LineSets that enables biologists to fulfill these tasks more effectively.Biologists make use of pathway visualization tools for a range of tasks, including investigating inter-pathway connectivity and retrieving details about biological entities and interactions. Some of these tasks require an understanding of the hierarchical nature of elements within the pathway or the ability to make comparisons between multiple pathways. We introduce a technique inspired by Extended LineSets, to facilitate new explorations of biological pathways. Our technique incorporates intuitive graphical representations of different levels of information and includes a well-designed set of user interactions for selecting, filtering, and organizing biological pathway data gathered from multiple databases.We introduce a novel technique, Based on interviews with domain experts and an analysis of two use cases, we show that our technique provides functionality not currently enabled by current techniques, and moreover that it helps biologists to better understand both inter-pathway connectivity and the hierarchical structure of biological elements within the pathways. Pathways Commons [Much effort has been expended to organize the body of knowledge that is available regarding the structure and function of biological pathways. The Reactome Database  and the  Commons , allow b Commons , who strPathways are typically represented as directed graphs, where nodes in the graph represent biological "participants," such as proteins or protein complexes, and where the edges represent a biological functionality, such as a biochemical reaction. Often different shapes for arrows and nodes are used to differentiate between the different types of molecules or reactions. Though this type of visual encoding is the most familiar, node-link diagrams are known to have a number of issues. A main issue is scalability; as the number of nodes or edges increases, it quickly becomes more difficult to make sense of the data . In the The importance of dynamic visualization has been discussed by Hu et al.  and KlukCytoscape [The structure of biological pathways can be formalized as a hypergraph, a graph that contains hyperedges which can connect to any number of nodes. Traditional node-link diagrams cannot provide sufficient information to represent all of the complexities in a biological pathway data: biochemical reactions can involve multi-participant relationships; pathways can contain multiple subpathways; nodes within a pathway can represent a nested structure containing many biological entities; and links between nodes can convey different biological meanings. Some techniques for the visualization of pathways propose representations which limit the complexity of the data structure in favor of a simpler design. This is the case of the SIF format, which is often used to encode data for generating visualizations with tools such as ytoscape . This foExtended LineSets, that more accurately represents the intricacy of interconnected pathways and subpathway connections, and moreover, that helps to reduce visual clutter that can interfere with visual analysis tasks. Additionally, we describe a prototype implementation that makes use of this technique so that biologists can more effectively search, filter, visualize, and compare pathways data.This paper presents a novel technique, Extended LineSets.We interviewed seven domain experts in order to understand the type of tasks that could be usefully accelerated or augmented by visualization techniques. The experts are professors and researchers in different domains of cellular biology, molecular genetics, and informatics. While each of the experts have different research interests, we identified three high-level tasks that were considered to be important to all of them. Through defining these tasks, we were able to identify the primary requirements necessary for an effective visualization technique. These tasks are not meant to be comprehensive, but rather to provide insight into the motivation for the development and design of Task 1: Examine the upstream and downstream connections between two entities within a pathway. Understanding how entities within pathways are connected is of critical importance to all of the researchers we interviewed, and is essential to most research related to pathway data. When discussing directed paths between entities, one entity is said to be upstream or downstream of another. Understanding upstream and downstream relationships is particularly important to domains such as cancer drug research, where a drug may affect a small subset of genes or gene products, which in turn will affect various downstream processes. In most cases, a directed relationship is meant to represent a biochemical reaction, where one entity is consumed as a reactant and another is produced as a product. Thus, an upstream entity may be connected to a downstream entity through a chain of several directed links. In the most basic sense, the "entities" mentioned above are genes, gene products (such as proteins or complexes), or other small molecules within a cell. A researcher may be interested in understanding the path of reactions (or other relationships) that connects two entities.Task 2: Understand the hierarchical structure of protein complexes. Protein complexes are represented as nested hierarchies of proteins. These hierarchies can be very intricate and potentially involve dozens of proteins and sub-complexes. Since the proteins and the complexes which compose these compound structures might be involved in other relevant complexes and biological processes, understanding their structure and organization is generally important for pathway analysis tasks.Task 3: Curate, edit, query and merge pathway data files, or construct user-defined pathways. Several of the researchers mentioned the importance of various tasks related to the curation, maintenance, and understanding of pathway data. They expressed the need to create "personalized pathways" that only include a user-determined subset of entities and relationships. They are especially interested in mixing-and-matching information that is contained within multiple pathways, but then being able to filter this information so that only information relevant for a particular task is displayed.node duplication, which considerably reduces the overlapping of edges and enables a visually appealing arrangement of the nodes. However, as pointed out by Bourqui et al. [It can be challenging to represent multiple pathways simultaneously in traditional node-link diagrams, due to issues of scalability and also due to difficulties in defining a layout that accurately and efficiently displays the topology of these pathways. In order to reduce the visual clutter introduced by dense networks with many edge crossings, many visualization tools rely on i et al. , analysiTask 3, researchers may need to merge pathway components from multiple sources, while still retaining an indication of the original source. For this reason, we also review visualization techniques that are used for presenting category information.This section presents an overview of features implemented in popular pathway visualization tools. One feature missing from all of these tools is a specific visualization technique for presenting category data within node-link diagrams. Indeed, as indicated by Entourage [Reactome Pathway Browser [VisAnt [MetaViz [VitaPad [ChiBe [Broadly speaking, current tools make use of one of three primary visual representations: node-link diagrams, node-link diagrams with compound nodes, and adjacency matrices. ntourage , Reactom Browser , VisAnt  [VisAnt , MetaViz[MetaViz  and Vita[VitaPad , each usd [ChiBe  extends Reactome Pathway Browser is a tool for visualizing pathway diagrams included in the Reactome database. It offers basic navigation and limited interactivity, making use of side panels to enable the inspection of complexes structure and analysis data. Entourage is a tool for pathway visualization that enables the exploration of the "cross-talk" between pathways via an "artificial partitioning" of the pathway structure, helping to reduce the complexity of the visualization. Multiple pathways and sub-pathways are represented in isolated views, which preserves the pathway structure but introduces node redundancy. Other tools represent multiple pathways in a single view, such as MetaViz, which enables topological analyses across multiple metabolic pathways simultaneously. Our work is inspired by MetaViz, but our technique tries to organize the representation of all pathways according to a topological ordering and enables the interactive inspection of hierarchical structures of certain nodes. BioFabric [VisAnt enables the user to search for the shortest path between two nodes [VisAnt permits a dynamic exploration of a biological network composed by multiple pathways. However, if more than one pathway includes the same node, multiple instances of the node will be present in the representation.The ioFabric  also enawo nodes  and to iVitaPad allows the user to incorporate microarray data into pathways, Entourage enables the integration of experimental data, and VisAnt permits the user to create and view annotations of nodes within the network.Although the majority of these pathway visualization tools use conventional graph representations, many of them decorate the node-link diagrams with experimental data or other additional information. For example, Combining multiple pathways in a single visualization introduces the need for displaying the relationships between biological entities and the pathway or pathways they are participants of. That is, biochemical reactions, proteins, or other biological compounds can be included in more than one pathway. This raises the further challenge of effectively displaying this membership information on top of the traditional node-link diagram without introducing visual clutter.Euler Diagrams to display set-based annotations on biological networks. However, this may lead to visually confusing representations if the data contains complex set intersections. For this reason a range of alternative visualization techniques has been developed to display elements belonging to multiple sets [Bubble Sets [Line Sets [KelpFusion [Bubble Sets and KelpFusion prevent these techniques from being applicable to pathway visualization. For example, Bubble Sets displays set relations using isocontours, which can make it difficult to distinguish elements belonging to multiple sets.Dinkla et al.  propose ple sets . Bubble ble Sets , Line Seine Sets , and KellpFusion  all addrLineSets, developed by Alper et al. [Bubble Sets technique when multiple sets overlap. KelpFusion uses continuous boundaries made by lines and hulls. The visual appearance is generally comparable to LineSets, but this strongly depends on the spatial arrangements of elements. Hulls are used to group elements that are both spatially close to each other and that belong to the same set. This technique can become confusing when it is applied to biological networks, which have an arbitrary or dynamic spatial layout. LineSets, though it has been applied to geographic datasets, is a more extensible technique that is able to effectively represent pathway data.r et al. , is a teUpSet [OnSet [None of the above techniques scale well when the number of sets and number of intersections between sets increases. UpSet  and OnSet [OnSet  are two Extended LineSets, a modified version of the LineSets technique which introduces the inclusion of hierarchical structure and which is presented as a more abstract node-link diagram, without the need for elements to have a spatial reference. Our technique aims to enhance analysis tasks that make use pathway visualization tools. It adds an extra information layer to the node-link diagram that represents the intricate relationships within and between pathways, and at the same time it provides an approach that reduces clutter while avoiding node duplication. In the following section we include a detailed description of the Extended LineSets technique. After presenting a prototype implementation, we introduce two use cases that demonstrate how our technique enables effective, real-world pathway exploration.In this paper we introduce Extended LineSets technique. The first representation combines node-link diagrams with LineSets, allowing the user to concurrently identify set memberships in a network and to visualize directional relationships of between elements. The second representation displays the hierarchical layout of elements within the network, allowing the user to interactively inspect their nested structure. These two primary representations are integrated to support the effective interactive investigation of biological pathways. Our prototype utilizes our technique and implements a variety of user-driven interaction to enable pathway analysis tasks.In this section we describe the two primary representations used in the Our first visual component presents a directed graph where nodes and edges belong to one or more sets. We identify every set with a color, and every element is depicted as either a square or a circle. Edges linking elements are represented using colored lines with sharp-pointed tip. Figure LineSets implementation. First, every set is not represented by a single smooth curve, but is identified only by the color of the links and nodes. Second, the layout of the elements flows from the top to the bottom, matching, when possible, the direction of the relationships. A completely consistent representation cannot be achieved when the graph contains cycles; in this case some directed edges will point upwards, but the general layout will still mostly adhere to the flow from the top to the bottom of the viewport. Additionally, the layout of the elements is designed to minimize nodes overlapping and edges crossing. The resulting visualization combines the efficient identification of elements belonging to particular sets from the LineSets technique with a traditional node-link graph representation. Figure LineSets , the pruned tree is updated to reflect the hierarchy of elements from the root to that currently selected element. The display is "pruned" because it does not show the whole structure, but instead only displays the direct path from the root to the parent that contains the selected element. That is, the pruned tree enables the inspection of all the components directly included in the parent.G, which brings up a pruned tree that indicates the hierarchy of parent complexes that contain G, fist A, then B, and then the direct parent C. The right side of the pruned tree shows protein G and its siblings D, E and F. Figure B and its corresponding pruned tree.Figure ORC:origin, and the related complex and five proteins. For clarity, the hovered component in the symbolic view is underlined and emphasized in the pruned tree. This visual component is integrated in the node-link component previously described. For this reason, the color palette used to differentiate nested complexes is limited to one color hue and two different color intensities, as this minimizes the potential conflict with other colors used to visualize biological elements. Our technique aims to find an optimal balance between reducing the complexity of the displayed structure while at the same providing as much information as necessary for a particular task.The symbolic overview provides a compact overview of the structure of the complex, where the squares represent complexes and the white dots are proteins contained within those complexes. Figure Extended LineSets technique. It enables the user to narrow down a potentially large set of proteins and complexes contained within multiple pathways to an arbitrary small group of relevant components. That is, a biologist or bioinformatician can interactively expand or reduce the visible pathways and components that is relevant for his or her research. The prototype uses a force-directed layout [https://github.com/CreativeCodingLab/pathways. By default, for demonstration purposes, the prototype loads in six pathways related to the Cell Cycle and 80 sub-pathways. The pathways are referred to as the field of interest. The field of interest is comprised of the pathways that the biologist or bioinformatician considers relevant for his or her research. The field of interest initially needs to contain all of the pathways that might be required for the analysis task.We created a prototype application that implements our d layout  to help We designed a set of user interactions for exploring, extending and inspecting the pathways at different level of detail. The interaction tasks, discussed below, include the following:• Pathway highlighting and labelling;• Pathway hiding and unhiding;• Keyword filtering;• Upstream and downstream expansion;• Layout rearranging, zooming, and panning;• Complex structure inspection;• Finding intermediate steps between two nodes in a pathway.Task 1 and Task 3. By default, labels for all complexes and proteins are hidden. This reduces the potential clutter that might be introduced by having too many overlapping labels shown in the graphical representation. Instead, when the user hovers over a component with the mouse pointer, only the labels of the components which are included in the same pathway as the selected component are displayed. The pathways which include this component also stand out because components and reactions belonging to any other pathway are temporarily desaturated , the user can hide or reveal any pathway included in the field of interest by clicking on its name from the list on the left of the screen  whose name matches at least one of the searched keywords.upstream step and one downstream step, starting from the selected component. A downstream step of one protein or complex is composed of all the reaction relationships that start from the given component. Similarly, an upstream step involves all the reaction relationships that end at that component. To facilitate this operation, we place a triangle-shaped maker pointing upwards on the node if it has an hidden upstream step, and a triangle-shaped maker pointing downwards on the node if it has a hidden downstream step. This interaction technique enables the following workflow to support both Task 1 and Task 3 : first the user searches for a set of proteins and complexes, then he or she progressively explores the network of interconnections by revealing the upstream and downstream nodes.By clicking on a protein or complexes the tool will expand the visualization with one Considering that the number of complexes and proteins involved might lead the visualization to exceed the boundary of the screen, the user can interactively zoom in, zoom out, and pan across the viewport. Since the representation might occasionally lead to unavoidable edge intersections, the user can also drag and drop any component on a preferred location to improve viewability.Task 2), our prototype enables the hierarchical inspection of complex structures, as described earlier in this paper. A complex can be enlarged by double-clicking on it. This enlarged complex then reveals its inner structure; all the other components are pushed away to avoid unwanted overlapping. The pruned tree is updated on the right side of the screen when the user moves the mouse over different parts of the hierarchy structure. A single click on an enlarged complex will hide the hierarchical structure, causing it to shrink the complex to the default size. More than one complex can be enlarged at the same time, enabling the user to easily jump from the inspection of one complex structure to another, and to find the same sub-complex in multiple complexes. Indeed, when the user moves the mouse on a component inside a complex, the same component will be highlighted in all the enlarged complexes that contain it.In order to support the user's understanding of the hierarchical structure of protein complexes  [M/G1 Transition pathway and activates the Assembly of the pre-replicative complex (As-preRC) and Activation of the pre-replicative complex (Ac-preRC) subpathways. The user then searches for ORC1-6 and preRC in the text field. The visualization is updated with the six ORC proteins and the preRC complex that match the keywords entered by the user. The color coding shows that the ORC proteins are involved in the As-preRC pathway, whereas preRC in involved in both As-preRC and Ac-preRC.In this use case we explore some of the interactions involved in the assembly of the  (preRC) . We pre-ORC proteins to the pre-replication complex. Furthermore, the user expands the network to view the immediate downstream elements of preRC, revealing reactions involved in the Ac-preRC pathway, such as Mcm10:preRC, which is expanded again to show the upstream MCM10 protein and the downstream Mcm10:active preRC protein.The user then interacts with the prototype application to reveal the intermediate steps from the ORC:origin, revealing the MCM8 protein, and expands the downstream complex (CDC6:ORC:origin), revealing the upstream protein CDC6. Then the user chooses to inspect the hierarchical structure of preRC, ORC:origin, along with other two complexes. By hovering over a sub-complex of preRC, the user can see that these same sub-complexes appear in all the visible complex structures . This enables the user user to better understand the steps involved in the preRC assembly. This use case is depicted via the three screenshots shown in Figures The user then expands the upstream of Regulation of DNA replication (RDR), M-G1 Transition (MG1) and Miotic G1-G1/S phases (G1S). The user types into the textbox to search for the preRC complex and for proteins that match the ORC3 and Ubiquitin keywords. By selecting two nodes and dragging between them the user is able to explore the missing steps between, first, ORC3 and preRC and, second, between Ubiquitin and preRC. The user can then easily detect which proteins and complexes are involved in all three pathways, such as the DNA replication factor (Cdt1) and the Mini chromosome maintenance complex (MCM2-7) complex [In this use case, the user selects three pathways from the larger set of pre-loaded pathways:  complex .preRC and the other complexes, showing, for example, the location within the hierarchical structure of MCM2-7 and its six related polypeptides in all the expanded complexes. This use case is explained via the four screenshots shown in Figures Using the integrated visualization of the hierarchy of elements within protein complexes, the user can further inspect the structure of We received detailed feedback from two domain experts who confirmed that the design choices implemented in our prototype were effective for pathway analysis tasks. Both of the experts are professors in a biology department at a large public research university. Each of them told us that the ability to see elements shared across multiple pathways (via the different colors) is a very important feature of our prototype. Both experts found the integration of the hierarchical views within the network to be a novel and potentially useful feature. They also appreciated the two coordinated views (the overview within the expanded node and the pruned tree) for inspecting the hierarchical structure of biological complexes.BioPAX files or different databases. He also said that the ability to quickly reveal all of the steps between two nodes could be useful for his research into interconnected pathways.Additionally, we received positive feedback regarding the search-by-keyword functionality, and especially the step-by-step exploration of the network interconnections. One of the experts agreed that in some cases a more natural way of exploring the network is to first search for a set of known entities and then to freely explore the interconnections to their neighbors. He was especially excited by the fact that these neighbors could be located in different One expert argued that some direct labeling of the nodes would be necessary, at least as an option, as an alternative to the dynamic labelling described earlier. Furthermore, he asked for the possibility to interactively dim all the other pathways to focus on a single pathway. The other domain expert commented that it would be helpful to be able to display the name or type of the reaction between two nodes, perhaps when a user mouses over the link. Moreover, he suggested that it would be useful to include an "undo" operation for when a particular exploration of the network turned out not to be relevant; rather than starting over, he wanted to rewind his exploration to a previous state. For instance, after a node is expanded and its interconnections are revealed, the user might want to revert this operation and hide all of its neighbors. We plan to incorporate these suggestions in a future implementation.Extended LineSets was developed through an iterative process whereby design choices were informally evaluated through discussions with expert users. While we are for the most part happy with the visual encodings and interaction methods that are currently used, we will continue to experiment with alternative encodings and interactions in future iterations. We plan to conduct a thorough empirical evaluation in order to test the effectiveness of our design choices. Although our technique eliminates node redundancy, multiple edges often originate from the same biological reaction. New interactions and visual paradigms should be designed to better represent this information.BioPax 3 format, and a future improvement will be to integrate our tool with the Pathway Commons Web API [Our prototype will require further work in order to become a full-fledged application for enabling pathway visualization tasks. Currently our tool assumes that all relevant pathways are pre-loaded; but the selection of pathways could instead be dynamically loaded from a public database of pathways. Our prototype already reads the Extended LineSets is a novel technique that enables the interactive visualization of a network of multiple pathways. It introduces a novel graph representation and an effective interactive interface. The visualization aims to provide a better understanding of the biological components and reactions shared among different pathways. Colors are assigned to pathways for a clear visual encoding. The user can dynamically focus on a limited set of pathways to reduce the amount of information and the number of different colors displayed at the same time. User-driven interactions enable a procedural exploration of the network, with the aim of limiting the visual clutter of the graphical representation and minimizing excessive edge crossings. Furthermore, the dynamic labeling of components allows the user to focus on the pathway topology to further reduce the complexity of the visualization. Finally, the inspection of the hierarchical structure of complexes permits the user to change the level of detail of the representation, enabling the simultaneous display of different abstraction layers. Based on real-world use cases and expert feedback, our initial prototype of Extended LineSets has already proven to be an effective representation for a range of tasks common to domain experts in biology and bioinformatics.The authors declare that they have no competing interests.FP and AGF conceived of the interactive visualization technique. FP implemented the prototype application and applied it to the two case studies. FP and AGF drafted, read, and approved the final manuscript.


In our recent paper , we descMany genomic studies focus on identifying DNA mutations associated with therapy response and prognosis, however many results have failed to be clinically actionable . First, In addition to the above, most efforts to identify diagnostic or predictive biomarkers are data driven, and fail to exploit the rapidly expanding biological cancer knowledge. We argue that this may be a costly omission, since using genomics data within a cancer biology knowledge framework enables reduction of data noise and focused extraction of relevant information for tumor characterization. Furthermore, the huge number of available data features versus the number of patient samples, combined with the fact that such sample sets are generally very heterogeneous, makes a data-driven approach prone to finding spurious patterns. Limiting biomarker search by using biological knowledge leads to more robust findings, which can be translated more straightforwardly into clinical practice as diagnostic assays.In our paper , we addrClinical validation of predictive and prognostic biomarker profiles is a difficult task, with uncertain factors such as population/tumor heterogeneity and long patient follow-up periods. Our approach, however, enables validation of the models in two stages: (i) biological validation of pathway activity assessment, followed by (ii) validation of clinical utility of the pathway test. Biological validation can be performed most effectively on well-defined cell line model systems allowing tight control of pathway status. This increases chances of successful clinical validation on patient samples in the second stage. In this way, we show that Bayesian models of the canonical Wnt and estrogen receptor (ER) pathway correctly assess pathway activity across various cancer tissue types and cell line experiments, after which we show that ER pathway activity is associated with better prognosis for ER-positive breast cancer patients treated with adjuvant hormonal therapy .Our computational pathway models can be used in several ways. First, to predict response to pathway-targeted therapy, e.g. in neoadjuvant settings, and to monitor therapy response using a repeat biopsy after installing treatment, where in case of response a reduction in pathway activity is expected. Second, for prognosis and to measure potential therapy resistance, e.g. ER-positive breast cancer patients may not respond to hormonal therapy if another pathway like Wnt is active and driving tumor growth instead, and Wnt activity hence indicates a worse prognosis. The models may also be used to guide additional diagnostics, e.g. the search for tumor-driving mutations can be focused on the pathway(s) that show abnormal activity, reducing noise caused by passenger mutations in other pathways.In summary, by interpreting genomics data in the context of the biology underlying cancer growth, we derive more clinically actionable information to select targeted therapies for individual patients, bringing “precision diagnostics” a step closer.


The Library of Integrated Network-Based Cellular Signatures (LINCS) project aims to create a network-based understanding of biology by cataloging changes in gene expression and signal transduction that occur when cells are exposed to a variety of perturbations. It is helpful for understanding cell pathways and facilitating drug discovery. Here, we developed a novel approach to infer cell-specific pathways and identify a compound's effects using gene expression and phosphoproteomics data under treatments with different compounds. Gene expression data were employed to infer potential targets of compounds and create a generic pathway map. Binary linear programming (BLP) was then developed to optimize the generic pathway topology based on the mid-stage signaling response of phosphorylation. To demonstrate effectiveness of this approach, we built a generic pathway map for the MCF7 breast cancer cell line and inferred the cell-specific pathways by BLP. The first group of 11 compounds was utilized to optimize the generic pathways, and then 4 compounds were used to identify effects based on the inferred cell-specific pathways. Cross-validation indicated that the cell-specific pathways reliably predicted a compound's effects. Finally, we applied BLP to re-optimize the cell-specific pathways to predict the effects of 4 compounds  according to compound-induced topological alterations. Trichostatin A and MS-275 (both HDAC inhibitors) inhibited the downstream pathway of HDAC1 and caused cell growth arrest via activation of p53 and p21; the effects of digoxigenin were totally opposite. Staurosporine blocked the cell cycle via p53 and p21, but also promoted cell growth via activated HDAC1 and its downstream pathway. Our approach was also applied to the PC3 prostate cancer cell line, and the cross-validation analysis showed very good accuracy in predicting effects of 4 compounds. In summary, our computational model can be used to elucidate potential mechanisms of a compound's efficacy. The gene expression data in LINCS (L1000) were cataloged for human cancer cells treated with compounds and genetic reagents. Similar to Connectivity Map (CMap) The identification and functional understanding of a compound's effects on the pathway level is becoming more and more important A key question is how to integrate these two types of data to systematically infer the cell-specific pathways induced by those perturbations, and then to predict the compound's effects. Because the measurements in P100 data only cover one time point (6 hours after administration of the compounds), traditional pathway modeling with ordinary differential equations may not be suitable to handle such kind of mid-stage phosphoproteomics data To address this challenge, we developed a binary linear programming (BLP) approach to predict a compound's efficacy by integrating L1000 gene expression and P100 phosphoproteomics data Figure 1In this study, we considered L1000 gene expression profiles and P100 phosphoproteomics data for MCF7 and PC3 cell lines (both included in L1000 and P100 data) treated with 15 compounds Table 1.http://cmap.github.io/l1ktools). The raw data of P100 (log2 ratio of treatment to control) were converted to binary values (0 or 1) according to the sign of raw data, where 1 corresponds to the fully activated state and 0 to no activation. In addition, if the targets or other co-regulators (some key proteins inhibited or activated after treatment) of some compounds were already validated in the literature, this prior knowledge was presented as constraints in our BLP approach.L1000 data were downloaded and processed as normalized log2 fold change value (http://www.ingenuity.com) and the literature The workflow of the proposed approach is presented in To construct the generic pathway map, we needed to determine the potential targets of compounds in this study. If some targets were already validated in literature, they were directly included in our generic pathway map. For example, the compound Scriptaid's target is HDAC1, and thus HDAC1 is one protein included in our generic pathway map Text S1 in File S1.We used an algorithm from Kolmogorov–Smirnov test-based gene set enrichment analysis GSEA  to calcuTable S1 and Table S2 in File S1.Next, we created the corresponding downstream pathways of those targets by integrating the PPI data HPRD , transcrTable S1 and Table S2 in File S1.After determining the potential targets of compounds and their downstream pathways, we manually created the generic pathway map. First, we searched the names of important pathways which were widely discussed in literatures; and then we used IPA to pick up the pathway topological structure of those pathways, including the estrogen signaling pathway, EGFR signaling, TNFR signaling, PI3K/AKT pathway, MEK/ERK pathway, JNK/p53/p21 pathway, p38 MAPK pathway, and HDAC1 pathway . In the current study, we also considered two more cases in Text S3 and Figure S6 in File S1. Text S2 in File S1. For more details regarding Boolean representation of pathways, please refer to Next we represented the generic pathway map with Boolean operations (Boolean network) To infer cell-specific pathways based on the generic pathway map constructed above, we then minimized the differences between the measurements and the simulated values, as well as the complexity of the signaling pathway structure's topology. To simplify this optimization problem, we developed a binary linear programming (BLP) approach to optimize such multi-objective functions. The concept behind BLP is that the states of the proteins (variables) are normalized to binary numbers (activated state or no activation); edges between two proteins are also represented as binary numbers (inhibition or promotion); binary linear constraints are used to describe the relationship between upstream and downstream proteins; and the optimization is done with binarized values taken by variables, edges, and constraints File S2) for MCF7, which proves that our model works well on mid-stage phosphoproteomics data.The BLP is solved with the optimization toolbox in MATLAB that guarantees minimal differences between phosphoproteomics data and predicted data, as well as the Boolean topology of the generic pathway map. Because P100 data are captured at the mid-stage of signal transduction, we developed constraints to simulate the change from early to mid-stage so that we can still obtain many important causal relationships of phosphorylation. The fitting precision of the optimized cell-specific pathways is 87.66% . According to the leave-one-out cross-validation we did on MCF7 cell line, we calculated the similarity of topological structures among 11 cases (each corresponds to one compound) where each case represents cell-specific pathway map trained from the remaining 10 compounds after leaving one compound out). The connected edges in all inferred pathways are 80. The similarity of the inferred cell-specific pathways based on the different combinations is defined as the ratio of the number of connected edges in each cell-specific pathway divided by the total number of connected edges (80). The average value of the similarity of 11 cell-specific pathways generated by leave-one-out cross-validation was 98.9%. This again indicates our inferred cell-specific pathway map was reliable.To prove the reliability of the developed approach, we examined the effects of leave-one-out cross-validation on the phosphoproteomics data of 11 compounds. For each compound, we employed data from 10 compounds to optimize a cell-specific pathway topology and evaluate the treatment effects of the remaining one compound on this pathway map by predicting the states of all the proteins via BLP. The mean values for fitting precision in MCF7 and PC3 cell lines were 84.85% and 86.20%, respectively, which suggests the inferred cell-specific pathway map is reliable for prediction of a compound's effects (File S2). The detailed results were discussed below.Our assumption is that the cell-specific pathways are fixed when there is no perturbation. So we can try to infer the cell-specific pathways first with some drug treated data and then use the reconstructed cell-specific pathways to predict effects of new drugs. Here, we first used the BLP model to train the generic pathways with the first part of phosphorylation data (11 training compounds), and obtained the cell-specific pathways. Then we fit the second part of data (4 testing compounds) to the optimized cell-specific pathways with BLP model, the state of phosphoproteins could be inferred to represent the compound's treatment effects via topological alterations. For identification of a compound's effects, we applied the inferred MCF7-specific pathways to 4 compounds: trichostatin A, MS-275, staurosporine, and digoxigenin. The first two compounds are HDAC inhibitors MS-275 With regard to staurosporine Digoxigenin induced the activation of HDAC1 and inhibition of p53 .As shown in In this paper, we present a computational approach to optimize generic pathways and identify a compound's effects on an inferred cell-specific pathway map by integrating gene expression profiles and phosphoproteomics data collected from various types of perturbations. For constructing the generic pathways, we combined the pathway information from the literature and the potential targets of compounds inferred from gene expression profiles under perturbations. A generic pathway map of MCF7 cell line with 60 proteins and 94 reactions was obtained. With our binary linear programming (BLP) approach for pathway topology optimization, a cell-specific pathway map of 54 proteins and 80 reactions that cover 27 measured phosphoproteins was finally inferred. In the optimized cell-specific pathways, we monitored 4 cases of compound-induced topological alterations to the pathways to predict a compound's effects using BLP.We used BLP formulations in two ways. For inference of the cell-specific pathways using the perturbation-induced data of 11 compounds, the BLP system included 4,014 constraints and 2,002 variables in the generic pathways. When the optimized cell-specific pathway map was utilized to predict the effects of each compound, it had about 306 constraints and 155 variables every time. In addition, there were 27,887 constraints and 9,732 variables used in a generic pathway map of 74 proteins and 105 reactions in ILP approach in vitro or in vivo assays, our method uses perturbation-induced gene expression profiles to infer the potential targets and downstream paths Compared to other phosphoproteomics-based and mass spectrometry-based target identification approaches, which use compound affinities measured either by An important aspect of our approach is the inference of the cell-specific pathways. Our approach, which is based on Boolean modeling, can simplify the representation of pathway topology and boost the optimization process. Compared with ordinary differential equation-based methods, a Boolean logic model has limited abilities to model kinetic behavior, but as a non-parametric estimation approach it can be applied for the simulation of large-scale topological structures. The actual signaling pathways have many types of topological structures, but the ILP approach can only deal with two examples of the topological structure, and its rules are rather redundant Figure S7 in File S1 presents the MCF7-specific pathway network inferred using BASC A approach. After comparing with the pathway network inferred using our normalization method in Figure S8 in File S1). In conclusion, BASC algorithm also can be applied to binarize p100 data. Some detailed results about BASC approach applied to our MCF7 data are presented in Text S4 in File S1. In addition, we also selected two well-defined binarization techniques on MCF7 phosphoproteomics data: (A) we used the mean value as a threshold We also notice that different binarization techniques resulted different networks. We investigated the effects of three binarization approaches on our resulting networks When applied to the MCF7 cell line, our approach identified both known and unanticipated results. Trichostatin A and MS-275 both are HDAC inhibitors; thus, their inhibition of HDAC1 can be seen in the pathway map and cell cycle is blocked via the up-regulated p53. Digoxigenin acts on MCF7 cells to inhibit p53 and induce cell cycle changes and cell growth promotion. In the case of staurosporine, a pro-apoptotic compound, double effects were detected from the pathway's topological alterations: p21 blocked the cell cycle by inhibiting the phosphorylation of the Rb-E2F complex, and DDB2-induced cell growth occurred via activated HDAC1. Finally, we detected no obvious topological alterations for the proteins BAD, 4E-BP1, RPS6, or c-Myc Figure 5File S2). We also evaluated the effects of 4 compounds  on the PC3 cell line. All these compounds induced the inhibition of EGFR, which plays an important role in apoptosis Figure S2 in File S1, MS-275, staurosporine and digoxigenin induced the inhibition of TNFR and part of its downstream proteins such as IKK which is very important for cell survival of PC3 Figure S2C and S2D in File S1: p21 blocked cell cycle; however, HDAC1 induced cell growth via DDB2.When we applied the proposed approach to the PC3 cell line, the precision of fit for the inferred cell-specific pathways on the experimental data of 11 compounds was 90.91%  have obvious angles; the average value of the correlation coefficient between any two training compounds is 0.31. Therefore, there was low redundancy of these 11 compounds. Based on the result shown in Figure S4 in File S1, we considered our LOO cross-validation on training set as reliable.In our study, the redundancy analysis (RDA) of compounds was performed on our P100 dataset via R-package “Vegan” Although network behavior is not the focus of this study, we tried to evaluate our network behavior over time after a drug is applied based on the inferred pathway topology when most proteins researched to saturation conditions through phosphorylation. Because the state of most proteins (in cell-specific pathways) at time In summary, we provide a novel methodology to infer the cell-specific signaling pathways and to identify the effects of compounds on the pathways. Our BLP approach can quickly infer the best-fitting pathways from topological structures calculated from the perturbation-induced data. We believe the proposed approach complements standard biochemical drug profiling assays and sheds new light on the discovery of possible mechanisms for drug effects.http://lincscloud.org/exploring-the-data/data-api/). The L1000 database is a catalog of gene-expression profiles collected from human cells treated with compounds and genetic reagents. These data are used to reveal connections between genes and compounds and the related molecular pathways for underlining disease states. The L1000 database includes information on 5,178 small-molecule compounds; 3,712 genes perturbed using lentivirally-delivered shRNAs; and the effects of overexpression of those genes. All the data were collected from 15 cancer cell lines on 1,000 carefully chosen landmark genes, which can reduce the number of measurements and will not be biased for a particular cellular model. The data from the 1,000 landmark genes will be converted to expression data for about 22,000 genes (http://lincscloud.org/the-data-set/the-landmark-genes/)In the LINCS project, L1000 gene expression profiles and P100 phosphoproteomics data have been generated by the Broad Institute (http://lincscloud.org/exploring-the-data/data-api/phosphoproteomics). Treated cells were grown in SILAC medium  or heavy  growth medium; control cells treated with DMSO were grown in light  growth medium. All raw values in P100 represent log2 ratios of the treatment vs. DMSO.The P100 database contains measurements of phosphorylation caused by 26 different compounds for three cell lines . The treatments were each administered for 6 hours at 37°C and were repeated twice in complete biological replicates. Details of the experimental design can be found at  (B) If the state of enzyme (C) When treated with a compound, enzyme to time  . (3)Similar to two previous reports Text S2 in File S1). Due to the different mechanisms of compounds, a reaction will take place after treatments with some compounds but not others. The goal of the proposed formulation is to remove the redundant and inconsistent reactions which do not occur with any compounds.A pathway is defined as a set of reactions For a generic pathway map, a set of experiments (indexed as If the phosphorylation level of species urements : (4)Text S2 in File S1).The second objective Here, we use a linear solution to simultaneously optimize the two objectives above: Figure S3 in File S1).In The constraints in our BLP approach can be summarized as: Text S2 in File S1. The states of all variables in According to these formulas, the constraints (7)–(15) in our BLP system are used to infer the states of all the species and reactions according to the pathway's topological structure. These constraints can handle four types of linking patterns between species in the pathway map. The detailed explanations of constraints (7)–(15) are described in the For the fitting precision Click here for additional data file.File S2The fitting precision of training compounds and testing compounds in two cell lines. (XLSX)(XLSX)Click here for additional data file.File S3The fitting precision of cross validation in two cell lines. (XLSX)(XLSX)Click here for additional data file.


Vitis vinifera “Pinot Noir” cultivar with 12X v1 annotations and was built on BioCyc platform using Pathway Tools software and MetaCyc reference database. Furthermore, VitisCyc was enriched for plant-specific pathways and grape-specific metabolites, reactions and pathways. Currently VitisCyc harbors 68 super pathways, 362 biosynthesis pathways, 118 catabolic pathways, 5 detoxification pathways, 36 energy related pathways and 6 transport pathways, 10,908 enzymes, 2912 enzymatic reactions, 31 transport reactions and 2024 compounds. VitisCyc, as a community resource, can aid in the discovery of candidate genes and pathways that are regulated during plant growth and development, and in response to biotic and abiotic stress signals generated from a plant's immediate environment. VitisCyc version 3.18 is available online at http://pathways.cgrb.oregonstate.edu.We have developed VitisCyc, a grapevine-specific metabolic pathway database that allows researchers to (i) search and browse the database for its various components such as metabolic pathways, reactions, compounds, genes and proteins, (ii) compare grapevine metabolic networks with other publicly available plant metabolic networks, and (iii) upload, visualize and analyze high-throughput data such as transcriptomes, proteomes, metabolomes etc. using OMICs-Viewer tool. VitisCyc is based on the genome sequence of the nearly homozygous genotype PN40024 of  Vitis spp) is one of the most economically important and widely cultivated fruit crops in the world, covering ~8 million hectares and producing ~67.5 million tons of berries (http://www.oiv.int/). The majority of grape berries are processed into wine, but significant portions of the harvest provide fresh table grapes, raisins, juice, vinegar, and distilled spirits (http://faostat.fao.org/). In addition to its economic importance, grapes are rich in nutrients, metabolites, vitamins, volatile compounds, and dietary fibers—all of which offer potential benefits to human health  to probe the genetic diversity in the cultivated and wild grape accessions. Genetic linkage maps are available for various cultivars of V. vinifera  (http://www.cribi.unipd.it/). VitisCyc was built on widely used BioCyc platform      that represents 29,971 protein coding genes. We used the Pathways Tools software  with 12X v1 annotations provided by CRIBI  and FASTA sequence files , the “PathoLogic” option was executed (with taxonomic filtering on) to find best matches for grape proteins in the reference database MetaCyc. When a match was found for an entity, such as, an enzyme name, gene name, synonym, EC number, reaction etc., then the respective reaction and pathway were projected in the VitisCyc.To ensure consistency and accuracy, a quality control check was performed after initial assembly of VitisCyc, following the standard procedure of the Pathway Tools suite  described previously  from t-tests were used to compare the mean log expression intensities between the berry classes; GS compared with PV, PS compared with GS, RS compared with PS and RS compared with GS. The R/Bioconductor Limma package  confirmation of computational mappings of genes to reactions and pathways, (ii) addition of grapevine-specific pathways, reactions, metabolites and biochemicals, and (iii) editing of reactions, sub-pathways, pathways and super-pathways including assignment of genes to a given enzyme and relevant pathways based on published grape literature. Examples of the manually curated pathways include four pathways of gibberellins and gibberellin precursors biosynthesis  entities by mining the published literature.Based on the published literature from The OMICs-Viewer tool, available from the VitisCyc entry page Figure  or from We observed major changes at the cellular transcriptome during PV to GS transition that affects a wide array of genes associated with both primary and secondary metabolisms, hormone metabolism, photosynthesis, and energy pathways Figure . In contTaking advantage of the OMICs-Viewer's interactive environment, we compiled a list of differentially expressed genes coding for enzymes of flavonol, flavonoid, anthocyanin, terpene, polysaccharides and plant hormone pathways , and also provide the gene annotation from 8X and 12X v0 annotations, if the gene model has not changed (Supplementary Figure VitisCyc aims to provide a comprehensive view of grapevine-specific cellular metabolic networks, and a framework for analysis of systems level OMICs expression data. VitisCyc was constructed using Pathway Tools software (Karp et al., The OMICs-Viewer, a built in tool (Paley and Karp, Gouthu et al.  have preIn addition, our analysis shows interplay between several hormones that is required to enable the fruit to transition from berry maturation to the ripening phase (Mcatee et al., We view publicly available experimental data as a vital resource for improvement of the functional annotation of the grapevine genes and curation of enzymes, reactions, pathways, and compounds in the VitisCyc databases. Manual curation is an ongoing process and the growth of VitisCyc is expected as more data becomes available and integrated. Many grape researchers rely on another publicly available grape database VitisNet (Grimplet et al., We expect that VitisCyc will aid grape researchers in discovering the broader role of new and known genes and/or molecular interactions in context of plant development, cells, tissues and organ differentiation. It could help in understanding how a plant's environment and genetic diversity relate to yields and the quality of its fruit's taste, aroma, flavor, and texture, and how the plant's overall metabolism and its ability to adapt is affected in response to climate change.http://pathways.cgrb.oregonstate.edu. The data content in the VitisCyc will be maintained and updated twice a year by continuously surveying the public resources and research articles as long as we have some support for curation and afterwards an archive copy will be maintained.The VitisCyc is freely accessible online at The Guest Associate Editor Mario Pezzotti declares that, despite having collaborated with author Laurent G. Deluc, the review process was handled objectively and no conflict of interest exists. The author Laurent G. Deluc declares that, despite having collaborated with the editor Mario Pezzotti, the review process was handled objectively and no conflict of interest exists. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


RXRA), which has been implicated in a previous epistasis network analysis of smallpox vaccine.Although many diseases and traits show large heritability, few genetic variants have been found to strongly separate phenotype groups by genotype. Complex regulatory networks of variants and expression of multiple genes lead to small individual-variant effects and difficulty replicating the effect of any single variant in an affected pathway. Interaction network modeling of GWAS identifies effects ignored by univariate models, but population differences may still cause specific genes to not replicate. Integrative network models may help detect indirect effects of variants in the underlying biological pathway. In this study, we used gene-level functional interaction information from the Integrative Multi-species Prediction (IMP) tool to reveal important genes associated with a complex phenotype through evidence from epistasis networks and pathway enrichment. We test this method for augmenting variant-based network analyses with functional interactions by applying it to a smallpox vaccine immune response GWAS. The integrative analysis spotlights the role of genes related to retinoid X receptor alpha ( In the current study, instead of using main effects only, we prioritize by main effect and epistasis network centrality. In addition, we use pathway enrichment and IMP to further characterize the functional network of complex phenotypes.Recently, Greene et al.  combinedThe integrative component of the current approach builds upon our previous feature-selection algorithms that incorporate main and interaction effects: simulated evaporative cooling (SEC) , regressRXRA) using a gene network centrality algorithm in immune response to smallpox vaccine [RXRA gene in immune response to hepatitis B, measles, and rubella vaccines [The focus of the applied analysis in the current study is a GWAS of the human immune response to smallpox vaccination. A previous analysis in a different population implicated a variant (rs1805352) in retinoid X receptor alpha  and RXRA, and the functional network showed an enrichment of RXR and retinoic acid pathways. The integration of multiple levels of information provides a more comprehensive view of genes and pathways likely to be involved in the vaccine immune response phenotype. between 2002 and 2006. All subjects had a documented vaccine “take” at the vaccination site. Smallpox antibody response GWAS data collected at Mayo Clinic  [Subjects from a previously described smallpox vaccine cohort (18–40 years old) were utilized for this study . Study ster, MN) ,13 incluVaccinia-specific neutralizing antibody (NA) titers were quantified for each subject using a method that has been previously published by us . MeasureOur bioinformatics strategy  integratSimulated Evaporative Cooling (EC) is a machine-learning algorithm that incorporates main effect contributions with interaction effects to prioritize variants ,15. The Many of the interactions and main effects are already captured in Step 1, so one could set a threshold for the EC scores and skip to Step 4. However, Steps 2 and 3 help further filter noise variants and construct an epistasis network that may be integrated with other network information. From the top 1,000 EC-filtered SNPs, we used the generalized linear model (GLM) to calculate the pairwise interaction strengths and main effects. We use these regression model coefficients to construct a regression GAIN (reGAIN) matrix , which iUsing the reGAIN matrix from Step 2, we employed an eigenvector centrality algorithm called SNPrank  to priorTo characterize the pathways and processes associated with the most central genes in our epistasis network, we used the Reactome Functional Interaction (FI) database to identify enriched biological pathways in the top genes . As we pThe epistasis network centrality reflects main effects and statistical interactions from the GWAS data, and the prioritization of genes by pathway enrichment reflects their coordinated effect on the phenotype. To identify additional potential interaction partners and relevant genes, we queried IMP, which integrates multiple sources of evidence for functional interactions . We usedAfter integrating our GWAS-driven genes from the statistical epistasis network with prior functional interactions from IMP (Step 5), we next identified pathway enrichment present in the posterior functional interaction network. The posterior functional interaction network from IMP includes additional predicted genes and interactions. Unlike the enrichment in Step 4, which reflected biological pathways strictly from factors present in the phenotype-specific results, the resulting biological pathways from Step 6 represent biological pathways enriched when integrating prior biological knowledge with the enriched processes responding to smallpox vaccine response.After EC filtering , Step 2,The epistasis network effects and main effects of the top SNPrank genes can be visualized in the “Positive/Negative Epistasis Degree Plot” , which wZBTB16, SOS1, and THRB. Phosphorylation causes SMRT (silencing mediator of retinoic acid and thyroid hormone receptor) to unbind from transcription factor complexes like RXR and RAR. In this top pathway, the gene ZBTB16 (zinc finger and BTB domain containing 16), which also goes by the name PLZF (promyelocytic leukemia zinc finger), has been shown to regulate the transcriptional activity of Retinoic Acid Receptor (RARs) transcriptional activity [ZBTB16 with retinoid receptors is also suggested by the association of a chromosomal translocation to a rare variant of acute promyelocytic leukemia (APL), which fuses the ZBTB16 (PLZF) protein to retinoic acid receptor (RARA) [THRB  in the pathway has a physical interaction with RXRA [The top 200 variants from the SNPrank centrality analysis were mapped to corresponding genes based on variant proximity to the gene body and analyzed to infer pathway enrichment from the Reactome database. The top pathway “Map kinase inactivation of SMRT corepressor (B)”  contains epistasis network genes activity . The invr (RARA) . The genith RXRA .ZBTB16 and THRB in the top pathway suggest the involvement of RXR pathways; however, as mentioned previously, the particular RXRA SNP variant was not genotyped in the current GWAS. In order to further characterize the involvement of RXR genes and other genes involved in smallpox vaccine immune response, we used the genes in the most enriched epistasis network pathways as seeds for predicting an interaction network from integrated prior knowledge , likely account for additional variation and may help identify subtle indirect effects missed by univariate models [Previous studies of the retinoid X receptor (RXR) have implicated its role in immunity, particularly through naïve T helper cell differentiation, innate inflammatory response, and cellular apoptosis ,26. Notae models . ConsequRXRA that influenced immune response to smallpox vaccine [A previous study identified an interaction network effect involving a variant in  vaccine ,6. Altho vaccine , may smoTHRB and other genes that share biological processes and interactions with RXRA. We note that this pathway effect of RXRA is not found when using univariate variant prioritization. The coordinated variation in this biologically relevant pathway provides candidate genes or “seeds” to query the IMP database for additional functional interactions that may be related to the immune response phenotype. The final posterior network implicated RXR pathways involved in vaccine response and included a high-confidence functional interaction between RXRA and THRB. This final posterior network and enriched pathways reflected the phenotype-specific effects associated with vaccine response while utilizing phenotype-independent biological features.The data-driven portion of the approach led to the enrichment of the pathway “Map kinase inactivation of SMRT co-repressor,” which includes In addition to RXR pathways, our integrated network analysis implicated “T helper-2 cell differentiation” and “interleukin-5 production,” each of which has been implicated in immune response,29, but RXRA has been implicated in the cellular processes leading to apoptosis [As association studies uncover new genes linked to complex immune responses, such as antibody response to vaccination, a major challenge is to understand how multiple implicated genes work in concert to produce a complex phenotype. Understanding the effects on gene expression–as an intermediate between SNP and phenotype–will lead to more extensive functional models of immune response. For example, in a SNP-SNP interaction analysis of an expression quantitative trait loci (eQTL) study of stimulated and unstimulated cells with the smallpox vaccine, we found a set of genes enriched for apoptosis . Furtherpoptosis , and thepoptosis . Future poptosis ,40,41,42


A number of epidemiological studies have identified statistical associations between persistent organic pollutants (POPs) and metabolic diseases, but testable hypotheses regarding underlying molecular mechanisms to explain these linkages have not been published.We assessed the underlying mechanisms of POPs that have been associated with metabolic diseases; three well-known POPs  were studied. We used advanced database search tools to delineate testable hypotheses and to guide laboratory-based research studies into underlying mechanisms by which this POP mixture could produce or exacerbate metabolic diseases.For our searches, we used proprietary systems biology software (MetaCore™/MetaDrug™) to conduct advanced search queries for the underlying interactions database, followed by directional network construction to identify common mechanisms for these POPs within two or fewer interaction steps downstream of their primary targets. These common downstream pathways belong to various cytokine and chemokine families with experimentally well-documented causal associations with type 2 diabetes.Our systems biology approach allowed identification of converging pathways leading to activation of common downstream targets. To our knowledge, this is the first study to propose an integrated global set of step-by-step molecular mechanisms for a combination of three common POPs using a systems biology approach, which may link POP exposure to diseases. Experimental evaluation of the proposed pathways may lead to development of predictive biomarkers of the effects of POPs, which could translate into disease prevention and effective clinical treatment strategies.Ruiz P, Perlina A, Mumtaz M, Fowler BA. 2016. A systems biology approach reveals converging molecular mechanisms that link different POPs to common metabolic diseases. Environ Health Perspect 124:1034–1041; http://dx.doi.org/10.1289/ehp.1510308 These tools help a user to analyze information from experimental results or to mine the underlying content from the MetaBase™ database content directly.50 < 1 μM.” Using Advanced Search allows us to create a Boolean query and to retrieve the results. A detailed methodological description of the systems biology procedures and protocols for using the software are available (http://lsresearch.thomsonreuters.com/).Advanced Search is a java application tool in MetaCore™ that facilitates searching combined information, for example, “find all compounds that inhibit EGFR with ICWhen querying content from MetaBase™, Advanced Search allows us to differentiate low- and high-trust annotation information behind interactions. “High-trust” content has been confirmed to have published small-scale experimental evidence . Low-trust interactions have only been validated via high-throughput screening/co-expression or predictive analysis studies and lack more rigorous experimental evidence. Molecular entities can affect a target directly and indirectly. Mechanisms used to describe direct physical interactions include binding, covalent modification, phosphorylation, and so on. Indirect mechanisms include influence on expression, co-regulation of transcription, unspecified, and others, as stated in the legend. Only high-trust direct interactions with known effects (activation or inhibition) were used in this study.To map all of the possible pathways from the three selected POPs to their downstream targets, primary targets  were first determined using MetaCore™ and MetaDrug™ content. Of those primary targets, only direct binding targets that had further downstream interactions were considered. To reduce the complexity presented by primary targets that already participate in thousands of annotated molecular interactions, the Advanced Search tool was used. This tool allowed the construction of direct database queries of interactions leading from primary/direct targets of each POP to common targets shared by all three POPs in three or fewer steps and with an inferred activating effect. These conditions meant that from the compound itself, the allowed network depth/distance would be three or fewer steps. The focus on downstream activation targets was presumed to be of greatest utility if such targets were to be used for detection as biomarkers. All combinations of path lengths within three interactions were considered. For example, some targets may have been two steps downstream of one POP but three steps downstream of the other two POPs. For all of the multi-step paths, the assumption was made that to achieve downstream activation, not only activation interactions but also inhibitory interactions could be considered, provided that they added up to the final effect of activation. For example, inhibition of an inhibitor can result in subsequent activation. Thus, all combinations of the following interaction paths were considered in our search queries:Two-step paths:activation, activationinhibition, inhibitionThree-step paths:activation, activation, activationactivation, inhibition, inhibitioninhibition, inhibition, activationinhibition, activation, inhibitionNote that there were no common primary targets for all three POPs; hence, no one-step paths were obtained in this analysis.The resulting combined list of potential common activation targets was used for network construction. The “shortest paths” network-building option was used. The maximum number of interaction steps is defined by the user from a range of 1 to 10 interaction steps and was set to three as a maximum in this study; the algorithm attempts to build the shortest direct paths between selected objects using up to the maximum number of interactions defined by the user. This operation yielded the resulting interconnected network diagram. Interaction effects were then checked for concordance . Only direct activation or inhibition interactions were used for network construction. “Direct” refers to small-scale molecular physical interactions, as described earlier, the effects of which are either activation or inhibition, meaning that interactions that are indirect or that have an unspecified effect would not be considered by the shortest-paths algorithm. Only concordantly regulated molecules  were displayed on the final network, with only those interactions that led to downstream activation. Therefore, the terminal nodes (those genes/proteins that only had upstream interactions) had only those sequences of interactions leading toward them that would result in activation. Even though advanced queries produced target lists (queries retrieve lists of genes), network building was still needed for a visual representation of the interaction space that met the requirements of the queries and mechanistically tied the POPs to the targets. Network building was also needed to manually check and remove any signaling conflicts that arose between intermediate nodes along the queried paths. For example, if gene A is a logical linker downstream of a POP target and its downstream common activation target but is regulated in the opposite direction by the primary target of another POP, then a mixed message would result, and gene A would have to be removed from the final network. In other words, a mixed message occurs when an intermediate protein receives a signal from a POP through its target to behave in one way , but through a second target for another POP, the effect is in opposition ; thus, the intermediate protein receives conflicting signals.The final network was narrowed to only those downstream targets associated with the following: diabetes/insulin resistance (IR), obesity, and metabolic syndrome X. Finally, to clarify the specific signaling paths, the final network was subdivided into subnetworks with smaller portions of information based on one downstream target at a time for increased resolution.p,p´-DDE, we identified primary targets using advanced database search queries, followed by directional network construction. Because these three POPs are so structurally different, they bind different primary targets: the pregnane X receptor (PXR) for PCB 153, the androgen receptor (AR) for p,p´-DDE, and the aryl hydrocarbon receptor (AhR) for TCDD. The difference in targets suggested different modes of action and downstream effects for these three chemicals. However, with the addition of only one or two more interaction steps, the literature-based pathway reconstruction/modeling approach made it possible to determine which activation targets of one or two of these compounds could also be activated by the third compound. Such converging common activation targets were identified from cumulative results obtained from the Advanced Search queries: 349 concordance targets were identified in three steps or fewer (data not shown) and had known positive disease association. Only high-trust direct physical interactions of known effects (activation or inhibition) were used for the queries and for network building. Networks were built downstream of all three POPs using three steps or fewer for Dijkstra’s shortest paths algorithm , IL-8, RelA (p65), c-Jun, FKHR, and Cyclin D1] were activated by the three POPs in 2.33 steps on average ; therefore, the average of each set of chemical steps was (2 + 2 + 3)/3 = 2.33. The network also showed that 35 targets were activated by the three POPs in 2.67 steps on average , yielding a total of 41 targets that could be activated by the three POPs in fewer than three network interaction steps on average.The complete final network  reveals p,p´-DDE via the AR (inhibition of an inhibitor) and activated by TCDD via the AhR (activation of an activator)    . PCB 153tivator) . CREB1 ctivator)   . The potp,p´-DDE can inhibit RelA (inhibition of an inhibitor) via PXR and AR, respectively, and then RelA activates IL-6. There is a direct pathway for activation of IL-6 from p,p´-DDE via the AR (inhibition of an inhibitor). IL-6 could be activated from PCB 153 in the same pathway as that described for IL-8 . PCB 153 activates TNF-α through the same pathways as those described for activation of IL-6 and IL-8. PXR (inhibition of an inhibitor) also provides a direct path for activation of TNF-α from PCB 153. CREB1 could be an important link to these cytokine activation pathways. RelA could represent a common step in the activation of TNF-α by all of these individual POPs. c-Jun plays an important role in all of the inflammation pathways, and together with RelA, promotes the inflammation pathway  via the PXR and the AR, respectively, and then RelA can activate fetuin A. Again, it is a noteworthy observation that RelA could represent a common step in the activation of fetuin A, IL-6, and TNF-α by all of these POPs. Thus, the counterpart proinflammatory effects of these proteins that are activated through intracellular signaling pathways may involve RelA.As illustrated in p,p´-DDE. Thus, based on our analysis, we hypothesize that there is an overlap of mechanisms between inflammatory processes and cancer development and progression that increases the potential for the carcinogenicity of mixtures of these POPs.Overall, the whole resulting network is populated with a combination of metabolic genes, insulin signaling, immune response signaling, and the inflammation cascade of cytokines and transcription factors. Based on our analysis, we hypothesize that common pathways that converge through the cytokines may contribute to inflammatory processes that may lead to metabolic diseases via circulation and via creation of a chronic inflammatory background in adipocytes and in liver and pancreatic tissues. These circumstances can lead to adipogenesis, pancreatic β-cell dysfunction, insulin resistance, glucose intolerance, liver disease, and inability to cope with increased dietary intake, which over time can result in the development of serious metabolic disease phenotypes. In addition to inflammation, some cancer-associated targets are also present. For example, as shown in By integrating the available information and bridging the gap between toxicology, epidemiology, and chemistry within the world of disease mechanisms, we can look beyond the primary targets of the individual POPs to two or three steps down the relevant pathway. Analysis of molecular networks and all possible downstream targets is extremely complex. Our approach, based on mechanistic annotated networks, allows identification of common targets that are beyond the primary targets. Although molecular interaction data have been reported for individual POPs and have been confirmed by published experimental studies , to our p,p´-DDE, TCDD, and PCB 153 can act as agonists or antagonists of the AR, the AhR, and the PXR, respectively (For example, ectively , 2012. Tectively .p,p´-DDE have overlapping and interconnected pathways that have the potential to cause a variety of biological perturbations. The AhR directly activates and transcriptionally regulates expression of IL-8 (A close look at the nuclear receptor signaling pathway revealed that PCB 153, TCDD, and  of IL-8 , and IL- of IL-8 . Our sysVarious toxic compounds may trigger abnormal inflammatory responses directly or indirectly by interfering with the normal physiological functioning of cells or tissues . These eAs noted previously, numerous studies have shown connections between cytokines and metabolic disease, cytokine levels and POPs, and POP levels and metabolic diseases. However, few give a clear articulation of the underlying mechanisms, particularly for mixtures of similar and dissimilar chemicals. In the study of disease biology and the pathogenesis of disease, much effort is given to elucidating and validating new pathways. It is relatively uncommon to actually trace pathways all the way back to identify how toxicant exposures, to individual toxicants or to mixtures, could lead to disturbances in these molecular regulatory systems.At present, there has been no clear explanation for the differences reported in epidemiological studies for POP exposures and T2D. The apparent inconsistencies may be related to the idea that POPs are involved in the pathogenesis of T2D by interfering with endocrine signaling pathways. Low-dose effects have been proposed as possible biological responses to POPs as endocrine disruptors . EndocriNovel methods of analysis including machine learning, bioinformatics, and systems biology tools are available and can be used to identify specific outcome pathways from complex data . These tThe resulting global network of common downstream activation targets was significantly enriched with metabolic disease–related targets. Interestingly, neoplasms were also over-represented among the common targets, with transcription factors, receptor tyrosine kinases, and cyclin genes identified by our queries. This common pathway could guide our understanding of the potential carcinogenic mechanisms shared by the POPs.in vitro studies of target cell populations such as liver cells and adipocytes; moreover, cell-line studies can be performed using pancreatic cells, hepatocytes, and brown adipocytes. Complementary in vivo studies in both normal and obese mouse strains dosed with POPs could be performed to determine whether the observed in vitro study findings are also observed after in vivo exposure. In addition, studies could be performed using transgenic mouse models with human fatty acid metabolism genes as well as with other potential monogenic or polygenic rodent models. Both in vitro and in vivo studies should be conducted using exposure to the three selected POPs on an individual or mixture basis using a factorial design approach. Specific receptors or pathway nodes of interest identified using these combined in silico laboratory model approaches could be technically evaluated using genomic, proteomic, or metabolomic methods. Putative biomarkers identified by these combined approaches could be further developed/translated into commercial test kits for clinical applications.The final network presents a novel systems biology and toxicology model of different molecular mechanisms of POP action that point to common disease outcomes. Future experimental evaluation of this model might lead to the development of new predictive markers of POP effects that could translate into new strategies for disease prevention and clinical use. Specific avenues of laboratory research might include, but are not limited to, We examined three representative POPs and their potential combined effects via possible protein–protein interactions. Our results, obtained using the inflammatory biomarkers pathway, showed that looking beyond the pathway for an individual chemical reveals a complex network of pathways that could be the basis of a mechanism of joint toxicity of mixtures. Hence, the body burden of chemical mixtures, particularly mixtures of lipophilic chemicals such as POPs, should be considered within the larger framework of diabetes, metabolic syndrome, and other chronic diseases. Biomarkers identified through such pathway analyses could be studied thoroughly and used to test real-world mixture exposures. Further investigations of the influence of factors such as multiple chemical exposures, nutrition, age, sex, and genetic variations will help develop personalized, specific treatment protocols for these complex diseases.


Diabetic retinopathy (DR) is a serious microvascular complication of diabetes, which causes visual disability and blindness. Several studies have used gene expression profiling of DR to identify the key genes involved in this process; however, few studies have focused on the associated pathways and transcription factors (TFs), or on the co-expression patterns at the multiple pathways level. In this study, we employed a microarray dataset from the public database library of the Gene Expression Omnibus (GEO) associated with DR and applied gene set enrichment analysis (GSEA) to this dataset and performed candidate TF selection. As a result, 10 upregulated pathways, including the type I diabetes mellitus and peroxisome proliferator-activated receptor (PPAR) signaling pathways, as well as 59 downregulated pathways, including the ErbB signaling pathway and the mammalian target of rapamycin (mTOR) signaling pathway, were identified as DR-related pathways. The majority of these pathways have been previously identified, but some were novel. Finally, co-expression networks of related pathways were constructed using the significant core genes and TFs, such as PPARγ and SMAD4. The results of our study may enhance our understanding of the molecular mechanisms associated DR at the genome-wide level. Diabetic retinopathy (DR), a specific microvascular complication of diabetes, is the most common cause of visual disability and blindness. The prevalence of DR increases with the duration of diabetes , and neaDespite DR being a common complication of diabetes, little is known about the underlying molecular mechanisms. In recent years, the complex association that exists between the most relevant contributors to the onset and progression of DR, such as AGEs, oxidative stress, inflammation and angiogenesis have been elucidated and analyzed, particularly via whole-genome expression analyses using cells and animal models ,7. Basedhttp://www.ncbi.nlm.nih.gov/geo/)  ,21, whicov/geo/) –24. The ov/geo/) .www.ncbi.nlm.nih.gov/geo/) for gene expression profiling studies associated with DR. Data were included in our re-analysis if they met the following conditions: i) the data were genome-wide; ii) a comparison was conducted between DR samples and control (CT) samples; and iii) complete microarray raw or normalized data were available. Finally, we chose the GSE12610 dataset for our re-analysis, which was contributed by Liang et al (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE12610). In this dataset, a total of 5 RNA samples extracted from retinas were examined for RNA quality and then hybridized to 2 different GeneChip® Mouse Genome 430 2.0 arrays . There were 3 biological replicates for DR  and 2 for CT .We searched the GEO database algorithm from the affy package .http://www.genome.jp/kegg-bin/get_htext?br08901.keg). The annotation of significant genes in each pathway was performed using the biomaRt package, BioMart v 0.8 rc3 . Next, clustering of groups and genes was performed based on the expression of the identified genes in each significant pathway, using the hierarchical clustering method and Pearson’s correlation co-efficient.Our GSEA of pathways and genes was performed using the Category package in version 2.6.0 of Bioconductor . The goahttp://dire.dcode.org/), which uses the Enhancer Identification (EI) method, to predict common REs for our input genes that have co-function in each identified, significantly related pathway (Mus musculus 9 (mm9) as the background genes. As a result, our predicted TFs have two major parameters, including TF occurrence (the percentage of candidate REs containing a conserved binding site for a particular TF) and TF importance (the product of TF occurrence and TF weight). From our candidate associated TFs with input gene sets, we selected the cut-off value of TF importance as >0.05.We used a web server known as the DiRE , the 69 significant pathways could be mapped into 6 functional classes: cellular processes, environmental information processing, genetic information processing, human diseases, metabolism and organismal systems. The details of the pathways involved in each class are described in Tables ICompared to the approach of DEGs, the strategy of GSEA that we used in this study is likely to be more powerful than conventional single-gene methods in the study of complex diseases, in which many genes make subtle contributions. According to our GSEA of the dataset of 5 samples, achieved by comparing the DR to the CT samples, there were 69 significant pathways associated with DR, whose P-values were <0.05, including 10 upregulated and 59 downregulated pathways. The coregulated pathways network is highlighted in In the functional class of cellular processes, there were 6 significantly downregulated pathways related to DR . These pThere were 7 significantly downregulated pathways in the functional class of environmental information processing, 5 significantly downregulated pathways in the functional class of genetic information processing, and only 2 significantly upregulated pathways in the functional class of genetic information processing related to DR . The ATPThirteen significantly associated pathways were classified and assigned to the functional class of human diseases, including 2 upregulated endocrine and metabolic diseases related pathways, 1 upregulated immune diseases related pathways, 3 downregulated cardiovascular diseases related pathways, 1 downregulated infectious diseases: bacterial related pathways, 4 downregulated infectious diseases: parasitic related pathways and 2 downregulated cancer related pathways . Of thesde novo synthesis of diacylglycerol (DAG), the key activator of PKC in physiology  within promoters. They play a critical physiological role as lipid sensors and regulators of lipid metabolism . More poThe TGF-β signal is predominantly transduced by a family of TFs, the Smad proteins . After bIn conclusion, we applied GSEA to re-analyze the published microarray datasets of DR and performed candidate TF selection. Finally, we identified 10 upregulated pathways, including the type I diabetes mellitus and PPAR signaling pathways, as well as 59 downregulated pathways, including the ErbB signaling pathway and the mTOR signaling pathway. Furthermore, co-expression networks of related pathways were constructed using the significant core genes and TFs, such as PPARG and SMAD4. These may be helpful to systematically understand the molecular mechanisms of diabetic retinopathy in genome-wide.


The functionality of the new software tool is illustrated using combined mass spectrometry and DNA microarray data. This application confirms jasmonate biosynthesis as important metabolic pathway that is upregulated during the wound response of Arabidopsis plants.A central aim in the evaluation of non-targeted metabolomics data is the detection of intensity patterns that differ between experimental conditions as well as the identification of the underlying metabolites and their association with metabolic pathways. In this context, the identification of metabolites based on non-targeted mass spectrometry data is a major bottleneck. In many applications, this identification needs to be guided by expert knowledge and interactive tools for exploratory data analysis can significantly support this process. Additionally, the integration of data from other omics platforms, such as DNA microarray-based transcriptomics, can provide valuable hints and thereby facilitate the identification of metabolites via the reconstruction of related metabolic pathways. We here introduce the MarVis-Pathway tool, which allows the user to identify metabolites by annotation of pathways from cross-omics data. The analysis is supported by an extensive framework for pathway enrichment and meta-analysis. The tool allows the mapping of data set features by ID, name, and accurate mass, and can incorporate information from adduct and isotope correction of mass spectrometry data. MarVis-Pathway was integrated in the MarVis-Suite (The online version of this article (doi:10.1007/s11306-014-0734-y) contains supplementary material, which is available to authorized users. Metabolomics studies  files. Additionally, a detailed protocol of the corresponding data analysis within the MarVis-Suite and project files which can be loaded directly into the MarVis-Suite interfaces (Load project function in the File menu) are provided.Installation packages for the MarVis-Suite including MarVis-Pathway and a detailed handbook are available on the project homepage Arabidopsis thaliana (ecotype Columbia-0) wild type (wt) and jasmonate-deficient dde2-2 mutant plants , three biological replicate samples were analyzed with two platforms: The four metabolomics data sets derive from Ultra Performance Liquid Chromatography (UPLC) coupled to a Time-Of-Flight (TOF) MS analysis of the non-polar and polar extraction phases in positive and negative ionization mode, respectively . The selection was reduced to all features with a ratio higher than 2 (1506 features) and labeled (’wt’). For the functional analysis in MarVis-Pathway, all features (labeled and unlabeled) were then selected (see Goto MarVis-Pathway function in the MarVis-Suite handbook).The combined data set was clustered and visualized in MarVis-Cluster  contains one reference and about 3,000 organism-specific databases. The included BioCyc collection  provides about the same number of organism-specific databases and one reference database (MetaCyc). Each KEGG reference pathway is associated with a number of compound, EC (Enzyme Commission), and KO (KEGG ORTHOLOGY) IDs and names. Each MetaCyc reference pathway variant is associated with a number of compound and EC IDs/names. For all compounds in the databases, the monoisotopic masses were calculated based on the molecular formula. In case of the organism-specific databases, the pathways are associated with compound IDs, names, and masses and gene IDs/names instead of the EC and KO numbers. Additionally, customized databases may be loaded from comma separated values (CSV) files (see the MarVis-Suite handbook for details).MarVis-Pathway implements pathway databases from the KEGG and the BioCyc collection  Set Enrichment Analysis (SEA)  SEA of combined data sets. For this purpose, the pathway-specific p-values were calculated based on a hypergeometric test and the initial filtering of the data sets , jasmonoyl isoleucine (JA-Ile), 11/12-Hydroxy-JA, 12-Hydroxy-JA-Ile, and 12-Carboxy-JA-Ile were obtained by LC 1290 Infinity  coupled with a 6540 UHD Accurate-Mass Q-TOF-MS instrument  with Dual Jet Stream Technology as electrospray ionization (ESI) source . The analysis was performed in the negative ESI mode with minor modifications as described by Floerl et al. .The plant’s response to wounding is part of the defense against insects and is mainly regulated by the isoleucine conjugate of jasmonic acid JA-Ile  are not exclusively associated with labeled features due to isomers and ambiguous mass matching . By means of the mapping of the filtered microarray data set, the coverage of pathway entries is significantly increased, as represented by much lower FDRs in enrichment analysis, and the wt-specific enzymatic steps towards the biosynthesis of jasmonate are clearly highlighted (see the lower branch of the pathway). Noteably, all but two mapped transcript features are labeled as wt-specific .The pathway enrichment analysis was repeated for the metabolomics M1–M4) in combination with the transcriptomics (T1) data set. For the S-SEA, the sample labels in the metabolomics and transcriptomics data sets were linked . These two microarray spots show high expression levels for the dde2-2-associated conditions independent of the wounding and may be an interesting starting point for further studies on the dde2-2 mutant.In contrast to the wt-constitutive intensity pattern, the prototype heatmap see Fig.  does notThe MarVis-Suite combines a statistical framework with highly interactive interfaces for exploratory data analysis. Data sets from different omics platforms can be filtered, combined, clustered, and visualized. By means of the new MarVis-Pathway interface, filtered or selected data set features may be annotated in the context of organism-specific pathway databases or custom pathway/entry set definitions which represent expert knowledge. The signal-to-noise ratio allows the ranking and filtering of heterogeneous data sets within a common framework and can easily be customized for the search for particular intensity patterns. The framework allows many other options, including alternative ratios, e.g. the signal-to-level ratio, or moderation/shrinkage of the noise term Smyth . By meanSupplementary material 1 (XLS 38 kb)Supplementary material 2 (CSV 19 kb)Supplementary material 3 (XLS 31 kb)Supplementary material 4 (PDF 258 kb)Supplementary material 5 (XLS 8 kb)Supplementary material 6 Supplementary material 7 (PDF 35 kb)Below is the link to the electronic supplementary material.