Type 2 diabetes mellitus (T2D), affecting >90% of the diabetic patients, is one of the major threats to human health. A comprehensive understanding of the mechanisms of T2D at molecular level is essential to facilitate the related translational research. Here, we introduce a comprehensive and up-to-date knowledgebase for T2D, i.e. T2D@ZJU. T2D@ZJU contains three levels of heterogeneous connections associated with T2D, which is retrieved from pathway databases, protein–protein interaction databases and literature, respectively. In current release, T2D@ZJU contains 1078 T2D related entities such as proteins, protein complexes, drugs and others together with their corresponding relationships, which include 3069 manually curated connections, 14 893 protein–protein interactions and 26 716 relationships identified by text-mining technology. Moreover, T2D@ZJU provides a user-friendly web interface for users to browse and search data. A Cytoscape Web-based interactive network browser is available to visualize the corresponding network relationships between T2D-related entities. The functionality of T2D@ZJU is shown by means of several case studies.Database URL:http://tcm.zju.edu.cn/t2d Diabetes mellitus is one of the major threats to human health, which is expected to affect 552 million people by 2030 . More thNowadays, the existing knowledge of T2D is located in different formats at scattered places , 6. The Here, we introduce a comprehensive and up-to-date knowledgebase for T2D, i.e. T2D@ZJU. T2D@ZJU contains three levels of data associated with T2D, which is retrieved from pathway databases, PPI databases and literature, respectively. In comparison with the previous T2D-related databases, which focus on the information of the T2D-related genes, T2D@ZJU organizes the integrated information in a manner of network, which could be further expanded as desired with data of three distinguishable levels, in terms of data reliability and information coverage, to meet the needs of network pharmacology studies. To enhance the readability of these data, T2D@ZJU provides an interactive web tool for users to visualize the network relationships between T2D-related entities, which will offer a better understanding of T2D for basic and clinical researchers and hereby facilitate the T2D-related studies of network pharmacology and multi-target therapeutics.As illustrated in http://www.biocarta.com) and PANTHER  in search box. Once given a query, the background program can find all the directly related entities/entity classes and draw the corresponding networks. Information of all the connections in the network is listed below the network, and the PMIDs of the references are linked to the respective literature sources. Clicking on any node of a given network, the detailed information would be listed with external links. Users also can search the pathway associated with T2D by selecting a pathway name in the dropdown box, and all the connections related to the corresponding pathway will be returned.To demonstrate the utility of the search function and the associated knowledge discovery, a use case was applied. A search with ‘SOCS3’ as the query keyword resulted in five entities  related to SOCS3 directly. As displayed in Therapies aimed at inhibiting SOCS3 in skeletal muscle may be a favorable strategy to reverse glucose intolerance and insulin resistance . NFκB plUsers are allowed to browse the T2D networks. Moving the mouse over any node of the network, all the directly related interactions will be highlighted, whereas clicking on a node, details about the node will be listed below. Double-clicking on any node of a given network, all the dirctly related entities from different data sets  will be listed, and users are able to select any of listed entities to extend the network.Nodes for which knowledge is still limited have fewer connections with other entities in the T2D network. However, some of them are proposed to be the associated with enhanced risk of T2D, such as HMGA1 ,30. ConnAll three data sets are freely available at the download page. We also provide an illustration and examples to guide the users through the different functions of the web interface at the help page.et al. , Program for New Century Excellent Talents in University (NCET-12-0488) and Fundamental Research Funds for the Central Universities. Funding for open access charge: National S&T Major Project (No. 2012ZX09503001).Conflict of interest. None declared.


Understanding the information-processing capabilities of signal transduction networks, how those networks are disrupted in disease, and rationally designing therapies to manipulate diseased states require systematic and accurate reconstruction of network topology. Data on networks central to human physiology, such as the inflammatory signalling networks analyzed here, are found in a multiplicity of on-line resources of pathway and interactome databases , PANTHER, Reactome, I2D, and STRING). We sought to determine whether these databases contain overlapping information and whether they can be used to construct high reliability prior knowledge networks for subsequent modeling of experimental data.We have assembled an ensemble network from multiple on-line sources representing a significant portion of all machine-readable and reconcilable human knowledge on proteins and protein interactions involved in inflammation. This ensemble network has many features expected of complex signalling networks assembled from high-throughput data: a power law distribution of both node degree and edge annotations, and topological features of a “bow tie” architecture in which diverse pathways converge on a highly conserved set of enzymatic cascades focused around PI3K/AKT, MAPK/ERK, JAK/STAT, NFκB, and apoptotic signaling. Individual pathways exhibit “fuzzy” modularity that is statistically significant but still involving a majority of “cross-talk” interactions. However, we find that the most widely used pathway databases are highly inconsistent with respect to the actual constituents and interactions in this network. Using a set of growth factor signalling networks as examples , we find a multiplicity of network topologies in which receptors couple to downstream components through myriad alternate paths. Many of these paths are inconsistent with well-established mechanistic features of signalling networks, such as a requirement for a transmembrane receptor in sensing extracellular ligands.a priori. However, by incorporating prior knowledge, it is possible to successively build out network complexity with high confidence from a simple linear signal transduction scaffold. Such reduced complexity networks appear suitable for use in mechanistic models while being richer and better justified than the simple linear pathways usually depicted in diagrams of signal transduction.Wide inconsistencies among interaction databases, pathway annotations, and the numbers and identities of nodes associated with a given pathway pose a major challenge for deriving causal and mechanistic insight from network graphs. We speculate that these inconsistencies are at least partially attributable to cell, and context-specificity of cellular signal transduction, which is largely unaccounted for in available databases, but the absence of standardized vocabularies is an additional confounding factor. As a result of discrepant annotations, it is very difficult to identify biologically meaningful pathways from interactome networks  Cells monitor their external environment, transmit information across membranes, and make cell fate decisions using multi-protein receptor-mediated signal transduction networks  that repMathematical models of cell signalling exist on a spectrum in which a trade-off exists between scope and molecular detail -11. The http://www.pathguide.org), which is intended to serve as a single point of access to interaction data involving biomolecules, links to over 300 on-line information resources  were searched for inflammation-associated pathways, with inclusion criteria based on extensive literature curation of ligands and processes involved in chronic inflammatory diseases , and the GeneGo ontology was used to annotate protein functions , and STRING v8.2 (http://string-db.org/) databases. The Cancer Cell Map (CellMap: http://cancer.cellmap.org/cellmap), NCI-PID, and Reactome pathways were downloaded as SIF files from Pathway Commons (http://www.pathwaycommons.org/pc/). While many additional similar databases exist, these arguably represent the most widely used, and by extension presumed highest quality resources available. All of the databases considered represent long-term collaborative efforts, maintained by full-time staff, regularly updated, and supported by large academic institutes as well as a commercial organization. GeneGo (Thompson Reuters) is the sole commercial, pay-for-access database. This was included so as to cover both publicly available and commercial resources. In addition, A manually-curated network of molecular interactions involved in macrophage activation (Macrophage) as defined in [http://www.cytoscape.org).The 6 Pathway databases listed in Tablefined in  was downJaccard Index measures similarity between sample sets, defined as the size of the intersection divided by the size of the union of the sample sets. The Jaccard Index between pathways i and j ) is thus defined as:The n is the number of common genes, and N(i) and N(j) are the total number of genes in pathway i and j respectively.Where Edge Consistency ) between databases i and j as :Similar to the Jaccard index, but accounting for vast differences in database coverage, we define the e is the number of common edges, and E(i)is total number of Edges in database i between genes also present in database j.Where NetworkAnalyzer Cytoscape plugin .Betweeness centrality of a node is defined as the fraction of all shortest paths in a network that pass through it. The Betweeness centrality for all genes in the Ensemble network was computed using the To examine the enrichment of protein functions within topological regions of the ensemble network, we used the hypergeometric Z-score, an easily computable metric for assessing gene set enrichment . The hypN = total number of elements (genes), R = total number of positive elements , n = sample size, and r = number of positive elements in the sample.Where The metric is derived from the standard, or Z-score:μ) and standard deviation (σ) for the Hypergeometric distribution:Substituting the average  a sample (size n) is assessed for enrichment of a categorical classification . The sample window (n/N) is then successively widened across the gene set, and the score iteratively computed, eventually covering the entire set of genes. The Z-score can was plotted as a function of the sample window size (n/N), to examine the distribution of functional categories within the network.Genes comprising the network are first rank ordered, in this case based on PT) is defined as the sum of all pathway gene degrees (K);The Total Pathway Connectivity  can be divided into Internal (PIN) vs. External (PEX), defined as edges which connect 2 genes within a pathway vs. edges which connect a gene within the pathway to a gene outside of the pathway.For M) is defined based on the ratio of internal to total pathway edges (PIN/PT) compared to what would be expected by chance, given a completely random association of genes to pathways. The expected ratio of internal to total pathway edges (EIN/ET) for each of the 128 pathways was determined by randomizing the gene lists while maintaining the Pathway Maps for each gene. The number of internal and total edges were then counted for 100 randomizations, and the average values taken as the expected ratio (EIN/ET). These calculations were performed using the Ensemble network, as well as the 7 interaction database-specific networks. Pathway Modularity is then given by:Pathway Modularity (PIN/PT) and (EIN/ET) distributions using the non-parametric Mann–Whitney U test.Statistical significance of the Pathway Modularity scores for each of the 6 pathway databases were assessed by comparing the .The authors declare that they have no competing interests.DCK conceived of the study and conducted all analyses. DCK and PKS wrote the manuscript. JSR, JS, JMB, and DAL contributed key ideas and critically revised the manuscript. All authors read and approved the final manuscript.(A), TNF (B), and WNT (C) to their respective sentinel markers of pathway activity, SMAD4, NFKB1, and GSK3B. Shortest paths (i), and total number of paths as a function of path length (ii), color-coded by database source.Ensemble representation of TGFB, TNF, and WNT signalling networks. Alternate signal transmission routes connecting the extracellular ligands TGFB1 Click here for fileS2 Gene Annotations. S3 Edge Annotations. S4 Pathway Gene sets.List of Pathways. Click here for file


Small molecule effects can be represented by active signaling pathways within functional networks. Identifying these can help to design new strategies to utilize known small molecules, e.g. to trigger specific cellular transformations or to reposition known drugs.CellFateScout that uses the method of Latent Variables to turn differential high-throughput expression data and a functional network into a list of active signaling pathways. Applying it to Connectivity Map data, i.e., differential expression data describing small molecule effects, we then generated a Human Small Molecule Mechanisms Database. Finally, using a list of active signaling pathways as query, a similarity search can identify small molecules from the database that may trigger these pathways. We validated our approach systematically, using expression data of small molecule perturbations, yielding better predictions than popular bioinformatics tools.We developed CellFateScout can be used to select small molecules for their desired effects. The CellFateScout Cytoscape plugin, a tutorial and the Human Small Molecule Mechanisms Database are available at https://sourceforge.net/projects/cellfatescout/ under LGPLv2 license. Searching for chemical substances  that trigger a desired effect in cells is frequently done in the laboratories of pharma and biotech companies. Computational work enables a speed-up of this search, based on three ingredients: (1) high-throughput data describing the desired effect of turning cells of type A into cells of type B, e.g. by measuring gene or protein amounts in diseased cells and in healthy cells, (2) high-throughput data measuring how such amounts change in cells if small molecules are applied, and (3) knowledge about possibly relevant cellular signaling pathways. Our procedure contributes towards the improvement of methods for finding small molecules that may turn, e.g., diseased cells into healthy cells, based on such data. The suggested small molecules can then be directly tested with a higher chance of success.Over the last decades, drug discovery and development was the focus of many studies. Although advances were made, traditional drug development is still time-consuming, risky and costly . An alteVarious network-based methods have been developed for identifying gene expression signatures, i.e., sets of genes combined with a pattern of their expression that is typical for some cellular state. These methods have been applied e.g. to the prediction of response to drug treatment . BioinfoCellFateScout that explores an expression signature in a network context and then suggests the repositioning of small molecules to emulate the given signature. The workflow of our study is illustrated in Figure In this study, we developed a network-based bioinformatics tool and Cytoscape plugin called In this study, we introduce a bioinformatics tool called CellFateScout that is implemented as a Cytoscape plugin. Using the method of Latent Variables ,14, our We performed a validation of our method against four other bioinformatics tools answering the question: how far away from the known targets of the small molecules are the active pathways we find? In , the perBased on our validation protocol, we obtained p-values describing the closeness of the known targets of selected small molecules to the active subnetworks/pathways estimated by bioinformatics. For better visualization, we turned the p-values into histogram bar heights, where large height corresponds to small p-values (indicating closeness). The resulting histograms are shown in Figures To study further the impact of the choice of the network construction source in our validation, we compared p-values for both sources and both species. Since samples are dependent and only the network construction methodology is varying, we used a one-sided paired t-test. For mouse, the mean of the differences of the p-values is −0.1938 between STRING and iRefIndex, and based on a one-sided paired t-test this difference is significantly smaller than 0 with p-value = 0.002. Thus, using STRING we observe an improvement of results; the active subnetworks/pathways are closer to the targets, and this is supported by smaller p-values. Based on the validation weighting function (see Methods), targets and genes directly connected to these targets obtain high weights and have a pivotal impact on the weighted mean distance. We noticed that on average, incoming links to these pivotal genes comprise 15.14% of all links in a STRING network, but only 8.56% in an iRefIndex network. In other words, given a more interconnected neighborhood close to the small molecule targets in STRING networks, more differentially expressed genes are connected to each other. Thus, in case of STRING, bioinformatics tools are able to report more links (being part of subnetworks or pathways) that connect genes in a neighborhood close to a small molecule target. For human, the mean of the differences of the p-values is -0.05209, which is not significantly smaller than 0 . Our findings confirm that in the context of our studies, STRING enables superior results than iRefIndex.In summary, we find that CellFateScout together with STRING has the most success to discover pathways that are close to a small molecule’s known targets. Second best tool is ExprEssence, which is better than KeyPathwayMiner and jActiveModules by one or two cases. Our validation cannot confirm results from , where KUsing CellFateScout together with expression data taken from the Connectivity Map project, small molecule targets from STITCH and functional networks from STRING, we performed a systematic search for active pathways triggered by small molecules and stored them in a Small Molecule Mechanisms Database as illustrated in Figure More specifically, we developed a Small Molecule Mechanisms Database (SMMD) comprising 818 small molecule records (see below) associated with the mechanisms triggered by them. These mechanisms, presented as active signaling pathways, can, e.g., be used for scouting cell fate by small molecules that have specific desired effects as found by our plugin. Right now the database features only human mechanisms, however this can be extended to other species.https://sourceforge.net/projects/cellfatescout/.To construct the SMMD, we used the Connectivity Map  databasUsing CellFateScout, we can discover active signaling pathways. Having a list of these, we can perform an automated query in the Small Molecule Mechanisms Database to identify records of small molecules that may trigger the same pathway activity. For each active pathway, we thus search for SMMD records featuring database pathways with a low p-value and with matching genes from the active pathway. For an active pathway (AP) and a database pathway (DP) pair, we define a similarity score:where the overlap in the numerator is defined as follows,We sort the query result based on the similarity score. Then, we collect the 100 best small molecule records with their respective database pathways and present these in CellFateScout.In contrast to bioinformatics tools designed to focus on finding pathways or connected subnetworks composed of genes that are significantly differentially expressed, CellFateScout uses the Latent Variable Model and considers the joint expression level of all genes in a pathway under investigation and thus takes into account the expression level of all genes in the entire network, and their interactions. Therefore, genes that have only few connections and that are not exhibiting significant expression change may still be considered components of active pathways. In such cases, signaling is done through e.g. phosphorylation of the protein product. This is an important aspect to consider when analyzing small molecule responses. Accordingly, there is evidence in drug target studies that significantly differential expression analysis is only partially effective in detecting target genes  and onlyBased on our findings, we constructed a Small Molecule Mechanisms Database. Rather than focusing on single genes as small molecule targets, we believe it is more useful and gives more insight to consider groups of genes that constitute active signaling pathways. Our database is a collection of small molecules and their target mechanisms in the form of active signaling pathways. Thus, we can utilize the SMMD to search for small molecules that have an expression signature closely matching the signature found in an experimental setup. An example of such a search and its result is given in Figure There are some limitations to our plugin. The most important caveat is a consequence of the noisiness and context-sensitivity of biological data, resulting in false positive results due to spurious expression values or network information. In particular, the activity of subnetworks, pathways or single interactions is a function of many confounding influences, ranging from obvious issues such as cell-line specific effects to more hidden ones, such as the temperature at which specific experiments were conducted. Moreover, high throughput technology is not all-encompassing and it may not detect all changes in the system under investigation. Limited accuracy and adequacy of the network structure in general is another caveat and limitation of network analysis; certain elements of biological systems are not readily portrayed in a network context . FinallyMore specifically to our method, in the Latent Variable framework, the network structure is represented as an adjacency matrix and its dimension increases with the number of genes included. Thus, calculations performed to estimate model parameters may be computationally demanding in case of large networks. Along these lines, CellFateScout gives the user a choice to focus only on nodes (genes) that she would like to investigate as start or end points of pathways. Performing exhaustive exploration of the pathways from all genes to all other genes may take a long time.CellFateScout is a powerful tool, easily usable as a plugin for Cytoscape, developed to support the design of experiments involving small molecules, with applications in cell transition research and drug repositioning. Having high throughput data and a functional network, the user can reveal active signaling pathways in the underlying network. These active signaling pathways may be prime candidates for more detailed modeling as in , to furtThe CellFateScout tool is designed as a Cytoscape 2.8.3  plugin. We define a functional network as a gene/protein interaction network. Thus, the nodes in such a network represent genes and gene products (proteins), and the links between the nodes may be regulatory (where a protein controls the expression of a gene) or physical (where two proteins interact). As in other work along the same lines, gene expression data is used as a proxy for protein expression (which is hard to obtain in a quantitative manner), and it is assumed that gene and protein expression correlate. To simplify terminology, we therefore refer to ‘genes’. In this study, a pathway is a linear chain in a functional network. Such a linear chain may reflect the backbone of a canonical pathway such as a KEGG  pathway,activity of a pathway as the amount of change in expression level across its nodes (genes), which can be thought of as the information  flow along the pathway. Pathway activity is discovered depending on a specific formula that varies depending on the aims of the study. Usually, change in expression level is considered for a pair or a set of linked genes, following the idea that signaling includes patterns of gene activity that are measurable by differential expression data along the links between the genes. More specifically, this measurement will be done in our case by adapting the “Latent Variable” approach, as described in the next paragraph. We define the active signaling pathways in a network as a set of pathways with an activity above a specific threshold.We define the g to its expression level is called a latent variable, and is modeled as a normally distributed random variable. On the other hand, the influence of other genes on the expression level of g is calculated from the expression level of the genes linked to g, considering the incoming edge weights. The Latent Variable Model can be represented as a Mixed Linear Model. Then, the fixed effects of the latent variables can be tested. In our adaptation, we consider pathways in the form of linear chains. The authors of [The Latent Variable Model ,14 can ethors of . In our We selected datasets of small molecules with corresponding gene expression and functional network data and developed a validation scheme as described below that estimates the closeness between known targets of the small molecules and the active subnetworks/pathways (or corresponding gene sets) found by the bioinformatics tools.For our validation effort, we collected publicly available microarray experiments for two organisms, mouse and human, where only one small molecule is investigated per microarray. Target genes of these small molecules had to be included in the STITCH database version 3.1, a chemical-protein interactions resource . There, For mouse, we investigated small molecules that are known to modulate embryonic stem cell fate and somatic cell reprogramming. Zhang collected these in a review ; out of For human, we selected small molecules applied in chromatin-modification therapeutic studies in cancer and a summary of such studies  is assembled in Mai  in the sFocusing on protein-protein and protein-gene interactions to identify active signaling pathways from expression data  and N the number of all nodes in the network. Thus, the weighting function is monotonically decreasing with respect to i, when N is fixed. A visualization of this approach is presented in Figure Here, log10.As a result of our validation, we obtained p-values, measuring our improvement over chance findings, i.e. how much closer to the known targets are the active pathways we find, compared to random pathways, and how significant is this difference? We obtained the same kind of p-values for the other bioinformatics tools. In order to visualize the validation results, we transformed the resulting p-values from the randomization test, taking the − links. The former are usually undirected, while the latter may be directed . Links may be weighted by their strength, which may be measured experimentally. By default, the network is treated as undirected and unweighted. Nevertheless, the user can select edges and define their directionality and assign weights. The expression values must be imported as node attributes and due to the statistical model utilized, only data with at least two replicates per condition can be processed.The plugin requires a functional network and at least two high-throughput datasets  imported into Cytoscape. The network consists of nodes and edges. As described, with respect to the nodes, we do not distinguish between genes and proteins. Thus, the edges may describe physical interactions between gene products (proteins) as well as regulatory interactions between genes and proteins. Both kinds of interactions are called http://sourceforge.net/projects/cellfatescout and as Additional file The CellFateScout user interface is implemented in a way that it is easy to grasp for non-computer scientists, but still functional and comprehensive. A tutorial is available at Complex networks feature a high fraction of all possible links and analyzing all resulting pathways is very often neither possible nor necessary. Therefore, the plugin lets the user decide how to explore the network, by marking the nodes or edges that correspond to the start and to the end of the pathways to be explored. The user tags these nodes or edges in the Cytoscape network view. Additionally, CellFateScout allows selecting the entire neighborhood of a specified node. An edge is considered as the set of its two nodes. These multiple ways of node and edge selection give the user the opportunity to exploit the network in a way that suits best. In the minimalist scenario the user can select a single node for the start and for the end, whereas selecting all nodes as start as well as end points will trigger an exhaustive exploration. Finally, the user selects Cytoscape node attributes representing two conditions (e.g. case and control) from a high-throughput experiment. After computation, the most active signaling pathways with lowest p-values in the input Cytoscape network are shown in a table and marked by colors in the table and the Cytoscape network view. We also provide adjusted p-values after multiple testing correction using the Benjamini-Hochberg procedure . The resOnce we know the activity pattern of the signaling pathways in the network, we can perform an automatic similarity search in the Small Molecule Mechanisms Database (SMMD) for matching pathways based on Connectivity Map data. Only user-selected active pathways from the results table will be queried for matching pathways. The matching pathways from the SMMD are associated with small molecules, each with information about the small molecule concentration, the cell line with which the experiment was conducted and the microarray platform used. . The matching database pathways are sorted based on a similarity score (see above) and up to 100 unique records with database pathways having the best similarity scores are shown. User selection of specific records will highlight, for all matching pathways, their nodes and edges in the Cytoscape network view. To investigate the pathways belonging to a specific record, the user can obtain their description in a Cytoscape Results Panel. In turn, the described pathways can be highlighted one-by-one in the Cytoscape network view by selecting them in the Results Panel. Since not all nodes or edges of a pathway belonging to a record have to be present in the original network for which the matching pathway was calculated, only some elements may be highlighted in the Cytoscape network view. An example view of a Cytoscape session while using CellFateScout is presented in Figure SMMD: Small molecule mechanism database; CMap: Connectivity map; GEO: Gene expression omnibus; SM: Small molecule; AP: Active pathway; DP: Database pathway.The authors declare that they have no competing interests.MS developed the software, analyzed the data and wrote the paper. VL contributed to data analysis and paper writing. GF conceived the study and contributed to paper writing. All authors read and approved the final manuscript.Mouse validation summary. Table that summarizes detailed results from mouse validation including weighted means distances, p-values, and the number of genes at a certain distance to the small molecules.Click here for fileHuman validation summary. Table that summarizes detailed results from human validation including weighted means distances, p-values, and the number of genes at a certain distance to the small molecules.Click here for fileSmall Molecule Mechanisms Database records. Summary of all small molecules from Connectivity Map included in the SMMD, including their respective concentration, cell line and Affymetrix microarray platform.Click here for fileSmall Molecule Mechanisms Database. The database is constructed with ObjectDB (http://www.objectdb.com), a Java oriented database management system. Content of this file can be viewed with the ObjectDB Explorer tool, which is a part of the ObjectDB project. This file is also available at http://sourceforge.net/projects/cellfatescout.Click here for fileCellFateScout step-by-step tutorial for a case study. Tutorial that presents how CellFateScout can be utilized on an expression dataset. For this, we used the public microarray expression dataset available at GEO [e at GEO  under ace at GEO . This stClick here for file


ABL1, GRB2, INPP5D, KIF1B, PIK3R1, PLCG1, PRKCD, SRC, TUBA1A and TUBA4A were identified as common to all 3 MS phenotypes. We observed that the highest number of first degree interactors were shared between MS susceptibility and MS severity (p = 1.34×10−79) with UBC as the most prominent first degree interactor for this phenotype pair from the prioritisation analysis. As expected, pairwise comparisons showed that MS susceptibility and severity interactomes shared the highest number of pathways. Pathways from signalling molecules and interaction, and signal transduction categories were found to be highest shared pathways between 3 phenotypes. Finally, FYN was the most common first degree interactor in the MS drugs-gene network. By applying the systems biology based approach, additional significant information can be extracted from GWAS. Results of our interactome analyses are complementary to what is already known in the literature and also highlight some novel interactions which await further experimental validation. Overall, this study illustrates the potential of using a systems biology based approach in an attempt to unravel the biological significance of gene loci identified in large GWAS.Recent genome-wide association studies (GWAS) have successfully identified several gene loci associated with multiple sclerosis (MS) susceptibility, severity or interferon-beta (IFN-ß) response. However, due to the nature of these studies, the functional relevance of these loci is not yet fully understood. We have utilized a systems biology based approach to explore the genetic interactomes of these MS related traits. We hypothesised that genes and pathways associated with the 3 MS related phenotypes might interact collectively to influence the heterogeneity and unpredictable clinical outcomes observed. Individual genetic interactomes for each trait were constructed and compared, followed by prioritization of common interactors based on their frequencies. Pathway enrichment analyses were performed to highlight shared functional pathways. Biologically relevant genes  Multiple Sclerosis (MS) is a neurodegenerative disease of the central nervous system (CNS) and is one of the most common CNS diseases in young adults affecting more than 2.5 million people worldwide In the current study we have used a similar methodology to Menon and Farina −5). Data relevant to both MS susceptibility and severity was obtained directly from the database. Since the results of the 2 GWAS investigating variable response to IFN-ß therapy in MS patients did not meet the stringent statistical significance threshold in the catalog, no genes were listed in the database. However, for the purpose of this study, we sought to prioritise the genes most likely to be associated with IFN-ß response. As such, genes with p-values less than 0.05 in the validation phase were obtained directly from the 2 pharmacogenomics publications. An overview of the studies is given in GWAS relating to MS susceptibility (n = 11) To investigate the biological interactions and associations of these GWAS-genes from the aforementioned studies, we used VisANT To investigate the relationships between MS susceptibility, severity and IFN-ß response modifying genes we performed shared comparative interactome analysis between these 3 phenotype categories in a pairwise fashion using methodology similar to Menon and Farina The paired normalisation factor and interactor score for each phenotype pair were also determined as described previously Further, to identify common interactors of functional relevance, we prioritized these genes based on their frequencies. Details of these common interactors and frequencies of their interaction between any 2 MS related interactomes are given in To elucidate biological networks or pathways most relevant to these common interactors, we performed pathway enrichment analysis using an online tool, ToppGene Suite From the list of pathways generated by ToppGene Suite, we classified them based on Kyoto Encyclopedia of Genes and Genomes (KEGG) database’s functional hierarchy schema A search of the literature revealed 7 MS immunomodulatory drugs with reasonably well understood molecular targets/effects. A list of 29 unique candidate genes which encode molecules that directly interact with or are thought to be modulated by these 7 drugs was compiled . Using VA total of 15 published GWAS within the 3 phenotype categories; MS susceptibility, MS severity and IFN-ß response, were identified from the GWAS catalog. A total of 134 genes were identified as associated with these traits, with some overlap. The MS susceptibility GWAS identified 94 genes – the highest number, whereas studies examining MS severity and IFN-ß response identified 21 and 19 genes, respectively . Using VABL1, GRB2, INPP5D, KIF1B, PIK3R1, PLCG1, PRKCD, SRC, TUBA1A and TUBA4A were shared between all 3 GWAS categories , followed by the susceptibility - IFN-ß response pair , and then the severity - IFN-ß response pair . Interactor scores were calculated with the intention of eliminating a bias due to varying numbers of studies, generating varying number of interactors and interactions for each gene  was ranked second, interacting with 8 susceptibility genes and 2 severity genes. In the susceptibility - IFN-ß response comparison, GRB2 which codes for growth factor receptor-bound protein 2, was the most commonly encountered gene interacting with 11 susceptibility genes and 2 IFN-ß response genes. FYN and PIK3R1 were next highly ranked for this pair, both interacting with 8 susceptibility genes and 2 IFN-ß response associated genes. Finally, among the severity - IFN-ß response pair, PIK3R1 was again top ranked along with v-src sarcoma (Schmidt-Ruppin A-2) viral oncogene homolog (SRC). Both of these genes interacted with 2 IFN-ß response genes and 2 severity genes.Given the large number of common interactors for each paired analysis, we attempted to prioritise those most likely to be of functional relevance by determining the frequency with which they interacted with GWAS-genes within each category, and used this to rank them within the paired analyses . For exaIn order to investigate the common signalling pathways and the biological functions of the first degree interactors for each gene set, we performed pathway enrichment analysis using ToppGene Suite. Using the default settings, we identified significant pathways enriched by the first degree interactors for each gene set. As ToppGene Suite did not identify some of the genes , only 87We then determined pathways shared between the 3 phenotype categories. Pairwise comparisons showed that susceptibility - severity interactomes had the highest number of shared pathways, with a total of 80 pathways common between them, while susceptibility - IFN-ß response interactomes and severity - IFN-ß response interactomes had 65 and 54 common pathways, respectively. Overall, 41 pathways were shared between all 3 phenotype categories . Details−16 and are highlighted in The common pathways were then classified and tabulated according to their functions and source database. Details of the pathways, their biological functions and statistical significance in terms of combined p-values are presented in Signalling molecules and interaction. In this class, the most significant shared signalling pathways were those mediated by CXCR4, stem cell factor receptor (c-Kit), scatter factor/hepatocyte growth factor, two IL2 related, and IL6 pathways. These 6 pathways were shared among all 3 phenotype categories.A total of 23 pathways were involved in Signal transduction class, 23 statistically significant shared pathways were identified. The 2 most significant among them were the ErbB and Jak-STAT signalling pathways. The ErbB signalling pathway was shared between all 3 phenotype categories, whereas the Jak-STAT signalling pathway was shared between the susceptibility and IFN-ß response categories only.In the Cell growth and death, among which the 9 most significant common pathways were related to signalling mediated by EGF (3 databases), ERbB, PDGFR, FGF, hepatocyte growth factor (2 databases), and also apoptosis signalling. These were, for the most part, shared between all 3 phenotype categories with the exception of apoptosis, FGF, and Hepatocyte growth factor receptor (1 database) signalling which were shared by MS susceptibility and MS severity categories.Twenty-two statistically significant common pathways were related to Immune system category, 19 pathways were shared between 2 or more phenotype categories. TCR signalling in naïve CD4+ T cells, T cell activation, FoxO family signalling and Fc-epsilon receptor I signalling in mast cells were among the most statistically significant common pathways between MS susceptibility and severity. The Toll-like receptor signalling pathway was highlighted as one of the most significant pathways common to both MS susceptibility and IFN-ß response. Among the 8 pathways that were part of the Endocrine system, the EPO signalling pathway was the most significant and was shared between all 3 phenotype categories.In the Cancer category. The most significant of these were pathways in cancer, colorectal cancer and chronic myeloid leukaemia, which were common to all 3 phenotypes.There were 5 statistically significant pathways in the Infectious diseases, Cell communication, and Cell development. In the Infectious diseases class, NFkB activation by nontypeable Hemophilus influenzae was the most significant pathway  and was shared amongst susceptibility and IFN-ß response categories. Signalling events mediated by PTP1B was the most significant pathway in the Cell communication class which was shared between susceptibility and IFN-ß response categories. In the Development category, there were 2 particularly notable pathways. Keratinocyte differentiation was common between the susceptibility and severity categories, while the Angiogenesis pathway was shared between the severity and IFN-ß response categories.Four significant common pathways were identified in each of the following categories: Nervous system. Of these, the most significant was Genes involved in signalling by NGF, which was shared amongst all 3 phenotype categories. Finally, 2 shared pathways were classified under Neurodegenerative diseases. The Parkinson’s disease pathway, which was shared amongst MS susceptibility and severity categories, was the most significant .Three statistically significant common pathways were related to the In order to quantify the significance of the pathway overlap, we again performed a pairwise analysis of the phenotype categories using a hypergeometric test. The results of this analysis, performed using the data for each of the 8 individual databases, are tabulated in −8, 3.86×10−4 and 1.59×10−5 respectively). Interestingly, only BioCarta and PantherDB databases ranked the pathways common between susceptibility and severity as most significant . Pathway Ontology database ranked the susceptibility - IFN-ß response pair as most significant in terms of pathway overlaps .The severity - IFN-ß response pair had the most significant pathway overlaps for KEGG, Reactome and NCI-Nature Curated databases . The list of drugs and interacting proteins is provided in CD52, NT5C1A, SLC28A2, SLC28A and SLC29A2) did not have any first degree interactors and were excluded from further analysis. Of the remaining 24 genes, we then determined first degree interactors which were common between multiple drugs. There were 26 first degree interactors associated with 2 or more of the aforementioned 7 MS drugs. Details of the VisANT output and drug sets with common interactors are given in FYN interacts with pathways modulated by 4 drugs, alemtuzumab and cladribine (CASP3), fingolimod (SPHK1 and SPHK2) and ocrelizumab (MS4A1).We then used VisANT to determine the first degree interactors for the genes encoding these 29 molecules. Five genes  and teriflunomide (ABCG2), and is additionally a second degree interactor of genes associated with alemtuzumab , cladribine  ocrelizumab (MS4A1), fingolimod , mitoxantrone (TOP2B) and natalizumab .CASP3 which had a total of 10 interactions, was observed to be one of the primary drug modulated/modulating genes, in relation to both alemtuzumab and cladribine. Of particular interest, it is also a second degree interactor of the actual molecular targets of fingolimod (S1P), mitoxantrone , natalizumab (ITGA4) and ocrelizumab (MS4A1).In the interaction network, GPC5, which is both a susceptibility Systems biology based approaches exploring genetic pathways and networks offer insights into the pathogenesis of complex diseases at the cellular level and may aid in the identification of novel drug targets. Whereas shared GWAS genes such as INPP5D was found to be critical for normal Th17 cell development and known to play a key role in the reciprocal regulation of regulatory T cells and Th17 cells INPP5D, involved in signal transduction was found to be significantly upregulated as a consequence of disease development in EAE mice, the animal model of MS when compared to healthy mice  in silico study of MS lesion-specific proteins, INPP5D was suggested to be a part of gene network associated with chronic active MS plaques Results of the pairwise analysis indicated a substantial overlap of the MS susceptibility, severity and IFN-ß response interactomes. The raw data suggested greatest sharing between the susceptibility - severity pair, and least between the severity - IFN-ß response pair, and this trend was later observed for the pathway analysis. This is somewhat distorted by the large number of susceptibility studies relative to severity and IFN-ß response studies . By calculating normalised interactor scores we were able to show that, based on the evidence available to date, the greatest degree of sharing actually occurs between the susceptibility and IFN-ß response interactomes. This finding should be interpreted with caution until additional evidence better covering the latter two phenotypes becomes available. We identified 10 first degree interactors common to all 3 phenotypes. The overlap of these first degree interactors highlights the importance of genetic interaction among genes and their pathways influencing determinants of MS susceptibility, disease severity and IFN-ß response. Indeed, the biological functions, cellular pathways and regulatory mechanisms of the proteins encoded by these genes in the nervous and immune systems have been previously documented. It has been demonstrated that mice deficient of ABL1 have impaired development and responsiveness of T cells and B cells GRB2, PIK3R1, PLCG1 are key players in a major signal-transduction pathway mediated by brain-derived neurotrophic factor (BDNF). Genetic analyses of polymorphisms in the BDNF gene region have generated inconclusive associations in terms of both MS susceptibility and severity Proteins encoded by PIK3R1 which was the only first degree interactor shared among all 3 phenotypes. UBC, which had a total of 15 connections with MS susceptibility and severity genes, was the most well connected gene in the MS drugs-gene network (see further discussion below). UBC is known to be involved in various critical processes in the CNS FYN was specifically found to be a frequent and unique interactor in the susceptibility - IFN-ß response pair. Fyn, a member of the Src kinase family, is known to be a key regulator of multiple downstream signalling pathways leading to differentiation of oligodendrocytes, the myelinating cells of the CNS Prioritisation of common genes in the paired analysis based on their frequency again identified Pathway analysis of the first degree interactors indicated 41 pathways common to 2 or more phenotypes, suggesting a plausible link between these different aspects of the disease. Interestingly, most of the statistically noticeable common pathways identified by the pairwise analysis  activateFYN was the most common first degree interactor in the drug network. This was particularly interesting since FYN was also a frequent and unique first degree interactor between IFN-ß response and susceptibility in the first stage of the analysis. Furthermore, given the functional relevance of this protein (as described above) we believe FYN is a good candidate for further investigation as a modulator of disease activity in MS. Some other functionally interesting but less common first degree interactors included paxillin (PXN) which is believed to play a critical role in oligodendrocyte differentiation and ultimately regulation of myelination LYN was also a common interactor between susceptibility and IFN-ß response interactomes. MYC was a common first degree interactor for 3 drugs  and was noted to be an interactor in the susceptibility- IFN-ß response paired analysis. A subset of common first degree interactors of drugs were also highlighted in the shared susceptibility- severity analysis: SPP1, PRKDC, HSPD1, YWHAE, CTNNB1, ESR1, TP53 and UBC.With our group’s specific interest in pharmacogenomics, we were particularly keen to identify the most likely genes/pathways involved in IFN-ß response and other MS treatments. By finding common first degree interactors and points of convergence in the extended networks of treatments with well understood mechanisms of action, we hoped to identify commonalties which may be potential drug targets. UBC and CASP3 were the 2 genes with the most number of connections. Ubiquitin, a highly conserved protein, is known to play a critical role as a signalling molecule in various proteolytic and non-proteolytic pathways. Along with UBB, UBA52 and UBA80, UBC maintains the ubiquitin homeostasis, a critical factor in the ubiquitin-proteasome system (UPS). UPS is responsible for the degradation of irreversibly damaged and misfolded proteins. Along with this critical housekeeping role, UPS is also responsible for regulation of various protein functions via ubiquitination UBC as a common first degree interactor in our analysis of the IFN-ß interactome with susceptibility and/or severity interactomes.In the convergent pathways analysis of MS drugs, JAK2–IL10–CASP3 allelic combination to be predictive of response status  CASP3 was a common interactor in the severity- IFN-ß response pair but not in the susceptibility- IFN-ß response analysis.The pharmacological actions of both alemtuzumab and cladribine seem to involve the activation of CASP-3, a member of the cysteine-aspartic acid protease (caspase) family which is the second most connected gene in the convergent pathways analysis. Sequential activation of caspase proteins regulates various critical functions including cell survival, proliferation, differentiation and particularly apoptosis The aim of this study was to primarily determine the shared genes/pathways between 3 MS related characteristics: susceptibility, severity, and IFN-ß response. We hypothesised that these traits must intertwine at a biological level, and thus those overlapping genes/pathways would be the most functionally relevant. To date, only the data from susceptibility studies has been sufficiently powered and extensively validated, and this is a major limitation of our analysis. However, we believe that by using this robust susceptibility data our study has been able to tease out the most likely genes/pathways influencing severity and/or IFN-ß response. A further caveat of this study is that current IFN- ß response criteria may incidentally capture patients with a less/more aggressive disease course. On this basis, it is difficult to determine whether shared genes/pathways in the response/severity interactomes are indeed common, or if they are all MS severity related genes/pathways with other distinct IFN- ß interactors representing true response modulators. Additionally, results related to this phenotype pair are not underpinned by the adequately powered/validated susceptibility data.ABL1, GRB2, INPP5D, KIF1B, PIK3R1, PLCG1, PRKCD, SRC, TUBA1A and TUBA4A, and identified a number of common genes in drug signalling pathways. FYN, in particular, is an appealing candidate for further investigation due to its frequency in our drugs network, presence in the susceptibility - IFN-ß response shared interactome, and its functional relevance in MS. Thus, by utilizing the wealth of information generated by GWAS and employing such complementary systems-based analyses, it is plausible to identify the underlying biological themes driving the various disease traits in a complex disease like MS. Appropriate follow-up studies are needed to further investigate and validate the role of the genes and pathways identified in this study with respect to various disease processes in MS.Our investigation of MS related GWAS-genes, their first degree interactors and related functional pathways led to the identification of genes and pathways that are highly plausible candidates as MS biomarkers and/or therapeutic targets. We identified 10 interactors common to all three MS phenotypes – Table S1Detailed records of GWAS from the GWAS catalog used in these analyses.(XLSX)Click here for additional data file.Table S2Interactome details – List of 3 MS related GWAS-genes and their interactors.(XLS)Click here for additional data file.Table S3List of the shared interactors from all three GWAS categories.(XLSX)Click here for additional data file.Table S4Statistical significances for interactor sharing – calculations of VisANT interactor score and VisANT interaction ratio.(XLS)Click here for additional data file.Table S5Frequency of common interactors.(XLS)Click here for additional data file.Table S6Details of pathway enrichment analysis – input data and results.(XLS)Click here for additional data file.Table S7List of shared pathways between 3 MS related phenotypes.(XLS)Click here for additional data file.Table S8Details of statistical significances and ranking of overlapping pathways between 3 MS related phenotypes.(XLS)Click here for additional data file.Table S9List of MS drugs and their possible mechanism of action along with gene/s involved. Interactome analysis of MS drugs-genes.(XLS)Click here for additional data file.Table S10Functional classification of common pathways between three phenotype categories based on the database from which they were derived.(XLS)Click here for additional data file.


Using data from an Arabidopsis BayGraphical models describe the linear correlation structure of data and have been used to establish causal relationships among phenotypes in genetic mapping populations. Data are typically collected at a single point in time. Biological processes on the other hand are often non-linear and display time varying dynamics. The extent to which graphical models can recapitulate the architecture of an underlying biological processes is not well understood. We consider metabolic networks with known stoichiometry to address the fundamental question:  High-throughput profiling data are pervasive in modern genetic studies. The large-scale nature of the data can make interpretation challenging. Methods that estimate networks or graphs have become popular tools for proposing causal relationships among traits. However, it is not obvious that these methods are able to capture causal biological mechanisms. Here we address the power and limitations of causal inference methods in biological systems. We examine metabolic data from simulation and from a well-characterized metabolic pathway in plants. We show that variation has to propagate through the pathway for reliable network inference. While it is possible for causal inference methods to recover the ordering of the biological pathway, it should not be expected. Causal relationships create subtle patterns in correlation, which may be dominated by other biological factors that do not reflect the ordering of the underlying pathway. Our results shape expectations about these methods and explain some of the successes and failures of causal graphical models for network inference. Understanding the nature of cause and effect is fundamental to all fields of scientific investigation, but the concept of causality can present special difficulties in biology Recent advances in high-throughput phenotyping technologies have made large-scale measurements of molecular traits possible. Expression QTL (eQTL), metabolic QTL (mQTL) and protein QTL (pQTL) can be used to link thousands of molecular phenotypes to genetic loci, as well as to clinical phenotypes The interpretation of a directed edge between nodes Several algorithms have been proposed for the inference of causal relationships among phenotypes using genetic data in silico predictions of the response of a system to perturbations. We present a simulation study in which we generate synthetic mQTL data from dynamical models of pathway motifs with two sources of perturbation. We vary the rate parameters in a manner that mimics a genetic cross and we drive the simulations models with an input function that includes stochastic noise.Deterministic models of cellular metabolism can be defined by ordinary differential equations (ODEs) derived from simple laws of mass-balance Arabidopsis BayGlucosinolates are secondary metabolites that influence the interaction of plant and pest and have a wide range of important functions in human health pathway motifs and real data from a well characterized metabolic network. We demonstrate that correlation structure can be shaped by a variety of factors, including, genetic variation, pathway architecture, position in the pathway and feedback. Our results highlight the necessity of biological variation outside of the variation contributed by genetic factors for reliable network inference. Substrate-product relationships are not always reflected in the correlation structure of the system and recovery of the biochemical ordering of species should not be expected. Substrate inhibition, which is pervasive in metabolic pathways, can diminish or mask these relationships and lead to missing edges in network inference. An accurate genetic model is also critical to the inference process, especially when epistasis is involved. Our findings should temper expectations and provide new insights into the interpretation of causal genotype-phenotype networks.In order to address these questions, we have inferred causal networks from mQTL data using simulated metabolic models of common Arabidopsis BayPathway motifs were constructed using ODEs . Flux ra input,  . The alistigated . For eacmerging pathway via metabolic reaction; there are no QTL for the bi-substrate reaction that occurs at the merge point  A mathematical model is constructed and described by ODEs, (2) The system is genetically perturbed and propogated. The output of the simulation serves as data for graphical model construction. (3) The correlation structure is observed and graphical models are constructed. The resulting correlation and inferred network is compared to the metabolic pathway.(TIF)Click here for additional data file.Figure S2Simulated branching pathway with epistasis. The signal (TIFF)Click here for additional data file.Figure S3Chr4:Chr5 interaction plots. Interaction plots are shown for each phenotype in the aliphatic glucosinolate pathway.(TIFF)Click here for additional data file.Table S1Aliphatic metabolites. Abbreviations and the number of lines that had measurements below detection level are indicated. Non-detection may be due to biological or technical reasons.(PDF)Click here for additional data file.Table S2Summary of single-locus genome scans for aliphatic glucosinolates. The chromosome, position, locus, LOD score and peak marker are indicated for each QTL. A significance level of LOD = P(PDF)Click here for additional data file.Table S3Summary of two-locus genome scans for aliphatic glucosinolates. Summary of two-locus genome scans for the metabolites measured in the Bay×Sha RIL panel. Two dimensional genome scans were performed to identify significant interactions. For each pair of chromosomes, the following LOD scores are calculated. lod.full: The difference in the maximum LOD score for the full model (two main effect terms and interaction) and the maximum LOD score for the additive model (main effect terms only). lod.fv1: The difference in the maximum LOD score for the full model and the maximum LOD score for the LOD score from a single-QTL mapping of the two chromosomes. lod.add: The maximum additive LOD score. lod.av1: The difference between the maximum additive LOD score and the maximum LOD score from a single-QTL mapping of the two chromosomes. The positions for the full and additive models (pos.f and pos.a respectively) are indicated. Significance thresholds were set at the R/qtl suggested values for a backcross.(PDF)Click here for additional data file.Text S1Graphical models were reconstructed using a MCMC algorithm. The result is an ensemble of graphs, each with a posterior probability. Here we present different summarizations of the Bay×Sha reconstructed networks based on model selection and marginal summaries over the most probable graphs.(PDF)Click here for additional data file.


We present here a computational tool, SEAS, for pathway enrichment analysis over a given set of genes in a specified organism against the pathways (or subsystems) in the SEED database, a popular pathway database for bacteria. SEAS maps a given set of genes of a bacterium to pathway genes covered by SEED through gene ID and/or orthology mapping, and then calculates the statistical significance of the enrichment of each relevant SEED pathway by the mapped genes. Our evaluation of SEAS indicates that the program provides highly reliable pathway mapping results and identifies more organism-specific pathways than similar existing programs. SEAS is publicly released under the GPL license agreement and freely available at http://csbl.bmb.uga.edu/~xizeng/research/seas/.Pathway enrichment analysis represents a key technique for analyzing high-throughput  The rapid accumulation of the omic data has provided unprecedented new opportunities for biologists to study substantially more complex problems at a systems level omic techniques P-value on each pathway and lists the enriched pathways in a linear table based on the hyper-geometric distribution assumption High-throughput E. coli genes while SEED covers only 2,181 while exceptions exist. For instance, KEGG covers 2,296 B. subtilis genes while SEED covers 2,303.Currently there are a few popular pathway databases in the public domain, without a particular one being the predominant one SEED-based Enrichment Analysis System). SEAS provides three ways for gene mapping to subsystems through gene ID, orthology or homology mapping based on the availability of the relevant information, and identifies the statistically enriched pathways in SEED. We have extensively tested the performance of SEAS by re-annotating known pathways of E. coli and B. subtilis in SEED, and found that the mapped pathways are highly reliable, achieving 79% precision and 95% coverage for E. coli and 66% precision and 74% coverage for B. subtilis. Our additional evaluation results on microarray data and newly sequenced genome suggest that SEAS can identify more organism-specific pathways than KEGG-based pathway annotation. To the best of our knowledge, SEAS is the first software for SEED pathway enrichment analysis.We have previously developed a software tool KOBAS versus the ratio of the query genes out of the whole gene set of the query genome or some other background ratio prepared by the user, and identifies significantly enriched subsystems.The workflow of SEAS consists of two main steps as shown in blastp for DNA, blastx for protein) , to compare the query genes/proteins against one or more reference genomes in SEED specified by the user, in which we select the top hit with known annotation in SEED. The SEAS program provides the option for the user to choose one of the options to do gene mapping.Mapping the query genes to pathways involves searching the well-annotated gene database in SEED that currently has 1,414 organisms. We have implemented three strategies in SEAS, one of which will be used depending on the availability of the relevant information. When the query genes are already in SEED, we will use the original (pathway) annotation in SEED directly if the SEED ID is available for the query or through ID mapping using the NCBI GI number as the universal ID. When the genes are not covered but have available genome in SEED, we will use the mapping results between the query genes and the pathway genes in SEED given by the official RAST server using Bi-Directional Best Hit (BDBH) E. coli and B. subtilis  based on SEED pathways encoded by other genomes (as references). The annotation is quite time-consuming if all genomes in SEED are used as references, but the coverage could be low if only one is used considering the reference genome may not be evolutionarily close enough to contribute useful annotation templates. To balance the annotation performance and coverage, our idea is to combine some reprehensive genomes for each group of reference genomes having similar evolutional distances to the query genome. To assess this idea, we have evaluated different combinations of reference genomes in an iterative manner  has more annotated genes than Y. pestis   ; (ii) munotated) ; and , SEAS will automatically switch to Fisher's exact test. The binomial test is the fastest method when the number of sequences is large false discovery rate (FDR) using multtest (2.8.0) provided in the R package We have employed four statistic methods for pathway enrichment analysis, and a user can choose one as we have done with the KOBAS software E. coli, consisting of 42 differentially expressed genes with fold change ≥2 or ≤0.5 in the E. coli ackA mutant (able to produce acetyl phosphate but not metabolize it) in comparison with the E. coli pta-ackA mutant (unable to produce acetyl phosphate) E. coli pta-ackA mutant versus ackA mutant 28 factor, a minor sigma factor responsible for initiation of transcription at a number of genes involved in motility pta and ackA affect the activity of σ28 factor and thus regulates the expression of the genes related with flagellum and flagellar motility . 14 out of 147  KEGG pathways are enriched for N. profundicola and 46 out of 225 (covering 856 genes) SEED pathways are enriched, as shown in We have also compared the pathway annotation performance by the two programs on a newly sequenced genome, annotate and pathfind, respectively. The program is implemented using the Mono cross platform (http://www.mono-project.com), open source.NET development platform, which can run on Windows, Linux and Mac OS X. All the programs are well documented, which can be quickly accessed by the ‘-h’ option. SEAS is released under the GNU General Public License (GPL), and the program along with related data are freely available at http://csbl.bmb.uga.edu/~xizeng/research/seas/.The SEAS system consists of two main steps: pathway annotation and enrichment analysis, each of which can be run through a command-line, E. coli on a Linux workstation (6 CPUs and 8G memory). To support large-scale pathway annotation, SEAS also accepts outputs from BLAST using the (-i blastout) option, making the program very fast if the BLAST results are done in advance.SEAS runs very fast for ID-mapping based pathway annotation and pathway enrichment analysis; the only slow step of the system is the BLAST search, which takes about 1.5 hours with a single reference genome and 8.5 hours with 10 reference genomes for pathway (re)annotation of If the user has a list of protein sequences (fasta format), a typical session of pathway enrichment analysis is as follows:-b specifies the BLAST program (blastp for protein sequence and blastx for DNA sequences), -i for the input format, -o for reference genome(s), -f for the input and “>example.annotations” specifies the output.Pathway annotation of the given list of proteins: seas.exe annotate –b blastp –i fasta –o “Escherichia coli,Bacillus subtilis” –f example.fasta>example.annotations, where E. coli genome as background: seas.exe pathfind –m hyper -1 example.ann -2 “Escherichia coli” >example.pathways, where -m specifies statistical method , -1 for sample annotation file from the above step and -2 specifies background annotation file, with built-in whole genome by species name or from the above step.Pathway enrichment analysis with the whole We have developed a new pathway enrichment analysis system, SEAS, for prokaryotes, which maps a given set of genes to SEED pathways along with a statistical significance assessment. Our evaluation result showed that SEAS-based pathway annotations tend to provide more reliable pathway predictions with slightly smaller coverage compared to a KEGG-based pathway enrichment tool KOBAS, hence it provides a new pathway enrichment tool complementary to KOBAS. We anticipate that the performance by SEAS will continue to improve as the coverage of SEED pathways continues to increase rapidly. As the only available tool specifically designed for SEED pathway enrichment analysis in the public domain, we believe that SEAS will add to the value of the SEED database, which is now being widely used by bacteriologists.ftp://ftp.ncbi.nih.gov/genomes/Bacteria on 12/30/2010. The SEED database was downloaded from ftp://ftp.theseed.org/genomes/SEED on 12/30/2010. The KEGG database was downloaded from ftp://ftp.genome.jp/pub/kegg/genes/organisms on 12/30/2010.The genome sequences and relevant annotations were downloaded from E-value≤10−5; (ii) its E-value ranks among the top five hits (Rank≤5); and (iii) the gene has pathway information in SEED. If the user specifies multiple reference genomes, SEAS merges them into a single “genome” using the NCBI BLAST program and then applies the aforementioned algorithm for the subsequent pathway annotation. We have implemented a Ruby (http://ruby-lang.org) script to help select multiple reference genomes that are diverse in taxonomic distances. The script selects the organism out of those with the same taxonomic distance that has the most similar number of genes to that of the query genome. Currently the default value for the number of multiple reference genomes is set to be ten to ensure our aforementioned re-annotation result on the two genomes have the best precision and coverage  script that is easy to extend with new methods and that to do enrichment analysis with other pathway databases outside of SEAS. The script is integrated seamlessly into the SEAS program.The statistic test methods are implemented as a separate R (Table S1Missing annotations of E. coli and B. subtilis.(XLS)Click here for additional data file.Table S2Significant pathways in two E. coli mutants.(XLS)Click here for additional data file.Table S3N. profundicola.Comparison of pathway enrichment analysis on the genome of (XLS)Click here for additional data file.


The integration of sequencing and gene interaction data and subsequent generation of pathways and networks contained in databases such as KEGG Pathway is essential for the comprehension of complex biological processes. We noticed the absence of a chart or pathway describing the well-studied preimplantation development stages; furthermore, not all genes involved in the process have entries in KEGG Orthology, important information for knowledge application with relation to other organisms.In this work we sought to develop the regulatory pathway for the preimplantation development stage using text-mining tools such as Medline Ranker and PESCADOR to reveal biointeractions among the genes involved in this process. The genes present in the resulting pathway were also used as seeds for software developed by our group called SeedServer to create clusters of homologous genes. These homologues allowed the determination of the last common ancestor for each gene and revealed that the preimplantation development pathway consists of a conserved ancient core of genes with the addition of modern elements.The generation of regulatory pathways through text-mining tools allows the integration of data generated by several studies for a more complete visualization of complex biological processes. Using the genes in this pathway as “seeds” for the generation of clusters of homologues, the pathway can be visualized for other organisms. The clustering of homologous genes together with determination of the ancestry leads to a better understanding of the evolution of such process. Bioinformatics tools currently allow research to focus on the integration of large-scale data generated by sequencing, differential expression analysis, gene interaction studies and others. Several initiatives exist to organize this knowledge in secondary databases, thus allowing easier access and visualization. Databases containing interaction information are a good source for novel research. iHOP  allows uet al.).Metabolic and regulatory pathways are an example of organized knowledge that allow a better visualization of a complex system and can be found in databases such as iPath , BioCyc The available tools described raise the possibility of integrating current information and generating complex regulatory pathways. Previous publications individually reported the regulatory interactions that control preimplantation embryo development -15. HoweIn humans, the preimplantation phase of embryonic development is a period of approximately six days after fertilization prior to attachment of the embryo to the uterine wall. Implantation can occur before or in the seventh embryonic day (E7), a time during which the uterus is receptive . MammaliIn this work, we noticed the absence in databases of a pathway describing the preimplantation phase of embryo development and sought to develop the given pathway using text-mining tools, complementing it with orthology information. The resulting pathway comprises 86 genes and the interactions between them. Clusters of orthologous groups were generated for each gene represented in the pathway and provided the necessary information to determine the last common ancestor. This determination revealed that the preimplantation development pathway is an ancient Chordata pathway with addition of modern elements throughout evolution.Initially, we used the PubMed platform to search for articles related to the embryo preimplantation development (query: “preimplantation development”) and obtained 3524 entries as a result. To obtain a more efficient set of articles with relevancy to our work, the result entries were submitted to MedlineRanker . This soet al.) an online platform for friendly operation of the LAITOR software  PMID: 22070195.The authors declare that they have no competing interests.ERD and JMO conceived the project and wrote the paper. ERD performed the research and pathway construction. MJK curated the pathway biointeractions. ABS (author of SeedLinkage and LAITOR) and MAAN designed the PESCADOR platform. RLMG designed the SeedServer software and conducted the ortholog search. HV was responsible for the LCA determination. GRF constructed the UEKO database. All authors read and approved the final manuscript.Homolog clusters. Clusters of homologous sequences found by SeedServer for each of the genes in the preimplantation pathway. For each gene, the left column shows the clustered sequence Uniprot ID and the right column shows the Taxonomy ID for this sequence.Click here for fileDrosophila melanogaster.Ortholog functions in  This figure represents the corresponding D. melanogaster orthologs found by SeedServer and their respective interactions and functions in fruit fly development. Note that these orthologs are involved in processes related to D. melanogaster embryo development. See Additional file Click here for fileGene correspondence table. Human and Drosophila melanogaster gene name correspondence for the orthologs grouped by SeedServer. Column 3 lists the PubMed identifiers (PMIDs) from the papers where functions described in Additional file Click here for file


Integrative and comparative analyses of multiple transcriptomics, proteomics and metabolomics datasets require an intensive knowledge of tools and background concepts. Thus, it is challenging for users to perform such analyses, highlighting the need for a single tool for such purposes. The 3Omics one-click web tool was developed to visualize and rapidly integrate multiple human inter- or intra-transcriptomic, proteomic, and metabolomic data by combining five commonly used analyses: correlation networking, coexpression, phenotyping, pathway enrichment, and GO (Gene Ontology) enrichment.3Omics generates inter-omic correlation networks to visualize relationships in data with respect to time or experimental conditions for all transcripts, proteins and metabolites. If only two of three omics datasets are input, then 3Omics supplements the missing transcript, protein or metabolite information related to the input data by text-mining the PubMed database. 3Omics’ coexpression analysis assists in revealing functions shared among different omics datasets. 3Omics’ phenotype analysis integrates Online Mendelian Inheritance in Man with available transcript or protein data. Pathway enrichment analysis on metabolomics data by 3Omics reveals enriched pathways in the KEGG/HumanCyc database. 3Omics performs statistical Gene Ontology-based functional enrichment analyses to display significantly overrepresented GO terms in transcriptomic experiments. Although the principal application of 3Omics is the integration of multiple omics datasets, it is also capable of analyzing individual omics datasets. The information obtained from the analyses of 3Omics in Case Studies 1 and 2 are also in accordance with comprehensive findings in the literature.http://3omics.cmdm.tw.3Omics incorporates the advantages and functionality of existing software into a single platform, thereby simplifying data analysis and enabling the user to perform a one-click integrated analysis. Visualization and analysis results are downloadable for further user customization and analysis. The 3Omics software can be freely accessed at  The development and integration of transcriptomics, proteomics and metabolomics comprise the majority of current systems biology studies and provide a significant capacity for the investigation of biological mechanisms and their associations with diseases in a “big picture” approach -3. IntegCurrent visualization tools for integrating omics data can be loosely classified as biological network-based or pathway-based tools . BiologiAlternatively, pathway visualization tools allow users to explore the biochemical activities found in experimental datasets along different interactive pathways. Pathguide  providesA summary and comparison of accessibility, data export and exchange, functions and utilities, and source data types found in the currently available software with those in 3Omics is presented in Table 3Omics is specifically designed for the analysis of human data because all of the incorporated transcripts, proteins, metabolites, pathways, and gene information are human-specific. The majority of the existing software described in Table 3Omics is a platform-independent web application constructed with Perl and PHP scripts and running on a Linux-based Apache web server. A typical session workflow is illustrated in Additional Users can export network images in SVG or SIF formats. SVG is an XML-based file format for describing two-dimensional vector graphics that is compatible with multiple platforms. Processed SIF files can be imported and edited in Cytoscape. 3Omics users can also download all of the processed data for further analysis. All analyzed data and network/pathway images can be downloaded in PNG or SVG format. Detailed descriptions of the supported data formats are found on the 3Omics online help page. All uploaded data files are temporarily stored during a 3Omics session and automatically deleted after processing to safeguard data confidentiality. In the following subsections, we briefly explain the methods used in 3Omics.3Omics offers four types of multiple omics analysis depending on the data provided by the user  under specific experimental conditions  or mass spectrometry mass-to-charge ratios). Acceptable IDs include Entrez Gene IDs, UniprotKB IDs and PubChem CIDs . Users cTable 3Omics incorporates the “corr” function from R  to compuTo generate a correlation network, the PCCs are calculated from two sets of expression values for two entities (inter- or intra-omics data). The PCC correlation matrix is calculated for the omics data, which are then used for visualization. Correlation networks can be generated automatically, and substances can be clustered according to similar behavior over time or into different experimental groups. Nodes denoted by squares, triangles and circles represent transcripts, proteins and metabolites, respectively. Transcript, enzyme and metabolite can be presented in the context of the correlation network. Correlated relationships (PCC > 0.9) are represented by solid lines, and the text-mining results between pairs of input molecules and literature-derived molecules are indicated by dotted lines. The results can be downloaded from the web interface. Navigation functions support visual exploration of the data-enriched networks Figure A.When only two of the three omics datasets are available for correlation network analysis, 3Omics supplies the missing omics information using the following approach. First, 3Omics identifies related transcript-protein, protein-metabolite, or transcript-metabolite pairs by incorporating 48,631 human genes from NCBI Entrez Gene, 20,370 human proteins from UniProt, and 16,339 metabolite entries from KEGG Compound  into an Once the missing information is identified, 3Omics uses the transcripts or proteins to search iHOP. The missing omics data are supplied from the iHOP results. For example, transcriptomics and proteomics data are inputs, and 3Omics searches for protein-metabolite pairs. The missing metabolomics data are recovered by text mining of the iHOP results. Relationships described in the literature are depicted as dotted edges in the plot, as shown in Figure Coexpression analysis is performed using the R statistical programming language . HeatmapA phenotype is defined as any observable characteristic or trait of an organism arising from gene expression, the influence of environmental factors, and interactions between gene expression and environmental factors. A total of 21,746 phenotypes listed in OMIM from the March 27, 2012 snapshot were downloaded from the OMIM website and stored in the internal 3Omics database. The OMIM data are used to identify genes and genetic disorders based on information that relates genes in the human genome with specific phenotypes.Phenotype analysis is not available for metabolomics datasets alone because no transcriptomic or proteomics data are available.N metabolites in set A and n metabolites in set B, and there are m metabolites in set A and i metabolites in set B in a given KEGG human pathway. The probability of the occurrence of x or fewer metabolites within set B in a given pathway is calculated by hypergeometric distribution [A total of 499 human pathways from KEGG Pathway and 793 human pathways from HumanCyc were downloaded and stored in the internal 3Omics database (Release 62.0 and Version 16.0). HumanCyc provides more than 250 human pathways with literature-based curation for at least one year by experts. The pathways in HumanCyc are small and similar to biologically functional units. Therefore, enriched pathways from HumanCyc provide meaningful information from input metabolomics data. Two modes are available in 3Omics’ KEGG pathway enrichment analysis: normal and enrichment. The normal mode displays user-provided metabolites via simple metabolite mapping to a pathway from the KEGG Pathway database. The enrichment mode requires users to upload two datasets: (A) a metabolite set and (B) a significantly changed metabolite set. Significantly enriched pathways are identified with a hypergeometric test for a given list of metabolites. For example, there are ribution  accordinN) is selected and the total number of mapped metabolites in set A (m) is also large, the cumulative probability in the hypergeometric test will be very high.The hypergeometric test is a standard method for calculating pathway enrichment. Note that when a large population . Three independent GOs are included: (i) biological processes, (ii) cellular components, and (iii) molecular functions. The input transcripts are used in 3Omics to calculate the p-value and FDR  of each GO term using a modified Fisher’s exact test in the DAVID API. The enriched GO terms associated with the given Entrez Gene IDs are reported in 3Omics. By default, enriched terms with p-values less than 0.05 are presented in an interactive bar chart generated with Google Chart Tools .Using GO enrichment analysis, only the enriched terms are displayed, thus avoiding the display of general terms, such as “cellular component” or “metabolic process”, which are of limited use because many transcripts and proteins can be mapped to them. GO-based enrichment analysis requires transcriptomics data to calculate the GO-term enrichment; therefore, GO-based functional enrichment analysis is available for transcriptomics-proteomics, transcriptomics-metabolomics, transcriptomics-proteomics-metabolomics and single transcriptomics analyses. GO enrichment analysis allows users to explore genes represented by GO terms with automated organization functionality, thereby avoiding the need for manual editing.Two cases were selected to demonstrate the main functions of 3Omics and its usefulness for omics analysis: a transcriptome and proteome dataset for an acute promyelocytic leukemia human cell line  and a urTo demonstrate transcriptome-proteome analysis with literature-derived metabolites, transcriptome and proteome data from promyelocytic leukemia cells treated with retinoic acid (RA), arsenic trioxide (ATO), or a combination of the two were selected for this case study. The experiments were performed with the NB4 cell line, a human acute promyelocytic leukemia (APL) cell line. In this case study, correlation network and GO-enrichment analyses were used to demonstrate the user interface and functionality.A correlation network can be constructed by calculating the PCCs between pairs of entities to uncover possible interactions in omics data. Figure Blue circles in the correlation network indicate metabolites, and dotted edges between two nodes indicate relationships described in the literature. The literature-derived metabolite relationships provide insight into the possible mechanism or phenotype. One input protein may be associated with many literature-derived metabolites. One literature-derived metabolite may likewise be associated with many input proteins. Thus, both proteins and literature-derived metabolites may appear several times in the association table , and the other contains all of the metabolites included in the study . These two datasets are used as the inputs for this 3Omics intra-omics analysis (intra-metabolomics) with the “Enrichment Test” . This observation is in accordance with the 3Omics KEGG pathway enrichment analysis, which also maps the following pathways: valine, leucine and isoleucine degradation and biosynthesis; alanine, aspartate and glutamate metabolism; amino sugar and nucleotide sugar metabolism; and cyanoamino acid metabolism.Escherichia coli using transcriptomics, proteomics, and metabolomics data. Similarly, Trauger et al. studied the adaptation of Pyrococcus furiosus to a temperature shift by integrating transcriptomics, proteomics, and metabolomics analyses [Integration of multi-omics datasets in one online tool helps researchers to unravel the complete picture of a biological system. For example, Ishii et al.  studied analyses . HoweverCurrently, 3Omics accepts human-only data and provides human-specific analyses, such as literature-derived information and human phenotypes from human genes/proteins. However, 3Omics works on human data with a provision for incorporating data for other species.We developed a one-click web tool for fast analysis and visualization of multi-omics data. 3Omics integrates transcriptomics, proteomics, and metabolomics datasets in combination or as single omics datasets. For integration, analysis, and visualization of multi-omics datasets, we incorporated five common features to present the information from input molecules: correlation and coexpression analysis to display the relationships among input molecules; GO-based enrichment analysis to provide information on biological events, molecular functions, and the cellular localization of transcripts and proteins; phenotypic analysis to expose related phenotypes of transcripts; and pathway-enrichment analysis to cover mapped KEGG/HumanCyc pathways in the normal mode and enriched pathways in the enrichment mode. From Case Studies 1 and 2, the results of the 3Omics analyses are consistent with the original reports based on the inter- and intra-omics datasets. Furthermore, possible mechanisms and biological functions are provided without manual editing in 3Omics. 3Omics incorporates the functionality of existing software into one piece of software, thereby simplifying data analysis and enabling users to perform a one-click integrated analysis.Project name: 3Omicshttp://3omics.cmdm.twProject home page: Operating system: Platform independentProgramming language: Perl and PHPOther requirements: Web browsers with JavaScript enabled. Microsoft Internet Explorer 7.0 or later, Google Chrome 5.0 or later, or Mozilla Firefox 2.0 or later.License: NoneNon-academic use restrictions: NoneKEGG: Kyoto Encyclopedia of Genes and Genomes; OMIM: Online Mendelian Inheritance in Man; GO: Gene Ontology; iHOP: information Hyperlinked Over Proteins.The authors declare that they have no competing interests.T-CK developed core functions, implemented programs, enhanced the user interface, and wrote the manuscript. T-FT developed the main format of the web interface, reviewed literature, prepared samples, and wrote the manuscript. YJT initiated the project, developed the core functions, adjusted the user interface, edited the manuscript and served as the advisor for this project. All authors read and approved the final manuscript.A typical Workflow of 3Omics. After a user uploads their data to the server, the experimental data is processed by a series of analytical and visualization methods. The Name ID converter is optional for converting molecule names into database IDs. Correlation network analysis and co-expression analysis -omics analysis. Phenotypic analysis, Pathway and Gene Ontology Enrichment Analysis only utilize part of the analysis flowutilize all seven type of the analysis flow.Click here for fileSummary Table of Clusters in the Correlation Network. All input molecules formed as clusters in correlation analysis. Each molecule has a link to external database. Transcripts are linked to NCBI Entrez Gene, proteins are linked to UniProt, and metabolites are linked to Human Metabolome.Click here for fileAssociation Table of the Literature-derived Metabolites and the Proteins. Literature-derived metabolites associates with proteins are reported in this table. Each literature-derived metabolite has a link to KEGG, PubChem, HMDB, and HMO (Human Metabolome Ontology).Click here for file