Host-pathogen systems biology

TRustDB: A comprehensive bioinformatics resource for understanding the complete Wheat-Stem rust host-pathogen interactome

The increasing infectious diseases in wheat immensely reduce crop yield and quality, thus affecting global wheat production. The evolution in phytopathogens hinders the understanding of the disease infection mechanisms. TRustDB is an open-access, comprehensive database that is specifically focused on the disease stem rust (also known as black rust) in Triticum aestivum, which is caused by the fungal pathogen Puccinia graminis (Pgt), strains 'Ug99' and '21-0'. The database aims at a broader focus of providing the researchers with comprehensive tools to predict the protein-protein interactions and avail the functional annotations of the proteins involved in the interactions that cause the disease. The network of the predicted interactome can also be visualized on the browser. Various modules for the functional annotations of the host and pathogen proteins such as subcellular localization, functional domains, gene ontology annotations, pathogen orthologs and effector proteins have been implemented. The host proteins that serve as transcription factors, along with the respective Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways are also available, which further enhance the understanding of the disease infection mechanisms and the defense responses of the host. The database is also linked with several other databases such as InterPro, KEGG pathways, Ensembl and National Center for Biotechnology Information (NCBI). TRustDB has a user-friendly web interface, which can be accessed through . Database URL http://bioinfo.usu.edu/trustdb/.

Structural proteomics, electron cryo-microscopy and structural modeling approaches in bacteria-human protein interactions

A central challenge in infection medicine is to determine the structure and function of host-pathogen protein-protein interactions to understand how these interactions facilitate bacterial adhesion, dissemination and survival. In this review, we focus on proteomics, electron cryo-microscopy and structural modeling to showcase instances where affinity-purification (AP) and cross-linking (XL) mass spectrometry (MS) has advanced our understanding of host-pathogen interactions. We highlight cases where XL-MS in combination with structural modeling has provided insight into the quaternary structure of interspecies protein complexes. We further exemplify how electron cryo-tomography has been used to visualize bacterial-human interactions during attachment and infection. Lastly, we discuss how AP-MS, XL-MS and electron cryo-microscopy and -tomography together with structural modeling approaches can be used in future studies to broaden our knowledge regarding the function, dynamics and evolution of such interactions. This knowledge will be of relevance for future drug and vaccine development programs.

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.

Systematic expression profiling analysis mines dys-regulated modules in active tuberculosis based on re-weighted protein-protein interaction network and attract algorithm

About 90% of tuberculosis (TB) patients latently infected with Mycobacterium tuberculosis (Mtb) show no symptoms, yet have a 10% chance in lifetime to progress active TB. Nevertheless, current diagnosis approaches need improvement in efficiency and sensitivity. The objective of this work was to detect potential signatures for active TB to further improve the understanding of the biological roles of functional modules involved in this disease. First, targeted networks of active TB and control groups were established via re-weighting protein-protein interaction (PPI) networks using Pearson's correlation coefficient (PCC). Candidate modules were detected from the targeted networks, and the modules with Jaccard score >0.7 were defined as attractors. After that, identification of dys-regulated modules was conducted from the attractors using attract method, Subsequently, gene oncology (GO) enrichment analyses were implemented for genes in the dys-regulated modules. We obtained 33 and 65 candidate modules from the targeted networks of control and active TB groups, respectively. Overall, 13 attractors were identified. Using the cut-off criteria of false discovery rate <0.05, there were 4 dys-regulated modules (Module 1, 2, 3, and 4). Based on the GO annotation results, genes in Modules 1, 2 and 4 were only involved in translation. Most genes in Module 1, 2 and 4 were associated with ribosomes. Accordingly, these dys-regulated modules might serve as potential biomarkers of active TB, facilitating the development for a more efficient, and sensitive diagnostic assay for active TB.

MPLEx: a method for simultaneous pathogen inactivation and extraction of samples for multi-omics profiling

The continued emergence and spread of infectious agents is of great concern, and systems biology approaches to infectious disease research can advance our understanding of host-pathogen relationships and facilitate the development of new therapies and vaccines. Molecular characterization of infectious samples outside of appropriate biosafety containment can take place only subsequent to pathogen inactivation. Herein, we describe a modified Folch extraction using chloroform/methanol that facilitates the molecular characterization of infectious samples by enabling simultaneous pathogen inactivation and extraction of proteins, metabolites, and lipids for subsequent mass spectrometry-based multi-omics measurements. This single-sample metabolite, protein and lipid extraction (MPLEx) method resulted in complete inactivation of clinically important bacterial and viral pathogens with exposed lipid membranes, including Yersinia pestis, Salmonella Typhimurium, and Campylobacter jejuni in pure culture, and Yersinia pestis, Campylobacter jejuni, and West Nile, MERS-CoV, Ebola, and influenza H7N9 viruses in infection studies. In addition, >99% inactivation, which increased with solvent exposure time, was also observed for pathogens without exposed lipid membranes including community-associated methicillin-resistant Staphylococcus aureus, Clostridium difficile spores and vegetative cells, and adenovirus type 5. The overall pipeline of inactivation and subsequent proteomic, metabolomic, and lipidomic analyses was evaluated using a human epithelial lung cell line infected with wild-type and mutant influenza H7N9 viruses, thereby demonstrating that MPLEx yields biomaterial of sufficient quality for subsequent multi-omics analyses. Based on these experimental results, we believe that MPLEx will facilitate systems biology studies of infectious samples by enabling simultaneous pathogen inactivation and multi-omics measurements from a single specimen with high success for pathogens with exposed lipid membranes.

Data-based Reconstruction of Gene Regulatory Networks of Fungal Pathogens

In the emerging field of systems biology of fungal infection, one of the central roles belongs to the modeling of gene regulatory networks (GRNs). Utilizing omics-data, GRNs can be predicted by mathematical modeling. Here, we review current advances of data-based reconstruction of both small-scale and large-scale GRNs for human pathogenic fungi. The advantage of large-scale genome-wide modeling is the possibility to predict central (hub) genes and thereby indicate potential biomarkers and drug targets. In contrast, small-scale GRN models provide hypotheses on the mode of gene regulatory interactions, which have to be validated experimentally. Due to the lack of sufficient quantity and quality of both experimental data and prior knowledge about regulator–target gene relations, the genome-wide modeling still remains problematic for fungal pathogens. While a first genome-wide GRN model has already been published for Candida albicans, the feasibility of such modeling for Aspergillus fumigatus is evaluated in the present article. Based on this evaluation, opinions are drawn on future directions of GRN modeling of fungal pathogens. The crucial point of genome-wide GRN modeling is the experimental evidence, both used for inferring the networks (omics ‘first-hand’ data as well as literature data used as prior knowledge) and for validation and evaluation of the inferred network models.

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.

Image-based systems biology of infection

The successful treatment of infectious diseases requires interdisciplinary studies of all aspects of infection processes. The overarching combination of experimental research and theoretical analysis in a systems biology approach can unravel mechanisms of complex interactions between pathogens and the human immune system. Taking into account spatial information is especially important in the context of infection, since the migratory behavior and spatial interactions of cells are often decisive for the outcome of the immune response. Spatial information is provided by image and video data that are acquired in microscopy experiments and that are at the heart of an image-based systems biology approach. This review demonstrates how image-based systems biology improves our understanding of infection processes. We discuss the three main steps of this approach--imaging, quantitative characterization, and modeling--and consider the application of these steps in the context of studying infection processes. After summarizing the most relevant microscopy and image analysis approaches, we discuss ways to quantify infection processes, and address a number of modeling techniques that exploit image-derived data to simulate host-pathogen interactions in silico.

Inference of the oxidative stress network in Anopheles stephensi upon Plasmodium infection

Ookinete invasion of Anopheles midgut is a critical step for malaria transmission; the parasite numbers drop drastically and practically reach a minimum during the parasite's whole life cycle. At this stage, the parasite as well as the vector undergoes immense oxidative stress. Thereafter, the vector undergoes oxidative stress at different time points as the parasite invades its tissues during the parasite development. The present study was undertaken to reconstruct the network of differentially expressed genes involved in oxidative stress in Anopheles stephensi during Plasmodium development and maturation in the midgut. Using high throughput next generation sequencing methods, we generated the transcriptome of the An. stephensi midgut during Plasmodium vinckei petteri oocyst invasion of the midgut epithelium. Further, we utilized large datasets available on public domain on Anopheles during Plasmodium ookinete invasion and Drosophila datasets and arrived upon clusters of genes that may play a role in oxidative stress. Finally, we used support vector machines for the functional prediction of the un-annotated genes of An. stephensi. Integrating the results from all the different data analyses, we identified a total of 516 genes that were involved in oxidative stress in An. stephensi during Plasmodium development. The significantly regulated genes were further extracted from this gene cluster and used to infer an oxidative stress network of An. stephensi. Using system biology approaches, we have been able to ascertain the role of several putative genes in An. stephensi with respect to oxidative stress. Further experimental validations of these genes are underway.

Injury and immune response: applying the danger theory to mosquitoes

The insect immune response can be activated by the recognition of both non-self and molecular by-products of tissue damage. Since pathogens and tissue damage usually arise at the same time during infection, the specific mechanisms of the immune response to microorganisms, and to tissue damage have not been unraveled. Consequently, some aspects of damage caused by microorganisms in vector-borne arthropods have been neglected. We herein reassess the Anopheles-Plasmodium interaction, incorporating Matzinger's danger/damage hypothesis and George Salt's injury assumptions. The invasive forms of the parasite cross the peritrophic matrix and midgut epithelia to reach the basal lamina and differentiate into an oocyst. The sporozoites produced in the oocyst are released into the hemolymph, and from there enter the salivary gland. During parasite development, wounds to midgut tissue and the basement membrane are produced. We describe the response of the different compartments where the parasite interacts with the mosquito. In the midgut, the response includes the expression of antimicrobial peptides, production of reactive oxygen species, and possible activation of midgut regenerative cells. In the basal membrane, wound repair mainly involves the production of molecules and the recruitment of hemocytes. We discuss the susceptibility to damage in tissues, and how the place and degree of damage may influence the differential response and the expression of damage associated molecular patterns (DAMPs). Knowledge about damage caused by parasites may lead to a deeper understanding of the relevance of tissue damage and the immune response it generates, as well as the origins and progression of infection in this insect-parasite interaction.

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

Viruses, despite having small genomes and few proteins, make an array of interactions with host proteins as they solely depend on host machinery for their replication and reproduction. Hence, analysis of the Human-Virus Protein-Protein Interaction Network (Hu-Vir PPI network) helps us to gain certain insights into the molecular mechanisms underlying the hijacking of host cell machinery by viruses for their perpetuation. Here we report an analysis of the Human-Virus Bridged PPI Networks that has led us to identify viral articulation points (VAPs) which connect unconnected components of the Human-PPI (Hu-PPI) network. VAPs cross-link peripheral nodes to the giant component of the Hu-PPI network. VAPs interact with a number of relatively lower topologically central human proteins and are conserved among related viruses. The linked nodes comprise of those that are mostly expressed during viral infection, as well as those that are found exclusively in some metabolic pathways, indicating that the novel viral mediation of certain human protein-protein interactions may form the basis for virus-specific tuning of the host machinery. The functional importance of VAPs and their interaction partners in virus replication make them potential drug targets against viral infection. Our investigations also led to the discovery of an example of a Human Endogenous Retrovirus (HERV) encoded protein, syncytin, as an Articulation Point (AP) in the Hu-PPI network, suggesting that VAPs may be retained in a genome if they result in any beneficial function in the host.

Mining large-scale response networks reveals 'topmost activities' in Mycobacterium tuberculosis infection

Mycobacterium tuberculosis owes its high pathogenic potential to its ability to evade host immune responses and thrive inside the macrophage. The outcome of infection is largely determined by the cellular response comprising a multitude of molecular events. The complexity and inter-relatedness in the processes makes it essential to adopt systems approaches to study them. In this work, we construct a comprehensive network of infection-related processes in a human macrophage comprising 1888 proteins and 14,016 interactions. We then compute response networks based on available gene expression profiles corresponding to states of health, disease and drug treatment. We use a novel formulation for mining response networks that has led to identifying highest activities in the cell. Highest activity paths provide mechanistic insights into pathogenesis and response to treatment. The approach used here serves as a generic framework for mining dynamic changes in genome-scale protein interaction networks.

Multi-omic network signatures of disease

To better understand dynamic disease processes, integrated multi-omic methods are needed, yet comparing different types of omic data remains difficult. Integrative solutions benefit experimenters by eliminating potential biases that come with single omic analysis. We have developed the methods needed to explore whether a relationship exists between co-expression network models built from transcriptomic and proteomic data types, and whether this relationship can be used to improve the disease signature discovery process. A naïve, correlation based method is utilized for comparison. Using publicly available infectious disease time series data, we analyzed the related co-expression structure of the transcriptome and proteome in response to SARS-CoV infection in mice. Transcript and peptide expression data was filtered using quality scores and subset by taking the intersection on mapped Entrez IDs. Using this data set, independent co-expression networks were built. The networks were integrated by constructing a bipartite module graph based on module member overlap, module summary correlation, and correlation to phenotypes of interest. Compared to the module level results, the naïve approach is hindered by a lack of correlation across data types, less significant enrichment results, and little functional overlap across data types. Our module graph approach avoids these problems, resulting in an integrated omic signature of disease progression, which allows prioritization across data types for down-stream experiment planning. Integrated modules exhibited related functional enrichments and could suggest novel interactions in response to infection. These disease and platform-independent methods can be used to realize the full potential of multi-omic network signatures. The data (experiment SM001) are publically available through the NIAID Systems Virology (https://www.systemsvirology.org) and PNNL (http://omics.pnl.gov) web portals. Phenotype data is found in the supplementary information. The ProCoNA package is available as part of Bioconductor 2.13.

Zebrafish as a model for systems biology

Zebrafish offer a unique vertebrate model for research areas such as drug development, disease modeling and other biological exploration. There is significant conservation of genetics and other cellular networks among zebrafish and other vertebrate models, including humans. Here we discuss the recent work and efforts made in different fields of biology to explore the potential of zebrafish. Along with this, we also reviewed the concept of systems biology. A biological system is made up of a large number of components that interact in a huge variety of combinations. To understand completely the behavior of a system, it is important to know its components and interactions, and this can be achieved through a systems biology approach. At the end of the paper we present a concept of integrating zebrafish into the systems biology approach.

Predicting and analyzing interactions between Mycobacterium tuberculosis and its human host

The outcome of infection by Mycobacterium tuberculosis (Mtb) depends greatly on how the host responds to the bacteria and how the bacteria manipulates the host, which is facilitated by protein-protein interactions. Thus, to understand this process, there is a need for elucidating protein interactions between human and Mtb, which may enable us to characterize specific molecular mechanisms allowing the bacteria to persist and survive under different environmental conditions. In this work, we used the interologs method based on experimentally verified intra-species and inter-species interactions to predict human-Mtb functional interactions. These interactions were further filtered using known human-Mtb interactions and genes that are differentially expressed during infection, producing 190 interactions. Further analysis of the subcellular location of proteins involved in these human-Mtb interactions confirms feasibility of these interactions. We also conducted functional analysis of human and Mtb proteins involved in these interactions, checking whether these proteins play a role in infection and/or disease, and enriching Mtb proteins in a previously predicted list of drug targets. We found that the biological processes of the human interacting proteins suggested their involvement in apoptosis and production of nitric oxide, whereas those of the Mtb interacting proteins were relevant to the intracellular environment of Mtb in the host. Mapping these proteins onto KEGG pathways highlighted proteins belonging to the tuberculosis pathway and also suggested that Mtb proteins might use the host to acquire nutrients, which is in agreement with the intracellular lifestyle of Mtb. This indicates that these interactions can shed light on the interplay between Mtb and its human host and thus, contribute to the process of designing novel drugs with new biological mechanisms of action.

Current trends in modeling host–pathogen interactions

The rapid emergence of infectious diseases calls for immediate attention to determine practical solutions for intervention strategies. To this end, it becomes necessary to obtain a holistic view of the complex host–pathogen interactome. Advances in omics and related technology have resulted in massive generation of data for the interacting systems at unprecedented levels of detail. Systems‐level studies with the aid of mathematical tools contribute to a deeper understanding of biological systems, where intuitive reasoning alone does not suffice. In this review, we discuss different aspects of host–pathogen interactions (HPIs) and the available data resources and tools used to study them. We discuss in detail models of HPIs at various levels of abstraction, along with their applications and limitations. We also enlist a few case studies, which incorporate different modeling approaches, providing significant insights into disease. © 2013 Wiley Periodicals, Inc.

This article is categorized under:

Systems biology of pathogen-host interaction: networks of protein-protein interaction within pathogens and pathogen-human interactions in the post-genomic era

Infectious diseases comprise some of the leading causes of death and disability worldwide. Interactions between pathogen and host proteins underlie the process of infection. Improved understanding of pathogen-host molecular interactions will increase our knowledge of the mechanisms involved in infection, and allow novel therapeutic solutions to be devised. Complete genome sequences for a number of pathogenic microorganisms, as well as the human host, has led to the revelation of their protein-protein interaction (PPI) networks. In this post-genomic era, pathogen-host interactions (PHIs) operating during infection can also be mapped. Detailed systematic analyses of PPI and PHI data together are required for a complete understanding of pathogenesis of infections. Here we review the striking results recently obtained during the construction and investigation of these networks. Emphasis is placed on studies producing large-scale interaction data by high-throughput experimental techniques.

Metabolomic investigations of human infections

Metabolomics has a special place among other 'omics' disciplines (genomics, transcriptomics and proteomics) as it describes the most dynamic level of biological regulation and, as such, provides the most direct reflection of the physiological status of an organism. Quick development of the analytical technologies in the first place - MS and NMR - has enabled the metabolomics analysis of such complex biological phenomena as host-pathogen interactions in the development of infection. In this review, an overview of the metabolomics studies of infectious diseases carried out on human material is provided. The relevant papers on the metabolomics of human infectious diseases are comprehensively summarized in a table, including, for example, information on the study design, number of subjects, employed technology and metabolic discriminator. Future considerations, such as importance of the time-resolved study designs and the embedment of metabolomics in large-scale epidemiological studies are discussed.

Dynamics of influenza virus and human host interactions during infection and replication cycle

The replication and life cycle of the influenza virus is governed by an intricate network of intracellular regulatory events during infection, including interactions with an even more complex system of biochemical interactions of the host cell. Computational modeling and systems biology have been successfully employed to further the understanding of various biological systems, however, computational studies of the complexity of intracellular interactions during influenza infection is lacking. In this work, we present the first large-scale dynamical model of the infection and replication cycle of influenza, as well as some of its interactions with the host's signaling machinery. Specifically, we focus on and visualize the dynamics of the internalization and endocytosis of the virus, replication and translation of its genomic components, as well as the assembly of progeny virions. Simulations and analyses of the models dynamics qualitatively reproduced numerous biological phenomena discovered in the laboratory. Finally, comparisons of the dynamics of existing and proposed drugs, our results suggest that a drug targeting PB1:PA would be more efficient than existing Amantadin/Rimantaine or Zanamivir/Oseltamivir.

Influenza infection and therapy: a systems approach

Seasonal flu affects 5–20% of the human population each year. Although mortality rates are typically <0.1% and the pandemic 2009 H1N1 influenza strain has been well contained by vaccination and strict hygiene, particularly virulent pandemic forms have emerged three times in the last century, resulting in millions of deaths. Current vaccine and therapeutic strategies are limited by the ability of the virus to generate variants that evade vaccine-induced immune responses and resist the therapeutic effects of antiviral drugs. Host genetic variations affect immune responses and may induce adverse effects during drug treatment or against vaccines. To develop new, first-in-class therapeutics, new antiviral targets and new chemical entities must be identified in the context of the immunogenomic repertoire of the patient. Since influenza and so many other viruses need to escape innate immunity to become pathogenic, the viral proteins responsible for this, as well as the host cell molecular pathways that lead to the antiviral response, are an excellent potential source of new therapeutic targets within a systems approach against influenza infections.

Systems biology of fungal infection

Elucidation of pathogenicity mechanisms of the most important human-pathogenic fungi, Aspergillus fumigatus and Candida albicans, has gained great interest in the light of the steadily increasing number of cases of invasive fungal infections. A key feature of these infections is the interaction of the different fungal morphotypes with epithelial and immune effector cells in the human host. Because of the high level of complexity, it is necessary to describe and understand invasive fungal infection by taking a systems biological approach, i.e., by a comprehensive quantitative analysis of the non-linear and selective interactions of a large number of functionally diverse, and frequently multifunctional, sets of elements, e.g., genes, proteins, metabolites, which produce coherent and emergent behaviors in time and space. The recent advances in systems biology will now make it possible to uncover the structure and dynamics of molecular and cellular cause-effect relationships within these pathogenic interactions. We review current efforts to integrate omics and image-based data of host-pathogen interactions into network and spatio-temporal models. The modeling will help to elucidate pathogenicity mechanisms and to identify diagnostic biomarkers and potential drug targets for therapy and could thus pave the way for novel intervention strategies based on novel antifungal drugs and cell therapy.

RNA-seq based transcriptional map of bovine respiratory disease pathogen "Histophilus somni 2336"

Genome structural annotation, i.e., identification and demarcation of the boundaries for all the functional elements in a genome (e.g., genes, non-coding RNAs, proteins and regulatory elements), is a prerequisite for systems level analysis. Current genome annotation programs do not identify all of the functional elements of the genome, especially small non-coding RNAs (sRNAs). Whole genome transcriptome analysis is a complementary method to identify “novel” genes, small RNAs, regulatory regions, and operon structures, thus improving the structural annotation in bacteria. In particular, the identification of non-coding RNAs has revealed their widespread occurrence and functional importance in gene regulation, stress and virulence. However, very little is known about non-coding transcripts in Histophilus somni, one of the causative agents of Bovine Respiratory Disease (BRD) as well as bovine infertility, abortion, septicemia, arthritis, myocarditis, and thrombotic meningoencephalitis. In this study, we report a single nucleotide resolution transcriptome map of H. somni strain 2336 using RNA-Seq method.

The RNA-Seq based transcriptome map identified 94 sRNAs in the H. somni genome of which 82 sRNAs were never predicted or reported in earlier studies. We also identified 38 novel potential protein coding open reading frames that were absent in the current genome annotation. The transcriptome map allowed the identification of 278 operon (total 730 genes) structures in the genome. When compared with the genome sequence of a non-virulent strain 129Pt, a disproportionate number of sRNAs (∼30%) were located in genomic region unique to strain 2336 (∼18% of the total genome). This observation suggests that a number of the newly identified sRNAs in strain 2336 may be involved in strain-specific adaptations.

Systems biology of infectious diseases: a focus on fungal infections

The study of infectious disease concerns the interaction between the host species and a pathogen organism. The analysis of such complex systems is improving with the evolution of high-throughput technologies and advanced computational resources. This article reviews integrative, systems-oriented approaches to understanding mechanisms underlying infection, immune response and inflammation to find biomarkers of disease and design new drugs. We focus on the systems biology process, especially the data gathering and analysis techniques rather than the experimental technologies or latest computational resources.

Analysis of phagosomal proteomes: from latex-bead to bacterial phagosomes

Phagosomal proteome characterization has contributed significantly to the understanding of host-pathogen interaction and the mechanism of infectious diseases caused by intracellular bacteria. The latex bead-containing phagosome has been widely used as a model system to study phagosomal proteomes at a global level. In contrast, the study of bacteria-containing phagosomes at a similar level has just begun. A number of intracellular microbial species are studied for their proteomes during the invasion of a host, providing insight into their metabolic adaptation in host cells and interaction with host-cell antimicrobial environments. In this review, we attempt to summarize the most recent advancements in the proteomic study of microbial phagosomes, especially those originating from mouse or human cells. We also briefly describe the proteomics of latex bead-containing phagosomes because they are often used as model phagosomes for study. We provide descriptions on major biological and technological components in phagosomal proteome studies. We also discuss the role of phagosomal proteome study in the broader horizon of systems biology and the technological challenges in phagosomal proteome characterization.

Enzyme sequence similarity improves the reaction alignment method for cross-species pathway comparison

Pathway-based information has become an important source of information for both establishing evolutionary relationships and understanding the mode of action of a chemical or pharmaceutical among species. Cross-species comparison of pathways can address two broad questions: comparison in order to inform evolutionary relationships and to extrapolate species differences used in a number of different applications including drug and toxicity testing. Cross-species comparison of metabolic pathways is complex as there are multiple features of a pathway that can be modeled and compared. Among the various methods that have been proposed, reaction alignment has emerged as the most successful at predicting phylogenetic relationships based on NCBI taxonomy. We propose an improvement of the reaction alignment method by accounting for sequence similarity in addition to reaction alignment method. Using nine species, including human and some model organisms and test species, we evaluate the standard and improved comparison methods by analyzing glycolysis and citrate cycle pathways conservation. In addition, we demonstrate how organism comparison can be conducted by accounting for the cumulative information retrieved from nine pathways in central metabolism as well as a more complete study involving 36 pathways common in all nine species. Our results indicate that reaction alignment with enzyme sequence similarity results in a more accurate representation of pathway specific cross-species similarities and differences based on NCBI taxonomy.

Eimeria bovis-induced modulation of the host cell proteome at the meront I stage

The proteome of Eimeria bovis meront I-carrying host cells was analyzed by two-dimensional gel electrophoresis (2DE) at 14 days p.i. and compared to non-infected control cells. A total of 221 protein spots were modulated in their abundance in E. bovis-infected host cells and were subsequently analyzed by matrix-assisted laser desorption ionization time-of-flight mass spectometry (MALDI-TOF-MS). These analyses identified 104 proteins in total with 25 host cell proteins being up-regulated and 79 proteins being down-regulated in E. bovis-infected host cells. Moreover, 20 newly expressed proteins were identified exclusively in E. bovis-infected host cells and were most likely of parasite origin. Parasite-induced differences in protein abundance concerned distinct functional categories, with most proteins being involved in host cell metabolism, cell structure, protein fate and gene transcription. Some of the modulated molecules also indicated regulatory processes on the level of host cell stress response (HSP70, HSP90), host cell apoptosis (caspase 8) and actin elongation/depolymerization (α-actinin-1, gelsonin, tropomodulin-3, transgelin). Since merozoites I were already released shortly after cell sampling, the current data reflect the situation at the end of first merogony. This is the first proteomic approach on E. bovis-infected host cells that was undertaken to gain a rather broad insight into Eimeria-induced host cell modulation. The data processed in this investigation should provide a useful basis for more detailed analyses concerning Eimeria-host cell interactions.

[The virtual infected cell: a systems biology rational for antiviral drug discovery]

Infection caused by pathogens kills millions of people every year. Comprehensive understanding of molecular pathogen-host interactions, i.e. the infectome, is one of the key steps towards the development of novel diagnostic, therapeutic and preventive strategies. In this quest, progress in high-throughput << omics >> technologies applied to pathogens, i.e. infectomics, opens new perspectives toward systemic understanding of perturbations induced during infection. Deciphering the pathogen-host system also relies on the analytical and predictive power of molecular systems biology and by developing in silico models taking into account the whole picture of the molecules and their interactions. In this context, we have reconstructed a prototype of the human virtual infected cell based on 30 years of intensive research in the field of molecular virology. This model contains more than one hundred viral infectomes, including major human pathogens (HCV, HBV, HIV, HHV, HPV) and has led to the generation of novel systems-level hypotheses that could be suitable for the development of innovative antiviral strategies based on the control of cellular functions.

Response of the mosquito protein interaction network to dengue infection

Background

Two fifths of the world's population is at risk from dengue. The absence of effective drugs and vaccines leaves vector control as the primary intervention tool. Understanding dengue virus (DENV) host interactions is essential for the development of novel control strategies. The availability of genome sequences for both human and mosquito host greatly facilitates genome-wide studies of DENV-host interactions.

Results

We developed the first draft of the mosquito protein interaction network using a computational approach. The weighted network includes 4,214 Aedes aegypti proteins with 10,209 interactions, among which 3,500 proteins are connected into an interconnected scale-free network. We demonstrated the application of this network for the further annotation of mosquito proteins and dissection of pathway crosstalk. Using three datasets based on physical interaction assays, genome-wide RNA interference (RNAi) screens and microarray assays, we identified 714 putative DENV-associated mosquito proteins. An integrated analysis of these proteins in the network highlighted four regions consisting of highly interconnected proteins with closely related functions in each of replication/transcription/translation (RTT), immunity, transport and metabolism. Putative DENV-associated proteins were further selected for validation by RNAi-mediated gene silencing, and dengue viral titer in mosquito midguts was significantly reduced for five out of ten (50.0%) randomly selected genes.

Conclusions

Our results indicate the presence of common host requirements for DENV in mosquitoes and humans. We discuss the significance of our findings for pharmacological intervention and genetic modification of mosquitoes for blocking dengue transmission.

Unraveling human complexity and disease with systems biology and personalized medicine

We are all perplexed that current medical practice often appears maladroit in curing our individual illnesses or disease. However, as is often the case, a lack of understanding, tools and technologies are the root cause of such situations. Human individuality is an often-quoted term but, in the context of human biology, it is poorly understood. This is compounded when there is a need to consider the variability of human populations. In the case of the former, it is possible to quantify human complexity as determined by the 35,000 genes of the human genome, the 1-10 million proteins (including antibodies) and the 2000-3000 metabolites of the human metabolome. Human variability is much more difficult to assess, since many of the variables, such as the definition of race, are not even clearly agreed on. In order to accommodate human complexity, variability and its influence on health and disease, it is necessary to undertake a systematic approach. In the past decade, the emergence of analytical platforms and bioinformatics tools has led to the development of systems biology. Such an approach offers enormous potential in defining key pathways and networks involved in optimal human health, as well as disease onset, progression and treatment. The tools and technologies now available in systems biology analyses offer exciting opportunities to exploit the emerging areas of personalized medicine. In this article, we discuss the current status of human complexity, and how systems biology and personalized medicine can impact at the individual and population level.

Global approaches to study protein-protein interactions among viruses and hosts

While high-throughput protein-protein interaction screens were first published approximately 10 years ago, systematic attempts to map interactions among viruses and hosts started only a few years ago. HIV-human interactions dominate host-pathogen interaction databases (with approximately 2000 interactions) despite the fact that probably none of these interactions have been identified in systematic interaction screens. Recently, combinations of protein interaction data with RNAi and other functional genomics data allowed researchers to model more complex interaction networks. The rapid progress in this area promises a flood of new data in the near future, with clinical applications as soon as structural and functional genomics catches up with next-generation sequencing of human variation and structure-based drug design.

Comparative genomics of metabolic networks of free-living and parasitic eukaryotes

Background

Obligate endoparasites often lack particular metabolic pathways as compared to free-living organisms. This phenomenon comprises anabolic as well as catabolic reactions. Presumably, the corresponding enzymes were lost in adaptation to parasitism. Here we compare the predicted core metabolic graphs of obligate endoparasites and non-parasites (free living organisms and facultative parasites) in order to analyze how the parasites' metabolic networks shrunk in the course of evolution.

Results

Core metabolic graphs comprising biochemical reactions present in the presumed ancestor of parasites and non-parasites were reconstructed from the Kyoto Encyclopedia of Genes and Genomes. While the parasites' networks had fewer nodes (metabolites) and edges (reactions), other parameters such as average connectivity, network diameter and number of isolated edges were similar in parasites and non-parasites. The parasites' networks contained a higher percentage of ATP-consuming reactions and a lower percentage of NAD-requiring reactions. Control networks, shrunk to the size of the parasites' by random deletion of edges, were scale-free but exhibited smaller diameters and more isolated edges.

Conclusions

The parasites' networks were smaller than those of the non-parasites regarding number of nodes or edges, but not regarding network diameters. Network integrity but not scale-freeness has acted as a selective principle during the evolutionary reduction of parasite metabolism. ATP-requiring reactions in particular have been retained in the parasites' core metabolism while NADH- or NADPH-requiring reactions were lost preferentially.

Systems biology of influenza: understanding multidimensional interactions for personalized prevention and treatment

Influenza virus infection is a public health threat worldwide. It is urgent to develop effective methods and tools for the prevention and treatment of influenza. Influenza vaccines have significant immune response variability across the population. Most of the current circulating strains of influenza A virus are resistant to anti-influenza drugs. It is necessary to understand how genetic variants affect immune responses, especially responses to the HA and NA transmembrane glycoproteins. The elucidation of the underlying mechanisms can help identify patient subgroups for effective prevention and treatment. New personalized vaccines, adjuvants, and drugs may result from the understanding of interactions of host genetic, environmental, and other factors. The systems biology approach is to simulate and model large networks of the interacting components, which can be excellent targets for antiviral therapies. The elucidation of host-influenza interactions may provide an integrative view of virus infection and host responses. Understanding the host-influenza-drug interactions may contribute to optimal drug combination therapies. Insight of the host-influenza-vaccine interactions, especially the immunogenetics of vaccine response, may lead to the development of better vaccines. Systemic studies of host-virus-vaccine-drug-environment interactions will enable predictive models for therapeutic responses and the development of individualized therapeutic strategies. A database containing such information on personalized and systems medicine for influenza is available at http://flu.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.

A systems perspective of host-pathogen interactions: predicting disease outcome in tuberculosis

The complex web of interactions between the host immune system and the pathogen determines the outcome of any infection. A computational model of this interaction network, which encodes complex interplay among host and bacterial components, forms a useful basis for improving the understanding of pathogenesis, in filling knowledge gaps and consequently to identify strategies to counter the disease. We have built an extensive model of the Mycobacterium tuberculosis host-pathogen interactome, consisting of 75 nodes corresponding to host and pathogen molecules, cells, cellular states or processes. Vaccination effects, clearance efficiencies due to drugs and growth rates have also been encoded in the model. The system is modelled as a Boolean network. Virtual deletion experiments, multiple parameter scans and analysis of the system's response to perturbations, indicate that disabling processes such as phagocytosis and phagolysosome fusion or cytokines such as TNF-alpha and IFN-gamma, greatly impaired bacterial clearance, while removing cytokines such as IL-10 alongside bacterial defence proteins such as SapM greatly favour clearance. Simulations indicate a high propensity of the pathogen to persist under different conditions.

System-level analysis of Salmonella metabolism during infection

Infectious diseases represent a major threat to human health. To develop urgently needed new control strategies, a transition from research focusing on individual factors to a more integrated system-level analysis might be needed. Such an approach faces great challenges and might require development of new concepts in large-scale data analysis. Here, I discuss for the well-characterized model pathogen Salmonella, how extensively studied metabolism can be used as a training field for infection biology at the systems level. Extensive experimental data can be analyzed in context using metabolic network visualization tools and in silico modeling based on genome-scale metabolic reconstructions. Suitable approaches to obtain still missing comprehensive quantitative data on Salmonella nutrition in infected host tissues are described. Such an integrated investigation of Salmonella metabolism during infection will enable an unprecedented large-scale understanding of pathogen in vivo activities, help to evaluate concepts and strategies for system-level analysis of host/pathogen interactions in general, and provide a basis for rational development of novel antimicrobials and efficacious live vaccines.

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.

Multiplexed optical pathogen detection with lab-on-a-chip devices

Infectious diseases are still a main cause of human morbidity and mortality. Advanced diagnostics is considered to be a key driver to improve the respective therapeutic outcome. The main factors influencing the impact of diagnostics include: assay speed, availability, information content, in-vitro diagnostics and cost, for which molecular assays are providing the most promising opportunities. Miniaturisation and integration of assay steps into lab-on-a-chip devices has been described as an appropriate way to speed up assay time and make assays available onsite at competitive costs. As meaningful assays for infectious diseases need to include a whole range of clinical relevant information about the pathogen, multiplexed functionality is often required for which optical transduction is particularly well suited. The aim of this review is to assess existing developments in this field and to give an outlook on future requirements and solutions.

Collapsing glomerulopathy: beyond serendipity in mouse genetics

Clinical correlates suggest that collapsing glomerulopathy results from the pathogenic interaction between patients' intractable genetic susceptibilities and environmental insults. When the environmental insults include a virus that introduces its own pathogenic genes, the interactions become more complex. Chan et al. combine reverse and forward genetic techniques in mice toward understanding this complexity with HIV and identify candidate genetic modifiers of collapsing glomerulopathy.

Candida albicans–macrophage interactions: genomic and proteomic insights

Candida albicans infection is a significant cause of morbidity and mortality in immunocompromised patients. In vivo and in vitro models have been developed to study both the fungal and the mammalian immune responses. Phagocytic cells (i.e., macrophages) play a key role in innate immunity against C. albicans by capturing, killing and processing the pathogen for presentation to T cells. The use of microarray technology to study global fungal transcriptional changes after interaction with different host cells has revealed how C. albicans adapts to its environment. Proteomic tools complement molecular approaches and computational methods enable the formulation of relevant biological hypotheses. Therefore, the combination of genomics, proteomics and bioinformatics tools (i.e., network analyses) is a powerful strategy to better understand the biological situation of the fungus inside macrophages; part of the fungal population is killed while a significantly high percentage survives.

Towards a systems level analysis of health and nutrition

Although theoretical systems analysis has been available for over half a century, the recent advent of omic high-throughput analytical platforms along with the integration of individual tools and technologies has given rise to the field of modern systems biology. Coupled with information technology, bioinformatics, knowledge management and powerful mathematical models, systems biology has opened up new vistas in our understanding of complex biological systems. Currently there are two distinct approaches that include the inductively driven computational systems biology (bottom-up approach) and the deductive data-driven top-down analysis. Such approaches offer enormous potential in the elucidation of disease as well as defining key pathways and networks involved in optimal human health and nutrition. The tools and technologies now available in systems biology analyses offer exciting opportunities to develop the emerging areas of personalized medicine and individual nutritional profiling.

Systems analysis of metabolism in the pathogenic trypanosomatid Leishmania major

Systems analyses have facilitated the characterization of metabolic networks of several organisms. We have reconstructed the metabolic network of Leishmania major, a poorly characterized organism that causes cutaneous leishmaniasis in mammalian hosts. This network reconstruction accounts for 560 genes, 1112 reactions, 1101 metabolites and 8 unique subcellular localizations. Using a systems-based approach, we hypothesized a comprehensive set of lethal single and double gene deletions, some of which were validated using published data with approximately 70% accuracy. Additionally, we generated hypothetical annotations to dozens of previously uncharacterized genes in the L. major genome and proposed a minimal medium for growth. We further demonstrated the utility of a network reconstruction with two proof-of-concept examples that yielded insight into robustness of the network in the presence of enzymatic inhibitors and delineation of promastigote/amastigote stage-specific metabolism. This reconstruction and the associated network analyses of L. major is the first of its kind for a protozoan. It can serve as a tool for clarifying discrepancies between data sources, generating hypotheses that can be experimentally validated and identifying ideal therapeutic targets.

PHIDIAS: a pathogen-host interaction data integration and analysis system

PHIDIAS is a web-based database system serving as a centralized source to search, compare and analyse integrated genome sequences, conserved domains and transcriptional data related to pathogen-host interactions.

The Pathogen-Host Interaction Data Integration and Analysis System (PHIDIAS) is a web-based database system that serves as a centralized source to search, compare, and analyze integrated genome sequences, conserved domains, and gene expression data related to pathogen-host interactions (PHIs) for pathogen species designated as high priority agents for public health and biological security. In addition, PHIDIAS allows submission, search and analysis of PHI genes and molecular networks curated from peer-reviewed literature. PHIDIAS is publicly available at .

A virtual look at Epstein-Barr virus infection: simulation mechanism

Epstein-Barr virus (EBV) is an important human pathogen that establishes a life-long persistent infection and for which no precise animal model exists. In this paper, we describe in detail an agent-based model and computer simulation of EBV infection. Agents representing EBV and sets of B and T lymphocytes move and interact on a three-dimensional grid approximating Waldeyer's ring, together with abstract compartments for lymph and blood. The simulation allows us to explore the development and resolution of virtual infections in a manner not possible in actual human experiments. Specifically, we identify parameters capable of inducing clearance, persistent infection, or death.

A virtual look at Epstein-Barr virus infection: biological interpretations

The possibility of using computer simulation and mathematical modeling to gain insight into biological and other complex systems is receiving increased attention. However, it is as yet unclear to what extent these techniques will provide useful biological insights or even what the best approach is. Epstein–Barr virus (EBV) provides a good candidate to address these issues. It persistently infects most humans and is associated with several important diseases. In addition, a detailed biological model has been developed that provides an intricate understanding of EBV infection in the naturally infected human host and accounts for most of the virus' diverse and peculiar properties. We have developed an agent-based computer model/simulation (PathSim, Pathogen Simulation) of this biological model. The simulation is performed on a virtual grid that represents the anatomy of the tonsils of the nasopharyngeal cavity (Waldeyer ring) and the peripheral circulation—the sites of EBV infection and persistence. The simulation is presented via a user friendly visual interface and reproduces quantitative and qualitative aspects of acute and persistent EBV infection. The simulation also had predictive power in validation experiments involving certain aspects of viral infection dynamics. Moreover, it allows us to identify switch points in the infection process that direct the disease course towards the end points of persistence, clearance, or death. Lastly, we were able to identify parameter sets that reproduced aspects of EBV-associated diseases. These investigations indicate that such simulations, combined with laboratory and clinical studies and animal models, will provide a powerful approach to investigating and controlling EBV infection, including the design of targeted anti-viral therapies.

The possibility of using computer simulation and mathematical modeling to gain insight into biological systems is receiving increased attention. However, it is as yet unclear to what extent these techniques will provide useful biological insights or even what the best approach is. Epstein–Barr virus (EBV) provides a good candidate to address these issues. It persistently infects most humans and is associated with several important diseases, including cancer. We have developed an agent-based computer model/simulation (PathSim, Pathogen Simulation) of EBV infection. The simulation is performed on a virtual grid that represents the anatomy where EBV infects and persists. The simulation is presented on a computer screen in a form that resembles a computer game. This makes it readily accessible to investigators who are not well versed in computer technology. The simulation allows us to identify switch points in the infection process that direct the disease course towards the end points of persistence, clearance, or death, and identify conditions that reproduce aspects of EBV-associated diseases. Such simulations, combined with laboratory and clinical studies and animal models, provide a powerful approach to investigating and controlling EBV infection, including the design of targeted anti-viral therapies.

Modulation of the host cell proteome by the intracellular apicomplexan parasite Toxoplasma gondii

To investigate how intracellular parasites manipulate their host cell environment at the molecular level, we undertook a quantitative proteomic study of cells following infection with the apicomplexan parasite Toxoplasma gondii. Using conventional two-dimensional electrophoresis, difference gel electrophoresis (DIGE), and mass spectrometry, we identified host proteins that were consistently modulated in expression following infection. We detected modification of protein expression in key metabolic pathways, including glycolysis, lipid and sterol metabolism, mitosis, apoptosis, and structural-protein expression, suggestive of global reprogramming of cell metabolism by the parasite. Many of the differentially expressed proteins had not been previously implicated in the response to the parasite, while others provide important corroborative protein evidence for previously proposed hypotheses of pathogen-cell interactions. Significantly, over one-third of all modulated proteins were mitochondrial, and this was further investigated by DIGE analysis of a mitochondrion-enriched preparation from infected cells. Comparison of our proteomic data with previous transcriptional studies suggested that a complex relationship exits between transcription and protein expression that may be partly explained by posttranslational modifications of proteins and revealed the importance of investigating protein changes when interpreting transcriptional data. To investigate this further, we used phosphatase treatment and DIGE to demonstrate changes in the phosphorylation states of several key proteins following infection. Overall, our findings indicate that the host cell proteome responds in a dramatic way to T. gondii invasion, in terms of both protein expression changes and protein modifications, and reveal a complex and intimate molecular relationship between host and parasite.