PIG--the pathogen interaction gateway

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.

ConsensusPathDB 2022: molecular interactions update as a resource for network biology

Molecular interactions are key drivers of biological function. Providing interaction resources to the research community is important since they allow functional interpretation and network-based analysis of molecular data. ConsensusPathDB (http://consensuspathdb.org) is a meta-database combining interactions of diverse types from 31 public resources for humans, 16 for mice and 14 for yeasts. Using ConsensusPathDB, researchers commonly evaluate lists of genes, proteins and metabolites against sets of molecular interactions defined by pathways, Gene Ontology and network neighborhoods and retrieve complex molecular neighborhoods formed by heterogeneous interaction types. Furthermore, the integrated protein–protein interaction network is used as a basis for propagation methods. Here, we present the 2022 update of ConsensusPathDB, highlighting content growth, additional functionality and improved database stability. For example, the number of human molecular interactions increased to 859 848 connecting 200 499 unique physical entities such as genes/proteins, metabolites and drugs. Furthermore, we integrated regulatory datasets in the form of transcription factor–, microRNA– and enhancer–gene target interactions, thus providing novel functionality in the context of overrepresentation and enrichment analyses. We specifically emphasize the use of the integrated protein–protein interaction network as a scaffold for network inferences, present topological characteristics of the network and discuss strengths and shortcomings of such approaches.

Systematic evaluation of machine learning methods for identifying human-pathogen protein-protein interactions

In recent years, high-throughput experimental techniques have significantly enhanced the accuracy and coverage of protein-protein interaction identification, including human-pathogen protein-protein interactions (HP-PPIs). Despite this progress, experimental methods are, in general, expensive in terms of both time and labour costs, especially considering that there are enormous amounts of potential protein-interacting partners. Developing computational methods to predict interactions between human and bacteria pathogen has thus become critical and meaningful, in both facilitating the detection of interactions and mining incomplete interaction maps. In this paper, we present a systematic evaluation of machine learning-based computational methods for human-bacterium protein-protein interactions (HB-PPIs). We first reviewed a vast number of publicly available databases of HP-PPIs and then critically evaluate the availability of these databases. Benefitting from its well-structured nature, we subsequently preprocess the data and identified six bacterium pathogens that could be used to study bacterium subjects in which a human was the host. Additionally, we thoroughly reviewed the literature on 'host-pathogen interactions' whereby existing models were summarized that we used to jointly study the impact of different feature representation algorithms and evaluate the performance of existing machine learning computational models. Owing to the abundance of sequence information and the limited scale of other protein-related information, we adopted the primary protocol from the literature and dedicated our analysis to a comprehensive assessment of sequence information and machine learning models. A systematic evaluation of machine learning models and a wide range of feature representation algorithms based on sequence information are presented as a comparison survey towards the prediction performance evaluation of HB-PPIs.

Study of intra-inter species protein-protein interactions for potential drug targets identification and subsequent drug design for Escherichia coli O104:H4 C277-11

Protein-protein interaction (PPI) and host-pathogen interactions (HPI) proteomic analysis has been successfully practiced for potential drug target identification in pathogenic infections. In this research, we attempted to identify new drug target based on PPI and HPI computation approaches and subsequently design new drug against devastating enterohemorrhagic Escherichia coli O104:H4 C277-11 (Broad), which causes life-threatening food borne disease outbreak in Germany and other countries in Europe in 2011. Our systematic in silico analysis on PPI and HPI of E. coli O104:H4 was able to identify bacterial D-galactose-binding periplasmic and UDP-N-acetylglucosamine 1-carboxyvinyltransferase as attractive candidates for new drug targets. Furthermore, computational three-dimensional structure modeling and subsequent molecular docking finally proposed [3-(5-Amino-7-Hydroxy-[1,2,3]Triazolo[4,5-D]Pyrimidin-2-Yl)-N-(3,5-Dichlorobenzyl)-Benzamide)] and (6-amino-2-[(1-naphthylmethyl)amino]-3,7-dihydro-8H-imidazo[4,5-g]quinazolin-8-one) as promising candidate drugs for further evaluation and development for E. coli O104:H4 mediated diseases. Identification of new drug target would be of great utility for humanity as the demand for designing new drugs to fight infections is increasing due to the developing resistance and side effects of current treatments. This research provided the basis for computer aided drug design which might be useful for new drug target identification and subsequent drug design for other infectious organisms.

DenHunt - A Comprehensive Database of the Intricate Network of Dengue-Human Interactions

Dengue virus (DENV) is a human pathogen and its etiology has been widely established. There are many interactions between DENV and human proteins that have been reported in literature. However, no publicly accessible resource for efficiently retrieving the information is yet available. In this study, we mined all publicly available dengue-human interactions that have been reported in the literature into a database called DenHunt. We retrieved 682 direct interactions of human proteins with dengue viral components, 382 indirect interactions and 4120 differentially expressed human genes in dengue infected cell lines and patients. We have illustrated the importance of DenHunt by mapping the dengue-human interactions on to the host interactome and observed that the virus targets multiple host functional complexes of important cellular processes such as metabolism, immune system and signaling pathways suggesting a potential role of these interactions in viral pathogenesis. We also observed that 7 percent of the dengue virus interacting human proteins are also associated with other infectious and non-infectious diseases. Finally, the understanding that comes from such analyses could be used to design better strategies to counteract the diseases caused by dengue virus. The whole dataset has been catalogued in a searchable database, called DenHunt (http://proline.biochem.iisc.ernet.in/DenHunt/).

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.

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.

Molecular principles of human virus protein-protein interactions

MOTIVATION

Viruses, from the human protein-protein interaction network perspective, target hubs, bottlenecks and interconnected nodes enriched in certain biological pathways. However, not much is known about the general characteristic features of the human proteins interacting with viral proteins (referred to as hVIPs) as well as the motifs and domains utilized by human-virus protein-protein interactions (referred to as Hu-Vir PPIs).

RESULTS

Our study has revealed that hVIPs are mostly disordered proteins, whereas viral proteins are mostly ordered proteins. Protein disorder in viral proteins and hVIPs varies from one subcellular location to another. In any given viral-human PPI pair, at least one of the two proteins is structurally disordered suggesting that disorder associated conformational flexibility as one of the characteristic features of virus-host interaction. Further analyses reveal that hVIPs are (i) slowly evolving proteins, (ii) associated with high centrality scores in human-PPI network, (iii) involved in multiple pathways, (iv) enriched in eukaryotic linear motifs (ELMs) associated with protein modification, degradation and regulatory processes, (v) associated with high number of splice variants and (vi) expressed abundantly across multiple tissues. These aforementioned findings suggest that conformational flexibility, spatial diversity, abundance and slow evolution are the characteristic features of the human proteins targeted by viral proteins. Hu-Vir PPIs are mostly mediated via domain-motif interactions (DMIs) where viral proteins employ motifs that mimic host ELMs to bind to domains in human proteins. DMIs are shared among viruses belonging to different families indicating a possible convergent evolution of these motifs to help viruses to adopt common strategies to subvert host cellular pathways.

AVAILABILITY AND IMPLEMENTATION

Hu-Vir PPI data, DDI and DMI data for human-virus PPI can be downloaded from http://cdfd.org.in/labpages/computational_biology_datasets.html.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Key Hub and Bottleneck Genes Differentiate the Macrophage Response to Virulent and Attenuated Mycobacterium bovis

Mycobacterium bovis is an intracellular pathogen that causes tuberculosis in cattle. Following infection, the pathogen resides and persists inside host macrophages by subverting host immune responses via a diverse range of mechanisms. Here, a high-density bovine microarray platform was used to examine the bovine monocyte-derived macrophage transcriptome response to M. bovis infection relative to infection with the attenuated vaccine strain, M. bovis Bacille Calmette-Guérin. Differentially expressed genes were identified (adjusted P-value ≤0.01) and interaction networks generated across an infection time course of 2, 6, and 24 h. The largest number of biological interactions was observed in the 24-h network, which exhibited scale-free network properties. The 24-h network featured a small number of key hub and bottleneck gene nodes, including IKBKE, MYC, NFKB1, and EGR1 that differentiated the macrophage response to virulent and attenuated M. bovis strains, possibly via the modulation of host cell death mechanisms. These hub and bottleneck genes represent possible targets for immuno-modulation of host macrophages by virulent mycobacterial species that enable their survival within a hostile environment.

Viral proteins that bridge unconnected proteins and components in the human PPI network

Viral proteins bridging unconnected components of the Hu-PPI network play a crucial role in viral replication and hence form attractive targets for therapeutic interventions.

Multitask learning for host-pathogen protein interactions

Motivation: An important aspect of infectious disease research involves understanding the differences and commonalities in the infection mechanisms underlying various diseases. Systems biology-based approaches study infectious diseases by analyzing the interactions between the host species and the pathogen organisms. This work aims to combine the knowledge from experimental studies of host–pathogen interactions in several diseases to build stronger predictive models. Our approach is based on a formalism from machine learning called ‘multitask learning’, which considers the problem of building models across tasks that are related to each other. A ‘task’ in our scenario is the set of host–pathogen protein interactions involved in one disease. To integrate interactions from several tasks (i.e. diseases), our method exploits the similarity in the infection process across the diseases. In particular, we use the biological hypothesis that similar pathogens target the same critical biological processes in the host, in defining a common structure across the tasks.

Results: Our current work on host–pathogen protein interaction prediction focuses on human as the host, and four bacterial species as pathogens. The multitask learning technique we develop uses a task-based regularization approach. We find that the resulting optimization problem is a difference of convex (DC) functions. To optimize, we implement a Convex–Concave procedure-based algorithm. We compare our integrative approach to baseline methods that build models on a single host–pathogen protein interaction dataset. Our results show that our approach outperforms the baselines on the training data. We further analyze the protein interaction predictions generated by the models, and find some interesting insights.

Availability: The predictions and code are available at: http://www.cs.cmu.edu/∼mkshirsa/ismb2013_paper320.html

Contact:j.klein-seetharaman@warwick.ac.uk

Supplementary information:Supplementary data are available at Bioinformatics online.

PATRIC, the bacterial bioinformatics database and analysis resource

The Pathosystems Resource Integration Center (PATRIC) is the all-bacterial Bioinformatics Resource Center (BRC) (http://www.patricbrc.org). A joint effort by two of the original National Institute of Allergy and Infectious Diseases-funded BRCs, PATRIC provides researchers with an online resource that stores and integrates a variety of data types [e.g. genomics, transcriptomics, protein-protein interactions (PPIs), three-dimensional protein structures and sequence typing data] and associated metadata. Datatypes are summarized for individual genomes and across taxonomic levels. All genomes in PATRIC, currently more than 10,000, are consistently annotated using RAST, the Rapid Annotations using Subsystems Technology. Summaries of different data types are also provided for individual genes, where comparisons of different annotations are available, and also include available transcriptomic data. PATRIC provides a variety of ways for researchers to find data of interest and a private workspace where they can store both genomic and gene associations, and their own private data. Both private and public data can be analyzed together using a suite of tools to perform comparative genomic or transcriptomic analysis. PATRIC also includes integrated information related to disease and PPIs. All the data and integrated analysis and visualization tools are freely available. This manuscript describes updates to the PATRIC since its initial report in the 2007 NAR Database Issue.

Pathogenicity phenomena in three model systems: from network mining to emerging system-level properties

Understanding the interconnections of microbial pathogenicity phenomena, such as biofilm formation, quorum sensing and antimicrobial resistance, is a tremendous open challenge for biomedical research. Progress made by wet-lab researchers and bioinformaticians in understanding the underlying regulatory phenomena has been significant, with converging evidence from multiple high-throughput technologies. Notably, network reconstructions are already of considerable size and quality, tackling both intracellular regulation and signal mediation in microbial infection. Therefore, it stands to reason that in silico investigations would play a more active part in this research. Drug target identification and drug repurposing could take much advantage of the ability to simulate pathogen regulatory systems, host-pathogen interactions and pathogen cross-talking. Here, we review the bioinformatics resources and tools available for the study of the gram-negative bacterium Pseudomonas aeruginosa, the gram-positive bacterium Staphylococcus aureus and the fungal species Candida albicans. The choice of these three microorganisms fits the rationale of the review converging into pathogens of great clinical importance, which thrive in biofilm consortia and manifest growing antimicrobial resistance.

A systematic analysis of host factors reveals a Med23-interferon-λ regulatory axis against herpes simplex virus type 1 replication

Herpes simplex virus type 1 (HSV-1) is a neurotropic virus causing vesicular oral or genital skin lesions, meningitis and other diseases particularly harmful in immunocompromised individuals. To comprehensively investigate the complex interaction between HSV-1 and its host we combined two genome-scale screens for host factors (HFs) involved in virus replication. A yeast two-hybrid screen for protein interactions and a RNA interference (RNAi) screen with a druggable genome small interfering RNA (siRNA) library confirmed existing and identified novel HFs which functionally influence HSV-1 infection. Bioinformatic analyses found the 358 HFs were enriched for several pathways and multi-protein complexes. Of particular interest was the identification of Med23 as a strongly anti-viral component of the largely pro-viral Mediator complex, which links specific transcription factors to RNA polymerase II. The anti-viral effect of Med23 on HSV-1 replication was confirmed in gain-of-function gene overexpression experiments, and this inhibitory effect was specific to HSV-1, as a range of other viruses including Vaccinia virus and Semliki Forest virus were unaffected by Med23 depletion. We found Med23 significantly upregulated expression of the type III interferon family (IFN-λ) at the mRNA and protein level by directly interacting with the transcription factor IRF7. The synergistic effect of Med23 and IRF7 on IFN-λ induction suggests this is the major transcription factor for IFN-λ expression. Genotypic analysis of patients suffering recurrent orofacial HSV-1 outbreaks, previously shown to be deficient in IFN-λ secretion, found a significant correlation with a single nucleotide polymorphism in the IFN-λ3 (IL28b) promoter strongly linked to Hepatitis C disease and treatment outcome. This paper describes a link between Med23 and IFN-λ, provides evidence for the crucial role of IFN-λ in HSV-1 immune control, and highlights the power of integrative genome-scale approaches to identify HFs critical for disease progression and outcome.

Toward a three-dimensional view of protein networks between species

General principles governing biomolecular interactions between species are expected to differ significantly from known principles governing the interactions within species, yet these principles remain poorly understood at the systems level. A key reason for this knowledge gap is the lack of a detailed three-dimensional (3D), atomistic view of biomolecular interaction networks between species. Recent progress in structural biology, systems biology, and computational biology has enabled accurate and large-scale construction of 3D structural models of nodes and edges for protein–protein interaction networks within and between species. The resulting within- and between-species structural interaction networks have provided new biophysical, functional, and evolutionary insights into species interactions and infectious disease. Here, we review the nascent field of between-species structural systems biology, focusing on interactions between host and pathogens such as viruses.

Progress in computational studies of host-pathogen interactions

Host-pathogen interactions are important for understanding infection mechanism and developing better treatment and prevention of infectious diseases. Many computational studies on host-pathogen interactions have been published. Here, we review recent progress and results in this field and provide a systematic summary, comparison and discussion of computational studies on host-pathogen interactions, including prediction and analysis of host-pathogen protein-protein interactions; basic principles revealed from host-pathogen interactions; and database and software tools for host-pathogen interaction data collection, integration and analysis.

Comparative analysis of virus-host interactomes with a mammalian high-throughput protein complementation assay based on Gaussia princeps luciferase

Comparative interactomics is a strategy for inferring potential interactions among orthologous proteins or "interologs". Herein we focus, in contrast to standard homology-based inference, on the divergence of protein interaction profiles among closely related organisms, showing that the approach can correlate specific traits to phenotypic differences. As a model, this new comparative interactomic approach was applied at a large scale to human papillomaviruses (HPVs) proteins. The oncogenic potential of HPVs is mainly determined by the E6 and E7 early proteins. We have mapped and overlapped the virus-host protein interaction networks of E6 and E7 proteins from 11 distinct HPV genotypes, selected for their different tropisms and pathologies. We generated robust and comprehensive datasets by combining two orthogonal protein interaction assays: yeast two-hybrid (Y2H), and our recently described "high-throughput Gaussia princeps protein complementation assay" (HT-GPCA). HT-GPCA detects protein interaction by measuring the interaction-mediated reconstitution of activity of a split G. princeps luciferase. Hierarchical clustering of interaction profiles recapitulated HPV phylogeny and was used to correlate specific virus-host interaction profiles with pathological traits, reflecting the distinct carcinogenic potentials of different HPVs. This comparative interactomics constitutes a reliable and powerful strategy to decipher molecular relationships in virtually any combination of microorganism-host interactions.

Computational analysis of interactomes: current and future perspectives for bioinformatics approaches to model the host-pathogen interaction space

Bacterial and viral pathogens affect their eukaryotic host partly by interacting with proteins of the host cell. Hence, to investigate infection from a systems' perspective we need to construct complete and accurate host-pathogen protein-protein interaction networks. Because of the paucity of available data and the cost associated with experimental approaches, any construction and analysis of such a network in the near future has to rely on computational predictions. Specifically, this challenge consists of a number of sub-problems: First, prediction of possible pathogen interactors (e.g. effector proteins) is necessary for bacteria and protozoa. Second, the prospective host binding partners have to be determined and finally, the impact on the host cell analyzed. This review gives an overview of current bioinformatics approaches to obtain and understand host-pathogen interactions. As an application example of the methods covered, we predict host-pathogen interactions of Salmonella and discuss the value of these predictions as a prospective for further research.

Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection

Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease patients. With the increased prevalence of multi-drug resistant PA, the conventional “one gene, one drug, one disease” paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at http://research.cchmc.org/PPIdatabase/.

VIP DB--a viral protein domain usage and distribution database

During the viral infection and replication processes, viral proteins are highly regulated and may interact with host proteins. However, the functions and interaction partners of many viral proteins have yet to be explored. Here, we compiled a VIral Protein domain DataBase (VIP DB) to associate viral proteins with putative functions and interaction partners. We systematically assign domains and infer the functions of proteins and their protein interaction partners from their domain annotations. A total of 2,322 unique domains that were identified from 2,404 viruses are used as a starting point to correlate GO classification, KEGG metabolic pathway annotation and domain-domain interactions. Of the unique domains, 42.7% have GO records, 39.6% have at least one domain-domain interaction record and 26.3% can also be found in either mammals or plants. This database provides a resource to help virologists identify potential roles for viral protein. All of the information is available at http://vipdb.cgu.edu.tw.

BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference

Protein–protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.

Functional genomics of trypanosomatids

The decoding of the Tritryp reference genomes nearly 7 years ago provided a first peek into the biology of pathogenic trypanosomatids and a blueprint that has paved the way for genome-wide studies. Although 60-70% of the predicted protein coding genes in Trypanosoma brucei, Trypanosoma cruzi and Leishmania major remain unannotated, the functional genomics landscape is rapidly changing. Facilitated by the advent of next-generation sequencing technologies, improved structural and functional annotation and genes and their products are emerging. Information is also growing for the interactions between cellular components as transcriptomes, regulatory networks and metabolomes are characterized, ushering in a new era of systems biology. Simultaneously, the launch of comparative sequencing of multiple strains of kinetoplastids will finally lead to the investigation of a vast, yet to be explored, evolutionary and pathogenomic space.

Prediction and comparison of Salmonella-human and Salmonella-Arabidopsis interactomes

Salmonellosis caused by Salmonella bacteria is a food-borne disease and a worldwide health threat causing millions of infections and thousands of deaths every year. This pathogen infects an unusually broad range of host organisms including human and plants. A better understanding of the mechanisms of communication between Salmonella and its hosts requires identifying the interactions between Salmonella and host proteins. Protein-protein interactions (PPIs) are the fundamental building blocks of communication. Here, we utilize the prediction platform BIANA to obtain the putative Salmonella-human and Salmonella-Arabidopsis interactomes based on sequence and domain similarity to known PPIs. A gold standard list of Salmonella-host PPIs served to validate the quality of the human model. 24,726 and 10,926 PPIs comprising interactions between 38 and 33 Salmonella effectors and virulence factors with 9,740 human and 4,676 Arabidopsis proteins, respectively, were predicted. Putative hub proteins could be identified, and parallels between the two interactomes were discovered. This approach can provide insight into possible biological functions of so far uncharacterized proteins. The predicted interactions are available via a web interface which allows filtering of the database according to parameters provided by the user to narrow down the list of suspected interactions. The interactions are available via a web interface at http://sbi.imim.es/web/SHIPREC.php.

Infection Strategies of Bacterial and Viral Pathogens through Pathogen-Human Protein-Protein Interactions

Since ancient times, even in today’s modern world, infectious diseases cause lots of people to die. Infectious organisms, pathogens, cause diseases by physical interactions with human proteins. A thorough analysis of these interspecies interactions is required to provide insights about infection strategies of pathogens. Here we analyzed the most comprehensive available pathogen–human protein interaction data including 23,435 interactions, targeting 5,210 human proteins. The data were obtained from the newly developed pathogen–host interaction search tool, PHISTO. This is the first comprehensive attempt to get a comparison between bacterial and viral infections. We investigated human proteins that are targeted by bacteria and viruses to provide an overview of common and special infection strategies used by these pathogen types. We observed that in the human protein interaction network the proteins targeted by pathogens have higher connectivity and betweenness centrality values than those proteins not interacting with pathogens. The preference of interacting with hub and bottleneck proteins is found to be a common infection strategy of all types of pathogens to manipulate essential mechanisms in human. Compared to bacteria, viruses tend to interact with human proteins of much higher connectivity and centrality values in the human network. Gene Ontology enrichment analysis of the human proteins targeted by pathogens indicates crucial clues about the infection mechanisms of bacteria and viruses. As the main infection strategy, bacteria interact with human proteins that function in immune response to disrupt human defense mechanisms. Indispensable viral strategy, on the other hand, is the manipulation of human cellular processes in order to use that transcriptional machinery for their own genetic material transcription. A novel observation about pathogen–human systems is that the human proteins targeted by both pathogens are enriched in the regulation of metabolic processes.

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

Background

Biology is rapidly becoming a data intensive, data-driven science. It is essential that data is represented and connected in ways that best represent its full conceptual content and allows both automated integration and data driven decision-making. Recent advancements in distributed multi-relational directed graphs, implemented in the form of the Semantic Web make it possible to deal with complicated heterogeneous data in new and interesting ways.

Results

This paper presents a new approach, scenario driven data modelling (SDDM), that integrates multi-relational directed graphs with data streams. SDDM can be applied to virtually any data integration challenge with widely divergent types of data and data streams. In this work, we explored integrating genetics data with reports from traditional media. SDDM was applied to the New Delhi metallo-beta-lactamase gene (NDM-1), an emerging global health threat. The SDDM process constructed a scenario, created a RDF multi-relational directed graph that linked diverse types of data to the Semantic Web, implemented RDF conversion tools (RDFizers) to bring content into the Sematic Web, identified data streams and analytical routines to analyse those streams, and identified user requirements and graph traversals to meet end-user requirements.

Conclusions

We provided an example where SDDM was applied to a complex data integration challenge. The process created a model of the emerging NDM-1 health threat, identified and filled gaps in that model, and constructed reliable software that monitored data streams based on the scenario derived multi-relational directed graph. The SDDM process significantly reduced the software requirements phase by letting the scenario and resulting multi-relational directed graph define what is possible and then set the scope of the user requirements. Approaches like SDDM will be critical to the future of data intensive, data-driven science because they automate the process of converting massive data streams into usable knowledge.

Virus-host interactomes and global models of virus-infected cells

Novel high-throughput technologies such as yeast two-hybrid and RNA interference (RNAi) screens provide the tools to study interactions between viral proteins and the host on a genomic scale. In this review, we provide an overview of studies in which these technologies were applied and of computational approaches for the analysis of the identified viral interactors in the context of the host cell. The results of these studies illustrate the advantages of integrative systems biology approaches in the investigation of viral pathogens.

GPS-Prot: a web-based visualization platform for integrating host-pathogen interaction data

BACKGROUND

The increasing availability of HIV-host interaction datasets, including both physical and genetic interactions, has created a need for software tools to integrate and visualize the data. Because these host-pathogen interactions are extensive and interactions between human proteins are found within many different databases, it is difficult to generate integrated HIV-human interaction networks.

RESULTS

We have developed a web-based platform, termed GPS-Prot http://www.gpsprot.org, that allows for facile integration of different HIV interaction data types as well as inclusion of interactions between human proteins derived from publicly-available databases, including MINT, BioGRID and HPRD. The software has the ability to group proteins into functional modules or protein complexes, generating more intuitive network representations and also allows for the uploading of user-generated data.

CONCLUSIONS

GPS-Prot is a software tool that allows users to easily create comprehensive and integrated HIV-host networks. A major advantage of this platform compared to other visualization tools is its web-based format, which requires no software installation or data downloads. GPS-Prot allows novice users to quickly generate networks that combine both genetic and protein-protein interactions between HIV and its human host into a single representation. Ultimately, the platform is extendable to other host-pathogen systems.

Immunogenomics and systems biology of vaccines

Vaccines represent a potent tool to prevent or contain infectious diseases with high morbidity or mortality. However, despite their widespread use, we still have a limited understanding of the mechanisms underlying the effective elicitation of protective immune responses by vaccines. Recent research suggests that this represents the cooperative action of the innate and adaptive immune systems. Immunity is made of a multifaceted set of integrated responses involving a dynamic interaction of thousands of molecules, whose list is constantly updated to fill the several empty spaces of this puzzle. The recent development of new technologies and computational tools permits the comprehensive and quantitative analysis of the interactions between all of the components of immunity over time. Here, we review the role of the innate immunity in the host response to vaccine antigens and the potential of systems biology in providing relevant and novel insights in the mechanisms of action of vaccines to improve their design and effectiveness.

Systems-biology approaches to discover anti-viral effectors of the human innate immune response

Virus infections elicit an immediate innate response involving antiviral factors. The activities of some of these factors are, in turn, blocked by viral countermeasures. The ensuing battle between the host and the viruses is crucial for determining whether the virus establishes a foothold and/or induces adaptive immune responses. A comprehensive systems-level understanding of the repertoire of anti-viral effectors in the context of these immediate virus-host responses would provide significant advantages in devising novel strategies to interfere with the initial establishment of infections. Recent efforts to identify cellular factors in a comprehensive and unbiased manner, using genome-wide siRNA screens and other systems biology “omics” methodologies, have revealed several potential anti-viral effectors for viruses like Human immunodeficiency virus type 1 (HIV-1), Hepatitis C virus (HCV), West Nile virus (WNV), and influenza virus. This review describes the discovery of novel viral restriction factors and discusses how the integration of different methods in systems biology can be used to more comprehensively identify the intimate interactions of viruses and the cellular innate resistance.

Domain-domain interactions underlying herpesvirus-human protein-protein interaction networks

Protein-domains play an important role in mediating protein-protein interactions. Furthermore, the same domain-pairs mediate different interactions in different contexts and in various organisms, and therefore domain-pairs are considered as the building blocks of interactome networks. Here we extend these principles to the host-virus interface and find the domain-pairs that potentially mediate human-herpesvirus interactions. Notably, we find that the same domain-pairs used by other organisms for mediating their interactions underlie statistically significant fractions of human-virus protein inter-interaction networks. Our analysis shows that viral domains tend to interact with human domains that are hubs in the human domain-domain interaction network. This may enable the virus to easily interfere with a variety of mechanisms and processes involving various and different human proteins carrying the relevant hub domain. Comparative genomics analysis provides hints at a molecular mechanism by which the virus acquired some of its interacting domains from its human host.

Integration and visualization of host-pathogen data related to infectious diseases

MOTIVATION

Infectious disease research is generating an increasing amount of disparate data on pathogenic systems. There is a growing need for resources that effectively integrate, analyze, deliver and visualize these data, both to improve our understanding of infectious diseases and to facilitate the development of strategies for disease control and prevention.

RESULTS

We have developed Disease View, an online host-pathogen resource that enables infectious disease-centric access, analysis and visualization of host-pathogen interactions. In this resource, we associate infectious diseases with corresponding pathogens, provide information on pathogens, pathogen virulence genes and the genetic and chemical evidences for the human genes that are associated with the diseases. We also deliver the relationships between pathogens, genes and diseases in an interactive graph and provide the geolocation reports of associated diseases around the globe in real time. Unlike many other resources, we have applied an iterative, user-centered design process to the entire resource development, including data acquisition, analysis and visualization.

AVAILABILITY AND IMPLEMENTATION

Freely available at http://www.patricbrc.org; all major web browsers supported.

CONTACT

cmao@vbi.vt.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Virus interactions with human signal transduction pathways

Viruses depend on their hosts at every stage of their life cycles and must therefore communicate with them via Protein-Protein Interactions (PPIs). To investigate the mechanisms of communication by different viruses, we overlay reported pairwise human-virus PPIs on human signalling pathways. Of 671 pathways obtained from NCI and Reactome databases, 355 are potentially targeted by at least one virus. The majority of pathways are linked to more than one virus. We find evidence supporting the hypothesis that viruses often interact with different proteins depending on the targeted pathway. Pathway analysis indicates overrepresentation of some pathways targeted by viruses. The merged network of the most statistically significant pathways shows several centrally located proteins, which are also hub proteins. Generally, hub proteins are targeted more frequently by viruses. Numerous proteins in virus-targeted pathways are known drug targets, suggesting that these might be exploited as potential new approaches to treatments against multiple viruses.

BiologicalNetworks--tools enabling the integration of multi-scale data for the host-pathogen studies

BACKGROUND

Understanding of immune response mechanisms of pathogen-infected host requires multi-scale analysis of genome-wide data. Data integration methods have proved useful to the study of biological processes in model organisms, but their systematic application to the study of host immune system response to a pathogen and human disease is still in the initial stage.

RESULTS

To study host-pathogen interaction on the systems biology level, an extension to the previously described BiologicalNetworks system is proposed. The developed methods and data integration and querying tools allow simplifying and streamlining the process of integration of diverse experimental data types, including molecular interactions and phylogenetic classifications, genomic sequences and protein structure information, gene expression and virulence data for pathogen-related studies. The data can be integrated from the databases and user's files for both public and private use.

CONCLUSIONS

The developed system can be used for the systems-level analysis of host-pathogen interactions, including host molecular pathways that are induced/repressed during the infections, co-expressed genes, and conserved transcription factor binding sites. Previously unknown to be associated with the influenza infection genes were identified and suggested for further investigation as potential drug targets. Developed methods and data are available through the Java application (from BiologicalNetworks program at http://www.biologicalnetworks.org) and web interface (at http://flu.sdsc.edu).

ConsensusPathDB: toward a more complete picture of cell biology

ConsensusPathDB is a meta-database that integrates different types of functional interactions from heterogeneous interaction data resources. Physical protein interactions, metabolic and signaling reactions and gene regulatory interactions are integrated in a seamless functional association network that simultaneously describes multiple functional aspects of genes, proteins, complexes, metabolites, etc. With 155 432 human, 194 480 yeast and 13 648 mouse complex functional interactions (originating from 18 databases on human and eight databases on yeast and mouse interactions each), ConsensusPathDB currently constitutes the most comprehensive publicly available interaction repository for these species. The Web interface at http://cpdb.molgen.mpg.de offers different ways of utilizing these integrated interaction data, in particular with tools for visualization, analysis and interpretation of high-throughput expression data in the light of functional interactions and biological pathways.

Host genetics and HIV-1: the final phase?

This is a crucial transition time for human genetics in general, and for HIV host genetics in particular. After years of equivocal results from candidate gene analyses, several genome-wide association studies have been published that looked at plasma viral load or disease progression. Results from other studies that used various large-scale approaches (siRNA screens, transcriptome or proteome analysis, comparative genomics) have also shed new light on retroviral pathogenesis. However, most of the inter-individual variability in response to HIV-1 infection remains to be explained: genome resequencing and systems biology approaches are now required to progress toward a better understanding of the complex interactions between HIV-1 and its human host.

HPIDB--a unified resource for host-pathogen interactions

BACKGROUND

Protein-protein interactions (PPIs) play a crucial role in initiating infection in a host-pathogen system. Identification of these PPIs is important for understanding the underlying biological mechanism of infection and identifying putative drug targets. Database resources for studying host-pathogen systems are scarce and are either host specific or dedicated to specific pathogens.

RESULTS

Here we describe "HPIDB" a host-pathogen PPI database, which will serve as a unified resource for host-pathogen interactions. Specifically, HPIDB integrates experimental PPIs from several public databases into a single, non-redundant web accessible resource. The database can be searched with a variety of options such as sequence identifiers, symbol, taxonomy, publication, author, or interaction type. The output is provided in a tab delimited text file format that is compatible with Cytoscape, an open source resource for PPI visualization. HPIDB allows the user to search protein sequences using BLASTP to retrieve homologous host/pathogen sequences. For high-throughput analysis, the user can search multiple protein sequences at a time using BLASTP and obtain results in tabular and sequence alignment formats. The taxonomic categorization of proteins (bacterial, viral, fungi, etc.) involved in PPI enables the user to perform category specific BLASTP searches. In addition, a new tool is introduced, which allows searching for homologous host-pathogen interactions in the HPIDB database.

CONCLUSIONS

HPIDB is a unified, comprehensive resource for host-pathogen PPIs. The user interface provides new features and tools helpful for studying host-pathogen interactions. HPIDB can be accessed at http://agbase.msstate.edu/hpi/main.html.

Virus-host protein interactions in RNA viruses

RNA viruses exhibit small-sized genomes that only encode a limited number of viral proteins, but still establish complex networks of interactions with host cell components. Here we summarize recent reports that aim at understanding general features of RNA virus infection networks at the protein level.

Structural similarity-based predictions of protein interactions between HIV-1 and Homo sapiens

Background

In the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes.

Results

We developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence.

Conclusions

Understanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention.

Immunoinformatics and systems biology methods for personalized medicine

The immune system plays an important role in the development of personalized medicine for a variety of diseases including cancer, autoimmune diseases, and infectious diseases. Immunoinformatics, or computational immunology, is an emerging area that provides fundamental methodologies in the study of immunomics, that is, immune-related genomics and proteomics. The integration of immunoinformatics with systems biology approaches may lead to a better understanding of immune-related diseases at various systems levels. Such methods can contribute to translational studies that bring scientific discoveries of the immune system into better clinical practice. One of the most intensely studied areas of the immune system is immune epitopes. Epitopes are important for disease understanding, host-pathogen interaction analyses, antimicrobial target discovery, and vaccine design. The information about genetic diversity of the immune system may help define patient subgroups for individualized vaccine or drug development. Cellular pathways and host immune-pathogen interactions have a crucial impact on disease pathogenesis and immunogen design. Epigenetic studies may help understand how environmental changes influence complex immune diseases such as allergy. High-throughput technologies enable the measurements and catalogs of genes, proteins, interactions, and behavior. Such perception may contribute to the understanding of the interaction network among humans, vaccines, and drugs, to enable new insights of diseases and therapeutic responses. The integration of immunomics information may ultimately lead to the development of optimized vaccines and drugs tailored to personalized prevention and treatment. An immunoinformatics portal containing relevant resources is available at http://immune.pharmtao.com.

Informatics for Infectious Disease Research and Control

The goal of infectious disease informatics is to optimize the clinical and public health management of infectious diseases through improvements in the development and use of antimicrobials, the design of more effective vaccines, the identification of biomarkers for life-threatening infections, a better understanding of host-pathogen interactions, and biosurveillance and clinical decision support. Infectious disease informatics can lead to more targeted and effective approaches for the prevention, diagnosis and treatment of infections through a comprehensive review of the genetic repertoire and metabolic profiles of a pathogen. The developments in informatics have been critical in boosting the translational science and in supporting both reductionist and integrative research paradigms.

Understanding animal viruses using the Gene Ontology

Understanding the effects of viral infection has typically focused on specific virus-host interactions such as tissue tropism, immune responses and histopathology. However, modeling viral pathogenesis requires information about the functions of gene products from both virus and host, and how these products interact. Recent developments in the functional annotation of genomes using Gene Ontology (GO) and in modeling functional interactions among gene products, together with an increased interest in systems biology, provide an excellent opportunity to generate global interaction models for viral infection. Here, we review how the GO is being used to model viral pathogenesis, with a focus on animal viruses.

Enabling a systems biology approach to immunology: focus on innate immunity

Immunity is not simply the product of a series of discrete linear signalling pathways; rather it is comprised of a complex set of integrated responses arising from a dynamic network of thousands of molecules subject to multiple influences. Its behaviour often cannot be explained or predicted solely by examining its components. Here, we review recently developed resources for the systems-level investigation of immunity. Although innate immunity is emphasized here, its considerable overlap with adaptive immunity makes many of these resources relevant to both arms of the immune response. We discuss recent studies implementing these approaches and illustrate the potential of systems biology to generate novel insights into the complexities of innate immunity.