Jason E. Shoemaker

Adding Protein Context to the Human Protein-Protein Interaction Network to Reveal Meaningful Interactions

Interactions of proteins regulate signaling, catalysis, gene expression and many other cellular functions. Therefore, characterizing the entire human interactome is a key effort in current proteomics research. This challenge is complicated by the dynamic nature of protein-protein interactions (PPIs), which are conditional on the cellular context: both interacting proteins must be expressed in the same cell and localized in the same organelle to meet. Additionally, interactions underlie a delicate control of signaling pathways, e.g. by post-translational modifications of the protein partners - hence, many diseases are caused by the perturbation of these mechanisms. Despite the high degree of cell-state specificity of PPIs, many interactions are measured under artificial conditions (e.g. yeast cells are transfected with human genes in yeast two-hybrid assays) or even if detected in a physiological context, this information is missing from the common PPI databases. To overcome these problems, we developed a method that assigns context information to PPIs inferred from various attributes of the interacting proteins: gene expression, functional and disease annotations, and inferred pathways. We demonstrate that context consistency correlates with the experimental reliability of PPIs, which allows us to generate high-confidence tissue- and function-specific subnetworks. We illustrate how these context-filtered networks are enriched in bona fide pathways and disease proteins to prove the ability of context-filters to highlight meaningful interactions with respect to various biological questions. We use this approach to study the lung-specific pathways used by the influenza virus, pointing to IRAK1, BHLHE40 and TOLLIP as potential regulators of influenza virus pathogenicity, and to study the signalling pathways that play a role in Alzheimers disease, identifying a pathway involving the altered phosphorylation of the Tau protein. Finally, we provide the annotated human PPI network via a web frontend that allows the construction of context-specific networks in several ways.

A comprehensive map of the influenza A virus replication cycle

BackgroundInfluenza is a common infectious disease caused by influenza viruses. Annual epidemics cause severe illnesses, deaths, and economic loss around the world. To better defend against influenza viral infection, it is essential to understand its mechanisms and associated host responses. Many studies have been conducted to elucidate these mechanisms, however, the overall picture remains incompletely understood. A systematic understanding of influenza viral infection in host cells is needed to facilitate the identification of influential host response mechanisms and potential drug targets.DescriptionWe constructed a comprehensive map of the influenza A virus (‘IAV’) life cycle (‘FluMap’) by undertaking a literature-based, manual curation approach. Based on information obtained from publicly available pathway databases, updated with literature-based information and input from expert virologists and immunologists, FluMap is currently composed of 960 factors (i.e., proteins, mRNAs etc.) and 456 reactions, and is annotated with ~500 papers and curation comments. In addition to detailing the type of molecular interactions, isolate/strain specific data are also available. The FluMap was built with the pathway editor CellDesigner in standard SBML (Systems Biology Markup Language) format and visualized as an SBGN (Systems Biology Graphical Notation) diagram. It is also available as a web service (online map) based on the iPathways+ system to enable community discussion by influenza researchers. We also demonstrate computational network analyses to identify targets using the FluMap.ConclusionThe FluMap is a comprehensive pathway map that can serve as a graphically presented knowledge-base and as a platform to analyze functional interactions between IAV and host factors. Publicly available webtools will allow continuous updating to ensure the most reliable representation of the host-virus interaction network. The FluMap is available at http://www.influenza-x.org/flumap/.

CTen: a web-based platform for identifying enriched cell types from heterogeneous microarray data

BackgroundInterpreting in vivo sampled microarray data is often complicated by changes in the cell population demographics. To put gene expression into its proper biological context, it is necessary to distinguish differential gene transcription from artificial gene expression induced by changes in the cellular demographics.ResultsCTen (c ell t ype en richment) is a web-based analytical tool which uses our highly expressed, cell specific (HECS) gene database to identify enriched cell types in heterogeneous microarray data. The web interface is designed for differential expression and gene clustering studies, and the enrichment results are presented as heatmaps or downloadable text files.ConclusionsIn this work, we use an independent, cell-specific gene expression data set to assess CTens performance in accurately identifying the appropriate cell type and provide insight into the suggested level of enrichment to optimally minimize the number of false discoveries. We show that CTen, when applied to microarray data developed from infected lung tissue, can correctly identify the cell signatures of key lymphocytes in a highly heterogeneous environment and compare its performance to another popular bioinformatics tool. Furthermore, we discuss the strong implications cell type enrichment has in the design of effective microarray workflow strategies and show that, by combining CTen with gene expression clustering, we may be able to determine the relative changes in the number of key cell types.CTen is available at http://www.influenza-x.org/~jshoemaker/cten/

Multi-spectral fluorescent reporter influenza viruses (Color-flu) as powerful tools for in vivo studies

Seasonal influenza A viruses cause annual epidemics of respiratory disease; highly pathogenic avian H5N1 and the recently emerged H7N9 viruses cause severe infections in humans, often with fatal outcomes. Although numerous studies have addressed the pathogenicity of influenza viruses, influenza pathogenesis remains incompletely understood. Here we generate influenza viruses expressing fluorescent proteins of different colours (‘Color-flu’ viruses) to facilitate the study of viral infection in in vivo models. On adaptation to mice, stable expression of the fluorescent proteins in infected animals allows their detection by different types of microscopy and by flow cytometry. We use this system to analyse the progression of viral spread in mouse lungs, for live imaging of virus-infected cells, and for differential gene expression studies in virus antigen-positive and virus antigen-negative live cells in the lungs of Color-flu-infected mice. Collectively, Color-flu viruses are powerful tools to analyse virus infections at the cellular level in vivo to better understand influenza pathogenesis.

Identifying Fragilities in Biochemical Networks: Robust Performance Analysis of Fas Signaling-Induced Apoptosis

Proper control of apoptotic signaling is critical to immune response and development in multicellular organisms. Two tools from control engineering are applied to a mathematical model of Fas ligand signaling-induced apoptosis. Structured singular value analysis determines the volume in parameter space within which the system parameters may exist and still maintain efficacious signaling, but is limited to linear behaviors. Sensitivity analysis can be applied to nonlinear systems but is difficult to relate to performance criteria. Thus, structured singular value analysis is used to quantify performance during apoptosis rejection, ensuring that the system remains sensitive but not overly so to apoptotic stimuli. Sensitivity analysis is applied when the system has switched to the death-inducing, apoptotic steady state to determine parameters significant to maintaining the bistability. The analyses reveal that the magnitude of the death signal is fragile to perturbations in degradation parameters (failures in the ubiquitin/proteasome mechanism) while the timing of signal expression can be tuned by manipulating local parameters. Simultaneous parameter uncertainty highlights apoptotic fragility to disturbances in the ubiquitin/proteasome system. Sensitivity analysis reveals that the robust signaling characteristics of the apoptotic network is due to network architecture, and the apoptotic signaling threshold is best manipulated by interactions upstream of the apoptosome.

Disease Severity Is Associated with Differential Gene Expression at the Early and Late Phases of Infection in Nonhuman Primates Infected with Different H5N1 Highly Pathogenic Avian Influenza Viruses

ABSTRACT Occasional transmission of highly pathogenic avian H5N1 influenza viruses to humans causes severe pneumonia with high mortality. To better understand the mechanisms via which H5N1 viruses induce severe disease in humans, we infected cynomolgus macaques with six different H5N1 strains isolated from human patients and compared their pathogenicity and the global host responses to the virus infection. Although all H5N1 viruses replicated in the respiratory tract, there was substantial heterogeneity in their replicative ability and in the disease severity induced, which ranged from asymptomatic to fatal. A comparison of global gene expression between severe and mild disease cases indicated that interferon-induced upregulation of genes related to innate immunity, apoptosis, and antigen processing/presentation in the early phase of infection was limited in severe disease cases, although interferon expression was upregulated in both severe and mild cases. Furthermore, coexpression analysis of microarray data, which reveals the dynamics of host responses during the infection, demonstrated that the limited expression of these genes early in infection led to a failure to suppress virus replication and to the hyperinduction of genes related to immunity, inflammation, coagulation, and homeostasis in the late phase of infection, resulting in a more severe disease. Our data suggest that the attenuated interferon-induced activation of innate immunity, apoptosis, and antigen presentation in the early phase of H5N1 virus infection leads to subsequent severe disease outcome. IMPORTANCE Highly pathogenic avian H5N1 influenza viruses sometimes transmit to humans and cause severe pneumonia with ca. 60% lethality. The continued circulation of these viruses poses a pandemic threat; however, their pathogenesis in mammals is not fully understood. We, therefore, investigated the pathogenicity of six H5N1 viruses and compared the host responses of cynomolgus macaques to the virus infection. We identified differences in the viral replicative ability of and in disease severity caused by these H5N1 viruses. A comparison of global host responses between severe and mild disease cases identified the limited upregulation of interferon-stimulated genes early in infection in severe cases. The dynamics of the host responses indicated that the limited response early in infection failed to suppress virus replication and led to hyperinduction of pathological condition-related genes late in infection. These findings provide insight into the pathogenesis of H5N1 viruses in mammals.

Integrated network analysis reveals a novel role for the cell cycle in 2009 pandemic influenza virus-induced inflammation in macaque lungs

BackgroundAnnually, influenza A viruses circulate the world causing wide-spread sickness, economic loss, and death. One way to better defend against influenza virus-induced disease may be to develop novel host-based therapies, targeted at mitigating viral pathogenesis through the management of virus-dysregulated host functions. However, mechanisms that govern aberrant host responses to influenza virus infection remain incompletely understood. We previously showed that the pandemic H1N1 virus influenza A/California/04/2009 (H1N1; CA04) has enhanced pathogenicity in the lungs of cynomolgus macaques relative to a seasonal influenza virus isolate (A/Kawasaki/UTK-4/2009 (H1N1; KUTK4)).ResultsHere, we used microarrays to identify host gene sequences that were highly differentially expressed (DE) in CA04-infected macaque lungs, and we employed a novel strategy – combining functional and pathway enrichment analyses, transcription factor binding site enrichment analysis and protein-protein interaction data – to create a CA04 differentially regulated host response network. This network describes enhanced viral RNA sensing, immune cell signaling and cell cycle arrest in CA04-infected lungs, and highlights a novel, putative role for the MYC-associated zinc finger (MAZ) transcription factor in regulating these processes.ConclusionsOur findings suggest that the enhanced pathology is the result of a prolonged immune response, despite successful virus clearance. Most interesting, we identify a mechanism which normally suppresses immune cell signaling and inflammation is ineffective in the pH1N1 virus infection; a dyregulatory event also associated with arthritis. This dysregulation offers several opportunities for developing strain-independent, immunomodulatory therapies to protect against future pandemics.

Fathead minnow steroidogenesis: in silico analyses reveals tradeoffs between nominal target efficacy and robustness to cross-talk

BackgroundInterpreting proteomic and genomic data is a major challenge in predictive ecotoxicology that can be addressed by a systems biology approach. Mathematical modeling provides an organizational platform to consolidate protein dynamics with possible genomic regulation. Here, a model of ovarian steroidogenesis in the fathead minnow, Pimephales promelas, (FHM) is developed to evaluate possible transcriptional regulation of steroid production observed in microarray studies.ResultsThe model was developed from literature sources, integrating key signaling components (G-protein and PKA activation) with their ensuing effect on steroid production. The model properly predicted trajectory behavior of estradiol and testosterone when fish were exposed to fadrozole, a specific aromatase inhibitor, but failed to predict the steroid hormone behavior occurring one week post-exposure as well as the increase in steroid levels when the stressor was removed. In vivo microarray data implicated three modes of regulation which may account for over-production of steroids during a depuration phase (when the stressor is removed): P450 enzyme up-regulation, inhibin down-regulation, and luteinizing hormone receptor up-regulation. Simulation studies and sensitivity analysis were used to evaluate each case as possible source of compensation to endocrine stress.ConclusionsSimulation studies of the testosterone and estradiol response to regulation observed in microarray data supported the hypothesis that the FHM steroidogenesis network compensated for endocrine stress by modulating the sensitivity of the ovarian network to global cues coming from the hypothalamus and pituitary. Model predictions of luteinizing hormone receptor regulation were consistent with depuration and in vitro data. These results challenge the traditional approach to network elucidation in systems biology. Generally, the most sensitive interactions in a network are targeted for further elucidation but microarray evidence shows that homeostatic regulation of the steroidogenic network is likely maintained by a mildly sensitive interaction. We hypothesize that effective network elucidation must consider both the sensitivity of the target as well as the targets robustness to biological noise (in this case, to cross-talk) when identifying possible points of regulation.

Confidence from uncertainty - A multi-target drug screening method from robust control theory

BackgroundRobustness is a recognized feature of biological systems that evolved as a defence to environmental variability. Complex diseases such as diabetes, cancer, bacterial and viral infections, exploit the same mechanisms that allow for robust behaviour in healthy conditions to ensure their own continuance. Single drug therapies, while generally potent regulators of their specific protein/gene targets, often fail to counter the robustness of the disease in question. Multi-drug therapies offer a powerful means to restore disrupted biological networks, by targeting the subsystem of interest while preventing the diseased network from reconciling through available, redundant mechanisms. Modelling techniques are needed to manage the high number of combinatorial possibilities arising in multi-drug therapeutic design, and identify synergistic targets that are robust to system uncertainty.ResultsWe present the application of a method from robust control theory, Structured Singular Value or μ- analysis, to identify highly effective multi-drug therapies by using robustness in the face of uncertainty as a new means of target discrimination. We illustrate the method by means of a case study of a negative feedback network motif subject to parametric uncertainty.ConclusionsThe paper contributes to the development of effective methods for drug screening in the context of network modelling affected by parametric uncertainty. The results have wide applicability for the analysis of different sources of uncertainty like noise experienced in the data, neglected dynamics, or intrinsic biological variability.

The dynamics of single-substrate continuous cultures: the role of transport enzymes.

A chemostat limited by a single growth-limiting substrate displays a rich spectrum of dynamics. Depending on the flow rate and feed concentration, the chemostat settles into a steady state or executes sustained oscillations. The transients in response to abrupt increases in the flow rate or the feed concentration are also quite complex. For example, if the increase in the flow rate is small, there is no perceptible change in the substrate concentration. If the increase in the flow rate is large, there is a large increase in the substrate concentration lasting several hours or days before the culture adjusts to a new steady state. In the latter case, the substrate concentration and cell density frequently undergo damped oscillations during their approach to the steady state. In this work, we formulate a simple structured model containing the inducible transport enzyme as the key intracellular variable. The model displays the foregoing dynamics under conditions similar to those employed in the experiments. The model suggests that long recovery times (on the order of several hours to several days) can occur because the initial transport enzyme level is too small to cope with the increased substrate supply. The substrate concentration, therefore, increases until the enzyme level is built up to a sufficiently high level by the slow process of enzyme induction. Damped and sustained oscillations can occur because transport enzyme synthesis is autocatalytic, and hence, destabilizing. At low dilution rates, the response of stabilizing processes, such as enzyme dilution and substrate consumption, becomes very slow, leading to damped and sustained oscillations.