High-throughput metabolomics technology, such as gas chromatography mass spectrometry, allows the analysis of hundreds of metabolites. Understanding that these metabolites dominate the study condition from biological pathway perspective is still a significant challenge. Pathway identification is an invaluable aid to address this issue and, thus, is urgently needed. In this study, we developed a network-based metabolite pathway identification method, MPINet, which considers the global importance of metabolites and the unique character of metabolomic profile. Through integrating the global metabolite functional network structure and the character of metabolomic profile, MPINet provides a more accurate metabolomic pathway analysis. This integrative strategy simultaneously captures the global nonequivalence of metabolites in a pathway and the bias from metabolomic experimental technology. We then applied MPINet to four different types of metabolite datasets. In the analysis of metastatic prostate cancer dataset, we demonstrated the effectiveness of MPINet. With the analysis of the two type 2 diabetes datasets, we show that MPINet has the potentiality for identifying novel pathways related with disease and is reliable for analyzing metabolomic data. Finally, we extensively applied MPINet to identify drug sensitivity related pathways. These results suggest MPINet's effectiveness and reliability for analyzing metabolomic data across multiple different application fields. The development of high-throughput metabolomics technology, such as nuclear magnetic resonance and approaches based on mass chromatography, has enabled us to obtain metabolomic profiles of large numbers of metabolites . The incSeveral computational approaches for metabolite pathway analysis have been developed recently, including overrepresentation analysis (ORA) and set enrichment analysis (SEA) . ORA is Also, the currently available approaches of metabolomic functional analysis ignore several common important aspects. Firstly, from a biological point of view, some metabolites participate in many pathways; they are referred to as common metabolites, while other metabolites only participate in a few pathways, which are referred to as pathway-specific metabolites. If a pathway identification method is mainly based on common metabolites, there will be more false positive pathways due to the presence of metabolites that are involved in multiple pathways. In contrast, it is more reliable that metabolites within an identified pathway tend to be pathway-specific as these dysfunctional metabolites only particularly involved in this pathway. Thus, these pathway-specific metabolites are more important in the pathway identification.Secondly, dysfunction of metabolites may be compensated for by their functional partners in the biological network. Metabolites involved in a number of pathways usually have many functional partners, while other metabolites only participate in a few pathways and thus have few functional-compensation partners. If metabolites within a pathway tend to be pathway-specific, its deregulated signals are likely to be amplified to the entire pathway. In contrast, if the metabolites in a pathway tend to have many functional partners in the biological network, the dysregulation signal is more likely to be alleviated. Thus, the nonequivalence roles of metabolites should be considered in order to identify pathways correctly. a priori knowledge [Finally, from the perspective of metabolomic technology, there is bias existing in metabolite identification. Most current metabolomic technologies usually only analyze a small fraction of the entire metabolome (5–10%) , and thenowledge , 14. Thihttp://bioinfo.hrbmu.edu.cn/MPINet/) and R-based tool (http://cran.r-project.org/web/packages/MPINet/), supporting 3350 human pathways across 10 databases.In this study, we developed a network-based pathway-analysis method called MPINet that considers both the global nonequivalence of metabolites and the bias from metabolomics experimental technology. In addition, a classical statistical model is also integrated. We constructed a human metabolite functional network. The global nonequivalence of metabolites within pathways refers to the different importance of metabolites and was evaluated based on the global functional interactions of metabolites within networks. Initial bias scores of metabolites were assigned based on the metabolomic profile, and a monotonic cubic regression spline model was then fitted to integrate the global nonequivalence scores and the initial bias scores of metabolites, and weights were assigned to the metabolites (nodes) in the network. Finally, the pathway weight, which was calculated based on the global node-weighted network, was used as the parameter for Wallenius approximation  to evaluWe analyzed one metastatic prostate cancer dataset, two type 2 diabetes datasets, and one drug-sensitivity dataset. These datasets were obtained from metabolomics experiments or manually extracted from the literature.P < 0.1). Finally, 92 metabolites were identified as metastatic prostate cancer differential metabolites and used for pathway analysis.The metabolomic profile of prostate cancer was obtained from the study of Sreekumar et al. . It inclhttp://dx.doi.org/10.1155/2014/325697). Finally, 65 metabolites were identified as type 2 diabetes-associated metabolites.We analyzed two type 2 diabetes datasets: type 2 diabetes dataset 1 and type 2 diabetes dataset 2. We extracted type 2 diabetes-associated metabolites from the HMDB database  and textP < 0.05) and converted the metabolite names to PubChem CIDs. Sixty-six metabolites related to type 2 diabetes were used for subsequent analysis.Dataset 2 was obtained from Suhre et al. . Multiplhttp://dtp.nci.nih.gov/mtargets/download.html. The drug-sensitivity dataset included the −log(GI50) data for drugs in replicated experiment across NCI-60 cell lines. For each drug, the −log(GI50) values from different experiments were averaged. The metabolite measurement dataset included 352 metabolites across 58 cell lines, of which 160 named metabolites mapped to 159 distinct PubChem CIDs.A total of 121 drugs selected from Weinstein et al.  were anaP value. The significant drug-sensitivity-related metabolite cutoff was set at 60%. MPINet was then applied to drug-sensitivity-related metabolites to identify drug-sensitivity-associated pathways.For each drug, we calculated the Pearson correlation of the drug −log(GI50) value and the metabolite measurements obtained in the experiment by Metabolon across 58 NCI-60 cell lines. The Benjamin method was used to correct the http://cran.r-project.org/web/packages/MPINet/) and web-based tool (http://bioinfo.hrbmu.edu.cn/MPINet/), supporting 3350 human pathways across 10 databases, including KEGG, Reactome, PID, and Wikipathways from ConsensusPathDB [We have implemented MPINet as a freely available R-based (usPathDB . Input ohttp://stitch.embl.de/) [The preliminary metabolite interaction network was downloaded from the STITCH database (mbl.de/) . We consmbl.de/) , HMDB [1mbl.de/) , Reactommbl.de/) , MSEA [9mbl.de/) . We obtaBased on the notion that dysfunction of metabolites with strong functional-interaction partners will be more easily compensated for, we defined a global nonequivalence score (GN score) to measure the functional interaction between a metabolite and its functional partners in the global metabolite network.Firstly, we quantified the functional interactions between each pair of metabolites in the network by calculating the global connection strength (GCS) for each metabolite pair. The GCS measure was defined as the modified version of the strength of connection (SOC) measure in Campbell et al. , which mWe then defined the GN score of a given metabolite as the mean of the GCS values between it and the other metabolites in the network. A high GN score indicates that the metabolite has strong functional interactions with its functional partners, and dysfunction of these metabolites is thus likely to be compensated for. In contrast, metabolites with low GN scores have weaker interactions, and their dysfunction is less likely to be compensated for, which suggests that metabolites should be paid more attention.Our goal was to consider simultaneously the global nonequivalence of metabolites in a pathway and the bias in pathway identification based on metabolomic data. These two critical factors are not independent, given that metabolites with a high GN score are more likely to be identified in the profile and to be identified as differential. A simple sum of the values for these two factors is thus not adequate. We therefore used a monotonic cubic regression spline model  with sixk knots can be represented asbi(x) is the basis cubic function at knot i,  i = 1 ⋯ k. For a given response yj and covariates xj,yj in this study is a binary value for the corresponding metabolite, 1 for differential metabolites and 0 for nondifferential metabolites in the network; xj is the GN score for the corresponding metabolite. Suppose that the k knots are x1*, x2*, …, xk*, which are placed at the quantiles of the distribution ofthe GN score vector; then the basis cubic function bi(x) can be represented asM metabolites in the network, the binary initial bias score vector of these M metabolites Y can be represented asyj, xj  (j = 1 ⋯ M) are the initial bias score and the GN score of the corresponding metabolite, respectively. Consider yj = 1 for an interesting metabolite; otherwise yj = 0. The cubic spline model can be simplified asX is the model matrix, β is the parameter vector that contains β1, β2,…, βk. The penalized constrained least squares with monotonicity constraint are used to evaluate the parameter vector β, to minimizeS is a positive semidefinite matrix of coefficients. λ is the smoothing parameter which can be given by β. We then obtain the evaluated β vector, C, can be calculated asThe cubic regression spline with n); (ii) the number of background metabolites (N); (iii) the number of background metabolites annotated to this pathway (m1); (iv) the number of interesting metabolites annotated to this pathway (g); (v) the weight of this pathway (w1). First, the relative weight of the pathway is calculated as follows:P is the metabolite set of the pathway, |P| is the size of P, j is the metabolite in P, Cj is the CGNB score of the metabolite j, K is the number of pathways selected for analysis, Pk is the metabolite set in the kth pathway, |Pk| is the size of Pk, r is the metabolite in Pk, and Cr is the CGNB score of the metabolite r. Finally, the significance P-value of this pathway can then be calculated as follows:dx = w1*(m1 − x) + w2*(m2 − (n − x)), w1 = W6, w2 = 1, and m2 = N − m1.We used the Wallenius approximation  to evaluWe firstly confirmed that the bias in metabolomics experimental technologies impacts on pathway identification. Then, we applied MPINet to four datasets, including a prostate cancer metastasis dataset, diabetes dataset 1, diabetes dataset 2, and a drug-sensitivity dataset. In the analysis of the prostate cancer metastasis dataset, we aimed to compare the effectiveness of MPINet and other currently popular methods, including the hypergeometric test (ORA), MSEA (SEA), and MetPA. We then applied MPINet to a different biological phenotype (diabetes) to identify novel diabetes-related pathways. Diabetes dataset 2 was used to demonstrate the reproducibility of MPINet. Finally, we applied MPINet to a drug-sensitivity dataset to test its validity with a completely different biological problem.These identified metabolites which were provided by the metabolomic technology are small in amount and not random selected. These identified metabolites in the profiles are usually well-studied metabolites which tend to have many functional partners in the network. This will lead to the bias that pathways contain many well-studied metabolites are likely to be inappropriately identified.In order to validate the bias in metabolite pathway identification, we performed the following analysis. First, we validated our assumption that metabolites with high GN scores were more likely to be identified in profiles and thus be considered as differential . We calculated the GN scores of metabolites identified in three different metabolomic profiles and showed that metabolites identified in metabolomic profiles tended to have high GN scores . DiffereP < 0.05, For the pathways, we calculated the GN scores of all the metabolites in KEGG pathways and found a wide variation in the distribution of metabolite GN scores . We thenWe determined the effectiveness of MPINet for identifying pathways associated with prostate cancer metastasis and compared the results with those of three other popular methods, including ORA (hypergeometric test), MSEA, and MetPA.To demonstrate the advance of MPINet, we compared pathways identified by MPINet to those by ORA using a prostate cancer dataset. MPINet identified twenty-two significant pathways, associated with prostate cancer metastasis, with a strict false discovery rate (FDR) cut-off level (FDR < 0.01). Among these pathways, up to 14 were well documented to be implicated in cancer (detailed information is provided in supplementary Table S3). After applying the ORA method to the same prostate cancer dataset, we identified 12 significant pathways under the same strict cut-off level (FDR < 0.01). Compared with ORA, MPINet identified 18 unique pathways, 11 of which were reported to associate with metastatic cancer or cancer . The degradation of tryptophan mediated by indoleamine 2,3-dioxygenase can influence the tumoral immune response [The most significant additional pathway identified by MPINet was the “tryptophan metabolism” pathway. MPINet yielded a FDR of 2.18response . Recentlresponse . Furtherresponse . An inspresponse . Recent response . InteresMPINet identified the “arachidonic acid metabolism” pathway with FDR = 0.0023. Most metabolites in this pathway had high CGNB scores . Because MSEA uses the SMPDB pathways in the MSEA library as pathway databases, rather than KEGG, we also applied MPINet to the same pathway databases to ensure a fair comparison. MPINet identified 15 significant pathways (FDR < 0.01) when using SMPDB pathway databases. Most of the pathways (10 pathways) identified by MSEA were also included in the pathway list for MPINet , and thrWe applied MetPA to the prostate cancer dataset. For fair comparison, 72 pathways in our KEGG pathway data, which were also in the 80 human pathway library of MetPA, were selected for comparison. The results of MetPA are given as impact scores, and we therefore compared pathway rank list from MPINet and MetPA. MPINet detected 15 pathways at a significance level of FDR < 0.01, based on this pathway dataset, up to nine of which are known to be related to cancer . ArgininE − 05 (rank 3), compared with an FDR value of 0.216 (rank 34) yielded by ORA, and an impact value of only 0.07 (ranked 23) reported by MetPA. Most metabolites in this pathway tended to be pathway-specific and thus difficult to be identified  and up to six of which belonged to lipid metabolism, which has been reported to play an important role in the pathogenesis of type 2 diabetes, mainly through its influence on insulin resistance , 41. Furentified . Bile acE − 05 (rank 4), compared with an FDR value of 0.042 (rank 14) in the ORA method, and an impact score of only 0.09 in MetPA (rank 19). Inspection of this pathway showed that it included many metabolites with high CGNB scores  pathway was closely connected with “primary bile acid biosynthesis” (path: 00120) and “valine, leucine, and isoleucine degradation” (path: 00280) in the network . These rP = 2.36e − 11, hypergeometric test. Some critical pathways such as “valine, leucine, and isoleucine degradation,” “valine, leucine, and isoleucine biosynthesis,” “primary bile acid biosynthesis,” and “histidine metabolism,” which are implicated in the progression of type 2 diabetes, were also identified in diabetes dataset 2. These results suggest that MPINet performs reliably in metabolomics pathway identification.The type 2 diabetes dataset 2 was used to evaluate the reproducibility of MPINet. We selected 66 differential metabolites from the data of Suhre et al. see . Only 30We investigated the effectiveness of MPINet for identifying drug-sensitivity-related pathways. Platinum-based drugs are widely used to treat many types of tumors, such as colon cancer, lung cancer, breast cancer, and ovarian cancer. However, resistance to platinum-based drugs is a major bottleneck in cancer therapy. The identification of consistent pathways relevant to platinum-based drug sensitivity is therefore of considerable importance. We analyzed the platinum-based drugs tetraplatin, iproplatin, and cisplatin and identified 55, 17, and 44 significant metabolites (FDR < 60%) associated with these three drugs, respectively. Through inputting these metabolites, MPINet identified 19, 9, and 19 significant pathways as drug-sensitivity-related pathways, respectively (FDR < 0.01). As we expected, most of the pathways identified by MPINet were shared by at least two of the three drugs . These rThe major innovations of MPINet include the simultaneous evaluation of the global nonequivalence of metabolites based on the global network structure and consideration of the bias associated with metabolomic experimental technology. From a biological perspective, dysfunctions of metabolites with strong functional-interaction partners are more likely to be compensated for than those with weak functional-interaction partners. Taking account of these functional interactions (network structure) to evaluate the nonequivalence of metabolites will help to improve the power of pathway identification. From the experimental technology perspective, the low coverage of metabolomics  and metaMetabolites involved in fundamental pathways have often been well studied and tend to be easily detected in the profile . These fCompared with other current methods such as ORA, MSEA, and MetPA, MPINet can integrate not only metabolites from metabolomics experiments, but also the global metabolite network structure to enhance the ability of pathway identification. One of the limitations of current metabolomics technology is that many metabolites cannot be identified. MPINet utilizes the global metabolite functional network to compensate for the lack of information. Thus, in the case of pathways with weak signals from the annotation number point of view, MPINet can also identify these by considering the additional global network structure. MSEA can also detect some weak pathways , 55 by cFigure S1: Validation of bias via analyzing the 3189 human pathways in the ConsensusPathDB database.Figure S2: Tryptophan metabolism pathway identified by MPINet, in which the differential metabolites of prostate cancer metastasis were annotated.Supplementary text: Detailed description of calculating the global connection strength (GCS).Table S1: The type 2 diabetes associated metabolites from text mining and HMDB database.Table S2: The detailed information of human metabolite background from five databases.Table S3: The statistically significant pathways identified by MPINet method for differential metabolites from metastatic prostate cancer dataset (FDR<0.01).Table S4: The twenty-one pathways identified by MPINet method for interesting metabolites from the type 2 diabetes dataset 1 (FDR<0.01).


Integration of open access, curated, high-quality information from multiple disciplines in the Life and Biomedical Sciences provides a holistic understanding of the domain. Additionally, the effective linking of diverse data sources can unearth hidden relationships and guide potential research strategies. However, given the lack of consistency between descriptors and identifiers used in different resources and the absence of a simple mechanism to link them, gathering and combining relevant, comprehensive information from diverse databases remains a challenge. The Open Pharmacological Concepts Triple Store (Open PHACTS) is an Innovative Medicines Initiative project that uses semantic web technology approaches to enable scientists to easily access and process data from multiple sources to solve real-world drug discovery problems. The project draws together sources of publicly-available pharmacological, physicochemical and biomolecular data, represents it in a stable infrastructure and provides well-defined information exploration and retrieval methods. Here, we highlight the utility of this platform in conjunction with workflow tools to solve pharmacological research questions that require interoperability between target, compound, and pathway data. Use cases presented herein cover 1) the comprehensive identification of chemical matter for a dopamine receptor drug discovery program 2) the identification of compounds active against all targets in the Epidermal growth factor receptor (ErbB) signaling pathway that have a relevance to disease and 3) the evaluation of established targets in the Vitamin D metabolism pathway to aid novel Vitamin D analogue design. The example workflows presented illustrate how the Open PHACTS Discovery Platform can be used to exploit existing knowledge and generate new hypotheses in the process of drug discovery. While the approval rates for new drugs may be somewhat stable, pharmacological data of increasing size, dimensionality and complexity is being housed in public and proprietary databases A recent approach to address these issues is the integration of data from different sources by means of semantic web technologies http://www.openphacts.org/partners/consortium), the Open PHACTS project benefits from a wealth of market experience and technical expertise. Development of the Open PHACTS Discovery Platform is driven in an agile, stepwise fashion focused on scientific competency questions and use cases for analysis of underlying data concepts and associations http://www.openphacts.org/documents/events/130424_Researchathon_London_Participant%20List.pdf.As a collaboration between multiple European universities, the European Federation of Pharmaceutical Industries and Associations (EFPIA), and various SMEs  identify target-specific chemical compounds, ii) support pathway-driven drug discovery. We describe how the platform can be used to solve common queries that require linkage of the entities of targets, compounds, and pathways, using the examples of a single target, Dopamine Receptor DHere, we demonstrate the utility of Open PHACTS in early drug discovery projectsthrough the development and application of workflows based on the Open PHACTS API and pipelining software, thereby allowing scientists to find answers to complex research questions requiring a wide range of data sources.https://dev.openphacts.org/docs/1.3. Accessed 2014 Nov 30) to query across integrated public data sources: ChEMBL http://support.openphacts.org/support/home (Accessed 2014 Nov 30). Proprietary databases used in Use Case A are: GVKBio GOSTAR (www.gostardb.com), Thomson Reuters (integrity.thomson-pharma.com) and in-house pharmacology databases from Janssen.All use case workflows utilized the Open PHACTS API version 1.3 (http://accelrys.com/products/pipeline-pilot/) and KNIME version 2.9 (http://www.knime.com).Use case workflows were constructed in the following manner: 1) entities of interest  needed for the specific step in the workflow were identified, 2) URIs for the entities of interest were determined, 3) Open PHACTS API calls were executed, 4) results were parsed, 5) the steps were repeated multiple times if answers to previous cycles were needed to reach the final question. For each use case, the tasks were automated using the two most common cheminformatics workflow tools, namely Pipeline Pilot (https://community.accelrys.com/docs/DOC-6473. Accessed 2014 Nov 30).A custom Pipeline Pilot component library was co-developed with Accelrys to access the Open PHACTS API calls and parse the output. These components were used for the Use Case A workflow and are available on the Open PHACTS page on the Accelrys community website at (https://github.com/openphacts/OPS-Knime. Accessed 2014 Nov 30) were created to incorporate the Open PHACTS services into the KNIME workbench. These nodes use two-dimensional tables, such as named rows and columns, as input and generate equivalent output. Since the Open PHACTS API services produce nested output (e.g. JSON or XML), a KNIME 'unfolding' algorithm was implemented as a node, transforming the Open PHACTS output into a KNIME table. The Open PHACTS API services are described in the Swagger REST service description format, enabling automatic generation of templates in KNIME. The result of running this utility node is a URL that represents the desired service call within a workflow. These nodes were used to construct workflows for Use Cases B and C.A series of generic KNIME utility nodes , using the target protein sequence in a BLAST A Pipeline Pilot workflow was created to provide a collection of targets, assay numbers, activity data, and chemical structure information from the databases mentioned above. The final steps of the workflow merge information per assay and data source, and sort the tabular results to present a ranked list of chemical compounds and their activities. In a facultative step, the workflow can also be programmed to search for similar chemical compounds and their pharmacological effects. This returns a complete activity profile for a comprehensive list of compounds of interest. A schematic representation of the workflow is shown in Free text entered in the ‘Free Text to Concept’ API call can be used to find all corresponding concept URIs to enable usage of other API calls.URIs for all orthologues of a given target were obtained using the ‘Free Text to Concept for Semantic Tag’ API call. The name of the target was used as free text input as above; the branch parameter was set to return concepts only from SwissProt data; and the tag concept parameter (i.e. the semantic type) was set to retrieve only those concepts tagged with ‘Amino Acid, Peptide, or Protein'.http://www.wikipathways.org/index.php/Pathway:WP1531) or the title or identifier of the pathway can be used in the ‘Free Text to Concept’ API call to retrieve a URI. Here, the branch parameter can be set to return concepts of WikiPathways only.After choosing the pathway of interest on the WikiPathways website, the pathway can be used as input for queries with the Open PHACTS API in several different ways. Either the URI of the pathway is used directly  were transformed into their negative logarithmic Molar values . The same activity endpoints are available as ‘pCHEMBL values’ from the ChEMBL database, but in addition we also kept values with a relation different from ‘ = ’, but discarded the relation information for the following steps. For a binary representation , a cutoff value of ‘-logActivity values [molar]’ of at least six was applied to determine active molecules.To provide a better distribution for visualization, the activity values . In the case of the binary representation, ‘1’ (active) is chosen if an ambiguous classification is made. The resulting heat-maps were visualized with the HeatMap (JFreeChart) node in KNIME.In order to detect compound specificity for single versus two or more targets within the pathway, an overlap table was generated. From the pivot table generated as above, the number of times a compound ‘hits’ a target was counted using the node ‘Column Aggregator’. The ‘Numeric row splitter’ node splits compounds hitting more than one target from those hitting just one. The former set was used to generate an overlap table.8 are also removed to avoid potential data errors. The data can be filtered in many different ways, for example to return data for a specific activity (eg. IC50) or assay type  or to only return agonists/activators or inhibitors/antagonists. Several different values can be requested in one call (e.g. IC50|EC50|AC50|Ki|Kd|Potency). Activity values can be limited by different cut-off parameters, for example by setting max-activity_value = 2000. The number of results for a given query can be retrieved with the ‘Target Pharmacology: Count’ or ‘Compound Pharmacology: Count’ API calls.The ‘Target Pharmacology: List’ API and ‘Compound Pharmacology: List’ API calls can be used to retrieve pharmacology data from ChEMBL for single protein targets and protein complexes containing the target. If only single protein targets are sought, the type is specified as target_type  =  single_protein in the API parameters. The pharmacology output is always filtered to exclude records where compound activity is unspecified. Values larger than 10The data can be returned in one piece by using the parameter _pageSize = all. In cases which might return too many data points , a smaller _pageSize parameter can be used, in combination with a loop overall result sets with the _page parameter.The first approach uses the ‘Target Information’ API call where target URIs (gene or protein) are used as input. Compounds targeting this protein are derived from the DrugBank dataset where each molecule is labeled according to its type . The resulting data are filtered for ‘Drug type = approved’. The second approach uses the ‘Target Pharmacology: List’ API call to find all compounds active against a given target based on ChEMBL records. These compound URIs are then used in the ‘Compound Information’ API call and results filtered for approved drugs as before. The search retrieves all approved drugs that have bioactivity against a given target, even if not approved for that target in DrugBank. The results from both approaches are merged.ChEBI terms for a molecule are retrieved with the ‘Compound Classifications’ API call setting the tree parameter to ‘chebi’. The resulting data was restricted to classifications of the type “has role”, which includes the three sub-categories: ‘chemical role’, ‘biological role’, and ‘application’.GO terms for a target can be retrieved using the ‘Target Classifications’ API call by setting the tree parameter to ‘go’. This returns classifications from the three branches of GO . The resulting data was filtered for ‘biological process’.GO terms associated with the term ‘regulation of Vitamin D’ were obtained with the ‘Free text to Concept’ API call, the resulting data was restricted to ‘alternative’ exact match type, to include only GO terms. Children of these terms were retrieved using ‘Hierarchies: Child’ API call to enable separation of positive and negative regulators. Gene products associated with these GO terms were obtained using ‘Target Class Member: List’ API call2 (DRD2) and then found related targets in both public and proprietary pharmacology databases to aid in the design of a new compound library for the dopamine receptor drug discovery program. Use case B identified compounds active against all targets in the Epidermal growth factor receptor (ErbB) signaling pathway that have a relevance to disease. Use case C evaluated established targets in the Vitamin D metabolism pathway and then expanded the scenario to view these targets in other contexts.Three use case workflows were implemented to highlight different applications of the integrated Open PHACTS data. Use case A assembled a ranked list of compounds targeting the dopamine receptor D1 and D5) and D2-like  classes of dopamine receptors (DARs) on the basis of their structural, pharmacological, and biochemical properties in vitro and in vivo imaging agents. We aimed to rank existing compounds known to target the DRD2 to aid in the design of a novel DRD2-targeted screening library.The mesolimbic dopamine system is a central component of the brain reward circuit 50 values against the DRD2 (50) values>6, we identified 6194 bioactivity values; an additional 164 ‘inactive’ compounds are found with activity values below 50% or -log(IC50) values below 6 (50) data points are found in the in-house proprietary pharmacology screening database. The total number of compounds found is the sum of those found in the different sources as there is little overlap between them. This is because Open PHACTS/ChEMBL uses public information, Thomson Reuters uses patent information (often not published), and the in-house pharmacology databases use internal information (often not patented). Our workflow provides 2278 compounds that would have been missed altogether or difficult to find using approaches independent of Open PHACTS. In a facultative step, the workflow can also search for similar chemical compounds and their pharmacological effects, to present a complete activity profile for a comprehensive list of compounds of interest. Thus, using Open PHACTS we were able to produce a cohesive list of interesting DRD2-targeting compounds derived from heterogeneous data stored in multiple databases.Our workflow  for findthe DRD2 . Conside below 6 . The samThe most interesting compounds have a high activity, or are reported in patent literature to act on the target of interest. They must also have little reported activity on other targets. Conversely, the least interesting compounds have low or no reported activity on targets of interest and have higher reported activity on other targets. This sorting allows a more efficient processing of tables that sometimes contain data on several hundreds of compounds. A Pipeline Pilot script running all the steps described above automatically produces a relevant listing of compounds, activity data, and target information in under an hour, making the process of looking for compounds for new targets and target families a simple and reproducible task. The above script allows control of the different process steps, and has been successfully used at Janssen to support various drug discovery projects.Finally, programmatic access to the individual data sources previously required a specific case by case approach: for example, access to biological activity data from ChEMBL was via a locally installed MySQL database, from DrugBank from a copy of the XML, from GVKBio GOSTAR from a remotely installed Oracle database, from Thomson Reuters from a tab-delimited text file, and from the in-house pharmacology database from a local server-based Oracle database. Searching the different databases for target information was done mostly manually, where information had to be carefully assembled for each target in each database and the process repeated for each request for new target information. By using Open PHACTS, data from ChEMBL and DrugBank could be retrieved from a single source, reducing the effort needed for data integration. The custom Pipeline Pilot Open PHACTS component library enabled access to the databases in Open PHACTS, on par with components already in use for proprietary databases, thereby allowing a true integration of all available pharmacology data in one protocol. The workflows for retrieving the data from the different data sources are depicted in a Pipeline Pilot screenshot This example illustrates the benefit of accessing the Open PHACTS data in the competitive Pharmaceutical research environment, even for well-known targets that have already been extensively studied.http://www.wikipathways.org/index.php/Pathway:WP673): mitogen-activated protein kinase (MAPK) signaling and phosphatidylinositol 3-kinase (PI3K)-AKT pathway, SRC tyrosine kinase pathway, signal transducer and activator of transcription proteins (STATs), and mammalian target of rapamycin(mTor) pathway Epidermal growth factor receptors (known as ErbB) are receptor tyrosine kinases consisting of four members: ErbB1/EGFR, ErbB2/HER2, ErbB3 (HER3), and ErbB4 (HER4). Members of the EGF family of growth factors , are natural ErbB receptor ligands which upon binding induce homo- or heterodimerization of the receptor and subsequent activation of intrinsic kinase activity However, current therapy treats only a subset of patients carrying specific mutations and even within this population, tumor resistance is common. Identification of specific protein targets involved in ErbB-mediated cancer development is confounded by the multiplicity of pathways activated by ErbB receptors and the existence of more than 100 potential protein binding partners identified by large-scale phosphoproteomic screening In total, 54 NCBI Gene IDs were retrieved as targets from the ErbB signaling pathway. Of those, only 35 single proteins returned pharmacological data with the applied bioactivity filters. Additionally, data for 12 protein families, 5 protein complexes, 2 protein-protein interactions and one chimeric protein containing a target from the pathway were retrieved, increasing the total number of targets to 55. While a pharmacology query without any filters would retrieve nearly 150,000 data points, filtering reduced the data to 108,014 bioactivities and 65,780 unique compounds see . Using tTo compare the pharmacological data across different targets, each compound/target pair was represented by only one activity point, keeping the most active value in cases where several measurements were reported, and a cutoff was set for separating active from inactive compounds. A heat map representation of the compound/target space was retrieved for these binary representations .. Protei50 bioactivity endpoints. Inspecting the underlying assay descriptions however reveals the inclusion of compounds targeting either ErbB1, ErbB2, both proteins, or in some cases even upstream targets. For the sake of data completeness, we retained all target types in the query, but we advise to always go back to the original primary literature source and study the bioassay setup in order to make sure which effect was actually measured and if the data is reliable in cases where data is assigned to other target types than ‘single protein’.Apart from necessary filtering and normalization steps that limit the full illustration of the target space, we also recognized a lack of reliable compound bioactivity data specifically targeting oligomeric proteins in the pathway. For example, in ChEMBL_v17, the target ‘Epidermal growth factor receptor and ErbB2 (HER1 and HER2)’ is classified as being a ‘protein family’  with 115 ICDetermining the targets related to cancer or neurodegenerative diseases was accomplished by evaluating the GO In parallel to the identification of GO terms for the targets, we enriched the compounds with the addition of ChEBI terms However - like all hand-curated resources - ChEBI is biased towards its annotation criteria, which in that case are already approved drugs. Thus, to date it serves best for filtering out drugs related to a certain disease. As the ChEBI database and ontology is instantly growing, it will become a more comprehensive and increasingly reliable and useful resource.Using our Open PHACTS workflow, we could answer research questions related to complex regulatory pathways with a large number of druggable targets and requiring data from multiple sources. With an expansion of the data sources available in the next release of the Open PHACTS API (version 1.4), which will include more information on the distribution of targets in tissues and changes in relation to disease, more refinement of the antineoplastic agents found in our analyses will be possible.2D3 or calcitriol, the biologically active form of vitamin D 1,25(OH)http://www.wikipathways.org/index.php/Pathway:WP1531). Better understanding of these interactions and a pathway-focused approach will facilitate the design of a new generation of vitamin D analogues with a desired interaction profile against pathway components, resulting in improved therapeutic indices. Knowledge of the appropriate compound evaluation methodologies is also important to ensure that the desired bioactivity profile is being retained during chemical optimization stages. Finally, information about how the pathway is regulated, identifying novel points for therapeutic intervention, and estimating the impact of modulating these targets could allow alternative therapeutic strategies. Accordingly, our Open PHACTS workflows were designed to collect the above information and identify drug discovery opportunities in the Vitamin D metabolism pathway.Prior to reaching the nuclear VDR, calcitriol interacts with several key proteins, the serum vitamin D binding protein (DBP), the vitamin D-activating enzyme (CYP27B1), and the catabolic enzyme 24-hydroxylase (CYP24A1). The latter two enzymes are expressed and differentially regulated in VDR-expressing target tissues, providing a means for tissue-specific actions of VDR ligands. Affinity for the DBP is another means to control circulating calcitriol levels. The unique actions of calcitriol and its analogues thus result from their combined interactions with several key proteins in the Vitamin D pathway  could serve as starting points for selective CYP24A1 inhibition strategies given the lack of polypharmacology data and potential for off-target effects . In addiin vivo. However, of the 669 human VDR-activating compounds retrieved, only two have been tested for human DBP binding  data on the VDR as compared to the DBP, although the latter is a critical determinant of Vitamin D analogue availability  binding . The ami binding  to retri binding . We iden binding . Prelimi2D3 is upregulated by the VDR, providing an efficient negative feedback loop to terminate calcitriol actions in normal conditions  annotations nditions . Converspression . Interes2D3 analogue efficiency in different cellular contexts.In conclusion, knowledge of the Vitamin D metabolism pathway obtained through these workflows supports and informs on a multi-pronged drug discovery approach, wherein properties like DBP binding and sensitivity to CYP24A1 catabolism are evaluated in parallel using the appropriate bioassays, rather than focusing on VDR activation alone. An effective analogue should potently activate VDR, be resistant to catabolism by CYP24A1 and have low affinity for DBP. Alternatively, co-administration with a selective CYP24A1 inhibitor could also extend analogue lifetime. Most tissues express VDR, so tissue-specific actions of VDR ligands are instead governed by differential expression and regulation of CYP27B1, which permits localized synthesis of additional calcitriol, and CYP24A1, which inactivates the hormone. Tissue expression profiles as well as interacting proteins for a given target can be obtained in future versions of the Open PHACTS Discovery Platform with the incorporation of neXtProt data and tissue ontologies, thereby enabling a better prediction of 1,25(OH)The Open PHACTS Discovery Platform makes available the data needed to answer a wide range of questions applicable to pharmaceutical research by broadly covering critical aspects of chemistry and biology. A multitude of potential use cases of the Open PHACTS Discovery Platform can be envisaged: target identification and validation, discovery of interaction profiles of compounds and targets, detection of potential toxic interactions, repositioning of existing drugs to new therapeutic areas, and many other drug discovery questions 2. Using a proprietary dictionary generated for in-house data, the different target and compound nomenclatures were reconciled with the public domain data for a comprehensive and meaningful ranking of existing chemical compounds active against the target of interest. The other use case examples leverage the semantically integrated knowledge in the Open PHACTS Discovery Platform on pathways to derive testable hypotheses concerning therapeutic targets. The two pathways, ErbB signaling and Vitamin D metabolism, are representative of a) complex regulatory processes involving a large number of druggable targets and corresponding chemical compounds, and b) comparatively simple and well-defined metabolic processes with few druggable targets. The differences between the two pathways serve to highlight divergent analyses possible via differently combined queries. In one case, pharmacological bioactivity data and its enrichment by integrated annotation terms originating from GO and the ChEBI ontology was turned into a reasonable number of data points, and visualized as heat map representations. While in the other case, key pathway targets  were explicitly evaluated to identify strategies for designing improved Vitamin D analogues with the desired bioactivity profile.The three exemplar use cases demonstrate how the application of Open PHACTS API services can support drug-discovery research. One workflow emphasizes a search strategy across proprietary and public pharmacology databases for a comprehensive identification of chemical compounds targeting the dopamine receptor DThe workflows developed for the present use cases can be broadly used by drug discovery scientists to exploit the wealth of publicly available information for other targets and pathways of interest. As all the accessed data sets reside in the public domain, the results from the present use cases could, in principle, be derived without the use of the Open PHACTS Discovery Platform. However, it has been previously demonstrated that manual access methods require considerable time and resource investment due to the complexity of data access and licensing for multiple databases, the use of different data formats and identifiers, need for bio- and chemo-informatics expertise and post-processing of data retrieved http://www.openphactsfoundation.org). The Open PHACTS Foundation is the not-for-profit successor organization set up to sustain and continue the growth of the achievements of the Open PHACTS project. Specifically the mission is to maintain a sustainable, open, vibrant and interoperable information infrastructure for applied life science research and development.Together, these examples serve to demonstrate some of the operations made possible via a semantically integrated pharmacology platform. A plethora of other queries requiring the linkage of target-compound-pathway concepts can be envisioned and answered by combining an appropriate sequence of API calls with workflow tools; and, the possibilities for new use cases continue to grow as more data sources are added to the platform. In future releases of the platform, gene-disease association data, protein sequence features, and tissue expression data are scheduled for integration. Additionally, many opportunities exist for the inclusion of new data sets such as text mining data from scientific publications and patents as well as proprietary or commercial data sources S1 FigPipeline Pilot workflows for retrieving data for Use Case A; lines 1, 2, and 3 show the components used for retrieving data from Open PHACTS discovery platform; lines 4 and 5 show the components used for retrieving data from Thomson Reuters; and, lines 6, 7, and 8 show the components used for retrieving data from GVKBio GOSTAR.(TIF)Click here for additional data file.S2 FigBinary heatmap representation of the pharmacological space in the human ErbB signalling pathway ; abscissae: targets with ChEMBL target ID's; ordinate: compounds; red bars indicate ‘actives’, blue bars ‘inactives’, grey areas indicate that no activity value was reported.(TIF)Click here for additional data file.S3 FigBinary heatmap representation for compounds annotated with ‘antineoplastic agent’ in ChEBI ; abscissae: targets with ChEMBL target ID's; ordinate: compounds; red bars indicate ‘actives’, blue bars ‘inactives’, grey areas indicate that no activity value was reported.(TIF)Click here for additional data file.S1 TableList of current resources available through the Open PHACTS Discovery Platform.(XLSX)Click here for additional data file.S2 TableExamples of free text and URI inputs used in the API calls.(XLSX)Click here for additional data file.S3 TableList of all GO ‘biological process’ terms that have been annotated to at least 5 of the 23 prioritized targets (plus ChEMBL target IDs of those targets).(XLSX)Click here for additional data file.S4 TableList of all ChEBI classification terms for the 23 prioritized targets that have been annotated to at least 6 compounds.(XLSX)Click here for additional data file.S5 TableSpecificity of compounds targeting proteins in the Vitamin D pathway.(XLSX)Click here for additional data file.S6 TableAdditional pathways for targets in the Vitamin D pathway.(XLSX)Click here for additional data file.S7 TableList of VDR and DBP orthologues and corresponding bioactivity records.(XLSX)Click here for additional data file.S1 FileOrganic molecules active against DRD2 retrieved from Open PHACTS API.(XLSX)Click here for additional data file.S2 FilePharmacological profile of compounds with ChEBI term ‘antineoplastic agent’.(XLSX)Click here for additional data file.S3 FileAll compound bioactivity data for targets in the Vitamin D pathway.(XLS)Click here for additional data file.S4 FileCompounds tested against DBP and VDR orthologues. KNIME workflows: in http://www.myexperiment.org/groups/1125.html. Pipeline Pilot script: in https://community.accelrys.com/docs/DOC-6473.(XLS)Click here for additional data file.S1 MethodSelection of pathway use cases.(DOCX)Click here for additional data file.


High-throughput methods such as next-generation sequencing or DNA microarrays lack precision, as they return hundreds of genes for a single disease profile. Several computational methods applied to physical interaction of protein networks have been successfully used in identification of the best disease candidates for each expression profile. An open problem for these methods is the ability to combine and take advantage of the wealth of biomedical data publicly available. We propose an enhanced method to improve selection of the best disease targets for a multilayer biomedical network that integrates PPI data annotated with stable knowledge from OMIM diseases and GO biological processes. We present a comprehensive validation that demonstrates the advantage of the proposed approach, Recursive Random Walk with Restarts (RecRWR). The obtained results outline the superiority of the proposed approach, RecRWR, in identifying disease candidates, especially with high levels of biological noise and benefiting from all data available. A major research domain in molecular biology is the study of the causal association between genomic variations and clinical phenotypes –3. ClassA recent research trend consists of exploiting the topological properties of protein-protein interaction (PPI) networks combined with other biological data to envisage the underlying mechanisms of genetic diseases. Barabási et al.  and Joy While the previous methods focus on evaluation of the weights given to each node, a complementary strategy consists of evaluating the proximity of two given nodes in the network. Some common methods to conduct this task are the shortest path, log-likelihood, propagation matrix, and the RWR . Previous studies confirm that the RWR outperforms other methods –18. One In this paper, we propose a novel method to improve selection of the best disease targets for multiconcept graphs. Towards this aim we build a multilayer biomedical graph that stores PPI data, annotated with stable knowledge from OMIM diseases and Biological Process from Gene Ontology. The inherent improvements of the proposed method are (a) the use of multilayer networks formed with PPI data and by the terms' associations; (b) the combination of this data to establish new associations among nodes; and (c) the use of degree-based methods to evaluate node weights.Finally, we present comprehensive validation that demonstrates the superiority of the proposed approach, Recursive Random Walk with Restarts (RecRWR).The method proposed herein uses a graph representation of biomedical knowledge centred on proteins enriched with biomedical terms. The first step consisted of selecting and curating the required data and using it to construct the graph. For performance issues this network is represented as a matrix of adjacencies. Based on this ground-based biomedical graph we apply a modified version of the Hubs and Authorities (HITS)  algorithPPI data are retrieved from STRING database , where tDisease data are extracted from OMIM morbid map  data wheBiological Process from Gene Ontology (GO)  DirectedA graph-based representation is used to store the relations among the biomedical terms. Since we are integrating three distinct data sources, three interconnected subgraphs are obtained.ak representing the content of the ath term from the interval . Each term ak is a tuple of three elements that can be represented asta has an association with the element tb, with a confidence score w where  and .For each of the previous data sources a curated set of terms G = .vx ∈ V is obtained by identifying the unique entry ta-tb or tb-ta of all the association tuples contained in vector Each vertex ex ∈ E connects vertexes  representing an association between the terms represented by the vertices vm and vn contained in vector Each edge wvm,vn of each edge ex corresponds to the score between two nodes.The weight The set of vectors G =  is then mapped to an adjacency matrix representation that consists of a |V| × |V| = n × n matrix A:aij = aji.The graph E|), which is realistically equivalent to a memory space of 6.0 MB, excluding hash tables required for node mapping. The adjacency matrix requires a memory space of Θ(|V|2), 7.2 GB.The compiled graph resulted in 60.000 nodes with an average degree of 5. The memory space required to represent the graph is Θ.Previous use of RWR on molecular biology typically concentrates on PPI networks. One would expect that including additional data would contribute to an improved overall result. We evaluate the performance of the RecRWR method using the receiver operating characteristic (ROC) curves where each curve contains the results for each level of randomness. A higher AUC (area under curve) corresponds to a better overall performance. With 0% randomness the AUC is approximately the same for the three methods, the proposed one having the lowest minimum value, which can be perceived visually. This means that in the absence of biological noise the protein annotation data does not contribute to improving the final result. However, if randomness is introduced the proposed method shows a strong improvement.With 20% randomness the RecRWR AUC is 0.9834, which compared to 0.9453 on the RWR corresponds to a 4.0% of improvement. Comparing the behaviour of the RecRWR the 20% randomness reflects no real impact (−0.22%) on the obtained AUC.For 40% and 60% the difference is even higher (7.1% for 40% and 7.6% for 60%) demonstrating the greater capability of the proposed method.y-axis on the graphs from x-axis).It is also relevant to note a 1.0 TPR  is considered a complex disorder having 23 known genotypes that are shared with other cancer-related disorders. We have used RecRWR over the common expression profiles of breast cancer to explore the network of diseases that share common mechanism. The diseases most closely related to breast cancer are hepatocellular carcinoma, bladder cancer, and lung cancer.From analysing the network of associations, we can see that the proteins most related with breast cancer are responsible for important cellular functions, such as DNA repairing, cell cycle arrest and its regulation, induced cell death (apoptosis), and tumor suppression. Also, we can see that the more closely GO terms associated with breast cancer are protein binding and apoptotic process. This means that the probable causes of breast cancer are related to the impairment of all these functions. For instance, a genetic mutation causing loss of function on a tumor suppressor gene  product would result in unrestrained cellular proliferation. Conversely, the transformation of a protooncogene (a gene that participates in a cell-growth pathway) into an oncogene  requires a gain-of-function mutation that will allow its permanent activation. An example of this is the epidermal growth factor receptor , also present in The shortest path between the two diseases is mediated by the cellular tumor antigen p53. There are however other connections between the two nodes. For instance, the proteins receptor tyrosine-protein kinase erbB-2 (P04626), GTPase KRas (P01116), and caspase-8 (Q14790) also connect the two cancers. The influence of caspase-8 mutations on the onset of cancer was explained above. ERBB2 is a protooncogene, with the potential of being converted into an oncogene and inducing cancer. The GTPase KRas is involved in a great variety of important biological processes, including regulation of both of cell proliferation and gene expression, signal transduction, and cell signalling. The majority of the proteins analysed here are part of the same KEGG pathways: pathways in cancer (hsa05200), neurotrophin signaling pathway (hsa04722), and focal adhesion (hsa04510). The first pathway consists of an integration of the various cancer pathways. The neurotrophin signalling pathway is responsible for the differentiation and survival of neural cells. However, this second pathway is heavily regulated by other intracellular signalling cascades, in which some of the proteins presented in In this paper, we have proposed a graph-based approach to address the problem of selecting the best disease targets for multiconcept graphs. Towards this aim we build a multilayer biomedical graph that stores PPI data, annotated with stable knowledge from OMIM diseases and Biological Process from Gene Ontology. The inherent improvements of the proposed method are the use of multilayer networks formed with the PPI data and by the terms' associations; combination of this data to establish new associations among nodes; and use of degree-based methods for evaluating node weights.Finally, we have presented comprehensive validation that demonstrates the superiority of the proposed approach, Recursive Random Walk with Restarts (RecRWR). The obtained results outline the superiority of the proposed approach in identifying disease candidates, especially with high levels of biological noise and benefiting from all data available.


To determine significance thresholds for associated pathways, correction for multiple testing and over-representation permutation testing is applied.Genome wide association studies (GWAS) are applied to identify genetic loci, which are associated with complex traits and human diseases. Analogous to the evolution of gene expression analyses, pathway analyses have emerged as important tools to uncover functional networks of genome-wide association data. Usually, pathway analyses combine statistical methods with We systematically investigated the impact of three different permutation test approaches for over-representation analysis to detect false positive pathway candidates and evaluate them on genome-wide association data of Dilated Cardiomyopathy (DCM) and Ulcerative Colitis (UC). Our results provide evidence that the gold standard - permuting the case–control status – effectively improves specificity of GWAS pathway analysis. Although permutation of SNPs does not maintain linkage disequilibrium (LD), these permutations represent an alternative for GWAS data when case–control permutations are not possible. Gene permutations, however, did not add significantly to the specificity. Finally, we provide estimates on the required number of permutations for the investigated approaches.axon guidance, focal adhesion and calcium signaling as important DCM-related pathways and Intestinal immune network for IgA production as most significant UC pathway.To discover potential false positive functional pathway candidates and to support the results from standard statistical tests such as the Hypergeometric test, permutation tests of case control data should be carried out. The most reasonable alternative was case–control permutation, if this is not possible, SNP permutations may be carried out. Our study also demonstrates that significance values converge rapidly with an increasing number of permutations. By applying the described statistical framework we were able to discover  This catalogue lists currently  1,920 different papers in PubMed for 1,079 different traits/diseases with 13,380 associations between variants and the respective traits (for each publication at most 50 SNPs are considered). Among the most comprehensive GWAS considering the screened sample size, Teslovich and co-workers )t-test, WMW-test or Z-scores). Next, the gene-level statistics is aggregated into a pathway level statistics. Here, one of the most commonly applied approaches is the Gene Set Enrichment Analysis [The evolution of GWAS analysis can be compared to the past evolution of expression microarray analysis. While in first instance the expression of restricted sets of genes has been analyzed, more substantial sets of gene expression have been investigated later, and finally, more sophisticated bioinformatics approaches have been implemented to understand the biological importance and relevance of the high-throughput gene expression data. To this end, a large set of gene set enrichment tools and pathway analysis programs was developed such that pathway analyses are now a standard for gene expression studies (no matter whether expression data are generated through microarrays or high-throughput transcriptome sequencing). Historically, over-representation analysis (ORA) was the first method applied, which statistically evaluates the fraction of genes  in a particular biochemical pathway and compares it to a background distribution . Then for each separate pathway a significance value is calculated based on common test statistics, e.g., Hypergeometric distribution, binomial distribution or chi-square distribution. Holmans and co-workers have published a similar example of a respective method, however not relying on a standard distribution . In theiAnalysis  (GSEA), Analysis . GSEA apAnalysis . To carrAnalysis .Notably, ORA as well as FCS in their basic implementations do not consider pathway topologies but only sets of genes. Here, genes that are on different parts of the network have the same meaning as genes that are directly influencing each other. Since the direct relation and interaction of genes can potentially add value to the gene set analysis, a third generation of bioinformatics tools has been implemented, covering the topology of pathways. One class of tools combined classical algorithms such as GSEA with pathway topology as implemented in the FIDEPA algorithm . Other eet al.[not necessarily significantly associated with the considered disease. Besides comprehensive scoring approaches such as SPOT [et al. evaluate ORA and GSEA approaches for Alzheimer Disease [et al.[While gene set and pathway analyses have become a standard for gene expression profiling, only a fraction of published GWAS studies made use of such analyses. There is a particular challenge as described by Khatri et al., namely et al., MetaCycet al. or Reactet al.) specifyet al.) just th as SPOT , another as SPOT . Liu et  Disease . Additioe [et al..Additionally, although cohort sizes of GWAS studies are very large and frequently thousands of patients are screened, no SNP may pass genome wide significance after adjustment. This may be due to the fact that the considered trait actually does not depend on genetics in the respective study or that the effect sizes are too small. Here, pathway analysis can contribute to improve the power, while the single genes are not significant the overall pathway might be significant.For all approaches it is essential to identify real associations and reject as many false positive results as possible. In the present study, we systematically explore the effect of different permutation tests in two sets of GWAS data. The most common approach is permuting the case–control status (column permutations). However, frequently raw data are not available but rather aggregated SNP information. In addition, for web-based applications, uploading of raw data that are required for permuting case–control status can be too time-consuming. Thus, we also evaluated strategies that do not require the case–control status, including permuting significance values of the original case–control status (row permutation I) and randomly permuting the gene labels instead of the significance values of SNPs (row permutation II). In the latter case, the LD is maintained and the sizes of random gene sets correspond to the original size of gene sets. Beyond testing the different permutation test strategies, we assess the required number of permutations to reach statistically stable results. The pathway computations were conducted exemplary on two GWAS datasets for Dilated Cardiomyopathy (DCM) and for Ulcerative Colitis (UC) using the public gene set analysis toolkit GeneTrail ,26. Our -4) number of genes in the range of 5,500 genes per permutation test, as indicated by the red distribution in the Histogram plot  number of significantly associated genes , as demonstrated by the green distribution in Figure -10). In the third permutation test strategy, i.e. permuting the genes, the number of significant genes was preserved. The substantial difference between the three analyses is well explained by the completely different permutation approaches. While e.g. for the column permutations correlations between SNPs are obtained, this information is completely lost in the case of permuting SNPs. This fact is of particular importance when hypotheses are tested that combine information across SNPs.First, we evaluated how different permutation tests influence the number of significant genes. As lead application we employed our method to a GWAS dataset of 909 patients suffering from Dilated Cardiomyopathies (DCM) and 2,120 population-based controls. As first analytical step, we matched all SNPs to the respective genes according to the information provided by the manufacturer. When one SNP mapped to multiple genes, all genes were taken into account. Next, genes were considered as significant, if at least a single SNP was discovered in that gene . The SNPs were not adjusted for multiple testing since a standard Bonferroni correction did not yield any individual genome-wide significant SNPs in this study. For the original data set we calculated 6,226 significantly associated genes. By carrying out 20,000 permutation runs across the columns of the GWAS matrix, corresponding to permutation of case–control status, we found a significantly decreased , we calculated 54 significant pathways after adjusting for multiple testing. By considering SNPs with significance below 0.01 , just 11 enriched pathways remained. When increasing the stringency of the threshold to 0.001 (gene set size: 466), no significant pathway remained. These results suggest that 0.05 or 0.01 are reasonable thresholds. The results in the manuscript are based on the least stringent alpha level of 0.05.p as the fraction of all 20,000 column and row permutation tests with higher significance for pathway p as the original data set. The significance values resulting from the four sets of pathway analysis are presented as bar chart in Additional file -13) than the dissimilarity between original p-values and column permutations (2*10-10). The highest difference was however calculated for row permutations II with a p-value of < 10-16. Although row I and column permutation tests showed a slightly higher concordance to each other, the difference between both approaches was still significant (5*10-7). All significance values for all pathways and all permutation tests are provided in Additional file For each pathway, we calculated four different significance values. Two significance values correspond to the two distributions described above and outlined in Figure The original motivation for permutation tests is to cross-check the p-values obtained by classical tests such as the Hypergeometric distribution to discover putative false positive pathways. Based on the results above, we conclude that column permutation as well as row I permutation tests highlight relevant pathways. In contrast, row permutation tests II in all cases confirmed the results of the Hypergeometric test, even with substantially lower significance values, not adding to the specificity of the pathway analysis.-5 . As detailed in the discussion section, all three pathways are important key networks for cardiovascular disorders. Besides these, further 26 pathways have been excluded by both approaches, being significant just in the original data set results , demonstrating that the applied column and row I based permutation approaches effectively handles this size bias while the second row based permutation strategy does not.Again, column and row permutation tests I showed a generally good concordance in marking potentially interesting candidate pathways such as “vascular smooth muscle contraction” or “dilated cardiomyopathy” as likely false positives. Notably, for these pathways the second row permutation test strategy found the pathways as highly significant. In case of “dilated cardiomyopathy” the original p-value was 0.02 while row permutations II reveal a p-value of 0.0003. For “vascular smooth muscle contraction” the original p-value was 0.01 and for row permutations II as low as 10Our analyses suggest that row I and column permutations provide fully concordant results and that one of the two approaches will be sufficient. Nevertheless, while for the total set of networks included in the Venn diagram in Figure 2 value of 0.87. Here, the significantly enriched and depleted pathways are highlighted in green and red. Notably, many pathways are enriched or depleted in almost all permutation test runs including 32 pathways that are 100% enriched and located in the upper right corner in Figure Our analyses considered significantly enriched as well as depleted pathways, representing both tails of the distribution of all permutation tests. In many cases it makes sense to treat enriched and depleted pathways separately from each other, corresponding to a one-tailed analysis. While the enriched pathways are most affected by the disease, depleted pathways may provide information on molecular networks that are not affected by the trait of interest. We thus calculated for each of the 241 pathways how many percent of the row and column permutation tests are enriched and depleted. As shown in Figure Another important question in GWAS pathway analysis is how many permutations have to be carried out in order to obtain stable results with respect to the considered pathways? Here, one common choice is to generate 1,000 different permutations, just a small fraction of the exponentially growing permutation number. We explored the Coefficient of Variation (CV), the ratio of the standard deviation to the mean as potential criterion for estimating the required number of permutations. In detail, we started by sampling 100 of the 20,000 permutation tests and stepwise increased the number. For each permutation set size 1,000 random drawings were carried out to calculate average value, standard deviation and CV value for column as well as row permutations. First, we considered the average and standard deviation for all pathways with 1,000, 2,000 and 5,000 permutation tests for row and column permutations separately. Additional file To validate our approach, we evaluated GWAS data measured from Ulcerative Colitis (UC) patients. Analogous to our results in the previous chapters, we focused on the most complex permutation test approach and carried out 10,000 permutations of the case–control status permutations. Following our original analysis strategy, we selected an alpha level of 0.05 to consider a SNP as significant. For the original data set we calculated 7,082 significant genes. In line with the results for DCM, we also found a significantly decreased number of genes in the permutation tests with an average of 6,775 genes.-4 and 8*10-4, respectively were “Intestinal immune network for IgA production” and “Toxoplasmosis”. The next pathways with significance values of 0.001, 0.002 and 0.003 contain “Maturity onset diabetes of the young”, “Fat digestion and absorption” and “Glycerophospholipid metabolism”. The first pathway that has been excluded by the permutation tests was “Cell adhesion molecules”. All significance values are provided in Additional file In the enrichment analysis we discovered as much as 51 KEGG pathways to be significant following adjustment for multiple testing at an alpha level of 0.05. In the subsequent evaluation of the permutation tests, 30 of these pathways (59%) were marked as potentially false positive paths and 21 remained significant. The two most significant networks with p-values of 6*10Next, we evaluated the required number of permutations for the UC data set with the same approach as for DCM. Corresponding to our previous results on DCM, we again did not discover substantial differences for the relevant pathways (p = 0.05). The difference between 2,000 permutations (blue) and 5,000 permutations (green) was not significantly larger than between 1,000 and 2,000 permutations  were likewise detected for DCM, representing a substantial overlap. Considering the pathways that are significant according to the Hypergeometric distribution, still 24 of the 51 UC pathways are overlapping with DCM (47%). After applying the permutation tests, however just 2 of the 21 pathways are overlapping between both diseases (10%). The respective pathways are “GnRH signaling pathway” as well as “Toxoplasmosis”. This analysis indicates that the permutation tests filter out a substantial part of false positive pathway candidates.Pathway analysis for GWAS has already been applied to various diseases such as pancreatic cancer , type 2 x significant SNPs have been detected in that gene. This however may introduce a bias towards longer genes, as we demonstrate in our study. Here, the permutation tests of rows and columns were very effective to account for potential size bias. Other approaches that could be applied in order to take gene size into account are to normalize the number of significant variants per gene by the gene length or by the total number of variants on this gene included in the study. We explored the respective approaches but did not discover improved results compared to the straightforward method employed in our study.The first challenge is that existing pathway resources such as KEGG having a lower resolution and comprising relatively few genes compared to genome-wide SNP datasets. Additionally, GWAS consider multiple variants in each single gene and even more variants in non-coding regions. Thus, associated variants first have to be assigned to genes and significance scores per gene have to be calculated. Here, various algorithms such as SPOT  have beeAnother challenge is significance value calculation of permutation tests and permutation test numbers to be carried out. Generally, the significance score for permutation tests is calculated as fraction of all permutations with more significant result than in the original analysis. To avoid p-values of zero, the minimal significance score to be reached by this method is 1/(# of permutations). One approach to account for this is to calculate p-values based on tail approximation. Knijnenburg and co-workers  present -5) that remained significant after column and row permutation tests were discovered, including “axon guidance”, “calcium signaling pathway” and “focal adhesion”. The “focal adhesion pathway” for instance is an interacting network of proteins that is essential for maintaining cardiomyocyte integrity [Despite these challenges, pathway analysis helps to understand pathogenic processes on a molecular level. By applying the ORA based pathway analysis for DCM we detected association signals to be enriched in different pathways indicating their modulation by common variants. Most importantly, three very highly significant pathways , 6 genes in common between “calcium signaling” and “focal adhesion”  as well as 11 genes in common between “axon guidance” and “focal adhesion” , representing likely key-players for DCM. After removing overlapping genes respectively and repeating the same analysis, the pathways did not remain significant, demonstrating that these genes are of central importance for the pathogenesis of DCM in these three pathways. To further explore the role of these pathways we removed all genes from the respective networks and repeated the analysis. The results demonstrated a substantial shift with much less significant results. The only category which remained significant in this analysis and was also significant in the original results and all permutation test approaches, was “Graft-versus-host disease”. These results imply that not only the overlapping genes but also all genes on the respective paths and, thus, the pathways themselves play a crucial role in pathogenesis of DCM.-6) was ruled out by both permutation test strategies, where 3,564 permutations of case–control status and 7,510 permutations of original association p-values showed higher significance than the originally calculated 9*10-6.Permutation tests help to filter paths, which are strongly significant in standard analyses such as the Hypergeometric test, improving the specificity of network analyses. Remarkably, already the fourth most significant pathway in our original analysis, “neuroactive ligand-receptor interaction” with as many as 124 genes located within this network and being highly significant  is an antibody that plays a critical role in mucosal immunity. More IgA is produced in mucosal linings than all other types of antibody combined . In its Although our results already revealed interesting biological results, future approaches that integrate the topology of networks rather than sets of genes will enhance the discovery of sub-networks or specific pathways that are significantly perturbed in a certain trait.It has to be mentioned that the three tested permutation test approaches evaluate different null hypothesis. Particularly, it should be noted that permuting SNPs explicitly does not maintain the LD scattering any single, linked effect among genes and potentially introducing inflation in the null distribution. This effect may become even more important depending on how significant genes are calculated from a list of significant SNPs. Thus, the results of the SNP permutations have to be carefully evaluated and, where possible, case–control permutations should be carried out.Our study elucidates that for GWAS permutation of case–control status as well as permutation of the original associations’ p-values are reasonable in order to systematically uncover potential pathogenic pathways for human diseases. Especially in the latter case, results require careful interpretation since this kind of permutation test does not maintain the LD. While the gold standard of permuting case–control status should be carried out, permuting SNPs appears to represent a reasonable alternative when case–control permutations are not possible. The most specific results are obtained for those pathways, where all three approaches yielded significant results. Furthermore we demonstrate that few thousand permutations are sufficient in order to obtain reliable results for our data example. In summary, the following parameters for the GWAS pathway analysis showed reasonable performance in our analysis: significance threshold for SNPs – 0.05; permutation approach – case–control permutations; number of permutations – 2000; significance threshold for pathways – 0.05. Further analyses on other traits will show whether these parameters can be generalized or have to be adapted for other GWAS studies.http://pngu.mgh.harvard.edu/purcell/plink). SNPs exhibiting minor allele frequencies <3%, call rates ≤95%, or deviations from Hardy-Weinberg equilibrium considering a significance level of 0.05 for controls and 0.001 for cases were excluded from further analyses. Analyses were adjusted for sex and age of the included unmatched individuals by means of logistic regression. The genomic inflation factor was calculated as median of all SNPs divided by the median of a chi square distribution with 1 degree of freedom and was used to correct p-values of the association analyses for genomic control (GC) in order to effectively adjust for population stratification [The GWAS dataset for pathway analyses: data used for pathway analyses was retrieved from Meder et al.  Stage 1 fication .-4) were excluded from further analysis. As for the DCM data set, case–control association tests were conducted using the PLINK software package version 1.07 assuming an underlying additive genetic model with 1 degree of freedom. The analysis was adjusted for Genomic Control by using logistic regression.The second GWAS data set on Ulcerative Colitis was extracted from Ellinghaus et al. , consistPermutation tests: In order to validate the significance of results from pathway analyses, re-sampling approaches are commonly applied. In our study we carried out a permutation of the case–control status (permutation of columns) as well as randomly shuffling the significance value for each SNP (permutation of rows). First, the case–control status has been randomly shuffled 20,000 times and the respective runs have been evaluated according to the methodology described earlier (in the following denoted as column permutations). In order to permute the original associations’ p-values of the GWAS data analysis as described above, original significance values have been randomly assigned to arbitrary SNPs (in the following denoted as row permutations I). The latter procedure ensured that the total number of significant SNPs did not vary between the various permutation test runs. Please note that the number of significant genes nevertheless varies between different permutation test runs. Additionally, we tested a third permutation variant by randomly permuting the gene labels instead of the significance values of SNPs (row permutations II). In this case, the LD is maintained and the sizes of random gene sets correspond to the original size of gene sets.x cases and y controls and covering z SNPs (or g Genes), a total ofRemarkably the number of possible permutations between those approaches is substantially different. Considering a GWAS with z! (or g!) permutations of SNP significance values (or genes) can be carried out. Notably, for usual GWAS the number of SNPs is considerably higher than the number of screened individuals (z > > x + y) such that significantly more row permutations are possible.different permutations of case–control status are possible while up to R based on permutation tests (either row or column permutations) we applied the following approach:In order to calculate a p-value for a pathway pRn represents the p-value for pathway R in the n-th permutation test, pRoriginal represents the original p-value for that pathway as calculated by the Hypergeometric distribution, Ntot equals the number of permutations carried out  and I is the indicator function, evaluating to 0 or 1, depending whether the permutation test is less significant as compared to the original p-value. In order to avoid significance values of zero in case that no permutation test is more significant than the original data a pseudo-count can be added.Here, g significant genes of which k belong to pathway R and a total of h genes of which i belong to R, the p-value for enriched pathways is calculated asPathway analysis: A total of 60,001 different analysis runs have been carried out, three times 20,000 permutation tests for each column and row permutations along with the original data set. All calculations have been carried out with the freely available gene set analysis tool GeneTrail . As bioland accordingly for depleted pathways asAfter all significance values were calculated, p-values were adjusted for multiple testing using the Benjamini Hochberg approach . All patThe authors declare that they have no competing interests.CB carried out permutation tests and pathway analysis, FR did the primary data analysis of GWAS arrays, MS supported the primary data analysis of GWAS arrays, contributed in writing the manuscript, JH and KF participated in the analysis of the GWAS data, AF contributed in writing the manuscript and supported the data interpretation, WL, HEW, TW, and WK participated in the analysis of the GWAS data, HPL contributed in interpreting the data, EM contributed in writing the manuscript and in pathway analysis, HK contributed in study design, BM contributed in study design, data analysis and wrote the manuscript, AK contributed in data analysis and wrote the manuscript. All authors read and approved the final manuscript.Overview of the significance values resulting from the four sets of pathway analysis as bar chart.Click here for fileAll significance values for all pathways and all permutation tests for the DCM dataset.Click here for fileSpider diagram of 26 pathways that have been excluded by both permutation approaches, being significant just in the original data set results.Click here for fileAll significance values for enriched and depleted KEGG pathways.Click here for fileComparison of the average and standard deviation for all pathways with 1,000 (black), 2,000 (blue) and 5,000 (green) permutation tests for row and column permutations separately (DCM dataset).Click here for fileAll significance values for the KEGG pathways for the UC dataset.Click here for fileComparison of the average and standard deviation for all pathways with 1,000 (black), 2,000 (blue) and 5,000 (green) permutation tests for row and column permutations separately (UC dataset).Click here for fileOverview of the convergence of p-values with increasing permutation test number for the pathway “RNA polymerase” in the UC dataset.Click here for fileAll significant genes on the core networks for UC as well as DCM.Click here for file


CYP4B1) in module 1, CYP4F14 in module 2 and the tachykinin precursor 1 gene (TAC1) in module 3. The majority of enriched pathways of module 1 and 2 were oxidation reduction pathways (metabolism of xenobiotics by CYPs) and lipid metabolism-related pathways, including linoleic acid and arachidonic acid metabolism. The neuropeptide signaling pathway was the most significantly enriched functional pathway of module 3. In conclusion, our findings strongly suggest that intestinal microbiota depletion affects cellular metabolism and oxidation reduction pathways. In addition, this is the first time, to the best of our knowledge, that the neuropeptide signaling pathway is reported to be affected by intestinal microbiota depletion in mice. The present study provides a list of candidate genes and processes related to the interaction of microbiota with the intestinal tract.Complex communities of microorganisms play important roles in human health, and alterations in the intestinal microbiota may induce intestinal inflammation and numerous diseases. The purpose of this study was to identify the key genes and processes affected by depletion of the intestinal microbiota in a murine model. The Affymetrix microarray dataset GSE22648 was downloaded from the Gene Expression Omnibus database, and differentially expressed genes (DEGs) were identified using the limma package in R. A protein-protein interaction (PPI) network was constructed for the DEGs using the Cytoscape software, and the network was divided into several modules using the MCODE plugin. Furthermore, the modules were functionally annotated using the PiNGO plugin, and DEG-related pathways were retrieved and analyzed using the GenMAPP software. A total of 53 DEGs were identified, of which 26 were upregulated and 27 were downregulated. The PPI network of these DEGs comprised 3 modules. The most significant module-related DEGs were the cytochrome P450 (CYP) 4B1 isozyme gene ( Bifidobacterium and Lactobacillus are particularly present in the colon of healthy individuals, and they are generally regarded as desirable, owing to the reduction of the neutral pH to a more acidic pH that they cause . These data were obtained on a GPL6887 platform using MouseWG-6 v2.0 expression beadchips . Data from a total of 11 chips were analyzed, corresponding to colonic intestinal epithelial cell gene expression profiles of 5 replicates from mice with depleted intestinal microbiota and 6 replicates from control mice that were not treated with antibiotics (germ-free).The raw data and the probe annotation files from the gene expression profiling dataset GSE22648  were downloaded from the Gene Expression Omnibus database  allows visualizing complex networks and integrating these networks to any type of attribute data.The search tool for the retrieval of interacting genes (STRING)  databaseThe MCODE  plugin ihttp://www.genome.jp/kegg/pathway.html)  ,20. Visuay.html) . GenMAPPay.html) . P<0.05 The normalized expression values following preprocessing of the raw data are shown in Hierarchical clustering analysis was performed on the expression values of all genes and of the 53 DEGs. Clearly distinct expression patterns were observed between the microbiota-depleted and the control mice in both the total gene and DEG clustering analysis .CYP4B1), CYP2D10 and CYP2D26, which were upregulated    was pred4 (TAC1) . The top4 (TAC1)  were allPathway enrichment analysis using the GenMAPP software was performed on the list of DEGs the products of which are parts of the three PPI modules. Module 1 was found to be significantly enriched for a total of 12 pathways, module 2 for 5 and module 3 for 2 . The mosCYP4B1 in module 1, CYP4F14 in module 2 and TAC1 in module 3. The majority of enriched pathways of module 1 and 2 were oxidation reduction (metabolism of xenobiotics by CYPs) and lipid  metabolism pathways. In addition, the neuropeptide signaling pathway was the most significantly enriched pathway in module 3.The collective genome of the human intestinal microbiota was estimated to contain 3.3 million microbial genes, which is ~150 times more genes than the human genome. Intestinal microbiota mostly use fermentation to generate energy, converting sugars, in part, to short-chain fatty acids, which are used by the host as an energy source  and CYP4F14 (module 2) revealed that CYP4B1 and CYP4F14 are involved in almost all of the functions of each PPI module. In rats and rabbits, the CYP4B1 protein was shown to play an important role in mutagenic activation of procarcinogens in the organs (CYP4B1 and CYP4F14 genes were identified as significantly upregulated in the present study, which, in combination with previous reports, suggests that intestinal microbiota depletion may lead to inflammation and cancer in the body.Further analysis of the most significant DEGs e organs . Most ofe organs . CYP4Fs e organs . The CYPIt is notable that modules 1 and 2 were both enriched for the processes of arachidonic and linoleic acid metabolism. Arachidonic acid is a polyunsaturated ω-6 fatty acid that is released in response to tissue injury. Arachidonic acid is a pivotal signaling molecule, involved in the initiation and propagation of diverse signaling cascades regulating inflammation, pain and homeostatic functions . It is mTAC1 is predicted to be involved in this pathway. This gene encodes a neurotransmitter of the central and peripheral nervous system (In contrast to the reported effects of intestinal microbiota on oxidation reduction and lipid metabolism –32, an as system , and thes system . The guts system ; alterats system . Gut-relIn conclusion, our findings strongly suggest that intestinal microbiota depletion affects metabolism, oxidation reduction and neuropeptide signaling pathways in mice, involving a number of genes and interactions. Numerous diseases, as well as aging, can be induced by depletion of the intestinal microflora, and therefore, the dynamic equilibrium of the intestinal microflora plays a key role in human health. The neuropeptide signaling pathway was first reported in the present study to be affected by the depletion of the intestinal microflora, a result which reveals a potential link between the intestinal bacteria and the nervous system. However, further experimentation and additional studies are needed to confirm this link; such studies are expected to enhance our understanding of the interactions between the bacteria of the intestinal microflora and their host environment.