Jointly analyzing biological pathway maps and experimental data is critical for understanding how biological processes work in different conditions and why different samples exhibit certain characteristics. This joint analysis, however, poses a significant challenge for visualization. Current techniques are either well suited to visualize large amounts of pathway node attributes, or to represent the topology of the pathway well, but do not accomplish both at the same time. To address this we introduce enRoute, a technique that enables analysts to specify a path of interest in a pathway, extract this path into a separate, linked view, and show detailed experimental data associated with the nodes of this extracted path right next to it. This juxtaposition of the extracted path and the experimental data allows analysts to simultaneously investigate large amounts of potentially heterogeneous data, thereby solving the problem of joint analysis of topology and node attributes. As this approach does not modify the layout of pathway maps, it is compatible with arbitrary graph layouts, including those of hand-crafted, image-based pathway maps. We demonstrate the technique in context of pathways from the KEGG and the Wikipathways databases. We apply experimental data from two public databases, the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas (TCGA) that both contain a wide variety of genomic datasets for a large number of samples. In addition, we make use of a smaller dataset of hepatocellular carcinoma and common xenograft models. To verify the utility of enRoute, domain experts conducted two case studies where they explore data from the CCLE and the hepatocellular carcinoma datasets in the context of relevant pathways. PTEN is a part of the phosphoinositide 3-kinase signaling pathway, which regulates cell-growth [PTEN is mutated it does not fulfill its function and shuts down the pathway, which can lead to tumor growth. Jointly analyzing experimental data and pathways can help in reasoning about and predicting such effects for different conditions. Knowledge about how pathways are modulated by the genetic profile of groups or individual samples can help improving prognosis, treatment, and patient well-being.Biological networks, such as interactions between proteins, biochemical reactions, and signaling processes are commonly depicted in pathway maps. Pathway maps are often hand-crafted and only show the part of the whole known biological network that is immediately relevant for a particular natural process, such as the tyrosine metabolism, or for a particular disease, such as HIV or diabetes. The network described by these pathways is based on published research on the interactions and interdependencies between the various nodes. As a consequence, pathway maps are static and are only valid for the specific processes or disease states they are designed for and fail to adapt to the variation found in real-world data. It is not uncommon, for example, that a de-activation of a node in a cascade invalidates reactions further downstream. For example, the gene l-growth . If PTENCaleydo, an open-source biomolecular visualization framework , which features various other visualization techniques for analyzing tabular and network data.Current approaches for visualizing interdependencies between pathways and experimental data do not scale to the now common large and heterogeneous experimental datasets, which often contain hundreds of experiments and multiple data types. We designed enRoute to remedy this. enRoute consists of two views: the pathway view, which shows the whole pathway and hints at interesting paths, and the enRoute view, which visualizes experimental data for parts of the pathway. In the pathway view, shown in Figure At the beginning of this paper, we give a brief introduction of the biological background, followed by a detailed analysis of the challenges of visualizing graphs with very large numbers of node-attributes. We continue by reviewing the literature and evaluating how existing approaches address the described challenges. Based on this discussion of the state-of-the-art and its limitations, we present our visualization technique, followed by a validation of our approach in case studies, conducted with experts in molecular biology. In the course of these case studies, we demonstrate how enRoute can be used to analyze large datasets in the context of pathways.This paper is based on and extends previously published work . In addiGlycolysis/Gluconeogenesis pathway of the KEGG database the buildup or degradation of glucose is described in great detail with all biochemical conversions and the connections to the Pentosephosphate pathway and the Citrate cycle pathway. A prominent example of the signaling pathway group is the MAPK signaling pathway, which contains a well-studied signaling cascade leading from an activated cell surface receptor to a phosphorylation cascade of several kinases to the activation of DNA binding complexes, which regulate transcription of genes involved in the proliferation of cells thus enabling the cell to react to growth stimuli from its environment. It is common for pathways to include the spatial organization of cells like cell walls, the Golgi apparatus, or the cell nucleus, thus allowing to depict transport of molecules and signaling through these compartments.Life scientists have accumulated intricate knowledge about biochemical and signaling processes in living cells, which has been used to build detailed biological interaction networks called pathways. These pathways summarize the molecular interactions in the biochemical conversions of molecules from source material to complex biomolecules or the signaling from a cell surface receptor via a second messenger to the nucleic transcription machinery. Several initiatives are drawing pathway maps and make these maps available to the scientific community, such as KEGG  or WikipPathway maps have become a valuable resource for molecular biologists summarizing broad knowledge about molecular interactions and presenting this information in a condensed view highlighting the functional aspects most interesting to the researcher. In the pathway maps nodes represent biological entities like proteins, metabolites, or chemical compounds. Protein nodes are annotated using the gene names from which these proteins are transcribed. These nodes usually contain several isoforms of the same protein and often several proteins of the same family catalyzing the same reaction, leading to extensive multi-mapping of many gene names to a single node. The nodes in a pathway are connected by links that depict biochemical reactions, activating or inhibiting modifications of proteins, or enzymatic reactions. The nodes together with the links between them allow to capture the interaction network of a biological system and present them in a structured graph with node-link diagrams following specific drawing conventions. Most pathway databases contain manually curated pathway maps that are trying to represent the molecular interactions in a visually appealing way while maintaining the scientific content.~omics technologies has generated large amounts of data. These datasets include genetic and expression data from large consortia like TCGA (http://cancergenome.nih.gov) and ENCODE (http://encodeproject.org/), but also high throughput metabolic screens via nuclear magnetic resonance (NMR) measurements or large scale proteomics by mass spectrometry. Data generated from these analysis includes dynamic data like gene expression levels, metabolite levels, and protein levels in various tissues, cells, and disease states, and also information about the genetic constitution of samples like copy number variation of genes, mutations in genes, or methylation patterns. All this data can only be interpreted in the context of the biological system present in cells, which are captured in the aforementioned pathway maps. The purpose of the techniques presented in this paper is to support the researcher in dynamically mapping biological data onto pathways. This allows researchers to compare, reason, and ultimately explain the complex biological systems and signaling cascades.In recent years, the advent of high throughput As a foundation for the design of our visualization technique and the evaluation of the related work, we have conducted a requirement analysis based on interviews with our collaborators from the Medical University of Graz. Our analysis resulted in five requirements that must be met by a visualization system to successfully enable the joint analysis of pathways and experimental data.R I: The Scale Requirement - While the scalability of the graph is addressed by the sub-division of the biological network into individual pathway maps, the experimental datasets we consider are quite large. Consequently, a joint pathway and experimental data visualization system must be able to scale to dozens of experimental conditions or groups and hundreds of samples.R II: The Heterogeneity Requirement - Modern biological studies often include a wide array of complementary but heterogeneous experimental datasets. While, for example, mRNA expression data measures the gene activity, copy number or mutation data can be used to reason about deviating expression values. These heterogeneous datasets need to be presented using different visualization techniques, as they differ in terms of data type. For example, mRNA expression data is numerical, copy number data is a hybrid categorical/numerical dataset, which is often binned into ordinal  data, and mutation status data is nominal . In order to analyze these different kinds of data in context of pathways, the visualization system needs to handle all of them simultaneously and also represent each of them using suitable visual encodings.R III: The Multi-Mapping Requirement - Pathway nodes can represent various different gene products, such as enzymes, proteins, or RNA. In some cases, pathways also summarize a whole gene family into a single node, where different genes produce functionally similar proteins. This is what we call a multi-mapping: One node in a pathway actually represents multiple entities and therefore multiple entries of an experimental dataset can be associated with this node. As understanding this complexity is essential for judging effects of experimental data on a pathway, it is critical to convey multi-mappings adequately.R IV: The Layout Constraint Requirement - Layouts for pathway maps can be either produced automatically or manually. To make pathway maps easier to understand, a number of drawing conventions have been established. Examples are cycles being drawn in circles or the predominance of orthogonal edges in many popular databases. Also, manually curated pathway maps typically contain rich meta-information indicating, for example, the cell compartments in which specific processes occur. Automatically drawn pathway maps can either try to respect these conventions , or optR V: The Topology-Attribute Coexistence Requirement - We distinguish between two main types of tasks conducted on a pathway: tasks that are based on the topology of the underlying graph, and tasks that are based on the node or edge attributes of the graph [Topology-based tasks are concerned with the connectivity of the graph, e.g., which nodes can be reached from a given node, what are the articulation points of a graph, etc. An example for a topology-based task in pathway analysis is to find all nodes that might be influenced by the inhibition of a node at the beginning of a pathway. Attribute-based tasks are concerned with analyzing the properties of node or edge attributes. Edge attributes in pathways commonly describe the type of a relationship between two nodes, such as biochemical conversion, while mapping experimental data represents the majority of node attributes. An example for an attribute-based task for pathway analysis is to find all nodes in a pathway that are mutated in a large number of the mapping samples.he graph . TopologVisualization techniques for graphs are usually optimized for either topology-based task or for attribute-based tasks, but are rarely suitable for both at the same time. Node-link diagrams, are, for example, well-suited for topology-based tasks, while matrix layouts, where nodes are shown on the sides of a matrix and the cells contain information on whether there is an edge connecting the nodes, are ideally suited for edge-attribute-based tasks . When anscale requirement (R I) with respect to the encoding of node or edge attributes. For a comprehensive review of systems biology visualization refer to the article by Gehlenborg et al. [While there is a wide body of literature on graph drawing and graph visualization, we focus on the discussion of techniques that are either directly relevant for pathways or that can address the g et al. . We iden• on-node mapping,• using multiple instances of the graph with different on-node mappings ,• using separate linked views for the graph and the attributes, and• adapting the graph layout.on-node mapping is that it makes it easy to address the layout constraint requirement (R IV). Consequently, on-node mapping has been widely used to augment pathways with multiple colored rectangles, each representing a single experiment or an aggregation of multiple experiments [R I) as the amount of distinguishable colored rectangles inside a node is severely limited.The benefit of using eriments -11, whereriments . The bigsmall multiples. Small multiples show the same configuration of a plot multiple times while changing one variable ([R I) is a limiting factor. Depending on the pathway about four to ten multiples are reasonable.An alternative strategy to using multiple colors within each node in one graph is to use multiple graphs where each of them uses a single experiment or a single aggregate of experiments to drive its color-coding. This approach is commonly referred to as ariable  and the heterogeneity requirement (R II) is using separate linked views for the experimental data and the pathways. This, of course, also preserves the topology (R IV) and can be used to address multi-mappings (R III). Separate linked views use synchronized highlighting (linking & brushing) between the multiple views to communicate relationships. If, for instance, a user selects a node in the pathway, the corresponding experimental values are highlighted in the views depicting the experimental data. Shannon et al. [R V, to simultaneously enable topology-and attribute-based tasks. As separate linked views require interaction to show relationships between a single node and its associated data, the joint analysis of the topology and attributes is severely hindered.A technique that can easily address the scale  and choose a node size that enables in-place encoding of experimental data with various visual encodings .Finally, there are methods that .g., bar ,19 and l.g., bar ). While GraphDice by Bezerianos et al. [R IV and R V.There are also more radical adaption approaches for the graph layout. Schulz et al. , for exas et al.  probablyPathline tool [Curvemap view, which displays experimental data for both genes and metabolites recorded in time series. While Pathline was the main inspiration for our approach, it suffers from the unconventional pathway layout, which can hinder understanding the graph topology (R V). Also, it currently requires manual creation of the linearized pathways, thereby making it difficult to integrate the large existing databases of pathways.A different approach on adapting the graph layout was taken by Meyer et al. with their ine tool . PathlinR V) as the most critical requirement to address, as current techniques usually either support only topology-based or attribute-based tasks. Only small-multiples and direct on-node mapping are able to address requirement R V, however, both neither scale to many experiments (R I), nor do they allow to simultaneously present heterogeneous data (R II). Our solution to this problem makes use of an observation we made in discussions with our collaborators: they usually reason about and analyze experimental data associated with a single path at any given time in detail, while the rest of the network merely informs them about the context of this path. They of course continuously change the path of interest, but do not need to see detailed data for multiple paths at the same time. This temporal separation of high-level topology-based tasks and low-level attribute-based tasks allowed us to create a solution that meets all five requirements. The enRoute visualization technique, as depicted in Figure pathway view, showing the pathway map in its original graph layout (meeting R IV), and the enRoute view where a user-selected path is shown in a linear fashion together with a potentially large number of experimental data from multiple sources (R I and R II). Due to the linear arrangement of the nodes from top to bottom, it is possible to encode multi-mappings (R III) by giving them more vertical space. enRoute thus makes use of the temporal separation of analysis focus by presenting an overview in one and the details of a selected path in another view. In the following, we discuss the components of our approach and their interplay in more detail.The goal of the enRoute visualization technique is to jointly visualize experimental data and pathways in a way that addresses all five requirements discussed. We identify the topology-attribute coexistence requirement  or a single sample in its high-level topological context. This allows analysts to consider experimental data associated with nodes that are not in the currently extracted path. For this purpose, the pathway view can be configured to show only the mapping of selected samples.Glioma pathway with on-node mappings of mRNA data, while Figure To address the overview task where analysts want to get a rough indicator of the mapped experimental values, we calculate the average of all experimental sample values and multi-mappings, if applicable, and color-code the nodes accordingly. If multiple data types are available, the analyst can choose which of them should be mapped. Figure mTOR node in the lower right part of Figure For numerical and ordinal data we use a blue-white-red color map. We decided to use white as a neutral base of the color map to be able to intuitively represent data that has a neutral base, as, for example is the case with copy number data, which has a "normal" status. In addition, the blue-white-red color map avoids the drawbacks of the common red-black-green color map for red-green color blind users. A two-color gray-red color map is used for nominal data with two categories, such as mutation status data. To indicate cases where experimental data is missing, we show a small rectangle in the lower left corner of the node, as can be seen, for example, in the PDGFR gene in Figure Since the aggregation of all samples and possible multi-mappings into an average value hides all variation, we additionally provide the standard deviation encoded as a green bar below each node, as shown in Figure data rows) right next to the nodes they are associated with. As a node can have multiple mapped data rows, we adapt the spacing between nodes of the path so that all rows can be shown with a uniform height. Such multi-mappings or the occurrence of complex nodes (nodes that consist of multiple subnodes) in the path make it very hard, if not impossible, to determine which data row belongs to which node using their position alone. Therefore, we connect each node with corresponding data rows using ribbons, as shown in Figure Once a path has been selected in the pathway view, it can be analyzed in detail in context of experimental data in the enRoute view. The path is displayed in a linear, top-down layout, which is ideally suited to show rows of experimental data (heterogeneity requirement (R II) by allowing the individual groups to originate from different datasets. However, all experiments within a group must be from a single dataset.Following the divide-and-conquer visualization strategy , we grouIn addition to showing the extracted path top-down in the enRoute view, we also display branches that join or leave the path in order to preserve some of the topological information present in the pathway maps. We indicate a branch by showing its first node relative to the node where the branching occurs in the extracted path. In order to maintain a compact path representation, multiple branches that join or leave a single node of the path are abstracted into expandable nodes, one for all joining and one for all leaving branches, as shown in Figure As illustrated in Figure R I and R II). enRoute supports the visualization of quantitative, ordinal, and binary categorical data. As previously mentioned, we organize experimental data in rows and columns. Each row shows data that maps to a certain node in the path and columns group the data by a homogeneity criterion. Different groups may also have overlapping experiments. The captions of the individual groups are displayed at the top and at the bottom of the corresponding columns. Their background color indicates the dataset they belong to. For example, in Figure Being able to display large amounts of heterogeneous experimental data is an integral part of the enRoute visualization technique  [Up to this point, we have assumed that decisions on which datasets and which groupings of the datasets to show are already made. However, given a large set of datasets and a variety of alternative groupings to choose from for every dataset, this presumably easy task is in fact not trivial. To support analysts in the task of selecting datasets and groupings and assigning them to views, Caleydo provides a dedicated view, the or (DVI) . As showCaleydo, which is an open source visualization framework for biomolecular data [http://www.caleydo.org. Both, our visualization technique and the visualization framework are implemented in Java and OpenGL using the freely available JOGL library (available at https://jogamp.org/jogl). In order to highlight selected pathways, we make use of a free implementation of the Bubble Sets technique [The approach presented in this paper is implemented as a module of lar data . Binary echnique  that we http://tcga.caleydo.org), which contain up to 900 samples (depending on the subtype stratification) and ~20,000 genes each. As demonstrated in Figure Caleydo allows analysts to import data sets with large amounts of experiments, such as mRNA, copy number, and mutation status datasets. We provide pre-packaged and preprocessed TCGA datasets , which contains the genetic and pharmacologic characterization of a large panel of human cancer cell lines. The second is a microarray dataset from a model of hepatocellular carcinoma collected at the Medical University of Graz, which can be found through the corresponding original article [We developed the enRoute visualization technique in close collaboration with a biologist from the Medical University of Graz, who is also an author of this paper. To evaluate the utility of the enRoute visualization technique, we conducted two case studies using different datasets together with this biologist. The first dataset is taken from the Broad-Novartis Cancer Cell Line Encyclopedia  for cell lines originating from ovary, lung, or breast tissue among others. By selecting all samples with an increased copy number of TNFSF10 in the histogram, the system also highlights these cell lines in the mRNA expression plots, as indicated by the arrows in Figure TNFSF10 in the mRNA expression plots. Based on this data, one could interpret that these cell lines should be prone to apoptosis, as they have high amounts of the cytokine triggering the cell death. However, given the nature of these cell lines which are derived from human tumors, which are by definition resistant to apoptosis, this conclusion would be misleading. An explanation for this seemingly strange behavior can be found by looking at the copy numbers of the genes TNFRSF10A to TNFRSF10D, which are receptors for TNFSF10 cytokine, further downstream the path. Here, it quickly becomes evident that these genes exhibit a frequent loss of copies, which is easy to see when looking at the many blue bars pointing downwards for these genes. This explains how these cells can still form a tumor, as the loss of the receptor makes the cells immune to the stimulus by the TNFSF10 cytokine, thus preventing apoptosis initiation.As first step of the analysis, the In summary, enRoute enabled the analyst to study copy number variation, gene expression, and mutation status of a large number of samples in parallel in a clearly visualized and linearized sub-path of the complex network initiating apoptosis. The researcher stated that the linearization of the signaling cascade and the clear association of the other data entities to the individual steps of this cascade are very intuitive and greatly facilitated the interpretation of the data and the deduction of a biological interpretation from the dataset.all-trans-Retinoate to all-trans-18-Hydroxy retinoic acid, which is a reaction contained in the Retinol (Vitamin A) metabolism pathway shown in Figure cytochrome p450 enzymes, which metabolize drugs and mediate oxidative stress. When studying the aforementioned conversion of retinoate to retinoic acid by cytochrome p450 enzymes, it can now be seen that the node CYP2A6 actually contains members of the whole CYP2 family of genes and that the expression of these genes is uniform in normal liver and cirrhosis, but very variable in HCC, dividing these cases into a low expressing and a high expressing group. It is immediately visible that the xenograft models are only representative of the low CYP2 expressing group of HCCs. Decomposition of the complex nodes allows the identification and investigation of the individual expression patterns of genes contained in the complex node. It becomes obvious that the expression of CYP2C18 (highlighted in yellow), contained in the node labeled CYP2A6, is higher in the Hep3B model than in the other cell lines.The second use case demonstrates the ability of the enRoute path extraction feature to aid in a common problem when interpreting pathway maps. In biological reaction systems enzymatic reactions are often carried out not by a single gene or protein but by a family of proteins encoded by several genes. Additionally, enzymatic reactions are often not specific to a single protein family, but can be substituted by other enzymes, which, however, often work with different efficiency. This biological diversity leads to functional nodes in pathways that contain a multitude of genes with historically designated gene names often not indicative of the real function of these genes. It is extremely difficult for the researcher to keep in mind which genes are behind a single node of a pathway and thus understand the mapped biological data. Cross referencing of expression levels to genes and functional nodes can thus only be achieved by resolving the multi-mapping in a node to gene names which can then be mapped to the individual gene expression levels. In enRoute, the researcher can select nodes upstream and downstream of the enzymatic reaction, which is then resolved in a linearized representation of all genes involved. An example is the conversion of CYP1A1 is highly expressed in HUH7 tissue culture cells. All this information was not visible to the researcher using conventional on-node mapping approaches and was successfully visualized using enRoute. Node decomposition is an integral feature of the enRoute path extraction and thus makes the association of many mapped nodes and their corresponding experimental data readily available to the researcher.Additionally, it can be detected that Enabling the joint analysis of biological pathways and large amount of experimental data by means of visualization is a challenging problem. In this paper, we provide a list of requirements that have to be met to support such analysis. We introduce the enRoute visualization technique, which addresses these requirements using a tightly-coupled dual-view approach. Our visualization technique allows experts to select a single path from a pathway map, which is then extracted and shown in linear form in a second view, the enRoute view. This view allows experts to investigate associated experimental data in detail by displaying it in a tabular layout right next to the extracted path. The conducted case studies using two different datasets confirmed the utility of our visualization technique.Although we currently already support pathways from both the Wikipathways and the KEGG database, we plan to integrate further resources for biological networks. For instance, an integration of the EBI IntAct database  will makenRoute allows to select nodes independent of the structure of the pathway graph. However, the boundaries of a single pathway graph also limit the potential of enRoute. Consequently, we plan to extend enRoute to also support the analysis of paths spanning multiple pathways, which introduces new challenges for scalability concerning the topology of the graph. Solving these problems is subject of future research. Another interesting area of research is the selection of pathways, which we have only briefly touched in this paper. We intend to implement common measures for identifying interesting pathways in the context of the available experimental data and integrate them in a unified data and pathway visual analysis workflow.The authors declare that they have no competing interests.CP, AL, MS, DK, and DS developed the concept of the enRoute visualization technique. CP, AL, MS, and DK implemented the Pathway view and the enRoute view for the Caleydo visualization framework. KK consulted on biological relevance and evaluated the visualization technique with regard to its applicability for biological data analysis. All authors contributed to this manuscript.


Using transcriptomic and metabolomic measurements from the NCI60 cell line panel,together with a novel approach to integration of molecular profile data, we showthat the biochemical pathways associated with tumour cell chemosensitivity toplatinum-based drugs are highly coincident, i.e. they describe a consensusphenotype. Direct integration of metabolome and transcriptome data at the pointof pathway analysis improved the detection of consensus pathways by 76%,and revealed associations between platinum sensitivity and several metabolicpathways that were not visible from transcriptome analysis alone. These pathwaysincluded the TCA cycle and pyruvate metabolism, lipoprotein uptake andnucleotide synthesis by both salvage and de novo pathways. Extending theapproach across a wide panel of chemotherapeutics, we confirmed the specificityof the metabolic pathway associations to platinum sensitivity. We conclude thatmetabolic phenotyping could play a role in predicting response to platinumchemotherapy and that consensus-phenotype integration of molecular profilingdata is a powerful and versatile tool for both biomarker discovery and forexploring the complex relationships between biological pathways and drugresponse. Resistance to chemotherapy drugs in cancer sufferers is very common. Using apanel of 59 cell lines obtained from different types of cancer we study thelinks between the genes and metabolites measured in these cells and theresistance the cells show to common cancer drugs containing platinum. In orderto combine the information given by the genes and metabolites we introduce a newpathway-based approach, which allows us to explore synergy between the differenttypes of data. We then extend the procedure to look at a wider panel of drugsand show that the pathways we found were associated with platinum are not justthe pathways which are frequently selected for a large number of drugs. Giventhe increasing use of multiple sets of measurements  in biological studies, we demonstrate a powerful, yetstraightforward method for dealing with the resulting large datasets andintegrating their knowledge. We believe that this work could contribute todeveloping a personalised medicine approach to treating tumours, where thegenetic and metabolic changes in the tumour are measured and then used forprediction of the optimal treatment regime. In the quest to understand complex biological systems at multiple levels ofbiological organization, the need arises to combine knowledge from experiments ofdifferent types to create a full picture of a system's behavior. Modernmolecular profiling (“omics”) methods, such as transcriptomics,proteomics and metabolomics allow one to build up a global picture of systemcharacteristics, and to search for interactions and coordinated behavior between thedifferent levels. While each level can be studied separately, greater statisticaland explanatory power can be gained by integrating this knowledge into a singlecoherent model of the system. This is currently one of the greatest challenges insystems biology.Inter-omic data integration can be performed at different levels a priori (e.g. genes in establishedpathways) to identify those sets that show greater overlap with thephenotype-associated genes than would be expected by chance. The final list ofsignificantly over-represented or ‘enriched’ sets/pathways is used toaid biological interpretation of the data. As well as performing OR with genes,Metabolite Set Enrichment Analysis (MSEA) Many researchers have found that interpretation of omics data at the level ofindividual molecular entities can be difficult and have opted for an analysis at thepathway or functional level In addition to the inter-omic integration of metabolomic and transcriptomic data, ourapproach involves a further type of data integration that we callconsensus-phenotype integration. In this approach, several examples of the samephenotype, achieved in different ways, are used within the experimental design. Forexample, one may study a particular mechanism of toxicity via the use of differentchemical treatments that have a similar mode of action. One can thus identifyfeatures that are central to the phenotype in question across different types of“omics” data, as opposed to features that are specific to a singleinstance of the phenotype being studied.In this work, we aim to elucidate mechanisms of drug sensitivity through the use ofinter-omic statistical data integration using drug sensitivity, transcriptomic andmetabolomic data from the NCI60 cell line panel 50 values indicating the concentrationof the drug which inhibited cell growth by 50%) r|) to the −log(50GI)values across all cell lines. Setting the false discovery rate (FDR) For each drug we ranked all probe sets by their absolute Pearson correlation(|http://www.netpath.org),Biocarta (http://www.biocarta.com), HumanCyc To assess which pathways characterized the drug sensitivity phenotype we thenperformed OR analysis with pathways from the ConsensusPathDB To highlight pathways relevant to general platinum sensitivity, as opposed toparticular platinum compounds, we looked for pathways that were associated withmore than one drug response phenotype .After conducting the same inter-omic OR analysis as described previously, weobserved that the effects of this compound on the NCI60 panel was associatedwith 5 of the 6 pathways common to all 4 other platinum drugs along with afurther 12 pathways from 50 of allplatinum drugs , was significantly correlated to 19/22 metabolites inthis panel, with complete consistency in the direction of association with thetraining set data.In order to validate and to test the generalisability of our findings we thenexamined GIum drugs .In tota50 data for all 118 compoundsavailable within the NCI 60 dataset. In total 1262 pathways were significantlyassociated with the drug sensitivity of at least one compound, while 82compounds gave at least one significant pathway. To explore more broadly the relationships between chemosensitivity and biologicalpathways across a range of agents, and to ascertain the specificity of theconsensus phenotype analysis for platinum sensitivity pathways, inter-omic ORanalysis was performed using GIWhile such mechanistic relationships were readily observable, the most prominentdivision between the compounds, visible as the two largest clusters in To assess the specificity of the identified consensus platinum-sensitivitypathways we compared these to the most frequently associated pathways in theglobal inter-omic OR analysis . Of the Our results show that an inter-omic, consensus phenotype approach to integration ofmolecular profiles can reveal a cellular metabolic phenotype robustly associatedwith platinum chemosensitivity across the NCI-60 cell line panel. Many of thespecific aspects of this phenotype are consistent with the perturbations describedacross many studies of tumour cell metabolism, and several of these have beenassociated with the development, or likely acquisition, of drug resistancephenotypes. The classic hallmark of tumour cell metabolism is the Warburg effect: anincrease in glucose uptake and glycolysis to lactate even in normal oxygenconditions. In addition to the Warburg effect tumour cells are frequently reportedas exhibiting higher rates of glutaminolysis, fatty acid and lipid metabolism, andnucleotide synthesis The dependency of tumour cells on glycolysis for synthetic intermediates could beexploited in platinum chemotherapy; for example the clinically-relevant glycolysisinhibitor 2-deoxy-glucose (2-DG) has been shown to enhance cisplatin cytotoxicity inhead and neck cancer cells de novo and via recovery ofcatabolic intermediates, and tumour cell Pt sensitivity , expression of dUTP pyrophosphatase (DUTr = 0.38), expression of thymidylate synthase (TYSYr = 0.27) and platinum sensitivity. dUTP has to be hydrolysedto dUMP by DUT to prevent the incorporation of uracils into DNA and suppression ofDUT has been shown to sensitize cells to other chemotherapeutics such as pyrimidineanti-metabolites A particularly high degree of coordination between gene transcript and metabolitelevels was observed in nucleotide metabolism, revealing a robust association betweenincreased nucleotide synthesis, both sitivity . For exade novo synthesis and cisplatinresistance in yeast. Interestingly in the same study the addition of lowconcentrations of extracellular purines also abolished cisplatin cytotoxicity; thusthe metabolome may have a causal influence on platinum sensitivity and not justrepresent epiphenomena that is a passive consequence of aberrant cell division.Increased expression of nucleotide salvage pathway enzymes (e.g. uracilphosphoribosyl transferase or UPP (r = 0.20),hypoxanthine-guanine phosphoribosyl transferase or HPRT,r = 0.27) in sensitive cell lines was accompanied by decreasesin several intermediates of purine and pyrimidine catabolism  and increase in CMP, thenucleotide product of HPRT. Kowalski et al. Our pathway analysis also predicts that lipid metabolism has a direct impact onchemosensitivity. We observed lower cholesterol, glycerol, and hexadecanoic acid in more sensitive cell lines, together with negative correlationsbetween expression of apolipoprotein E , LDL receptor  and platinum sensitivity . All theWhile previous pathway analysis was conducted on gene expression profiles alone fromthe NCI60 dataset At present, our study is one of very few that presents a strategy for simultaneousinterpretation of gene expression data, metabolic profiles and physiologicalendpoints using biological pathway analysis, and has several advantages over otherapproaches. Multivariate analysis using pattern recognition algorithms such as PCA,All OR techniques share the limitation that they rely on a database containingpre-defined pathways, and therefore cannot identify novel pathways or functionalmodules. In this work we have tried to overcome this limitation somewhat throughdeconstructing the pathways which were significantly associated and thenfunctionally interpreting the elements of the pathways which showed significantassociations .Howeverin vitro derived GEMs outperformed thosederived directly from in vivo data. Recently it has also been shownthat pre-treatment metabolic profiles can be used to predict the metabolic fate oreffect of drugs in rodents Ultimately, a systems biology approach, such as the inter-omic pathway analysispresented in our study, could assist the development of anti-resistancechemotherapeutic strategies, and better individualization of treatment, i.e.personalized medicine. Using gene expression models (GEMs) based on cytotoxicity inthe NCI-60 panel, Williams et al. http://dtp.nci.nih.gov/mtargets/download.html on 27th August2008. For this work three datasets were used: metabolite levels, gene expressionlevels and drug sensitivities. The metabolite data consists of measurements of352 metabolites, 154 identified, across 58 cell lines, performed by MetabolonInc. 50), where GI50 is the dosage of the drugwhich inhibits the growth of the cells by 50%. . GI50 valueswere averaged across the replicates for each cell line, thus increasing therobustness of the primary phenotypic endpoint.The NCI60 data was downloaded from Pathways were derived from the ConsensusPathDB 50) values for each drug. Transcripts/metabolitessignificant below a false discovery rate threshold of 60% were retainedin each test set for OR analysis. Each UniProt identifier in the ConsensusPathDBpathways can be mapped to zero or more gene identifiers on the U133 chip. Thebackground estimate (m in equation 1) for OR analysis was adjusted to reflectthe following: 1) Where ConsensusPathDB proteins could not be mapped to any geneidentifiers, these were ignored; and 2) where ConsensusPathDB proteins mapped tomultiple probesets measuring genes in the transcript data, the number ofprobesets was used. In addition, several metabolites, often referred to as“currency metabolites”, which appear in many pathways and do notprovide specificity were removed before analysis. The currency metabolitesremoved were phosphate, diphosphate and NADP+. Thus, given thetranscriptomic and metabolomic data, an “effective size”,iN, could be defined for each pathway,I, in terms of genes and metabolites, The effective pathwaysize may be larger or smaller than the actual number of proteins/metabolites inthe pathway. Pathway significance was calculated using the hypergeometricdistribution,i is the number of genes ormetabolites from the pathway. P<0.05 was used as the criterion definingsignificance of pathway enrichment.Pearson correlations were calculated between all transcript/metabolite levels and−log. The number of times in which more pathwayswere significant (p<0.05) for the permuted lists than in the real data wasrecorded.n drugs.Cumulative false discovery rates for all models were calculated by dividing the‘expected’ number of pathways as given by the null model, by theactual (cumulative) number of pathways found in the real data in at leastTable S1Panels of genes and metabolites that were deemed to be associated with thesensitivity to each drug.(0.57 MB XLS)Click here for additional data file.Table S2The binary table used to produce the heatmap in (0.39 MB XLSX)Click here for additional data file.Table S3The pathways most frequently associated with drugs from the panel.(0.01 MB XLSX)Click here for additional data file.Table S4List of metabolites measured in the NCI60 panel, along with our assigned KEGGIDs.(0.05 MB XLSX)Click here for additional data file.


Metabolomics experiments have become commonplace in a wide variety of disciplines. By identifying and quantifying metabolites researchers can achieve a systems level understanding of metabolism. These studies produce vast swaths of data which are often only lightly interpreted due to the overwhelmingly large amount of variables that are measured. Recently, a number of computational tools have been developed which enable much deeper analysis of metabolomics data. These data have been difficult to interpret as understanding the connections between dozens of altered metabolites has often relied on the biochemical knowledge of researchers and their speculations. Modern biochemical databases provide information about the interconnectivity of metabolism which can be automatically polled using metabolomics secondary analysis tools. Starting with lists of altered metabolites, there are two main types of analysis: enrichment analysis computes which metabolic pathways have been significantly altered whereas metabolite mapping contextualizes the abundances and significances of measured metabolites into network visualizations. Many different tools have been developed for one or both of these applications. In this review the functionality and use of these software is discussed. Together these novel secondary analysis tools will enable metabolomics researchers to plumb the depths of their data and produce farther reaching biological conclusions than ever before. Over the past decade metabolomics has emerged as a powerful tool used in a variety of quite diverse fields for hypothesis development, to elaborate unknown gene functions, biomarker discovery and to complement proteomic and transcriptomic experiments. While considerable progress has been made, the datasets obtained from metabolomics experiments still remain extremely large and dense and thus subsequently a challenge to interpret and derive biological meaning. This challenge lies in the difficulty of understanding how dozens of chemically diverse compounds, a small subset of the hundreds to thousands of metabolites present within cells, are functionally related to each other and the perturbed condition of the experiment. While it is possible, and common, for experimenters to intuitively interpret these results using their knowledge of metabolism and the tested conditions, or manually map them onto known metabolic pathways, computational analysis allows for more comprehensive interpretation. As metabolomics remains a developing field, bioinformatic tools designed to perform this task continue to be developed and released by various groups using diverse algorithms. While many databases, tools and projects such as the human metabolome database  have focPreprocessing: Computational procedure where raw data  are converted into a useable form. Removes bias and makes samples comparable.Targeted metabolomics: Directed measurement of a group of metabolites suspected to be relevant in a particular system.Untargeted metabolomics: Quantification of as many metabolites as possible within the bounds of an instrument.Secondary Analysis: Data interpretation procedure where a finalized dataset is subject to higher level analysis using information obtained from biochemical databases.Metabolic Pathway: A series of enzyme-catalyzed biochemical reactions that bring a number of metabolites together under the umbrella of one particular biological function.‘Omics: General term for the high through-put technologies that identify and quantify large groups of targets at once including transcriptomics (mRNA), proteomics (proteins) and metabolomics (metabolites).Unannotated compound: A metabolite to which no biological function has been ascribed either in life in general or in the specific organism in question.Unknown compound: A compound that produces a unique chromatographic peak and mass spectrum, but who’s structure and name are unknown.The central dogma of molecular biology delineated the basic transfer of biological information as moving from DNA to RNA to protein . While muntargeted metabolomics as the problem of interpretation mainly needs to be dealt with for this kind of experiment. Untargeted metabolomics aims to identify and quantify as many of the metabolites in a sample as possible then determine which are important, rather than focusing on identifying and quantifying a specific set of metabolites which are expected to be important (the targeted approach) [et al.[Metabolomics requires many steps and choices before getting to the point of data interpretation which will affect how this process is undertaken. The main decisions are analytical platform (likely GC/LC-MS or NMR as they are the most common), each with their own advantages and disadvantages though the choice will more likely be dictated by instrument availability and analytical method (chemometric or quantitative) determined by the scope of the experiment. GC-MS is an extremely common metabolomics platform, resulting in a high frequency of tools which allow for the direct input of GC-MS spectra. The popularity of GC-MS is due to its relatively high sensitivity, broad range of detectable metabolites, existence of well-established identification libraries and ease of automation . Even wipproach) . When thpproach) .These te [et al..Advances in instrumentation and technical treatment of samples as well as data preprocessing and development of improved databases have been arriving rapidly in metabolomics leading to ever increasing numbers of metabolites identified and accuracy of their quantification. With thIt is in the final steps of interpretation where the most potential remains to improving the quality of information obtained from metabolomics , 29, 30.www.metabolomicssociety.org/database). Additionally, and more specific to the development of metabolomics, mass spectral databases like the Golm Metabolome Database (GMD), which link mass spectrum and chromatographic retention time to specific compounds have been developed for use in the identification stages of metabolomics [Arabidopsis[With the advent of the genomic age, the amount of biochemical knowledge has exploded in the last two decades which has necessitated its storage in large databases. A variety of top-down (gene to protein to metabolite) and bottom-up  approaches have been taken resulting in a rich expanse of metabolic knowledge bases available to query. These databases provide the contextual biochemical basis for metabolomics data interpretation. By supplying information about metabolites, such as defining which enzymatic reactions consume or produce them, and which pathways they’re involved in, researchers can use them to interpret their experiments to higher levels. An excellent review of these (top-down) types of databases is available in , while abolomics , 35. Sombolomics  warrantsbolomics . These dbolomics ), such abolomics  (human),bolomics  (plants)bolomics   . This lawww.R-project.org). Before continuing, enrichment analysis will be used as a synonym for over-representation analysis, which some tools prefer to use. For enrichment analysis two objects are needed, a (ranked) list of items (i.e. genes or metabolites) provided by the experiment and a background set of annotations, derived from biochemical databases, computationally, through manual curation or some combination thereof. The list of metabolites can be ranked based on some metric indicating how different the abundances of each metabolite is between two sample classes, which can be calculated a number of different ways. As such the list will then show the metabolites with the most different abundances at the top of the list and the most similar at the bottom. The background set should contain all known metabolic pathways in an organism, each pathway including all the involved metabolites. For example the ‘TCA cycle’ contains the metabolites succinate, oxaloacetate, isocitrate etc. Compounds can occur multiple times as they are parts of many pathways, such as oxaloacetate which also appears in glyoxylate metabolism, among other pathways.Metabolomics secondary analysis tools have been developed by a number of groups with diverse implementations, however there are many commonalities. One of the major benefits of many of these tools is their implementations of user-friendly GUIs, allowing greater accessibility and precluding the necessity of learning the complicated tools they’re based on, most prevalently, R are related with a particular network graph.Enrichment analysis: Statistical calculation that uses biological annotation to attempt to discern out of a set of (significantly changing) metabolites which higher level functional properties (pathways) are being affected.A danger with KEGG is that it includes pathways such as ‘metabolic pathways’ and ‘microbial metabolism in diverse environments’ which contain huge numbers of metabolites, and so as such are relatively meaningless when found as enrichment analysis hits. ‘Aminoacyl-tRNA biosynthesis’ is also a common hit to be taken with a grain of salt, as it is often highlighted when several amino acids are identified as significant. Careful scrutinization of metabolic pathways to ensure that they are logical is an important step in analyzing results produced by any platform. Other problems can arise when a dataset contains inordinate representation of certain pathways (either very few or very many). When many metabolites from one pathway are found in a dataset this pathway may be found to be significant mainly due to the large number of metabolites. Also the converse can happen if only a few metabolites are present in the dataset, but they changed significantly between classes, the pathway may not be found to be important due to the low number of metabolites . AnotherThese issues, and KEGG’s issues are somewhat alleviated by the BioCyc series oAs with the rest of the metabolomics field, the sub-field of secondary analysis is rapidly evolving. Many tools for metabolite mapping and enrichment have been recently developed and are available for use. Generally these tools can be divided into two categories: enrichment analysis and metabolite mapping. Enrichment analysis aims to provide higher level information about metabolism from a list of metabolite abundances in different sample classes. Metabolite mapping provides a visual representation of metabolomic data by showing the identified metabolites (and their abundances) on a network graph, often obtained from a biochemical database. Some tools provide other functions as well, or can perform both simultaneously. Additionally the option to integrate other ‘omics data is becoming more prevalent. MetaboAnalyst and MeltDB are two platforms that warrant special mention as they provide a comprehensive environment to analyze metabolomics data from raw spectra all the way to secondary analysis. Finally it should be noted that the platforms discussed here are by no means an exhaustive list, merely a representative set of the most used and promising tools at this time.MetaboAnalyst  providesThe Enrichment Analysis tool of MetaboAnalyst was one of the earliest implementations of GSEA for metabolomics datasets. As it stands, it is quite biased towards human metabolism as except for the custom option, all the available background sets for enrichment analysis are of various mammalian derived human-centric sets including blood, urine and disease associated metabolite sets. It is however possible to provide a custom background set thereby allowing any organism to be studied. This implementation of MSEA provides three options for input: a single column list of compounds , a two column list of compounds AND abundances  and a multi-column table of compound abundances in classed samples . Each option can provide different information. ORA will calculate whether a particular set of metabolites is statistically significantly higher in the input list than a random list, which can be used to examine ranked or threshold cut-off lists. SSP is aimed at determining whether any metabolites are above the normal range for common human biofluids. QEA is the most canonical and will determine which metabolite sets are enriched within the provided class labels, while providing a correlation value and p-value. MetaboAnalyst’s MSEA has been used for a number of applications including aiding in characterizing the metabolic basis of Fragile X syndrome , understC. elegans, A. thaliana, E. coli, M. musculus among others, as well as a custom option. Input options are the same as MSEA. Once the data is loaded, the background database selected, the test method (Fisher’s Exact or Hypergeometric) and network topology metric must also be chosen. Output from MetPa is an interactive set of graphs. One graph plots the p-values vs pathway impact for the computed metabolic pathways. This graph allows one to discover the highest significantly impacted pathways for further exploration. Clicking data points on this graph will cause the network to be displayed in an adjacent view, with the input metabolites highlighted. Clicking these metabolites will show a box whiskers plot for each class allowing one to visualize increases and decreases. Through these functions MetPa can be used to visualize metabolomic data within known metabolic pathways, along with calculating which pathways are significantly affected. This ability makes it a powerful tool for secondary analysis. It has been used to understand the diabetes-contextual effects of a high-fructose diet in rats [The Pathway Analysis tool of MetaboAnalyst, MetPa, performs somewhat similarly to MSEA, however it performs pathway enrichment and network topology analysis. It also provides broader options for organism databases including 17 common model organisms such as  in rats . The res in rats  to provi in rats . Finally in rats . This prC. glutanicum), which was automatically produced then manually curated produced better results than the use of the KEGG database. Two main reasons were provided. First, KEGG pathways are much larger and interconnected than their CglCyc pathways resulting in the obstruction of information when there are opposite fluxes in different parts of the pathway (the provided occurrence was in opposite abundances in the upper and lower regions of gluconeogenesis). Second, as previously noted, KEGG annotated pathways do not exist in all organisms. Further testing was performed on datasets obtained from a number of mutant lysine production strains, which generally showed the expected results of alterations to lysine and threonine metabolic pathways as their mutations targeted this split of branched chain amino acid metabolism. This new tool appears to be quite powerful, though also very reliant on the quality of the background set.MeltDB  is anothPathway Activity Profiling  is an R-Metabolic Biological Role  is anothMetabolite Pathway Enrichment Analysis  is a staThe Tool for automatic Interpretation of a Compound List  is an eaIntegrated Molecular Pathway-Level Analysis  is a tooMetaMapp  presentsMassTrix  is a plaPaintOmics  is an ‘oStaphylococcus aureus[The tool for the Visualization and Analysis of Networks with related Experimental Data (VANTED)  is anoths aureus, among mPathos  is a metProMeTra  is an ‘oBuchnera and its aphid host [MetExplore  is a methid host . For allMeta P-server  is a metAll of the above software are completely free to use, with most not even requiring registration of an account. There are also a number of commercial pieces of software available. These software are generally designed to be comprehensive solutions for use in multi-omics experiments including a number of integrated pathway analysis and contextual visualization choices. Additionally these software are more geared towards human/mammalian model  disease and drug investigations and use manually curated proprietary databases.www.ingenuity.com). Using their Ingenuity Knowledge Base, metabolomic data (among other ‘omics data types) can be mapped onto networks and enrichment analysis can also be performed. These features are among a large suite of systems biology analysis tools that are designed to allow biologically contextual representation of data. These data can come from a large variety of different types of experiments ranging from small-scale drug target experiments to combined transcriptomic and metabolomic studies. While many types of data can be used, IPA can take just a list of altered metabolites as input and use literature-characterized signaling and metabolic pathways to identify the biologically relevant effects of an experiment. This approach has been used to aid in the understanding of diverse metabolomics experiments, including colorectal cancer [Ingenuity Systems Inc. , offers a data analysis suite deemed Ingenuity Pathway Analysis , of Thompson Reuters, along with MetaMiner and MetaDrug is a series of pathway analysis and data mining tools highly geared towards human disease investigations. Among these tools are features for mapping multi-omics experiments, drug target prediction, and pathway perturbation analysis for toxicity studies. These tools all work against their proprietary manually curated databases, which for some features extend into common model and pathology-relevant organisms. Metacore has been used to perform over representation and network analysis on datasets combining metabolomic and either transcriptomic or proteomic data to understand toxin mode of actions  and biomAgilent Technologies  provides a comparable suite while also providing features for MS analysis of raw data by integrating with their Mass Profiler Professional, however this program has mostly been used for transcriptomic analysis.Metabolomics represents the apical step in the paradigm of systems biology. Its rapid development has provided unparalleled understanding of metabolic processes to a plethora of different fields. The secondary analysis of metabolomics data is a recent addition which will provide researchers with much more power in finding biological interpretation. Currently though, few researchers tend to be using enrichment tools and the provided results are rarely heavily discussed in their manuscripts. While the results from this type of analysis should not be the sole source of information for biological interpretation of metabolomics experiment, the provided results are highly useful in giving metabolic context for many metabolites at once, without having to search through databases one at a time. Still, a number of researchers have shown they provide an excellent springboard for diving into the depths of metabolomics data.As the field of metabolomics secondary analysis evolves, a number of challenges remain. Beyond the ever-changing processes of data pre-processing and statistical analysis for metabolite significance, the contextual interpretation of metabolomics results will also need improving. While the software described in this review are a good beginning, future analyses will need to be highly tailored towards organism-specific metabolic reconstructions. The MetaCyc derived series of databases have begun to fill this role, however as genome annotation remains a marginally accurate process, the models generated from such data are affected equally. Community driven confirmation and elucidation of genetic and metabolic annotation of databases like EcoCyc have shown that it is only a matter of time and effort for reasonable computational models to be built. For other organisms comparable tools will be developed as the research community deems them important. Another major improvement to be made would be decreasing the number of inaccurate and meaningless pathway hits made in enrichment analysis. This may be difficult though as metabolites will only become annotated into more metabolic pathways as their connections are elucidated. Great steps have been made in metabolite mapping techniques, especially with the chemical similarity connections provided by MetaMapp, however the interpretability of all maps remains difficult. Computational improvements in the graphical presentation and ease of producing legible maps will make metabolite mapping better for metabolomic secondary analysis.E. coli, and A. thaliana are much more convenient to perform metabolomics secondary analysis upon compared to, for example, a freshly isolated environmental bacterial strain. Again MetaCyc and its associated tools present the solution though producing an organism-specific –Cyc database from an annotated genome remains a complex time consuming endeavour. Fortunately the documentation continues to improve and SRI International is well-engaged with the research community for educational and software feedback purposes.Achieving good biological interpretations of metabolomics data is easier in medical studies of humans and generally when using highly studied model organisms due to the preponderance and quality of databases associated with these subjects. While it is generally true in science that studying organisms with a wealth of literature makes data interpretation easier, this effect is greatly amplified in metabolomics. Thus humans, Regarding the current set of available tools, they all have strengths and weaknesses and it should not come to the use of one over the other. Once a metabolomics dataset has reached the point of secondary analysis, applying any of the above tools is not a hugely time-consuming process and so it may be wise to use multiple tools and take the consensus results. Definitely the use of at least one enrichment analysis and one visualization/mapping tool is recommended. Given the complexity of metabolomic data, it is also important to carefully regard the results from secondary analysis as it is possible for enrichment analysis to produce significant pathway hits from only one or two metabolites in a pathway. As such, careful scrutinization and logical biological interpretation of the data must be undertaken. With this in mind metabolomics researchers should strive to integrate secondary analysis into their studies as these highly useful results can be obtained very rapidly. Clearly the field of secondary analysis is coming into its own and its continuing development will only serve to improve the success of the metabolomics approach.


How a living organism maintains its healthy equilibrium in response to endless exposure of potentially harmful chemicals is an important question in current biology. By transcriptomic analysis of zebrafish livers treated by various chemicals, we defined hubs as molecular pathways that are frequently perturbed by chemicals and have high degree of functional connectivity to other pathways. Our network analysis revealed that these hubs were organized into two groups showing inverted functionality with each other. Intriguingly, the inverted activity profiles in these two groups of hubs were observed to associate only with toxicopathological states but not with physiological changes. Furthermore, these inverted profiles were also present in rat, mouse, and human under certain toxicopathological conditions. Thus, toxicopathological-associated anti-correlated profiles in hubs not only indicate their potential use in diagnosis but also development of systems-based therapeutics to modulate gene expression by chemical approach in order to rewire the deregulated activities of hubs back to normal physiology. Living organisms are constantly exposed to potentially harmful chemicals within their living habitats. Detoxification that involves metabolizing and eliminating these chemicals are of great importance to maintain healthy equilibrium. In vertebrates, the liver is an essential organ in detoxifying xenobiotics and monitoring nutritional cues via homeostatic regulation in broad biochemical processes. This offers the liver as an excellent organ to study homeostatic processes in response to chemical imbalances and xenobiotic insults.Several omics studies had revealed that genes involved in xenobiotic metabolism, heat shock signaling, oxidative stress, cell proliferation, apoptotic pathway, nuclear receptor signaling and proteasome are among those mostly reported to show altered expression Thus, it is important to understand the intricate relationship among molecular pathways in the liver under various chemical insults as their deregulated activities often resulting metabolic disorders, cancers, and liver damage. In this study we used the zebrafish model to identify molecular pathways that act as hubs to respond to perturbations triggered by different chemicals. We exposed zebrafish to six different chemicals, Benzo-[a]-pyrene (BAP), 17-β estradiol (E2), 4-Nitrophenol (NP), 4-Chloroaniline (CA), Arsenic (V) acid (As) and Mercury (II) chloride (Hg), at various concentrations and under different exposure durations, and generated hepatic transcriptomic data by DNA microarray. These chemicals were chosen because they serve as representatives of selected environmental toxicants and pollutants that are potential health hazards to various organisms including humans, hence having considerable public health concern. BAP is a polycyclic aromatic hydrocarbon that serves as a representative of persistent organic pollutants while E2 is a steroid that represents estrogenic compounds and these two classes of compounds are known endocrine disruptors found in the environment Using gene set enrichment, functional correlation and network analyses, we successfully defined a group of molecular pathways acting as hubs for their frequent respond to multiple chemicals and high degree of functional correlations (connectivity degrees) to many other molecular pathways. Interestingly, we found that these hubs were organized into two groups showing inverted activity correlation with each other. Our current work revealed that the inverted activity profiles of hubs are obvious only when a fish is confronted with chemically-induced toxicopathological conditions. Since the identified hubs play important roles in liver homeostasis, their malfunctions suggest broad association to liver disorders. Furthermore, analyses using independent external datasets obtained from liver tissues of rat, mouse, and human cells exposed to chemicals We generated a total of 168 arrays  for zebrafish livers exposed to six different chemicals at various concentrations and durations, accounting for a total of 40 specific chemically-induced perturbations. For comparative study, we included 3 additional hepatic transcriptomic data obtained from zebrafish experiments with starvation and specific carbohydrate dietary manipulation To examine whether the expression of genes within a molecular pathway is enriched by a perturbation, enrichment analyses considering the relative ranks of gene expression profile were conducted by Gene Set Enrichment Analysis (GSEA) We next defined a function called normalized pathway coverage (NPC) in order to determine appropriate thresholds to partition pathways into categories composing of high responsive (HR), medium responsive (MR), and low responsive (LR) across multiple perturbations see . The rel plotted . From thAt cellular level, activities among molecular pathways are governed by intricate regulation and expression of their genes. To investigate the correlation and potential functional connectivity of molecular pathways, we performed Pearson correlation to assess linear correlation among 189 molecular pathways across 43 perturbations. Normalized Enrichment Score (NES) generated by GSEA see  were useA pair of pathways is considered functionally connected if the calculated Pearson correlation coefficient (PCC)>0.5 or PCC<−0.5  with p-vResults from Pearson correlation also revealed that pathways with known associated functions also show significant correlations in fish triggered by chemicals . For exaWe are interested in finding molecular pathways acting as hubs that they are not only frequently perturbed by various chemicals but also show high functional connectivity to many other pathways. We plotted the frequency of perturbations and the degree of connectivity in order to visualize their relationships for each of the molecular pathway . InteresWe identified a group of 16 hubs corresponding to 43 perturbations in this study  for rat, mouse, and human treated with various chemicals. Four expression series, GEO:GSE5509 Analysis of transcriptomic data GEO:GSE17184 We found most toxicopathological-associated states studied thus far are biased towards profile of “Scheme B”, with most hubs in Group B are activated accompanied with suppression of hubs in Group A . This isIn this study, we used zebrafish as a vertebrate model to identify hub molecular pathways that respond to multiple chemical perturbations in the liver and assess their functional correlations. We observed a substantial number of hubs involved in lipid metabolism. This is consistent with the fact that the liver plays a central role in lipid metabolism in response to nutritional cues by regulating the synthesis, oxidization, transport and excretion of lipids Other hubs such as ribosome, proteasome, circadian exercise, N-glycan biosynthesis, aminoacyl-tRNA biosynthesis, translation factors, and IL6 pathway form non-lipid metabolic category. Suppression of proteolysis is normally required when a rapid transcriptional response and protein synthesis are needed in a cell such as during cell division. Translation factors, aminoacyl-tRNA and ribosomes are anticipated to coordinate at the subsequent stage after the transcription step. Also, circadian clock had been reported to couple with proteasome It had been reported that amino acids such as isoleucine, leucine, tyrosine, phenylalanine, tryptophan, lysine, and arginine are capable of inhibiting the chymotrypsin-like activity of the proteasome in a dose-dependent manner The concept of “attractor” that is used to describe the behavior of complex systems in physics To maintain healthy equilibrium, a cell undergoes homeostasis processes constituting multiple tiers of negative and positive feedback loops to ensure all processes are tightly regulated such that all cellular processes only take place in the right amount, right place, and right time within a cell. The balanced activities of these negative and positive feedback loops can thus be viewed as balanced of “Yin” and “Yang” components, an ancient Chinese concept refers to opposite but mutually interdependent elements. In biology, this Yin-Yang concept had been utilized to describe the balance of various cellular activities, such as antioxidation/oxidation There are two toxicopathological schemes associated to the inverted profiles. “Scheme A” with activated Group A hubs and suppressed Group B hubs. The vice versa is applicable for “Scheme B”. Our current analyses indicate that most toxicopathological states favor Scheme B for fish and mammalian systems. We envisage lower “pathological barrier”  for Scheme B across an imaginary toxicopathological landscape with normal physiological equilibrium being a global minimum . Also, SThe double-headed arrow at the base of the pyramid in Although we observed the association of toxicopathological states to inverted activity profiles of shared molecular pathways identified from fish to mammals, at this stage, we cannot exclude the possibility that there are other hub pathways showing inverted activities but fail to capture in this study. Furthermore, it is possible that there are totally different sets of molecular pathways showing inverted activities owing to different toxicopathological mechanisms under particular conditions. For instance, as shown in http://toxnet.nlm.nih.gov/) and our preliminary acute toxicity exposure experiments conducted for the compounds Adult zebrafish (6 months–1 year old) were obtained from a local fish farm. The fishes were allowed to acclimatize in aquaria for several days before transferred into smaller containers for chemical exposure. Fishes were exposed to chemicals for up to 96 h at a density of one fish/200 ml at 27°C in a static condition. The treatment regimens used are summarized in Total RNA was extracted using Trizol reagent  according to the manufacturer's instructions. Reference RNA for microarray hybridization was obtained by pooling zebrafish whole adult male total RNA with female total RNA at 9∶1 ratio. The integrity of RNA samples was verified by gel electrophoresis, and their concentrations were determined by UV spectrophotometer.Reference RNA was co-hybridized with RNA samples either from chemically treated or control fish on a glass array spotted with 16.5K and 23K zebrafish oligo probes. Both reference and sample RNAs were reverse-transcribed and labeled differently using fluorescent dyes Cy-3 or Cy-5. After hybridization at 42°C for 16 hours in hybridization chambers, the microarray slides were washed in a series of washing solutions , dried by low-speed centrifugation and scanned for fluorescence detection using the GenePix 4000B microarray scanner (Axon Instruments). More detailed protocols for microarray experiment and data acquisition have been described recently by us All data used in this study is given in http://www.braju.com/R/). The zebrafish genes were mapped to human homologous genes as previously described 10(1/P) where P is the p-value of a gene obtained from Student's t-tests. Positive and negative values of log10(1/P) were assigned for up- and down-regulated genes, respectively. The genes were then ranked in descending order based on values of log10(1/P). The ranked genes were the input array for GSEA and were mapped to 639 pre-defined gene sets of canonical pathways obtained from the GSEA website (http://www.broadinstitute.org/gsea/). Gene sets with less than 10 genes mapped to input were excluded from further analysis.The raw microarray data was normalized using Lowess method in the R package  for partitioning pathway category corresponding to high responsive (HR), medium responsive (MR), and low responsive (LR), we defined a function called normalized pathway coverage (NPC) for one particular chosen threshold asIn this study, we used NES obtained from GSEA to represent the “meta-activity” of a pathway. We used Pearson correlation which assumes linear dependence between variables to infer functional association between pathways. Matrix of z-score-corrected NES values was used as input to calculate Pearson correlation coefficient (PCC) as stated below:To evaluate the significance between correlations, a matrix of p-values for testing the hypothesis of no correlation was performed. Each p-value is the probability of getting a correlation as large as the observed value by random chance, where the true correlation is zero. We considered the correlation between pathways is significant if PCC>0.5 or PCC<−0.5 with p-value<0.0001.We observed there are 20 conditions in our data showing obvious inverted activity profiles among hubs (bolded labels in 10(1/P) was computed for each gene and was used as ranking metric as described in the zebrafish data. Gene symbols provided by platforms in these selected GEO series were used as input for GSEA, with molecular pathways less than 10 genes mapped to the ranked input arrays were discarded. Pathways with nominal p-value<0.05 were considered significantly affected. Molecular pathways showing inverted profiles in zebrafish were selected to assess their activity profile in mammalian systems.In order to assess the present of inverted activity profiles of molecular pathways in mammalian systems, we collected independent external hepatic transcriptome data of rat and mouse treated with chemicals from the Gene Expression Omnibus (GEO). Transcriptome data of human hepatocellular carcinomas was also included. Four GEO expression series, GEO:GSE5509 Figure S1Network of molecular pathways showing anti-correlated behavior. Hubs are indicated with red and green fonts. Pink and green nodes represent pathways associated to Group A and B, respectively.(TIF)Click here for additional data file.Figure S2Demonstration of Normalized Pathway Coverage (NPC) function, which represents the average contribution of significant responses from each pathway with . Lets us assume that the number of significant responses of three pathways Pi, Pj, Pk, are larger or equal to a threshold, say 5, then the NPC for these pathways of threshold 5 will be NPC(5) = (5/43+7/43+6/43)/3≈0.1395. Similar computation is performed for other pathways with different values of thresholds. The computation results are shown in the figure for (JPG)Click here for additional data file.Data S1z-score corrected normalized enrichment scores for each molecular pathway across 43 chemical perturbations.(XLS)Click here for additional data file.Data S2Pairwise Pearson correlation between pathways.(XLS)Click here for additional data file.Data S3Statistical test  for pairwise correlation between pathways.(XLS)Click here for additional data file.Table S1Categories, types, and experimental summary of hepatic transcriptomic data used in this study. As = Arsenic (V); BAP = Benzo-[a]-pyrene; CA = Chloroaniline; E2 = Estradiol; Hg = Mercury (II) chloride; NP = 4-Nitrophenol.(DOC)Click here for additional data file.Table S2Categories of pathways at different levels of responsiveness and degree of connectivities. HR = Highly responsive; MR = Medium responsive; LR = Low responsive; HC = Highly connected; LC = Low connected.(DOC)Click here for additional data file.Table S3Categories of pathways with known associated functions showing significant correlation coefficients.(DOC)Click here for additional data file.Table S4Assigned anti-correlation scores to pathways showing significant responds to 20 selected conditions with obvious inverted activities of hubs as “seed”. Red and green fonts are hub pathways of Group A and B, respectively. Pink and green backgrounds are anti-correlated pathways assigned to Group A and B, respectively. See (DOC)Click here for additional data file.