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Lipid mediators are major factors in multiple biological functions and are strongly associated with disease. Recent lipidomics approaches have made it possible to analyze multiple metabolites and the associations of individual lipid mediators. Such systematic approaches have enabled us to identify key changes of biological relevance. Against this background, a knowledge-based pathway map of lipid mediators would be useful to visualize and understand the overall interactions of these factors. Here, we have built a precise map of lipid mediator metabolic pathways (LimeMap) to visualize the comprehensive profiles of lipid mediators that change dynamically in various disorders. We constructed the map by focusing on ω-3 and ω-6 fatty acid metabolites and their respective metabolic pathways, with manual curation of referenced information from public databases and relevant studies. Ultimately, LimeMap comprises 282 factors (222 mediators, and 60 enzymes, receptors, and ion channels) and 279 reactions derived from 102 related studies. Users will be able to modify the map and visualize measured data specific to their purposes using CellDesigner and VANTED software. We expect that LimeMap will contribute to elucidating the comprehensive functional relationships and pathways of lipid mediators.

Assessing a compound’s mutagenicity using machine learning is an important activity in the drug discovery and development process. Traditional methods of mutagenicity detection, such as Ames test, are expensive and time and labor intensive. In this context, in silico methods that predict a compound mutagenicity with high accuracy are important. Recently, machine-learning (ML) models are increasingly being proposed to improve the accuracy of mutagenicity prediction. While these models are used in practice, there is further scope to improve the accuracy of these models. We hypothesize that choosing the right features to train the model can further lead to better accuracy. We systematically consider and evaluate a combination of novel structural and molecular features which have the maximal impact on the accuracy of models. We rigorously evaluate these features against multiple classification models (from classical ML models to deep neural network models). The performance of the models was assessed using 5- and 10-fold cross-validation and we show that our approach using the molecule structure, molecular properties, and structural alerts as feature sets successfully outperform the state-of-the-art methods for mutagenicity prediction for the Hansen et al. benchmark dataset with an area under the receiver operating characteristic curve of 0.93. More importantly, our framework shows how combining features could benefit model accuracy improvements.

Maoto, a traditional kampo medicine, has been clinically prescribed for influenza infection and is reported to relieve symptoms and tissue damage. In this study, we evaluated the effects of maoto as an herbal multi-compound medicine on host responses in a mouse model of influenza infection. On the fifth day of oral administration to mice intranasally infected with influenza virus [A/PR/8/34 (H1N1)], maoto significantly improved survival rate, decreased viral titer, and ameliorated the infection-induced phenotype as compared with control mice. Analysis of the lung and plasma transcriptome and lipid mediator metabolite profile showed that maoto altered the profile of lipid mediators derived from ω-6 and ω-3 fatty acids to restore a normal state, and significantly up-regulated the expression of macrophage- and T-cell-related genes. Collectively, these results suggest that maoto regulates the host’s inflammatory response by altering the lipid mediator profile and thereby ameliorating the symptoms of influenza.

It is unclear how epidermal growth factor receptor EGFR major driver mutations (L858R or Ex19del) affect downstream molecular networks and pathways. This study aimed to provide information on the influences of these mutations. The study assessed 36 protein expression profiles of lung adenocarcinoma (Ex19del, nine; L858R, nine; no Ex19del/L858R, 18). Weighted gene co-expression network analysis together with analysis of variance-based screening identified 13 co-expressed modules and their eigen proteins. Pathway enrichment analysis for the Ex19del mutation demonstrated involvement of SUMOylation, epithelial and mesenchymal transition, ERK/mitogen-activated protein kinase signalling via phosphorylation and Hippo signalling. Additionally, analysis for the L858R mutation identified various pathways related to cancer cell survival and death. With regard to the Ex19del mutation, ROCK, RPS6KA1, ARF1, IL2RA and several ErbB pathways were upregulated, whereas AURK and GSKIP were downregulated. With regard to the L858R mutation, RB1, TSC22D3 and DOCK1 were downregulated, whereas various networks, including VEGFA, were moderately upregulated. In all mutation types, CD80/CD86 (B7), MHC, CIITA and IFGN were activated, whereas CD37 and SAFB were inhibited. Costimulatory immune-checkpoint pathways by B7/CD28 were mainly activated, whereas those by PD-1/PD-L1 were inhibited. Our findings may help identify potential therapeutic targets and develop therapeutic strategies to improve patient outcomes.

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution., Over the past two decades, scientists from different fields have been developing SBML, a standard format for encoding computational models in biology and medicine. This article summarizes recent progress and gives perspectives on emerging challenges.

Designing alternative approaches to efficiently screen chemicals on the efficacy landscape is a challenging yet indispensable task in the current compound profiling methods. Particularly, increasing regulatory restrictions underscore the need to develop advanced computational pipelines for efficacy assessment of chemical compounds as alternative means to reduce and/or replace in vivo experiments. Here, we present an innovative computational pipeline for large-scale assessment of chemical compounds by analysing and clustering chemical compounds on the basis of multiple dimensions—structural similarity, binding profiles and their network effects across pathways and molecular interaction maps—to generate testable hypotheses on the pharmacological landscapes as well as identify potential mechanisms of efficacy on phenomenological processes. Further, we elucidate the application of the pipeline on a screen of anti-ageing-related compounds to cluster the candidates based on their structure, docking profile and network effects on fundamental metabolic/molecular pathways associated with the cell vitality, highlighting emergent insights on compounds activities based on the multi-dimensional deep screen pipeline., Network pharmacology: merging the boundaries of network- and structural biology The ability to study the precise effect of chemical compounds on specific molecular entities plays a crucial role in understanding their safety and efficacy landscapes. Towards this goal, an international team of researchers led by Lionel Breton and Hiroaki Kitano proposed an innovative multi-dimensional computation pipeline for large-scale assessment of chemical compounds. The pipeline harnesses the benefits of both structural and network biology together to cluster chemicals on their network effects across pathways and molecular interaction maps. Further, the pipeline was applied to screen a set of anti-ageing-related compounds on fundamental metabolic/molecular pathways associated with the cell vitality. The results highlight the importance of network-based multi-dimensional screens in capturing emergent properties of compounds which may not be apparent from a single-dimensional analysis such as molecular docking or chemical similarity.

Lipid mediators are major factors in multiple biological functions and are strongly associated with disease. Recent lipidomics approaches have made it possible to analyze multiple metabolites and the associations of individual lipid mediators. Such systematic approaches have enabled us to identify key changes of biological relevance. Against this background, a knowledge-based pathway map of lipid mediators would be useful to visualize and understand the overall interactions of these factors. Here, we have built a precise map of lipid mediator metabolic pathways (LimeMap) to visualize the comprehensive profiles of lipid mediators that change dynamically in various disorders. We constructed the map by focusing on ω-3 and ω-6 fatty acid metabolites and their respective metabolic pathways, with manual curation of referenced information from public databases and relevant studies. Ultimately, LimeMap comprises 282 factors (222 mediators, and 60 enzymes, receptors, and ion channels) and 279 reactions derived from 102 related studies. Users will be able to modify the map and visualize measured data specific to their purposes using CellDesigner and VANTED software. We expect that LimeMap will contribute to elucidating the comprehensive functional relationships and pathways of lipid mediators.

Interleukin 34 (IL-34) constitutes a cytokine that shares a common receptor, colony-stimulating factor-1 receptor (CSF-1R), with CSF-1. We recently identified a novel type of monocytic cell termed follicular dendritic cell-induced monocytic cells (FDMCs), whose differentiation depended on CSF-1R signaling through the IL-34 produced from a follicular dendritic cell line, FL-Y. Here, we report the functional mechanisms of the IL-34–mediated CSF-1R signaling underlying FDMC differentiation. CRIPSR/Cas9-mediated knockout of the Il34 gene confirmed that the ability of FL-Y cells to induce FDMCs completely depends on the IL-34 expressed by FL-Y cells. Transwell culture experiments revealed that FDMC differentiation requires a signal from a membrane-anchored form of IL-34 on the FL-Y cell surface, but not from a secreted form, in a direct interaction between FDMC precursor cells and FL-Y cells. Furthermore, flow cytometric analysis using an anti–IL-34 antibody indicated that IL-34 was also expressed on the FL-Y cell surface. Thus, we explored proteins interacting with IL-34 in FL-Y cells. Mass spectrometry analysis and pulldown assay identified that IL-34 was associated with the molecular chaperone 78-kDa glucose-regulated protein (GRP78) in the plasma membrane fraction of FL-Y cells. Consistent with this finding, GRP78-heterozygous FL-Y cells expressed a lower level of IL-34 protein on their cell surface and exhibited a reduced competency to induce FDMC differentiation compared with the original FL-Y cells. These results indicated a novel GRP78-dependent localization and specific function of IL-34 in FL-Y cells related to monocytic cell differentiation.

Integrating virus-host interactions with host protein-protein interactions, we have created a method using these established network practices to identify host factors (i.e., proteins) that are likely candidates for antiviral drug targeting. We demonstrate that interaction cascades between host proteins that directly interact with viral proteins and host factors that are important to influenza virus replication are enriched for signaling and immune processes. Additionally, we show that host proteins that interact with viral proteins are in network locations of power. Finally, we demonstrate a new network methodology to predict novel host factors and validate predictions with an siRNA screen. Our results show that integrating virus-host proteins interactions is useful in the identification of antiviral drug target candidates., The positions of host factors required for viral replication within a human protein-protein interaction (PPI) network can be exploited to identify drug targets that are robust to drug-mediated selective pressure. Host factors can physically interact with viral proteins, be a component of virus-regulated pathways (where proteins do not interact with viral proteins), or be required for viral replication but unregulated by viruses. Here, we demonstrate a method of combining human PPI networks with virus-host PPI data to improve antiviral drug discovery for influenza viruses by identifying target host proteins. Analysis shows that influenza virus proteins physically interact with host proteins in network positions significant for information flow, even after the removal of known abundance-degree bias within PPI data. We have isolated a subnetwork of the human PPI network that connects virus-interacting host proteins to host factors that are important for influenza virus replication without physically interacting with viral proteins. The subnetwork is enriched for signaling and immune processes distinct from those associated with virus-interacting proteins. Selecting proteins based on subnetwork topology, we performed an siRNA screen to determine whether the subnetwork was enriched for virus replication host factors and whether network position within the subnetwork offers an advantage in prioritization of drug targets to control influenza virus replication. We found that the subnetwork is highly enriched for target host proteins—more so than the set of host factors that physically interact with viral proteins. Our findings demonstrate that network positions are a powerful predictor to guide antiviral drug candidate prioritization.

Pharmacological activities of the traditional Japanese herbal medicine (Kampo) are putatively mediated by complex interactions between multiple herbal compounds and host factors, which are difficult to characterize via the reductive approach of purifying major bioactive compounds and elucidating their mechanisms by conventional pharmacology. Here, we performed comprehensive compound, pharmacological and metabolomic analyses of maoto, a pharmaceutical-grade Kampo prescribed for flu-like symptoms, in normal and polyI:C-injected rats, the latter suffering from acute inflammation via Toll-like receptor 3 activation. In total, 352 chemical composition-determined compounds (CCDs) were detected in maoto extract by mass spectrometric analysis. After maoto treatment, 113 CCDs were newly detected in rat plasma. Of these CCDs, 19 were present in maoto extract, while 94 were presumed to be metabolites generated from maoto compounds or endogenous substances such as phospholipids. At the phenotypic level, maoto ameliorated the polyI:C-induced decrease in locomotor activity and body weight; however, body weight was not affected by individual maoto components in isolation. In accordance with symptom relief, maoto suppressed TNF-α and IL-1β, increased IL-10, and altered endogenous metabolites related to sympathetic activation and energy expenditure. Furthermore, maoto decreased inflammatory prostaglandins and leukotrienes, and increased anti-inflammatory eicosapentaenoic acid and hydroxyl-eicosapentaenoic acids, suggesting that it has differential effects on eicosanoid metabolic pathways involving cyclooxygenases, lipoxygenases and cytochrome P450s. Collectively, these data indicate that extensive profiling of compounds, metabolites and pharmacological phenotypes is essential for elucidating the mechanisms of herbal medicines, whose vast array of constituents induce a wide range of changes in xenobiotic and endogenous metabolism., Systems biology of traditional medicine: comprehensive analysis of a traditional Japanese medicine, maoto Pharmacological activities of Kampo, or traditional Japanese herbal medicine, are putatively mediated by complex interactions between the plant-derived compounds and endogenous molecules. To elucidate these properties, we performed comprehensive phytochemical profiling, and pharmacological and metabolomic analyses of maoto, an herbal remedy for flu-like symptoms. In the plasma of maoto-treated rats, we detected maoto-derived compounds, metabolites produced from the chemical transformation of maoto compounds by host metabolism and gut microbes, and endogenous metabolites that were appeared following maoto administration. In an acute inflammatory rat model, maoto ameliorated symptoms of sickness behavior, suppressed inflammatory cytokines, and extensively affected common metabolites and lipid mediators. These data suggest that the diverse chemical composition of Kampo broadly affects the host’s endogenous metabolism and exerts specific pharmacological effects.

SummaryHost factors required for viral replication are ideal drug targets because they are less likely than viral proteins to mutate under drug-mediated selective pressure. Although genome-wide screens have identified host proteins involved in influenza virus replication, limited mechanistic understanding of how these factors affect influenza has hindered potential drug development. We conducted a systematic analysis to identify and validate host factors that associate with influenza virus proteins and affect viral replication. After identifying over 1,000 host factors that coimmunoprecipitate with specific viral proteins, we generated a network of virus-host protein interactions based on the stage of the viral life cycle affected upon host factor downregulation. Using compounds that inhibit these host factors, we validated several proteins, notably Golgi-specific brefeldin A-resistant guanine nucleotide exchange factor 1 (GBF1) and JAK1, as potential antiviral drug targets. Thus, virus-host interactome screens are powerful strategies to identify targetable host factors and guide antiviral drug development.

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.

1 Systems Biology Group, Kitano Symbiotic Systems Project, ERATO, JST, M-31 Suite 6A 6-31-15 Jingumae Shibuya-ku, Tokyo 150-0001, Japan 2 Physical Biosciences Division, E.O.Lawrence Berkeley National Laboratory, Calvin 3-144, 1 Cyclotron Road, Berkeley, CA 94720, USA 3 Division of Biology, California Institute of Technology, M/C 216–76, Pasadena, CA 91125, USA 4 Control and Dynamical Systems, California Institute of Technology, M/C 216–76, Pasadena, CA 91125, USA 5 Department of Computer Science, Graduate School of Science and Technology, Keio University, 3-14-1 Hiyoshi Kohoku-ku, Yokohama 223-0061, Japan 6 Jet Propulsion Laboratory, NASA, MS 126-347 Pasadena, CA 91109-8099, USA 7 Medical Research Institute, Tokyo Medical and Dental University, 1-5-45 Yushima Bunkyo-ku, Tokyo 113-0034, Japan 8 Sony Computer Science Laboratories, Inc., 3-14-13 Higashi-Gotanda Shinagawa-ku, Tokyo 141-0022, Japan

Specialized languages used for describing computational models in the field of systems biology and physiology, such as Systems Biology Markup Language (SBML), CellML, and Physiological Hierarchy Markup Language (PHML), have been devised to enhance effective model reuse and sharing among researchers for developing large, multilevel models. Each language has its own specialty. By combining two of these languages, i. e. SBML for illustrating subcellular phenomena and PHML for expressing supracellular dynamics, a novel technology has been developed to describe models of multilevel biophysiological systems. For practical use of the aforementioned languages, consolidated software applications providing intuitive graphical user interfaces are necessary. Starting from 2011, a versatile platform called PhysioDesigner has been developed for multilevel modeling of physiological systems based on PHML. In this article, we focus on the newly developed distinguishing features of PhysioDesigner and PHML for the development of multilevel biophysiological models using SBMLPHML hybridization.

Understanding complex biological systems requires the extensive support of computational tools. This is particularly true for systems pharmacology, which aims to understand the action of drugs and their interactions in a systems context. Computational models play an important role as they can be viewed as an explicit representation of biological hypotheses to be tested. A series of software and data resources are used for model development, verification and exploration of the possible behaviors of biological systems using the model that may not be possible or not cost effective by experiments. Software platforms play a dominant role in creativity and productivity support and have transformed many industries, techniques that can be applied to biology as well. Establishing an integrated software platform will be the next important step in the field. © 2013 The Authors. Biopharmaceutics & Drug Disposition published by John Wiley & Sons, Ltd.

Parkinson's disease (PD) is a major neurodegenerative chronic disease, most likely caused by a complex interplay of genetic and environmental factors. Information on various aspects of PD pathogenesis is rapidly increasing and needs to be efficiently organized, so that the resulting data is available for exploration and analysis. Here we introduce a computationally tractable, comprehensive molecular interaction map of PD. This map integrates pathways implicated in PD pathogenesis such as synaptic and mitochondrial dysfunction, impaired protein degradation, alpha-synuclein pathobiology and neuroinflammation. We also present bioinformatics tools for the analysis, enrichment and annotation of the map, allowing the research community to open new avenues in PD research. The PD map is accessible at http://minerva.uni.lu/pd_map. Electronic supplementary material The online version of this article (doi:10.1007/s12035-013-8489-4) contains supplementary material, which is available to authorized users.

Motivation: LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner. Availability and implementation: Milestone 2 was released in December 2011. Source code, example files and binaries are freely available under the terms of either the LGPL v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net. Contact: sbgn-libsbgn@lists.sourceforge.net

A new type of big science is emerging that involves knowledge integration and collaboration among small sciences. Because open collaboration involves participants with diverse motivations and interests, social dynamics have a critical role in making the project successful. Thus, proper 'social engineering' will have greater role in scientific project planning and management in the future.