This user interface has been used to showcase relationships that can be inferred from the information available in several public databases.In the last few years, the Life Sciences domain has experienced a rapid growth in the amount of available biological databases. The heterogeneity of these databases makes data integration a challenging issue. Some integration challenges are locating resources, relationships, data formats, synonyms or ambiguity. The Linked Data approach partially solves the heterogeneity problems by introducing a uniform data representation model. Linked Data refers to a set of best practices for publishing and connecting structured data on the Web. This article introduces Database URL: The public Linked Data repository can be queried at http://sparql.kpath.khaos.uma.es using the graph URI “www.khaos.uma.es/metabolic-pathways-app”. The GUI providing navigational access to kpath database is available at http://browser.kpath.khaos.uma.es. Over the last two decades, the biological database community has witnessed a rapid growth in the number of available data sources. The growth is a result of an enormous increase in the availability and accessibility of biological data. In the beginning, the information was used by a few specialized disciplines but today, these databases have become essential resources used by scientists around the world.Arabidopsis displaying the data on a user-friendly interface. This database is comparable in terms of annotation quality with EcoCyc, a database that has been focused on collecting Escherichiacoli information since mid-1990 . This level of interoperability is sufficient for a simple navigational search, but does not allow a federated search across different resources. As a result, users need to perform many independent searches when they seek cross-database information. For example, if a user wants to see all the pathways from Kegg and Reactome, which involve a specific metabolite, they would have to run two independent searches for each of the databases. Even then, the actual overlap of those two sets of results would not be very clear. Furthermore, Stobbe et al.  recentlyTo address the problem of information integration, the Semantic Web community led by the W3C community proposed a set of standards such as the Resource Description Framework (RDF) which is a data model based on subject-predicate-object logical statements called triples . Since 2http://www.ncbi.nlm.nih.gov/pubmed), UMLS (The move towards the publication and linking of data has been continuously growing and the number of documents and resources stored in the Linked Data Cloud  has growd), UMLS , Entrez-d), UMLS  and Opend), UMLS .However, the use of Linked Data to distribute and provide access to public biological data and several well established databases provide their own points of RDF information access (SPARQL endpoints) , 27, 28.kpath (Khaos Metabolic Pathways) database has been published. On top of the database, we have implemented kpath Browser with functionalities such as navigation, search and visualization to make the integrated data easily accessible to users with a biological background.In Life Sciences, one of the main integration problems is related to the complexity of biological data. This feature makes it difficult to develop ‘wide-span’ applications for end-users. This article presents an integration solution in the context of the metabolic pathways that use Linked Data as a source of information. As a result of this process, The data generated from genomics using next-generation sequencing techniques and metabolomics has increased in the last few years. For this reason, there has been a need to develop new applications to visualize the information related to the metabolic pathways and genes involved in the metabolism. These applications can be divided into two groups: the first group is composed of those databases that provide visualization of indexed chemical reactions and the second group includes web-based or desktop software that visualizes metabolic pathways. For example, in the first group, Kegg and Ecocyc databases only display pathways whose information is stored in the database. These database tools which visualize pathways are limited in features such as pathway editing, query pathways, etc. The second group of applications provides some of the aforementioned features that are not present in the first group of tools.kpath database links genetic and metabolic information from Kegg, Uniprot and Reactome , which is an example of such an application, to access and exploit these integrated data. The main goals of this tool are:kpath database without prior knowledge of SPARQL.Provision of an easy-to-use tool for accessing the integrated Provision of a set of features to study the different pathways at different levels of detail, including at least the most common functionalities of other client tools see ; novel fProvision of the fine grain information (at the level of pathway components) inside the same client tool without needing to browse other client tools or databases; i.e. instead of providing links to external sources, the information is integrated and shown in the same tool.We also provide a specialized client, Kpath Browser covers most of the commonest functionalities and provides an advanced use of the data able to take advantage of the integration process. A comparative study has been performed with related tools based on the analysis of several dimensions, see supplementary material (see online supplementary file S1).The kpath database to access this integrated data, how it can be used and how the usage scenario behaves in comparison with manual access to the same. Use cases section describes several use cases with relevant biomedical implications. Conclusion section presents the conclusions resulting from this integration work and the consequent improvements to be implemented in the future.The article is divided as follows: System and methods section describes the process to provide access to heterogeneous biological information and the results obtained. The kpath user interface section presents a detailed description of the navigational interface of kpath takes the Bio2Rdf Kegg's data as the core, which is then extended with organism data from NCBI Taxonomy .For example the ‘hsa’ code from Kegg corresponds to NCBI's 9606  between SwissProt proteins end Kegg genes. Therefore, it is possible to query Kegg gene codes from certain Uniprot proteins. An example query with its results is presented in It is worth noting, that the data structure used by Metabolic pathways from Reactome were also included in a way corresponding to the simple data structure presented above. In order to include new pathway data, the imported pathways have been represented as a set of biochemical reactions, which, due to the highly general nature of our model, implies a simplification for the majority of relevant datasets.The instance matching approach we have proposed is based on relating basic concepts, i.e. compounds and enzymes, thus enabling querying for Kegg and Reactome pathways that share common components. Enzymes and enzyme groups can be easily connected through the E.C. numbers, while equivalence relationships for compounds were found through common CHEBI references found in the original datasets (Kegg and Reactome). As a result more than 25 K Reactome instances were mapped to a set of nearly 1 K Kegg compounds . Matching those basic pathway components leads to matching specific reactions with over 3.5 K (out of ca. 8 K) reactions from Reactome mapped to their Kegg counterparts. In the case of Reactome instances that can be mapped to Kegg instances, we have merged the Reactome data into Kegg data, while maintaining a separate namespace. For example, if we discover that object R from Reactome has an equivalent K in Kegg, we simply substitute the identifier of R with the identifier of K wherever it appears within the Reactome extracted data. At the same time we keep track of data provenance by putting the Reactome data in a separate namespace, so that we can either include or exclude the Reactome data from our queries. The effect of this implementation choice can be seen in the pathway browser. It is worth noting, that only metabolic pathways were incorporated from Reactome.http://sparql.kpath.khaos.uma.es. Users can run SPARQL queries, defined or parameterized queries using the Bioqueries platform (The integrated data is available through a public SPARQL (Virtuoso) endpoint at platform  and retrplatform .kpath Browser is publicly available through a Web interface, whose main page shows some options and links to quickly navigate to the different tools provided. As an entry point, the user can search a pathway using simple filters or the global metabolic map  of the pathways that are being displayed in the Pathway Graphical Viewer. The pathway components can be edited and saved as a new user pathway. The custom pathways can be stored so they can be loaded later. This new custom pathway, if saved, can also be used for comparisons in the Pathway Graphical Viewer.The Relationship Search Tool allows some components to be selected from the integrated data or, using a file, search for some of them. After choosing a set of components , the tool can be executed to find all the existing relations between each pair of the selected components.kpath Browser can be used to discover relevant knowledge from the integrated data through the description of two easy use cases regarding different pathological conditions.This section shows how kpath Browser is its capability to infer relationships from data. Many of these inferences correspond to new or not easily accessible information from biological databases by current standard methods. This situation demands improved biological data handling for users. For this reason, we have focused on these inferences to illustrate how data integration based on linked data technology provides comprehensive relationships and potential biomedical applications. To this end, we used kpath Browser to study biochemical reactions in two different medical conditions.One of the most interesting features of the kpath Browser to explore all the metabolic relationships between the two enzyme-coding genes. We found that GSS and HAGH  are included in distinct Kegg pathways, glutathione (hsa00480) and pyruvate (hsa00620) metabolism, respectively. The metabolic relationships retrieved from the kpath Browser allow us to easily identify that both enzymes are related to the biosynthesis of glutathione. Indeed, previous studies have concluded that GSS deficiency implies low glutathione levels, which is an essential cofactor for HAGH and this could explain a deficient activity of glyoxalase  or hydroxyacylglutathione hydrolase (HAGH), but it should be noted that these genes are not related to the same disease. HAGH and GSS are associated with glyoxalase II deficiency (MIM 614033) and haemolytic anaemia is due to glutathione synthetase deficiency (MIM 231900). However, both inherited metabolic disorders are characterized by glyoxalase deficiency, which is a very specific clinical feature, meaning that mutations in HAGH and GSS cause low levels of glyoxalase activity. For this reason, we used the yoxalase . This suyoxalase A.FigureIn a second example, we also studied a multifactorial and prevalent phenotype such as hypercholesterolemia. We used a similar approach as in the previous case, which consists in studying all the biochemical relationships between genes that are not associated with the same genetic disease but their mutations cause similar phenotypes. We found that mutations in lipase A (LIPA) and lipoprotein lipase (LPL) cause symptoms such as nodular changes affecting the eyelids, hypercholesterolemia, hepatosplenomegaly and xanthomatosis. In addition, LIPA and LPL can be also found in different KEGG pathways B.kpath Browser, a client interface, which covers end-users’ needs such as navigating easily through the integrated data stored in a SPARQL Endpoint. The publication of these data as an Open SPARQL endpoint allows third-parties to develop different user interfaces on top of this database. Additionally, the integration process is based on standard Linked Data technologies enabling the integration of additional data sources with a controlled cost to improve the data offered. This process is also updatable, that is, the integration process will detect changes in the sources of information and will update the SPARQL Endpoint. Thus, users will always be accessing up-to-date integrated dataThis article has described a database which integrates pathway data from four different databases. We have also presented the kpath Browser is provided with a user interface that integrates three useful tools: the Pathway Graphical Viewer, the Pathway Graphical Editor and the Relationship Search Tool. Their combination provides users with not only a browsing interface, but also some analytical features to discover new knowledge from the integration of public data. Furthermore, the use cases described show that this tool can be useful for extracting relevant biological information. The use cases show the benefits of querying different biological concepts within a common information space. This issue becomes even more complex if one considers the multitude of available datasets and their integration. For this reason, new tools for exploring the whole set of biochemical reactions between metabolic components, like those provided by the kpath Browser, represent a useful step towards more accessible platforms for accessing integrated data.The Supplementary data are available at Database Online.TIN2014-58304-R and TIN2011-25840 (Ministerio de Ciencia e Innovación) and P11-TIC-7529 and P12-TIC-1519 , and funds from Group BIO-267 . The “CIBER de Enfermedades Raras” is an initiative from the ISCIII (Spain).Supported by Grants Conflict of interest. None declared.


S. miltiorrhiza depside salt and aspirin using perspective of pharmacological molecular networks. Active components of S. miltiorrhiza depside salt and aspirin's related genes were identified via the STITCH4.0 and GeneCards Database. A text search engine (Agilent Literature Search 2.71) and MCODE software were applied to construct network and divide modules, respectively. Finally, 32, 2, and 28 overlapping genes, modules, and pathways were identified between active components of S. miltiorrhiza depside salt and aspirin. A multidimensional framework of drug network showed that two networks reflected commonly in human aortic endothelial cells and atherosclerosis process. Aspirin plays a more important role in metabolism, such as the well-known AA metabolism pathway and other lipid or carbohydrate metabolism pathways. S. miltiorrhiza depside salt still plays a regulatory role in type II diabetes mellitus, insulin resistance, and adipocytokine signaling pathway. Therefore, this study suggests that aspirin combined with S. miltiorrhiza depside salt may be more efficient in treatment of CHD patients, especially those with diabetes mellitus or hyperlipidemia. Further clinical trials to confirm this hypothesis are still needed.The aim of this study is to examine and compare the similarities and differences between active components of Salvia miltiorrhiza, a Chinese herbal medicine which promotes blood circulation to remove blood stasis drugs [ S. miltiorrhiza depside salt passed the certification of new drug application for chronic angina treatment at the State Food and Drug Administration (SFDA). 80% of its active components are magnesium lithospermate B (MLB) and its analogs  [ Salvia miltiorrhiza major water-soluble active ingredients. The other 20% are mainly rosmarinic acid (RA) and lithospermic acid (LA). A clinical noninferiority study showed that S. miltiorrhiza depside salt had definite therapeutic effect in patients with CHD angina pectoris, with no evidence of adverse drug reaction (ADR) [ S. miltiorrhiza depside salt are thus commonly used in CHD treatment, but the molecular relationships between the two drugs are still under study. Current evidence shows that both similar and different molecular therapeutic patterns exist between these two drugs, but the exact pattern of whether they are the same, different, or partially overlapping remains unknown. Therefore, a network pharmacology approach seems to be of interest as it would reveal the potential overlapping or unique modules that are affected by either treatment.Antithrombotic Therapy and Prevention of Thrombosis (Version 9) , announcis drugs , has bee acid B)  extracteon (ADR) . Aspirin. miltiorrhiza depside salt in this study. We believe that exploring the internal connection of potential molecular interactions between the two drugs can provide a clue for combination therapy of CHD.“Network pharmacology” , which chttp://www.genecards.org/) is a comprehensive and authoritative database that provides information of human genes [http://stitch.embl.de/) is a database of protein-chemical interactions integrating massive literature information and various databases of biological pathways and drug-target relationships [ S. miltiorrhiza depside salt), and “Aspirin” were entered into the integrated and searchable database of GeneCards and STITCH4.0 to search and export related genes in Homo sapiens, respectively.GeneCards  and “Aspirin” were submitted to the Agilent Literature Search software v.3.1.1 (http://www.agilent.com/labs/research/litsearch.html), which is a powerful automatic metasearch tool for querying multiple text-based PubMed and USPTO, for associations among genes of interest and constructing a network. Agilent Literature Search is a registered plugin and can be used in conjunction with Cytoscape (http://www.cytoscape.org/) to realize the visualization and analyzation of the network [ Homo sapiens; Interaction Lexicon = limited).The genes related to active components of network  (paramethttp://baderlab.org/Software/MCODE). MCODE is a clustering algorithm tool, which divides network module and provides detailed algorithm [The modular structures exist in a complex biological systematic network, so we detect highly interconnected regions clusters in the network by MCODE (lgorithm  (paramethttp://david.abcc.ncifcrf.gov/) to analyze the function of modules. DAVID software provides hypergeometric distribution tests and mainly includes typical batch annotation and gene-GO term enrichment analysis to highlight the most relevant GO terms associated with a given gene list [ Homo sapiens). DAVID functional annotation clustering uses an algorithm to measure relationships among the annotation terms based on the degrees of their coassociation genes to group similar, redundant, and heterogeneous annotation contents from the same or different resources into annotation groups. It can display genes from a user's list on pathway maps to facilitate biological interpretation in a network context by taking full advantage of the well-known KEGG pathways. The biological processes and KEGG pathway corresponding to the modules were identified and ranked by P values .DAVID software was used  and GeneCards Database , we found 55 genes related to active components of S. miltiorrhiza depside salt and 498 genes related to aspirin. There were 32 overlapping genes shared by the two drugs, and these genes accounted for 58.18% (32/55) of the identified genes related to active components of S. miltiorrhiza depside salt, 6.43% (32/498) of the identified genes related to aspirin, and 5.79% (32/553) of all genes related to both drugs. After entering 32 overlapping genes' list in GO functional annotation, it showed that the common roles included response to chemical stimulus, response to stress, multiorganism process, response to stimulus, and response to external stimulus  and 9064 edges (interactions) from the aspirin-related genes. Similar distributions of node degree that followed the power-law distribution appeared in two networks  and M(s33a100) ((s3a10) contains 7 nodes  and 21 edges. M(s33a100) contains 3 nodes  and 3 edges  . M), 17 immune system pathways, 8 cellular processes pathways (cell growth and death (6), regulation of autophagy, and focal adhesion), 5 endocrine system pathways, and so forth. The 80 pathways  , human diseases (5)  autoimmune thyroid disease), cellular processes (3) , signal transduction (3) , signaling molecules and interactions (2) (CAMs and cytokine-cytokine receptor interaction), neurotrophin signaling pathway (1), and progesterone-mediated oocyte maturation. Unique pathways of active components of S. miltiorrhiza depside salt were as follows: cancers (6), immune diseases or systems (4) , endocrine diseases or systems (4) , cellular processes (3) , and mTOR signaling pathway (1). Unique pathways of aspirin were as follows: metabolism pathways (17) , immune diseases and system (4)  pathway, and complement and coagulation cascades pathway), human diseases (3) , PPAR signaling pathway (1), ABC transporters pathway (1), and ECM-receptor interaction pathway (1).After eliminating repeated pathways, active components of S. miltiorrhiza depside salt are commonly used drugs for the treatment of CHD, but there is lack of relationship studies between the two drugs from large-scale molecular perspective. Two overlapping modules and many common biological processes and pathways between the two drugs were known in this investigation. These results are likely to reveal the overlap of potential therapy mechanism and internal connection between the two drugs. Except for the overlap, the two drugs also have some unique functions, which have an excellent advantage in different ranges.Aspirin and S. miltiorrhiza depside salt, such as JUN, VCAM-1, TGFB1, TGFB1, IL8, IL1B, NOS2, NOS3, MAPK1, MAPK8, CASP3, MMP1, and MMP2 that have been associated with the cardiovascular diseases  as biomarkers or therapeutic targets from the Comparative Toxicogenomics Database. Vascular endothelial cell proliferation and apoptosis are an early marker for atherosclerosis. Therefore, prevention of smooth muscle cell and endothelial cell proliferation and removal of superoxide radicals have a positive effect for treatment of CHD. JUN participates in the biological process of angiogenesis [There are 32 overlapping genes between aspirin and active components ofogenesis  and incrogenesis . IL8, ILogenesis . TGFB1 pogenesis . VCAM-1,ogenesis , which fogenesis , which iogenesis  and promogenesis . MAPK1, ogenesis . CASP3 cogenesis , but alsogenesis . MMP1 anogenesis , such asogenesis . Regulatogenesis . Thus, t S. miltiorrhiza depside salt and aspirin. Most of these pathways are related to the antitumor and inflammatory immune response . But there are also some pathways participating in the process of antiatherosclerosis. CAMs pathway participates in cell growth and death, which are associated with cardiovascular diseases [γ-treated HAECs, MLB inhibited IFN-γ-induced JAK-STAT signaling pathways and consequently suppressed IFN-γ-induced expression of chemokines, IP-10 promoter activity, IP-10 protein release, and monocyte adhesion to HAECs [There are 28 common pathways in overlapping and top 10 unique modules between active components ofdiseases . CAMs ardiseases . JAK-STAdiseases . In IFN-to HAECs .γ has played a pivotal role in anti-inflammation, atherosclerosis, insulin resistance, and antitumor [ S. miltiorrhiza depside salt may play a more important role in endocrine system, such as type II diabetes mellitus, insulin signaling pathway, and adipocytokine signaling pathway. Cardiovascular disease, especially CHD, is the primary cause of mortality among diabetes mellitus patients. Entirely due to more extensive coronary atherosclerosis, more than 50% of diabetes mellitus patients will die from a cardiovascular event. Meanwhile, diabetes mellitus is a risk factor for coronary heart disease [Antiplatelet treatment with aspirin is widely considered as a cornerstone of atherosclerotic vascular disease's primary  and secontitumor . Active  disease . Type II disease . The met disease . Control disease , scaveng disease , prevent disease , regulat disease , modulat disease , and pro disease . S. miltiorrhiza depside salt may be more efficient in treatment of CHD patients, especially those with diabetes mellitus or hyperlipidemia.Finally, through analyzing the molecular networks module, these two drugs were found not only sharing certain biological processes and pathways, but also having unique character. So using aspirin combined with S. miltiorrhiza depside salt and aspirin in great detail. A multidimensional framework of drug network showed that the two drugs reflected commonly in human aortic endothelial cells and atherosclerosis process. Aspirin plays a more important role in metabolism, such as the well-known AA metabolism pathway and other lipid or carbohydrate metabolism pathways. S. miltiorrhiza depside salt still plays a regulatory role in type II diabetes mellitus, insulin resistance, and adipocytokine signaling pathway. Therefore, this study suggests that aspirin combined with S. miltiorrhiza depside salt may be more efficient in treatment of CHD patients, especially those with diabetes mellitus or hyperlipidemia, but should be confirmed by further clinical trials.We adopted network-based approach to investigate the similarities and differences betweenThere are 5 tables in the Supplementary Material file. Table 1 contained the detailed genes related to active components of S. miltiorrhiza depside salt and aspirin. Table 2 and table 3 contained the modules of active components of S. miltiorrhiza depside salt and aspirin network, and ever modules' related genes. Table 4 and table 5 contained the detail of every biological function and KEGG pathway related to active components of S. miltiorrhiza and aspirin.


High-throughput technologies became common tools to decipher genome-wide changes of gene expression (GE) patterns. Functional analysis of GE patterns is a daunting task as it requires often recourse to the public repositories of biological knowledge. On the other hand, in many cases researcher's inquiry can be served by a comprehensive glimpse. The KEGG PATHWAY database is a compilation of manually verified maps of biological interactions represented by the complete set of pathways related to signal transduction and other cellular processes. Rapid mapping of the differentially expressed genes to the KEGG pathways may provide an idea about the functional relevance of the gene lists corresponding to the high-throughput expression data.Here we present a web based graphic tool KEGG Pathway Painter (KPP). KPP paints pathways from the KEGG database using large sets of the candidate genes accompanied by "overexpressed" or "underexpressed" marks, for example, those generated by microarrays or miRNA profilings.http://web.cos.gmu.edu/~gmanyam/kegg/KPP provides fast and comprehensive visualization of the global GE changes by consolidating a list of the color-coded candidate genes into the KEGG pathways. KPP is freely available and can be accessed at  High-throughput technologies became common tools to decipher genome-wide changes of gene expression (GE) patterns or relative protein abundance. Typical output of these large-scale studies is represented by the list comprised of hundreds of gene candidates with attached quantitative labels. Functional analysis of these gene lists is a daunting task as it requires regular recourse to the public repositories of biological knowledge or use of expensive databases of manually curated biological annotation ,2. On thFunctional analysis of markers identified from large-scale datasets can be performed using a wide variety of bioinformatics tools. As microarrays became a common tool to decipher global gene expression, centralized systems like Gene Expression Omnibus (GEO), ArrayExpress was developed to congregate the valuable profile data ,4. An anKEGG (Kyoto Encyclopedia of Genes and Genomes) is a compendium of databases covering both annotated genomes and protein interaction networks for all sequenced organisms. Its integral part, KEGG PATHWAY, is a compilation of manually verified pathway maps displaying both the molecular interactions and the biochemical reactions . The rechttp://www.genome.jp/kegg/tool/color_pathway.html). Similar task may be also executed using G-language Genome Analysis Environment [Pathways assembled into the KEGG database are displayed as semi-static objects that can be manipulated using tools like KGML and KEGG application programmable interface (API) ,12. KEGGironment . Both apironment . Howeverironment ,16. An aWith this notion, here we present a web-based tool, KEGG Pathway Painter (KPP). KPP performs a batch painting of relevant pathways according to the uploaded lists of up-regulated and down-regulated genes in KEGG. KPP returns a set of images that give a holistic perspective to the functional importance of the change in the GE patterns revealed by a given high-throughput experiment and facilitate the extraction of the biological insights.http://www.genome.jp/kegg/rest/).KPP was implemented using PERL/CGI. Pathways assembled into the KEGG database are displayed as semi-static objects that can be manipulated using tools like KGML (KEGG Markup Language) and KEGG API (Application Programming Interface). The API allows access to the resources stored in KEGG system in an interactive and user-friendly way (KEGG Pathway Painter (KPP) accepts the up-regulated and down-regulated gene lists as two different text files containing the gene identifiers of any sequenced organism. Permitted identifiers include GenBank id, NCBI GENE id, NCBI GI accession, Unigene ID and Uniprot ID. Conversion of the gene identifiers, extraction of the corresponding pathway and their painting is performed by specific API routines. The KPP processes data through direct interface to the KEGG database, and therefore, the KPP painted pathways are always up-to date with reference to KEGG knowledgebase. In KPP, genes of interest can be also highlighted with user-specified foreground and background colors allowing easy visual differentiating of up- and down-regulated genes.Mapped genes are automatically consolidated within each pathway. The number of the KPP returned pathways could be filtered by either the total number of the painted genes in a given pathway or the ratio of painted genes to the total number of genes in a given pathway. The chosen pathways passing the criteria on filter are color coded according to users' preferences. Users can browse through these high-resolution pathway images along with gene information and an archive of the painted pathways can also be saved for future reference. After completion of the query, the URL to the index of resulting output images is e-mailed to the user along with the job summary.The motivation for the development of KPP came up from the idea to build a user-friendly, platform-independent and simple tool to visualize the placement of genes in their associated pathways. The simplicity of KPP is due to the acceptance of gene identifiers without reference to respective microarray platform. This isolation enhances its utility for the studies of the data from RealTime-PCR or medium-throughput platforms or even for validation of the various hypotheses concerning an involvement of the groups of genes in one or another biological process.This utility of KPP was demonstrated by highlighting of cell cycle related events using the publicly available prostate carcinoma dataset GDS1439)  from the439  fromin vitro experiments with blocking antibodies, a pharmacological inhibitor, and siRNAs [In one of these examples, KPP-aided visual parsing the pathways encompassing molecular components relevant to HCV pathogenesis allowed to pinpoint the Janus kinase-signal transducers and activators of transcription signaling cascade as the major pathogenetic component responsible for not achieving SVR , a concld siRNAs .In another example, KPP allowed to visualize a sustained pattern of treatment-induced gene expression in patients carrying interferon/ribavirin-responding IL28B genotype C/C, while in patients with therapy-resistant IL28B T* genotype, the background pre-activation of interferon-dependent genes precluded further therapeutic boost . Thus, Khttp://www.cytoscape.org/). Another commonly used pathway parsing tool, Reactome Skypainter (http://www.reactome.org/), is restricted to underlying knowledge base and, therefore, limits the potential set of insights to be extracted.The major fetching point of the KPP tool lies in its tight connection with the KEGG database, as this will allow for the pathway visualization of every sequenced organism. However this flexibility comes at the cost of possible KEGG-attributed delay of the data transfer, the resultant tool is substantially more convenient for the user than the tools embed into existing pathway analysis environment, for example, Cytoskape (http://www.genome.jp/kegg/), however, the practicality of KPP is in its comprehensive visual representation of up- and downregulated genes in the KEGG dataset as a whole. In other words, KPP allows one to extract immediate and visual insights about cumulative change in each pathway under scrutiny. Users can browse through high-resolution pathway images and download an archive of the painted pathways that may be used as figures for upcoming manuscripts.It is important to note that the painting of individual pathways can be performed through by the KEGG website itself (In summary, KPP provides fast and comprehensive visualization of the global GE changes by consolidating a list of the color-coded candidate genes into the KEGG pathways.KPP - KEGG Pathway PainterGE - Gene ExpressionKGML - KEGG Markup LanguageAPI - Application Programming InterfaceSVR - Sustainer Virological ResponseThe authors declare that they have no competing interests.All three authors contributed to the study design, interpretation of results and producing the manuscript. All authors read and approved the final manuscript.


Inborn errors of metabolism (IEM) represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. The diversity of metabolic pathways involved explains the difficulties in establishing their diagnosis. However, early diagnosis is usually mandatory for successful treatment. Given the considerable clinical overlap between some inborn errors, biochemical and molecular tests are crucial in making a diagnosis. Conventional biological diagnosis procedures are based on a time-consuming series of sequential and segmented biochemical tests. The rise of “omic” technologies offers holistic views of the basic molecules that build a biological system at different levels. Metabolomics is the most recent “omic” technology based on biochemical characterization of metabolites and their changes related to genetic and environmental factors. This review addresses the principles underlying metabolomics technologies that allow them to comprehensively assess an individual biochemical profile and their reported applications for IEM investigations in the precision medicine era. The new field of precision medicine is revolutionizing current medical practice and reshaping future medicine. Precision medicine aspires to put the patient as the central driver of healthcare by broadening biological knowledge and acknowledging the greate diversity of individuals . It is wInborn errors of metabolism (IEM) are an appropriate model for systems medicine studies because the biological basis underlying these diseases has been, at least partly, revealed. IEM represent a group of about 500 rare genetic diseases with an overall estimated incidence of 1/2500. Even though these disorders are individually rare, they are collectively more common and cause a significant childhood morbidity and mortality. IEM are genetic disorders resulting from defects in a given biochemical pathway due to the deficiency or abnormality of an enzyme, its cofactor, or a transporter, leading to an accumulation of a substrate or lack of the product. Hence, the diversity of metabolic pathways involved explains the difficulties in establishing a diagnosis.Autosomal recessive transmission is most frequent, but autosomal dominant and X-linked disorders have also been described. IEM may involve mutations in mitochondrial DNA. The pathogenesis of IEM can be explained by mechanisms such as deficiency of an essential product or enzyme, systemic toxic effects of circulating metabolites, and activation or inhibition of alternative metabolism . Based oThe rise of “omic” approaches, enabled by the tremendous technological shift in both multiscale biological information capture and data management, offers an amazing opportunity to provide new effective tools for screening, diagnosis, treatment, and monitoring of these diseases. Omic technologies offer global views on the basic molecules that build a biological system at the cell, tissue, or organism level. Primarily, they aim to recover, in an untargeted, unbiased, and hypothesis-free fashion, the biological information carried by genes (genomics), mRNA (transcriptomics), proteins (proteomics), and metabolites (metabolomics). These holistic strategies clearly contrast with conventional studies, which are mainly hypothesis-driven and reductionist. To truly understand disease processes, a global investigative approach needs to be applied at multiple biological informational levels.Since the early days of medicine, the human body is viewed as a collection of separate and independent components, and thus, physicians typically treated disease by trying to identify the single abnormality related to a single component. This approach lacks contextual information which is vital for mechanistic understanding of pathophysiology and, thus, for designing treatment strategies ,6. IndeeSystems biology is a new scientific field that tries to achieve this systemic understanding of biology and to fill in the gap between information and context from a biological standpoint. Systems biology can be defined as a holistic and systemic analysis of complex system inter-connections and their functional interrelationships ,12,13,14The patient-centric approach is essential to achieve the promise of personal and stratified medicine. Indeed, unlike conventional medical biology practice based primarily on sequential studies of genes, proteins, and metabolites, the great challenge of modern biology is to apprehend a disease as a complex, integrated, and dynamic network. The dynamic view refers to the quantitative and qualitative assessment of changes and interactions between the different layers of the biological information ,19,20,21The omics surge presents an amazing opportunity to provide new innovative tools for rapid diagnosis of IEM. Furthermore, metabolomics approaches are relevant for IEM because their basic pathophysiology is tightly related to metabolism. These diseases present with nonspecific clinical symptoms and appropriate laboratory tests are crucial in making a diagnosis. However, conventional biological diagnosis procedures are based on a series of sequential and segmented biochemical tests on various separated analytical platforms. This approach is slow, time-consuming, and complex, whereas optimal patient management requires improved speed of biochemical tests to allow early diagnosis and better monitoring of IEM. To address this need of faster screening and diagnosis strategies, metabolic profiling is a promising candidate.In this review, we describe basic principles underlying metabolic phenotyping and metabolomic approaches that can be used to comprehensively assess an individual biochemical profile and their reported applications in IEM. Data for this review were identified by searches of PubMed and references from relevant articles using the search terms “metabolomics”, “metabonomics”, “metabolic profiling”, “inborn errors of metabolism”, and “inherited metabolic diseases”.The idea behind metabolomics goes back to ancient Greece, where doctors used the organoleptic characteristics of urine to link them to different medical conditions. Urine sweetness has been used to detect high glucose in diabetes . Such orMetabolomics allows researchers to characterize these interactions and to evaluate the biochemical mechanisms involved in such changes in a systematic fashion. Indeed, metabolites fulfill the key criterion in that they change rapidly in response to physiological changes and may generate vital information about biochemical pathways that are modified in patients and in treated patients. Hence, metabolic profiling is highly informative since metabolites act as substrates or products in biochemical metabolic pathways ,27,28,29Metabolomics has found applications in many disease studies and in complex interacting systems . The posFor biological information recovery, metabolomics generally uses biofluids, cells, or tissue extracts as primary sources of metabolic fingerprint data. Compared with intact or extracted tissues, urine and serum or plasma are the most commonly studied biofluids in clinical practice, because they are easily obtained and prepared ,37,38,39The human metabolome is a complex, highly responsive, and dynamic system. Thus, it raises different analytical challenges compared to other omics analysis approaches that are based on profiling large molecules built with a simple and limited set of subunits, such as nucleotides for genomics and transcriptomics and amino acids for proteomics. Thus, for identification and functional analysis of DNA, RNAs and proteins, the order combination of the subunits is what matters. It is the order of subunits that embodies the observed complexity that carries the biological information. Sequencing technologies rely basically on an incremental detection of these subunits. Researchers must figure out the order of the subunits to decode the carried biological information . HoweverTo retrieve the metabolic information, the metabolome requires a more complex analysis of chemical mixtures that allows components to be individually and selectively differentiated, identified and measured across a wide qualitative and quantitative chemical space.The diversity of the physicochemical properties of the various metabolites groups adds another layer of complexity to metabolomics studies. This supplemental challenge has been the key driver for the development of various analytical protocols and platforms. Indeed, scientists tackled this analytical challenge even before the term metabolomics was coined. The first scientific article about metabolomics was published by Pauling and colleagues, in which they described a method using gas chromatographic separation with flame ionization detection to analyze the breath . The autSince then, huge development has been made. The mainly used metabolic profiling technologies are nuclear magnetic resonance (NMR) spectroscopy ,59,60 anRecently, approaches using another gas phase separation, ion mobility spectrometry (IMS) , has beem/z) measurement accuracy and, hence, better metabolome coverage [Fourier transform mass spectrometry is another group of ultra-high-resolution methods that offer the highest resolving power, resolution, and mass-to-charge ratio  between groups. Second, predictive mathematical models based on multivariate statistical analysis can be built. These models predict a subsequent classification of unknown biological samples . The targeted approach focuses on identifying and quantifying selected metabolites according to their involvement in a metabolic pathway or their specific chemical or biochemical proprieties.In general, a metabolomic analysis involves mainly four steps. Step 1 is a preparatory step on both analytical and conceptual aspects. It is initiated by the biological question to consider and the definition of the study aim. It also defines the most informative biological matrix and the experimental design to implement. In addition, this step defines the appropriate sample preparation according to the considered analytical study. Step 2 includes analytical and instrumental strategy choices. During this step, data are collected and processed and then statistical analysis is performed. Step 3 involves the putative annotation, identification, and confirmation of the potential biomarkers generated by the data analysis. Chemical, biochemical, and spectral databases are queried. Step 4 aims to build a predictive mathematical model based on the identified biomarkers. This model is then validated analytically and clinically. This final step involves the integration of experimental data and their interpretation in the studied biological or clinical context . Figure Few highly reliable metabolites could be, at some extent, sufficient for diagnostic or monitoring purposes. However, a broader overview using more metabolites is more appropriate to assess, for example, a biochemical pathway. Thus, the choice of the most appropriate data modeling strategy is an important issue and is dependent on the underlying question to be addressed. In mechanistic studies, the structural data descriptions and the underlying extracted information yielded by the built model are more important than its predictive ability to classify new samples. However, in diagnosis applications, the predictive performances of the model are vital regarding samples classification. Hence, the clear and precise definition of the study aims has to be intelligible and purpose driven.The analytical performance improvements associated with metabolomics platforms led to the generation of complex and high-dimensional datasets. Handling, in a smoothly high-throughput fashion, the huge amount of generated data is a very important issue for transforming the data into clinically actionable knowledge.t-test and ANOVA are commonly applied, respectively. However, normality assumptions should be verified for consistent conclusions [U test or Kruskal–Wallis one-way analysis of variance could be used if normality is not assumed. Another important issue is that applying multiple univariate tests in parallel to a high dimensional dataset raises the multiple testing problem. In metabolomics studies, a large number of features are simultaneously analyzed. Thus, the probability to find a statistically significant difference accidentally  is high. In order to handle this multiple testing issue, different correction methods could be used. Each method tries to balance between avoiding false metabolite associations  and discarding true associations . In the Bonferroni correction, the significance level for a hypothesis is divided by the number of hypotheses tested simultaneously [Metabolomics data analysis can be approached from a univariate perspective using traditional statistical methods that consider only one variable at a time. Univariate methods are common statistical analysis tools and their main advantage is the convenient use and interpretation. To assess the differences between two or more groups, parametric tests such as Student’s clusions . Otherwianeously . Hence, aneously . Gene exFurthermore, it should be noted that potential confounding factors like gender, age or diet may affect the output results if not properly addressed. Furthermore, the main limit of these approaches is their lack of handling the correlations and interactions between the different metabolic features. Hence, advanced multivariate approaches are more suitable.Translating biological data into knowledge requires addressing biology as an informational science using tools that allow to track the information at large scales. To do so, an entire field was born “Bioinformatics” . BioinfoThe high-dimensionality of metabolic data requires adapted statistical tools to retrieve as much as possible chemical information from the data to translate it into biological knowledge. The major challenge is to reduce the dimensionality by selecting relevant signals from the noisy raw data. To achieve this goal, chemometric tools are widely used. Chemometrics is the science of extracting useful information from chemical systems using data-driven means . It is iThe data analysis methods are mainly divided into two types: unsupervised and supervised methods. The former are mainly exploratory, whereas the latter are explanatory and predictive. Unsupervised methods are used to analyze the behavior of the observations in the data set without taking into account any related outcome. Because there is no class labeling or response, the data set is considered as a collection of analogous objects. Unsupervised learning methods track patterns or clustering trends in the data to understand any spontaneous relationships between the samples. It can also highlight the variables that are responsible for these relationships. Based on effective visualization means, unsupervised learning helps to reveal categories of samples or variables that naturally cluster together based on their underlying similarities. In metabolomics data, it is the metabolic similarity that shapes the clustering. Principal component analysis  is a widIn supervised methods, the multivariate datasets can be modeled so that the class label of separate samples known as a validation set can be predicted based on a series of mathematical models derived from the original data, namely the training set. Various supervised methods could be used in metabolomics, including partial least squares (PLS) methods such as PLS-Discriminant Analysis (PLS-DA)  and OrthOf note, multivariate and univariate data analysis pipelines are not mutually exclusive and it is often recommended to use both to maximize the quality of the information extraction from metabolomics data.For further details on data analysis techniques and tools in metabolomics, refer to recent reviews on this issue ,104,105.The integration of experimental data and computational tools is mandatory to understand complex biological systems. This gave birth to computational biology which could be divided into two distinct branches: knowledge discovery or data-mining, and simulation-based analysis. The former extracts the hidden patterns from huge amount of experimental data, generating hypotheses. However, the latter tests hypotheses with in silico experiments, providing predictions to be confirmed by in vitro and in vivo studies .One of the biggest challenges of any metabolomics study is linking the identified metabolites to biology, which is a crucial step to move from biomarkers towards more mechanistic insights. To achieve this purpose, pathway and network analysis approaches aim to capitalize on the information generated by metabolomics studies to get insightful inference ,107. BotMetabolic pathways are sets of metabolites that are connected to the same biological process, and that are linked by one or multiple enzymatic reactions directly or indirectly. Biological databases are therefore seminal enablers providing rich information of different of metabolic pathways . Indeed,There are mainly three distinct methods to perform MSEA ,116:p-value and/or a multiple-hypothesis-test-corrected p-value. The ORA main advantage over non-knowledge-driven  analysis is that it gives metabolomic data a biological context. This allows formulating a hypothesis that could subsequently be test experimentally. Hence, ORA turns data analysis into a knowledge generation cycle, proper of the Systems Biology approach. However, PA exhibits some limits. Due to the selected cut-off method for statistical significance potentially important components could be omitted in the analysis. Furthermore, PA assume that pathways are independent from each other, which is contrary to the admitted interaction and overlapping between pathways [Overrepresentation analysis (ORA): The basic hypothesis in this method is that relevant pathways can be detected if the proportion of differential expressed metabolites, within a given pathway, exceeds the proportion of metabolites that could be randomly expected. A hypergeometric test or a Fisher’s Exact test is used to evaluate the statistical significance of whether the metabolite belongs to the pathway. The final result from an ORA method consists in a list of the most relevant pathways, ranked by pathways . Other mQuantitative enrichment analysis (QEA): In this approach, the input data are a set of quantified metabolite from multiple samples. Thus, absolute concentrations are used. Enriched pathways can be identified using different approaches like the Wilcoxon-based test , globaltSingle-sample profiling (SSP): Unlike the previous methods that are designed for studies involving multiple samples, this method is used at the sample level. In this case, SSP requires a list of metabolite concentrations in biofluids , tissue, or cell type and a database with the normal concentration ranges of the chosen metabolites in the analyzed sample. Thus, SSP identifies, from the data, the set of metabolites presenting significantly different levels compared to the normal ranges ,120.For better interpretability of pathway analysis outputs, MSEA results could be combined with pathway topological analysis (PTA). PTA measures assess the impact of the disturbed metabolites within the pathway. First, single impacts are evaluated using the degree and betweenness network centrality measures of each metabolite. This represents the number of shortest paths passing through a certain node to estimate its centrality (importance). Subsequently, the overall impact  is calculated as the sum of the single impact measures of the disturbed metabolites normalized by the sum of the measures of the impact of all the metabolites within the considered pathway . Indeed,From a topological standpoint, a metabolic network can be considered as an interconnected ensemble of nodes presented by metabolites, and edges representing reactions catalyzed by enzymes. Thus, unlike PA, network analysis uses the high degree of correlation in metabolomics data to build metabolic networks that characterize the complex relationships the measured metabolites. Biological data exhibit a high level of correlation that exists between the different biological components . Indeed, a given metabolite may be connected to different metabolic pathways and, thus, show correlation patterns. In other cases, the observed correlations may be due to other causes such as global changes  or specific changes due to the intrinsic variability of metabolomic data ,122. TheBased on the observed relationship patterns present in the experimental data, correlation-based methods allow building metabolic networks in which each metabolite represents a node. However, unlike the pathway analysis, the links between nodes denotes the level of mathematical correlation between each metabolites pair and called edge. High correlation coefficients are frequent in metabolomics data which is due to the presence of systemic associations . Hence, Metabolite identification is a challenging and time consuming task. Thus, a novel approach, named Mummichog, has been proposed by Li et al. for network analysis. This method predicts biological activity directly from mass spectrometry based untargeted metabolomics data without a priori identification of metabolites. The idea behind this strategy is combining network analysis and metabolite prediction under the same computational framework reducing significantly the metabolomics workflow time. This method has been elegantly illustrated by exploring the activation of innate immune cells. It yielded that glutathione metabolism is modified by viral infection driven by constitutive nitric oxide synthases .A wide variety of software tools are available to analyze metabolomic data at the pathway and network level. Contextual interpretation is crucial to fully embrace the potential of metabolomics. Indeed, metabolites carry out precious contextual biological information. In a metabolic network, flux is defined as the rate  at which metabolites are converted or transported between different compartments . Thus, mMetabolites embody physiological end-points and regulatory processes directly connected to the fluxome. Hence, the metabolome is very time sensitive and is constantly changing. Therefore, changes in metabolite concentrations are usually more suitable to describe the biochemical state of a biological system. Because metabolomics is the ultimate expression of the genes’ influences and proteins’ use of metabolites, it offers a rich and tremendous view on the phenotype. Indeed, metabolites carry out precious contextual biological information that could be used to assess pathophysiological states.Metabolic profiling as a diagnostic tool opens an informative metabolic window into disease, which makes metabolomics an appealing ally in disease diagnosis.What makes metabolomics a key driver in the post-genomic era is its tight relationship with the phenotype, whether the phenotype is driven by a monogenic or a multifactorial complex condition. Linking metabolic profile modulation with particular genetic variation  and/or eEarly biochemists such as Cori, Warburg, Meyerhof, and Krebs made seminal contributions to map most fundamental aspects of metabolic pathways and physiology. Therefore, urine chemical properties guided early physicians in founding the concept of IEM . Sir GarWith the expected improvements in the metabolic profiling scope and data quality, metabolomics is destined to play a major and disruptive role in the near future as an efficient screening and diagnostic tool . There aIEM being tightly connected with metabolism, the inherent pathophysiological changes are the main determinant of the metabolome and functional understanding of the disease. Hence, due to its intrinsic multidisciplinary nature, integrating biochemistry, analytical chemistry, advanced statistics and bioinformatics, metabolomics analysis represents a promising tool to achieve improved understanding and better diagnosis of IEM in the post-genomic and precision medicine era. For years, MS has been used in the assessment of inherited metabolic diseases. Several IEM are currently diagnosed using targeted MS-based metabolomics methods such as aminoacidopathies, organic acidurias, and fatty acid oxidation disorders ,154,155.To assess different IEM including aminoacidopathies, organic aciduria, and mitochondrial disorders, Janeckova et al. used targeted analysis combined with multivariate data analysis. Their work showed how combining chemometrics modeling and absolute quantification are eloquently complementary in the assessment of IEMs . DreckseGiven the increasing potential of metabolomics in IEM, different groups published work regarding the usefulness of untargeted-metabolomics-based approaches in IEM in disease characterization, diagnosis, and biomarker discovery. For characterization of disease biosignatures, respiratory chain deficiencies have been investigated by several research groups to track specific metabolic signatures using metabolomics ,162,163.Sholmi et al. presented an elegant computational approach for assessing metabolic profiles of red blood cells enzyme deficiencies. The developed predictive method yielded biomarkers for red blood cells alterations and revealed a strong correlation with disrupted metabolic concentrations. Over 200 metabolites were identified as potential biomarkers due to 176 enzyme deficiencies. Furthermore, already known disease indicators were retrieved by the developed prediction method. Importantly, potential novel biomarkers were also predicted. This approach proved to dramatically increase biomarker discovery performance .Because the metabolome is highly influenced by nutritional factors, diet monitoring also has been investigated using metabolomics. Phenylketonuria is an interesting example of diet monitoring in IEM. Using metabolomics, Mutze et al. showed that a long-term dietary fatty acid restriction influences mitochondrial beta-oxidation intermediates. No functional influence on unsaturated fatty acid metabolism and platelet aggregation in patients with phenylketonuria was detected .Regarding the use of metabolomics platforms as a diagnosis tool, several teams proposed metabolomics workflows. Using NMR and DESI-MS methods, Pan et al. clearly discriminated six patients with IEMs from six controls based on their respective urine metabolic profiles, identifying argininosuccinic aciduria, classic homocystinuria, classic methylmalonic acidemia, maple syrup urine disease, phenylketonuria, and type II tyrosinemia . Later, Regarding NMR-based platforms, Aygen and colleagues conducted a multi-center clinical study in 14 clinical centers in Turkey. Urine samples from 989 neonates were collected and investigated using NMR spectroscopy in two different laboratories to assess reproducibility. The objectives of their study were twofold: (1) to explore the metabolite variations to set pathological thresholds of specific metabolites in comparison with healthy neonates to develop predictive models; and (2) to build a NMR database from a healthy population of neonates for IEM metabolite identification .Metabolite identification is the main bottleneck of metabolomics for large adoption in both translational and clinical context. Despite spectral information becomes available in the literature or in spectral databases, metabolites identification is still a challenging task . To the Standardization is a vital aspect for a wide spread of any new technology. Thus, for clinical metabolomics, harmonization of the sample preparation, processing, analysis and reporting using validated and standardized protocols is mandatory ,174. ThiAutomation at different stages, instrument-, pre- and post-analytic levels are a very important issue for large clinical adoption of any diagnostic innovation. Metabolomics workflow automation is a key enabler regarding high-throughput, reproducibility and reliability which are pillars of modern laboratory medicine practice. To address this limit, current efforts are promising like the iKnife, which would allow real-time cancer diagnosis  and breaIt is common to perform early diagnosis of IEM by assessing specific metabolic biomarkers related to a genetic defect. However, the original paradigm of “one gene–one enzyme–one disease” is no longer viewed as a reality for IEM. The impact of an altered protein on metabolic flux is not easily predictable. Indeed, the metabolic pathways are not linear and metabolites are tightly linked with several interactions within a highly organized network ,177. DepMetabolomics is still in its infancy with regard to the investigation of IEM, and its great potential has yet to be explored worldwide at both the basic and clinical sides. Improving workflows for high-quality data acquisition, processing, and visualization is an important issue for effectively translating the biological information into actionable knowledge under clinically accessible formats for effective healthcare management. However, this innovative global approach also requires a paradigm shift in our practice at different levels. A complete change is needed in our screening and diagnosis strategies. Thus, a disruptive move from a hypothesis-driven approach to a more data-driven and hypothesis-generating approach is crucial to address the challenges of the post-genomic era. The core idea of the paradigm shift in IEM laboratory investigation is presented in Furthermore, totally new investigative thinking is needed to transform all aspects of the laboratory medicine enterprise, including education, research, and healthcare. Upgrading medical practitioners’ skill sets on both the clinical and laboratory sides is needed to smoothly achieve the full potential of systems medicine. These skills integrate biology, computing and data analytics to develop common communication channels for more effective medical interactions. This ongoing high digitization of the individual biological and clinical information offers a tremendous and exciting opportunity to fully embrace the promising era of precision medicine.


Epilepsy is a common chronic neurological disorder characterized by seizures or convulsions, and is known to affect patients with primary brain tumors. The etiology of epilepsy is superficially thought to be multifactorial; however, the genetic factors which may be involved in the pathogenesis of seizures have not yet been elucidated, particularly at the pathway level. In the present study, in order to systematically investigate the gene regulatory networks involved in epilepsy, we employed a microarray dataset from the public database library of Gene Expression Omnibus (GEO) associated with tumor-induced epileptogenesis and applied gene set enrichment analysis (GSEA) on these data sets and performed candidate transcription factor (TF) selection. As a result, 68 upregulated pathways, including the extracellular matrix (ECM)-receptor interaction (P=0.004) and peroxisome proliferator-activated receptor (PPAR) signaling pathways (P=0.045), as well as 4 downregulated pathways, including the GnRH signaling pathway (P=0.029) and gap junction (P=0.034) were identified as epileptogenesis-related pathways. The majority of these pathways identified have been previously reported and our results were in accordance with those reports. However, some of these pathways identified were novel. Finally, co-expression networks of the related pathways were constructed with the significant core genes and TFs, such as PPAR-γ and phosphatidylethanolamine-binding protein. The results of our study may contribute to the improved understanding of the molecular mechanisms of epileptogenesis on a genome-wide level. Epilepsy is a common chronic neurological disorder characterized by epileptic seizures, which vary in their duration. These episodes can range from brief and nearly undetectable seizures to long periods of vigorous shaking ,2. In ephttp://www.ncbi.nlm.nih.gov/geo/)  and G protein-coupled receptors have been identified as single genes in which defects cause epilepsy; however, epilepsy may occur due to the interaction of multiple genes and environmental factors . The etiov/geo/) ,6, whichIn the present study, we employed a microarray dataset of genome-wide gene expression profiling from GEO, which is associated with tumor-induced epileptogenesis. The most well-known method of gene set enrichment analysis (GSEA) was used to analyze the genomic data in order to uncover the regulatory mechanisms of human epilepsy caused by brain tumors at the multiple pathways level. GSEA is widely used to analyze gene expression profiles, particularly to identify pre-defined gene sets which exhibit significant differences in expression between samples from the control and treatment groups –9. The gwww.ncbi.nlm.nih.gov/geo/) for gene expression profiling studies related to epilepsy. Data were included in our re-analysis if they met the following criteria: i) the data were genome-wide; ii) the comparison was conducted between samples with epilepsy and controls; iii) complete microarray raw or normalized data were available. Finally, we selected the dataset of GSE32534 for our re-analysis, which was contributed by Niesen et al  algorithm in Affy package, as previously described .http://www.genome.jp/kegg-bin/get_htext?br08901.keg). The annotation of significant genes in each pathway was performed by using biomaRt package, BioMart v. 0.8 rc3 . Subsequently, clustering of the groups and genes was performed based on the identified gene expression in each significant pathway using the method of hierarchical clustering with Pearson correlation coefficient.Our GSEA of pathways and genes was performed using the Category package in version 2.6.0 of Bioconductor, as previously described . The goahttp://dire.dcode.org/), based on the enhancer identification (EI) method, to predict common REs for our input genes which have co-function in each identified significantly related pathway -receptor interaction and cell adhesion molecule (CAM) pathways.In the study of Niesen et al , a numbeet al (http://www.genome.jp/kegg/), these 72 significant pathways could be mainly mapped into 6 functional classes, including cellular processes, environmental information processing, genetic information processing, human diseases, as well as metabolism and organismal systems. The details of the involved pathways in each class are presented in Compared to the approach of DEGs, the strategy of GSEA we used in this study is likely to be more powerful than conventional single-gene methods in the study of complex diseases in which many genes make subtle contributions. According to our GSEA on the dataset of 10 samples by the comparison of epilepsy to the controls, in total, there were 72 significant pathways associated with epilepsy, whose P-values were <0.05, including 4 downregulated and 68 upregulated pathways. The 3 main pathways identified by Niesen In the functional class of cellular processes, there was 1 significantly downregulated and 8 significantly upregulated pathways associated with epilepsy . These pet al . PPARs belong to the nuclear hormone receptor superfamily. They play critical physiological roles as lipid sensors and regulators of lipid metabolism and are activated by fatty acids . Previouin vitro and in situ calpain substrate using an in vitro proteomics approach or serial analysis of gene expression (SAGE)  was identified in 3 of 4 downregulated pathways, including the GnRH signaling pathway, gap junction and long-term depression pathways. PEBP is alternatively named Raf-1 kinase inhibitor protein, the precursor of the hippocampal cholinergic neurostimulating peptide (HCNP) corresponding to its natural N-terminal fragment, which has been previously described to be released by hippocampal neurons –28. The n (SAGE) ,31. In pn (SAGE) .


Listeria monocytogenes is a food-borne pathogen of humans and other animals. The striking ability to survive several stresses usually used for food preservation makes L. monocytogenes one of the biggest concerns to the food industry, while the high mortality of listeriosis in specific groups of humans makes it a great concern for public health. Previous studies have shown that a regulatory network involving alternative sigma (σ) factors and transcription factors is pivotal to stress survival. However, few studies have evaluated at the metabolic networks controlled by these regulatory mechanisms. The L. monocytogenes BioCyc database uses the strain 10403S as a model. Computer-generated initial annotation for all genes also allowed for identification, annotation and display of predicted reactions and pathways carried out by a single cell. Further ongoing manual curation based on published data as well as database mining for selected genes allowed the more refined annotation of functions, which, in turn, allowed for annotation of new pathways and fine-tuning of previously defined pathways to more L. monocytogenes-specific pathways. Using RNA-Seq data, several transcription start sites and promoter regions were mapped to the 10403S genome and annotated within the database. Additionally, the identification of promoter regions and a comprehensive review of available literature allowed the annotation of several regulatory interactions involving σ factors and transcription factors. The L. monocytogenes 10403S BioCyc database is a new resource for researchers studying Listeria and related organisms. It allows users to (i) have a comprehensive view of all reactions and pathways predicted to take place within the cell in the cellular overview, as well as to (ii) upload their own data, such as differential expression data, to visualize the data in the scope of predicted pathways and regulatory networks and to carry on enrichment analyses using several different annotations available within the database.Database URL: http://biocyc.org/organism-summary?object=10403S_RAST Listeria monocytogenes is a food-borne bacterial pathogen and the etiological agent of a disease known as listeriosis, which affects humans and other animals. Although listeriosis is somewhat rare, with an estimated number of approximately 1590 cases per year in USA  found in L. monocytogenes and also has the largest regulon. The other three alternative σ factors, σ C, which is specific to lineage II strains, σH and σL are thought to be involved in more specific stress responses and to regulate a smaller number of genes (L. monocytogenes is an important pathogen and has been used as a model organism for immune response studies to intracellular pathogens (L. monocytogenes is the GenoList (L. monocytogenes genomes (i.e. strain EGD-e and F2365). This database was designed for comparative genomics analyses and allows user to (i) detect genes that were horizontally transferred and pseudogenes and to (ii) assess the gene content of genomes in comparison to other genomes , InterPro and gene ontology  studies  factor σof genes , 10. Altathogens , few resGenoList , which pGenoList  also sup genomes . The LEG genomes  is anothontology . The Kyoontology  also supenerated . Besides studies , compara studies , and gen studies .BioCyc is a collection of Pathway/Genome databases also known as PGDBs . Each PGL. monocytogenes computer-assisted and manually curated (Tier 2) database implemented within the framework of the Pathway Tools software and hosted within the BioCyc web site  was constructed based on the 10403S finished genome sequence. The genome was sequenced in a collaborative effort including Cornell University, UC Berkeley, Harvard Medical School, the Broad Institute, the Centers for Disease Control and Prevention (CDC), Cerus Corporation, and Institut Pasteur and released on March 24, 2010 as part of the Listeria monocytogenes sequencing project [Listeria monocytogenes Sequencing Project, Broad Institute of Harvard and MIT  definition of certain enzymes that could not be unambiguously called during the automated process, (ii) rescoring and rebuilding of pathways based on the refined annotation of new enzymes, (iii) manual creation of protein complexes suggested by the annotation, (iv) operon assignment and (v) other fine-tuning operation. Then, a consistency check was performed to verify for possible broken links across objects within the database, formatting errors, and validity of citations. Manual curation of the database was carried out using a series of tools for identification and annotation of genes and protein functions, literature mining and a combination of published , 85 encode for transporter RNAs (tRNAs) and 48 encode other noncoding RNAs (ncRNAs). A total of 1729 transcription units were annotated in a genome size of 2 903 106 bases. A total of 207 pathways can be accessed along with 1173 enzymatic reactions and 119 transport reactions. One pathway, the d-chiro-inositol degradation pathway, along with five reactions and four compounds within the pathway were created specifically for this database. Out of the 2828 protein-encoding genes, 961 encode for enzymes  and 233 encode transporters. Three protein annotations were specifically created for this database. A total of 62 proteins contain comments and 71 contain citations with direct links to PubMed. Gene ontology terms (GO terms) are provided for 877 proteins. A total of 262 proteins and genes have been curated as on 09 July 2014. Currently, the database also provides the curated localization and sequence information for 67 σA-dependent promoters, 75 σB-dependent promoters, 2 σL-dependent promoters and 1 σH-dependent promoter for a total of 145 promoter regions. Moreover, the database provides information on 34 curated DNA binding sites for 12 different transcription factors, including all described-to-date DNA binding sites for the positive regulator of L. monocytogenes virulence genes, PrfA, the motility regulators MogR and DegU and the stress response regulators CtsR and HrcA. There are a total of 788 citations in the L. monocytogenes 10403S PGDB, which include citations for genes, proteins and pathways.The L. monocytogenes 10403S PGDB can be used for genome browsing and to obtain information on regulators and regulatees, gene ontology, transcription units, reactions and pathways. Users will find a wide range of tools including a regulatory interaction map, which shows the regulatory network involving σ factors and transcription factors currently curated, and a cellular overview, which shows all reactions and pathways potentially taking place in the cell based on genome annotation and curation. In comparison to the L. monocytogenes strain EGD-e BioCyc database (L. monocytogenes EGD-e PGDB), which has gone through no curation, the L. monocytogenes 10403S PGDB has 77 more pathways, including 148 biosynthetic pathways in the L. monocytogenes 10403S PGDB versus 93 biosynthetic pathways in the L. monocytogenes EGD-e PGDB, 78 pathways involved in degradation, utilization or assimilation in the 10403S database versus 48 of such pathways in the L. monocytogenes EGD-e PGDB and 25 pathways involved in generation of precursor metabolites and energy in the 10403S database versus 18 of such pathways in the L. monocytogenes EGD-e PGDB. Moreover, the L. monocytogenes 10403S PGDB has 233 transporters listed compared to 130, in the EGD-e database. Users will benefit from using the L. monocytogenes 10403S PGDB instead of the L. monocytogenes EGD-e PGDB when accessing the regulatory content of the L. monocytogenes 10403S PGDB. Because regulatory information is only added to the databases by curation , the L. monocytogenes EGD-e PGDB has no information concerning regulation of genes, RNA or proteins. Conversely, the L. monocytogenes 10403S PGDB includes 191 regulatory interactions, 146 promoters, 14 proteins annotated as transcription factors and 176 transcription factor binding sites.The Users have the option to run a quick search for gene name, locus name, protein name, ontologies, pathway, reaction or compound or run an advanced search where several options are available such as DNA binding sites, regulation and citations. Every search result will provide a link that will take the user to the corresponding webpage. Users can also use the genome browser tool  to navigFor genes that were manually annotated, which can be identified by the credit at the bottom of the page, users will find several useful pieces of information including (i) gene name and gene accession number (i.e. locus name in strain 10403S); (ii) protein name; (iii) alternative protein name for selected proteins; (iv) gene locus name of the ortholog in strain EGD-e; (v) a regulation summary diagram showing the transcription regulators of the gene itself as well as other genes in the same operon; (vi) citations with direct links to NCBI PubMed, (vii) protein localization, map position, gene/protein length; (viii) predicted (from sequence) and experimental (when available) molecular weight of the protein; (ix) a genetic regulation schematic showing the regulatory interactions involving the regulators and regulatees of the selected gene; (x) GO terms associated with the selected protein; (xi) MultiFun terms, a Pathway Tools built-in ontology schema; (xii) a graphical representation of the genomic region surrounding the selected gene, including neighbor genes, transcription start sites and regulators involved in transcription regulation of the selected gene; (xiii) graphical representation of transcription units in which the selected gene is transcribed ; (xiv) a legend for understanding of the graphical representations and full references with links to NCBI PubMed .Figure L. monocytogenes BioCyc database annotation revealed that the L. monocytogenes genome codifies for all enzymes necessary for the utilization of ethanolamine and propanediol. Ethanolamine, a source of carbon and nitrogen, is present in eukaryotic cells , a cofactor necessary for both ethanolamine and propanediol degradation  L. monocytogenes strain 10403S BioCyc database can be accessed from the BioCyc website (Listeria monocytogenes 10403S database under ‘change organism database’. The 10403S BioCyc database is also available for download with the Pathway Tools program from (The web version of the  website  by selecram from . Pathway


At stage 2, networks andenriched pathways were estimated with Cytoscape for genes from stage 1, separatelyfor the 2 phenotypes, then examining network overlap. This overlap identified 4enriched pathways, and 3 of these pathways appear to interact, and are likelycandidates for playing a role in hypertension. At stage 3, using 157 maximallyunrelated individuals, partial least squares regression was used to find associationsbetween diastolic blood pressure and single-nucleotide polymorphisms in geneshighlighted by the pathway analyses. However, we saw no improvement in the adjustedcross-validated R2. Although our pathway-motivated regressionsdid not improve prediction of diastolic blood pressure, merging gene networks dididentify several plausible pathways for hypertension.In Genetic Analysis Workshop 18 data, we used a 3-stage approach to explore thebenefits of pathway analysis in improving a model to predict 2 diastolic bloodpressure phenotypes as a function of genetic variation. At stage 1, gene-based testsof association in family data of approximately 800 individuals found over 600 genesassociated at  R2 value in a partial least squares (PLS)regression model predicting phenotype as a function of single-nucleotide polymorphisms(SNPs). Our hypothesis was that pathway analysis would highlight genes containing manySNPs with effects too small to be detected in univariate analyses, but that the PLScomponents (or latent variables) might benefit from such signals.Pathway analysis of results from genetic association studies has become a very popularapproach, and usually the goal is to gain a better understanding of which genes orgroups of genes are related to the disease or trait being studied. However, "a betterunderstanding" is not usually well defined. We chose to investigate whether pathwayanalysis could improve the Analyses used a multistage approach to analyze associations with diastolic bloodpressure (DBP) in the Genetic Analysis Workshop 18 (GAW18) data. At stage 1, we testedfor association between sequence variation in each gene and DBP in the families. Atstage 2, we built gene networks from the significant genes identified in stage 1, usingCytoscape  and idenWe used 2 different DBP phenotypes: DBP at the first visit (DBP-1) and a measure ofDBP change with age (DBP-C). In the GAW18 dataset, 795 individuals had both a DBP-1measurement and genotype information; no covariates were used when analysing DBP-1.Slopes of DBP versus age were estimated from only 2 to 4 measurements in each of 855individuals, and then slopes were adjusted for smoking and antihypertensivemedication use, and categorized into 3 levels: none of the visits, some visits, orall visits. The resulting residuals formed our second phenotype, measuring whether anindividual's blood pressure changes more or less than the average. There were 611individuals with DBP-C and genotype information.http://genome.ucsc.edu) [pvalue summarized the gene result.Gene-based tests of association between the exome sequencing data and the DBP-1 andDBP-C phenotypes were performed using ASKAT , a methocsc.edu) , and we p value≤0.05 from stage 1. The networks for DBP-1 and DBP-C were then compared andmerged using "Advanced Network Merge-Intersection." Pathway analysis for genes incommon was performed using the "Analyze Module Function."For DBP-1 and DBP-C, network analysis was performed using methods in Cytoscape 2.8.2 ,8. Netwop value≤0.05 in gene-based analyses from stage 1, (b) stage 2 enriched pathways forDBP-1 or DBP-C with a false discovery rate (FDR) ≤0.05, or (c) stage 2 enrichedpathways in the overlapping gene network. Tenfold cross-validation was used to choosethe optimal number of PLS components, and we report the number of PLS components thatgave the smallest adjusted cross-validated R2.PLS regression analysis was used for prediction modeling of our 2 phenotypes as afunction of the number of minor alleles in SNPs identified by stages 1 and 2. PLSsearches for multidimensional linear combinations of SNPs that explain the maximumvariance direction of the phenotype, and can be thought of as constructing latentpredictor variables. PLS models, using plsr version 2.3-0 of library pls , were fip value ≤0.05), whereas 694 genes weresignificant for the DBP-C phenotype. Among the 20 lowest p values for eachphenotype, there were no genes in common  with FDR ≤0.05 for DBP-1,and 88 for DBP-C (59 different genes). There were 26 enriched pathways in common, butonly the cadherin signalling pathway (P) and G2/M pathway (R) were part of the top 20enriched pathways for both phenotypes. Merging the networks from the 2 phenotypesrevealed some gene networks in common  using the JAVAapplet GSEA software v2.07 from the Broad Institute . GSEA tapathways .R2 of a multivariate (PLS) regression model, which isone of many possible definitions, and we explored this in GAW18 DBP data. We selectedPLS regression in order to optimize construction of latent predictors, but the optimalnumber of PLS components identified through cross-validation was often zero for eitherphenotype and for any of the 3 gene and pathway selection strategies. We did not, infact, find evidence for improved prediction of DBP using our strategy. However, PLS wasnot designed for categorical data and may not be the best choice for capturingassociations between numerous SNPs and phenotype. When we forced a PLS fit with 1component, the per-SNP contributions to 2 Rfrom the overlappinggenes were larger than when we used other approaches. It must be noted that althoughstage 1 used family data and stage 3 used unrelated individuals data, there is someoverlap between these sets of individuals, and hence the per-SNP improvement in TablePathway analysis is often heralded as a solution for better understanding geneticeffects, but how to best benefit from it is unclear. We selected a definition,improvement in We chose to work with 2 DBP phenotypes, DBP at first visit, and a measure of DBP changewith age. In most individuals, blood pressure increases with age. Our DBP-C phenotype,the residuals from a regression on age, measures each individual's sensitivity to thefactors that lead to this general population trend of increasing DBP with age. Weadjusted for medication use and smoking in a second model because with only 2 to 4measurements per person, full longitudinal modeling was not possible. In this work, wehave not adjusted for the variable precision of the slope estimates or age for DBP-1,and we recognize that this is a limitation.Because our 2 phenotypes (DBP-1 and DBP-C) are closely related, we decided to focus ourpathway analysis on gene networks present for both phenotypes. This strategy led toidentification of 4 pathways significantly associated with DBP-1 and DBP-C. The first 3pathways (see Table Only odd-numbered chromosomes were included in GAW18, thus our identified pathways andnetworks will be incomplete. An analysis of the entire genome might validate ourfindings and provide additional significant associations. Furthermore, Cytoscape onlyassigned pathways to 120 genes out of the approximately 600 selected for eitherphenotype, thus providing further motivation to continue our analysis of a largercurated set of genes in pathways. Despite all the caveats in this work, it isinteresting that our approach of examining overlapping pathways identified 3 pathwaysthat are plausibly related to hypertension.R2 in multivariate regression models. Although we did notfind evidence for improved model fits, 3 enriched pathways contained plausiblehypertension-related genes.For DBP or DBP changes in GAW18 data, we examined whether using pathway analysis resultsimproved The authors declare that they have no competing interests.LD conducted statistical analyses and drafted the manuscript. KO designed ASKAT, andwith VF, helped with the statistical analyses. CMTG supervised the statistical analysesand the manuscript. All authors read and approved the final manuscript.