactational periods are largely unknown. Thus, we examined the toxicity of acrylamide on neuronal development in the hippocampus of fetal rats during pregnancy. Sprague-Dawley rats were mated with male rats at a 1:1 ratio. Rats were administered 0, 5, 10 or 20 mg/kg acrylamide intragastrically from embryonic days 6–21. The gait scores were examined in pregnant rats in each group to analyze maternal toxicity. Eight weaning rats from each group were also euthanized on postnatal day 21 for follow-up studies. Nissl staining was used to observe histological change in the hippocampus. Immunohistochemistry was conducted to observe the condition of neurites, including dendrites and axons. Western blot assay was used to measure the expression levels of the specific nerve axon membrane protein, growth associated protein 43, and the presynaptic vesicle membrane specific protein, synaptophysin. The gait scores of gravid rats significantly increased, suggesting that acrylamide induced maternal motor dysfunction. The number of neurons, as well as expression of growth associated protein 43 and synaptophysin, was reduced with increasing acrylamide dose in postnatal day 21 weaning rats. These data suggest that acrylamide exerts dose-dependent toxic effects on the growth and development of hippocampal neurons of weaning rats.

A review on host-pathogen interactions: classification and prediction

The research on host-pathogen interactions is an ever-emerging and evolving field. Every other day a new pathogen gets discovered, along with comes the challenge of its prevention and cure. As the intelligent human always vies for prevention, which is better than cure, understanding the mechanisms of host-pathogen interactions gets prior importance. There are many mechanisms involved from the pathogen as well as the host sides while an interaction happens. It is a vis-a-vis fight of the counter genes and proteins from both sides. Who wins depends on whether a host gets an infection or not. Moreover, a higher level of complexity arises when the pathogens evolve and become resistant to a host's defense mechanisms. Such pathogens pose serious challenges for treatment. The entire human population is in danger of such long-lasting persistent infections. Some of these infections even increase the rate of mortality. Hence there is an immediate emergency to understand how the pathogens interact with their host for successful invasion. It may lead to discovery of appropriate preventive measures, and the development of rational therapeutic measures and medication against such infections and diseases. This review, a state-of-the-art updated scenario of host-pathogen interaction research, has been done by keeping in mind this urgency. It covers the biological and computational aspects of host-pathogen interactions, classification of the methods by which the pathogens interact with their hosts, different machine learning techniques for prediction of host-pathogen interactions, and future scopes of this research field.

An improved method for predicting interactions between virus and human proteins

The interaction of virus proteins with host proteins plays a key role in viral infection and consequent pathogenesis. Many computational methods have been proposed to predict protein-protein interactions (PPIs), but most of the computational methods are intended for PPIs within a species rather than PPIs across different species such as virus-host PPIs. We developed a method that represents key features of virus and human proteins of variable length into a feature vector of fixed length. The key features include the relative frequency of amino acid triplets (RFAT), the frequency difference of amino acid triplets (FDAT) between virus and host proteins, and amino acid composition (AC). We constructed several support vector machine (SVM) models to evaluate our method and to compare our method with others on PPIs between human and two types of viruses: human papillomaviruses (HPV) and hepatitis C virus (HCV). Comparison of our method to others with same datasets of HPV-human PPIs and HCV-human PPIs showed that the performance of our method is significantly higher than others in all performance measures. Using the SVM model with gene ontology (GO) annotations of proteins, we predicted new HPV-human PPIs. We believe our approach will be useful in predicting heterogeneous PPIs.

Nucleolin interacts with influenza A nucleoprotein and contributes to viral ribonucleoprotein complexes nuclear trafficking and efficient influenza viral replication

Influenza viruses replicate their single-stranded RNA genomes in the nucleus of infected cells and these replicated genomes (vRNPs) are then exported from the nucleus to the cytoplasm and plasma membrane before budding. To achieve this export, influenza viruses hijack the host cell export machinery. However, the complete mechanisms underlying this hijacking remain not fully understood. We have previously shown that influenza viruses induce a marked alteration of the nucleus during the time-course of infection and notably in the nucleolar compartment. In this study, we discovered that a major nucleolar component, called nucleolin, is required for an efficient export of vRNPs and viral replication. We have notably shown that nucleolin interacts with the viral nucleoprotein (NP) that mainly constitutes vRNPs. Our results suggest that this interaction could allow vRNPs to "catch" the host cell export machinery, a necessary step for viral replication.

Networks of Host Factors that Interact with NS1 Protein of Influenza A Virus

Pigs are an important host of influenza A viruses due to their ability to generate reassortant viruses with pandemic potential. NS1 protein of influenza A viruses is a key virulence factor and a major antagonist of innate immune responses. It is also involved in enhancing viral mRNA translation and regulation of virus replication. Being a protein with pleiotropic functions, NS1 has a variety of cellular interaction partners. Hence, studies on swine influenza viruses (SIV) and identification of swine influenza NS1-interacting host proteins is of great interest. Here, we constructed a recombinant SIV carrying a Strep-tag in the NS1 protein and infected primary swine respiratory epithelial cells (SRECs) with this virus. The Strep-tag sequence in the NS1 protein enabled us to purify intact, the NS1 protein and its interacting protein complex specifically. We identified cellular proteins present in the purified complex by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and generated a dataset of these proteins. 445 proteins were identified by LC-MS/MS and among them 192 proteins were selected by setting up a threshold based on MS parameters. The selected proteins were analyzed by bioinformatics and were categorized as belonging to different functional groups including translation, RNA processing, cytoskeleton, innate immunity, and apoptosis. Protein interaction networks were derived using these data and the NS1 interactions with some of the specific host factors were verified by immunoprecipitation. The novel proteins and the networks revealed in our study will be the potential candidates for targeted study of the molecular interaction of NS1 with host proteins, which will provide insights into the identification of new therapeutic targets to control influenza infection and disease pathogenesis.

Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets

The protein-protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as "indispensable," "neutral," or "dispensable," which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network's control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets.

A model for intracellular movement of Cauliflower mosaic virus: the concept of the mobile virion factory

The genomes of many plant viruses have a coding capacity limited to <10 proteins, yet it is becoming increasingly clear that individual plant virus proteins may interact with several targets in the host for establishment of infection. As new functions are uncovered for individual viral proteins, virologists have realized that the apparent simplicity of the virus genome is an illusion that belies the true impact that plant viruses have on host physiology. In this review, we discuss our evolving understanding of the function of the P6 protein of Cauliflower mosaic virus (CaMV), a process that was initiated nearly 35 years ago when the CaMV P6 protein was first described as the 'major inclusion body protein' (IB) present in infected plants. P6 is now referred to in most articles as the transactivator (TAV)/viroplasmin protein, because the first viral function to be characterized for the Caulimovirus P6 protein beyond its role as an inclusion body protein (the viroplasmin) was its role in translational transactivation (the TAV function). This review will discuss the currently accepted functions for P6 and then present the evidence for an entirely new function for P6 in intracellular movement.

Proteomic approaches to uncovering virus-host protein interactions during the progression of viral infection

The integration of proteomic methods to virology has facilitated a significant breadth of biological insight into mechanisms of virus replication, antiviral host responses and viral subversion of host defenses. Throughout the course of infection, these cellular mechanisms rely heavily on the formation of temporally and spatially regulated virus-host protein-protein interactions. Reviewed here are proteomic-based approaches that have been used to characterize this dynamic virus-host interplay. Specifically discussed are the contribution of integrative mass spectrometry, antibody-based affinity purification of protein complexes, cross-linking and protein array techniques for elucidating complex networks of virus-host protein associations during infection with a diverse range of RNA and DNA viruses. The benefits and limitations of applying proteomic methods to virology are explored, and the contribution of these approaches to important biological discoveries and to inspiring new tractable avenues for the design of antiviral therapeutics is highlighted.

Interactome of the hepatitis C virus: Literature mining with ANDSystem

A study of the molecular genetics mechanisms of host-pathogen interactions is of paramount importance in developing drugs against viral diseases. Currently, the literature contains a huge amount of information that describes interactions between HCV and human proteins. In addition, there are many factual databases that contain experimentally verified data on HCV-host interactions. The sources of such data are the original data along with the data manually extracted from the literature. However, the manual analysis of scientific publications is time consuming and, because of this, databases created with such an approach often do not have complete information. One of the most promising methods to provide actualisation and completeness of information is text mining. Here, with the use of a previously developed method by the authors using ANDSystem, an automated extraction of information on the interactions between HCV and human proteins was conducted. As a data source for the text mining approach, PubMed abstracts and full text articles were used. Additionally, external factual databases were analyzed. On the basis of this analysis, a special version of ANDSystem, extended with the HCV interactome, was created. The HCV interactome contains information about the interactions between 969 human and 11 HCV proteins. Among the 969 proteins, 153 'new' proteins were found not previously referred to in any external databases of protein-protein interactions for HCV-host interactions. Thus, the extended ANDSystem possesses a more comprehensive detailing of HCV-host interactions versus other existing databases. It was interesting that HCV proteins more preferably interact with human proteins that were already involved in a large number of protein-protein interactions as well as those associated with many diseases. Among human proteins of the HCV interactome, there were a large number of proteins regulated by microRNAs. It turned out that the results obtained for protein-protein interactions and microRNA-regulation did not depend on how well the proteins were studied, while protein-disease interactions appeared to be dependent on the level of study. In particular, the mean number of diseases linked to well-studied proteins (proteins were considered well-studied if they were mentioned in 50 or more PubMed publications) from the HCV interactome was 20.8, significantly exceeding the mean number of associations with diseases (10.1) for the total set of well-studied human proteins present in ANDSystem. For proteins not highly poorly-studied investigated, proteins from the HCV interactome (each protein was referred to in less than 50 publications) distribution of the number of diseases associated with them had no statistically significant differences from the distribution of the number of diseases associated with poorly-studied proteins based on the total set of human proteins stored in ANDSystem. With this, the average number of associations with diseases for the HCV interactome and the total set of human proteins were 0.3 and 0.2, respectively. Thus, ANDSystem, extended with the HCV interactome, can be helpful in a wide range of issues related to analyzing HCV-host interactions in the search for anti-HCV drug targets. The demo version of the extended ANDSystem covered here containing only interactions between human proteins, genes, metabolites, diseases, miRNAs and molecular-genetic pathways, as well as interactions between human proteins/genes and HCV proteins, is freely available at the following web address: http://www-bionet.sscc.ru/psd/andhcv/.

Application of Functional Genomics for Bovine Respiratory Disease Diagnostics

Bovine respiratory disease (BRD) is the most common economically important disease affecting cattle. For developing accurate diagnostics that can predict disease susceptibility/resistance and stratification, it is necessary to identify the molecular mechanisms that underlie BRD. To study the complex interactions among the bovine host and the multitude of viral and bacterial pathogens, as well as the environmental factors associated with BRD etiology, genome-scale high-throughput functional genomics methods such as microarrays, RNA-seq, and proteomics are helpful. In this review, we summarize the progress made in our understanding of BRD using functional genomics approaches. We also discuss some of the available bioinformatics resources for analyzing high-throughput data, in the context of biological pathways and molecular interactions. Although resources for studying host response to infection are avail-able, the corresponding information is lacking for majority of BRD pathogens, impeding progress in identifying diagnostic signatures for BRD using functional genomics approaches.

Temporal transcriptional response to latency reversing agents identifies specific factors regulating HIV-1 viral transcriptional switch

Background

Latent HIV-1 reservoirs are identified as one of the major challenges to achieve HIV-1 cure. Currently available strategies are associated with wide variability in outcomes both in patients and CD4⁺ T cell models. This underlines the critical need to develop innovative strategies to predict and recognize ways that could result in better reactivation and eventual elimination of latent HIV-1 reservoirs.

Results and discussion

In this study, we combined genome wide transcriptome datasets post activation with Systems Biology approach (Signaling and Dynamic Regulatory Events Miner, SDREM analyses) to reconstruct a dynamic signaling and regulatory network involved in reactivation mediated by specific activators using a latent cell line. This approach identified several critical regulators for each treatment, which were confirmed in follow-up validation studies using small molecule inhibitors. Results indicate that signaling pathways involving JNK and related factors as predicted by SDREM are essential for virus reactivation by suberoylanilide hydroxamic acid. ERK1/2 and NF-κB pathways have the foremost role in reactivation with prostratin and TNF-α, respectively. JAK-STAT pathway has a central role in HIV-1 transcription. Additional evaluation, using other latent J-Lat cell clones and primary T cell model, also confirmed that many of the cellular factors associated with latency reversing agents are similar, though minor differences are identified. JAK-STAT and NF-κB related pathways are critical for reversal of HIV-1 latency in primary resting T cells.

Conclusion

These results validate our combinatorial approach to predict the regulatory cellular factors and pathways responsible for HIV-1 reactivation in latent HIV-1 harboring cell line models. JAK-STAT have a role in reversal of latency in all the HIV-1 latency models tested, including primary CD4⁺ T cells, with additional cellular pathways such as NF-κB, JNK and ERK 1/2 that may have complementary role in reversal of HIV-1 latency.

Electronic supplementary material

The online version of this article (doi:10.1186/s12977-015-0211-3) contains supplementary material, which is available to authorized users.

Integrative Genomics-Based Discovery of Novel Regulators of the Innate Antiviral Response

The RIG-I-like receptor (RLR) pathway is essential for detecting cytosolic viral RNA to trigger the production of type I interferons (IFNα/β) that initiate an innate antiviral response. Through systematic assessment of a wide variety of genomics data, we discovered 10 molecular signatures of known RLR pathway components that collectively predict novel members. We demonstrate that RLR pathway genes, among others, tend to evolve rapidly, interact with viral proteins, contain a limited set of protein domains, are regulated by specific transcription factors, and form a tightly connected interaction network. Using a Bayesian approach to integrate these signatures, we propose likely novel RLR regulators. RNAi knockdown experiments revealed a high prediction accuracy, identifying 94 genes among 187 candidates tested (~50%) that affected viral RNA-induced production of IFNβ. The discovered antiviral regulators may participate in a wide range of processes that highlight the complexity of antiviral defense (e.g. MAP3K11, CDK11B, PSMA3, TRIM14, HSPA9B, CDC37, NUP98, G3BP1), and include uncharacterized factors (DDX17, C6orf58, C16orf57, PKN2, SNW1). Our validated RLR pathway list (http://rlr.cmbi.umcn.nl/), obtained using a combination of integrative genomics and experiments, is a new resource for innate antiviral immunity research.

Improving the Understanding of Pathogenesis of Human Papillomavirus 16 via Mapping Protein-Protein Interaction Network

The human papillomavirus 16 (HPV16) has high risk to lead various cancers and afflictions, especially, the cervical cancer. Therefore, investigating the pathogenesis of HPV16 is very important for public health. Protein-protein interaction (PPI) network between HPV16 and human was used as a measure to improve our understanding of its pathogenesis. By adopting sequence and topological features, a support vector machine (SVM) model was built to predict new interactions between HPV16 and human proteins. All interactions were comprehensively investigated and analyzed. The analysis indicated that HPV16 enlarged its scope of influence by interacting with human proteins as much as possible. These interactions alter a broad array of cell cycle progression. Furthermore, not only was HPV16 highly prone to interact with hub proteins and bottleneck proteins, but also it could effectively affect a breadth of signaling pathways. In addition, we found that the HPV16 evolved into high carcinogenicity on the condition that its own reproduction had been ensured. Meanwhile, this work will contribute to providing potential new targets for antiviral therapeutics and help experimental research in the future.

Prediction of host - pathogen protein interactions between Mycobacterium tuberculosis and Homo sapiens using sequence motifs

Background

Emergence of multiple drug resistant strains of M. tuberculosis (MDR-TB) threatens to derail global efforts aimed at reigning in the pathogen. Co-infections of M. tuberculosis with HIV are difficult to treat. To counter these new challenges, it is essential to study the interactions between M. tuberculosis and the host to learn how these bacteria cause disease.

Results

We report a systematic flow to predict the host pathogen interactions (HPIs) between M. tuberculosis and Homo sapiens based on sequence motifs. First, protein sequences were used as initial input for identifying the HPIs by ‘interolog’ method. HPIs were further filtered by prediction of domain-domain interactions (DDIs). Functional annotations of protein and publicly available experimental results were applied to filter the remaining HPIs. Using such a strategy, 118 pairs of HPIs were identified, which involve 43 proteins from M. tuberculosis and 48 proteins from Homo sapiens. A biological interaction network between M. tuberculosis and Homo sapiens was then constructed using the predicted inter- and intra-species interactions based on the 118 pairs of HPIs. Finally, a web accessible database named PATH (Protein interactions of M. tuberculosis and Human) was constructed to store these predicted interactions and proteins.

Conclusions

This interaction network will facilitate the research on host-pathogen protein-protein interactions, and may throw light on how M. tuberculosis interacts with its host.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-015-0535-y) contains supplementary material, which is available to authorized users.

Computational approaches for prediction of pathogen-host protein-protein interactions

Infectious diseases are still among the major and prevalent health problems, mostly because of the drug resistance of novel variants of pathogens. Molecular interactions between pathogens and their hosts are the key parts of the infection mechanisms. Novel antimicrobial therapeutics to fight drug resistance is only possible in case of a thorough understanding of pathogen-host interaction (PHI) systems. Existing databases, which contain experimentally verified PHI data, suffer from scarcity of reported interactions due to the technically challenging and time consuming process of experiments. These have motivated many researchers to address the problem by proposing computational approaches for analysis and prediction of PHIs. The computational methods primarily utilize sequence information, protein structure and known interactions. Classic machine learning techniques are used when there are sufficient known interactions to be used as training data. On the opposite case, transfer and multitask learning methods are preferred. Here, we present an overview of these computational approaches for predicting PHI systems, discussing their weakness and abilities, with future directions.

An integrative approach for a network based meta-analysis of viral RNAi screens

BACKGROUND

Big data is becoming ubiquitous in biology, and poses significant challenges in data analysis and interpretation. RNAi screening has become a workhorse of functional genomics, and has been applied, for example, to identify host factors involved in infection for a panel of different viruses. However, the analysis of data resulting from such screens is difficult, with often low overlap between hit lists, even when comparing screens targeting the same virus. This makes it a major challenge to select interesting candidates for further detailed, mechanistic experimental characterization.

RESULTS

To address this problem we propose an integrative bioinformatics pipeline that allows for a network based meta-analysis of viral high-throughput RNAi screens. Initially, we collate a human protein interaction network from various public repositories, which is then subjected to unsupervised clustering to determine functional modules. Modules that are significantly enriched with host dependency factors (HDFs) and/or host restriction factors (HRFs) are then filtered based on network topology and semantic similarity measures. Modules passing all these criteria are finally interpreted for their biological significance using enrichment analysis, and interesting candidate genes can be selected from the modules.

CONCLUSIONS

We apply our approach to seven screens targeting three different viruses, and compare results with other published meta-analyses of viral RNAi screens. We recover key hit genes, and identify additional candidates from the screens. While we demonstrate the application of the approach using viral RNAi data, the method is generally applicable to identify underlying mechanisms from hit lists derived from high-throughput experimental data, and to select a small number of most promising genes for further mechanistic studies.

Merging and scoring molecular interactions utilising existing community standards: tools, use-cases and a case study

The evidence that two molecules interact in a living cell is often inferred from multiple different experiments. Experimental data is captured in multiple repositories, but there is no simple way to assess the evidence of an interaction occurring in a cellular environment. Merging and scoring of data are commonly required operations after querying for the details of specific molecular interactions, to remove redundancy and assess the strength of accompanying experimental evidence. We have developed both a merging algorithm and a scoring system for molecular interactions based on the proteomics standard initiative–molecular interaction standards. In this manuscript, we introduce these two algorithms and provide community access to the tool suite, describe examples of how these tools are useful to selectively present molecular interaction data and demonstrate a case where the algorithms were successfully used to identify a systematic error in an existing dataset.

Network representations of immune system complexity

The mammalian immune system is a dynamic multiscale system composed of a hierarchically organized set of molecular, cellular, and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein–protein interactions underlying intracellular signaling pathways and single‐cell responses to increasingly complex networks of in vivo cellular interaction, positioning, and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather nonlinear behaviors arising from dynamic, feedback‐regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multiscale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels, while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular‐ and organism‐level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. WIREs Syst Biol Med 2015, 7:13–38. doi: 10.1002/wsbm.1288

This article is categorized under: 1

Analytical and Computational Methods > Computational Methods

2

Laboratory Methods and Technologies > Macromolecular Interactions, Methods

A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks

Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions. Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus.