TICL – a web tool for network-based interpretation of compound lists inferred by high-throughput metabolomics Alexey V Antonov1, Sabine Dietmann1, Philip Wong1 and Hans W Mewes1,2 Helmholtz Zentrum Munchen, Institute for Bioinformatics and Systems Biology, Neuherberg, Germany ă Department of Genome-Oriented Bioinformatics, Technische Universitat Munchen, Freising, Germany ă ¨ Keywords bioinformatics tools for high-throughput metabolomics; metabolomics; statistical analysis and data mining; statistical and bioinformatics tools; web tools for metabolomics Correspondence A V Antonov, Helmholtz Zentrum Munchen ă German Research Center for Environmental Health (GmbH), Institute for Bioinformatics and Systems Biology, Ingolsta ădter Landstraòe 1, D-85764 Neuherberg, Germany Fax: +49 89 3187 3585 Tel: +49 89 3187 2788 E-mail: a.antonov@helmholtz-muenchen.de High-throughput metabolomics is a dynamically developing technology that enables the mass separation of complex mixtures at very high resolution Metabolic profiling has begun to be widely used in clinical research to study the molecular mechanisms of complex cell disorders Similar to transcriptomics, which is capable of detecting genes at differential states, metabolomics is able to deliver a list of compounds differentially present between explored cell physiological conditions The bioinformatics challenge lies in a statistically valid interpretation of the functional context for identified sets of metabolites Here, we present TICL, a web tool for the automatic interpretation of lists of compounds The major advance of TICL is that it not only provides a model of possible compound transformations related to the input list, but also implements a robust statistical framework to estimate the significance of the inferred model The TICL web tool is freely accessible at http://mips.helmholtz-muenchen.de/proj/ cmp (Received 12 November 2008, revised 28 January 2009, accepted February 2009) doi:10.1111/j.1742-4658.2009.06943.x Knowledge of the molecular basis of metabolism is crucial for our understanding of most cellular processes [1–3] In recent years, technologies have been developed that allow the systematic investigation of large numbers of different metabolites [1,4–6] This has led to metabolomics becoming an attractive technology for exploring the molecular basis of complex cell disorders [7–10] In most genomics and proteomics studies aimed at deciphering the molecular mechanisms of complex biological phenomena, the output is usually a list of genes ⁄ proteins [11–13] The next common step is the application of bioinformatics and statistical methods to obtain a statistically valid interpretation of the derived gene list There are dozens of bioinformatics tools available for the interpretation of gene lists A standard solution is the inference of over- ⁄ under-represented gene ontology terms [14–22] The significance of the produced results is usually supplied in the form of a P-value The P-value represents a probability of inferring a similar or greater enrichment (for any gene ontology term) for a randomly sampled gene list [19] More complex methods have been proposed to exploit the database information currently available for metabolic and signaling pathways, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) [23] or BioCarta (http://www.biocarta.com) In this case, pathway topology was taken into account by developing specialized scoring functions The method developed by Rahnenfuhrer et al [24] includes, in Abbreviations KEGG, Kyoto Encyclopedia of Genes and Genomes; SHR, spontaneously hypertensive rat; WKY, Wistar Kyoto rat 2084 FEBS Journal 276 (2009) 2084–2094 ª 2009 Helmholtz Zentrum Munchen – German Research Center for Environmental Health (GmbH) Journal compilation ê 2009 FEBS ă A V Antonov et al addition, the distance between genes within the metabolic pathway The impact of a pair of genes is weighted with respect to the distance between genes within the metabolic pathway Another procedure (impact analysis) proposed recently by Draghici et al [25,26] goes beyond gene pairs and fully captures the topology of signaling pathways by propagating the perturbations measured at gene levels through the entire pathway This technique can capture information about the position of the genes on the pathway, because perturbation of the genes at the top of the signaling cascade will propagate through the entire pathway, unlike perturbation of the downstream genes Metabolomics is a relatively new ‘omics’ technology Experimental studies of complex cell disorders, which employ high-throughput metabolomics as a basic instrument, have just started to appear Several studies of different diseases have demonstrated the successful application of metabolomics in clinical research [7–9] There is no doubt that the number of such clinical studies will grow exponentially in the near future Similar to transcriptomics and proteomics, metabolomics allows for the detection of a list of markers, present at different concentrations under various explored cell physiological conditions In the case of metabolomics, the markers are compounds (not genes or gene products) There is a great demand for bioinformatics to provide a statistically valid interpretation of compound lists produced experimentally Currently, several bioinformatics approaches are available for metabolomics Each approach was developed to solve different practical problems related to the analysis of metabolomics data [5,27–30] Most of the proposed tools for metabolomics deal with the mass peak annotation problem [31] The MassTrix web server has recently been presented [30] and provides the possibility of uploading a high-precision mass spectrum, automatically annotating mass peaks and mapping identified compounds onto KEGG metabolic pathways Most of the available tools aim to interpret the whole mass spectra rather than a sparse list of compounds differentially present between samples Other tools are available that provide visualizations of a compound list in the context of metabolic networks [32,33] The KEGG atlas accepts a list of compounds as an input The output of the KEGG atlas is a graphical visualization of compounds in the context of the global metabolic reaction network The KEGG atlas, however, does not provide quantitative and statistical analyses It is important to know whether experimentally selected compounds are related, for example, whether they belong to a chain or network of metabolic reac- TICL – a tool for interpretation of compound lists tions A partial answer to this question can be obtained from the KEGG atlas However, without quantitative analysis, there are no clues about the quality of these relations To fill the gap, we propose an analytical framework for the interpretation of molecular mechanisms that unite a list of compounds This analytical framework is implemented as the freely accessible web tool TICL As we demonstrate using data from recently published metabolomics studies, TICL translates compounds into a set of linked metabolic reactions and provides quantitative estimates of the significance of the inferred models Results We consider several recently published experimental studies that report lists of compounds found to be differentially present under diverse physiological conditions We demonstrate that the proposed statistical framework can be helpful in understanding the biological context of the reported compound lists We start with the study by Lu et al [9], which reports metabolic variation related to hypertension and age-related conditions To characterize the development of hypertension, the spontaneously hypertensive rat (SHR), and its normotensive control, the Wistar Kyoto (WKY) rat, were investigated, and their blood plasma was analyzed using GC ⁄ time-of-flight MS In total, 187 peaks were quantitatively determined after deconvolution, and 78 of them were identified Plasma compositional differences for many identified compounds showed significant age-related variations for both SHR and WKY Also, many identified compounds showed significant variations between hypertension-related SHR and control WKY rats Table in Lu et al [9] reports  20 compounds that show significantly increased or decreased levels from 10 to 18 weeks of age in both SHR and WKY rats In total, 16 compounds can be mapped to the global compound network inferred from the KEGG Submission of this list to the KEGG atlas gives the graphical visualization presented in Fig At first glance, these compounds have nothing in common; they not represent any specific canonical metabolic pathway In this case, visual analyses of Fig cannot give a clear answer as to whether and how the compounds are related By contrast, submission of this list to the TICL gives quantitative values that describe the quality of the relations between the input compounds and provides a confidence score for such relations in the form of a P-value (the probability that randomly generated compound lists are involved in relations of similar quality) The report for the analyzed list is given in Table FEBS Journal 276 (2009) 2084–2094 ª 2009 Helmholtz Zentrum Munchen – German Research Center for Environmental Health (GmbH) Journal compilation ª 2009 FEBS ă 2085 TICL a tool for interpretation of compound lists A V Antonov et al Fig Output returned by the KEGG atlas after submission of 20 compounds that have significantly increased or decreased levels from 10 to 18 weeks of age in both SHR and WKY rats Red points correspond to submitted compounds 2086 FEBS Journal 276 (2009) 2084–2094 ª 2009 Helmholtz Zentrum Munchen – German Research Center for Environmental Health (GmbH) Journal compilation ª 2009 FEBS ă A V Antonov et al TICL a tool for interpretation of compound lists Table The quantitative report ‘Enriched subnetworks’ returned by TICL after the submission of 20 compounds with significantly increased or decreased levels from 10 to 18 weeks of age in both SHR and WKY rats Model Maximum distance between compounds No input compounds in the subnetwork P-value 6 11 12 < < < < < < 0.125 0.015 0.05 0.06 0.01 0.015 Table The quantitative report ‘Enriched subnetworks’ returned by TICL after the submission of 22 compounds with significantly different levels between SHR and WKY rats Model Maximum distance between compounds No input compounds in the subnetwork P-value 6 1 < < < < < < 0.99 0.99 0.99 0.24 0.13 0.18 From Table we can see the dependency between the numbers of input compounds, which are involved in the network model, and the number of allowed missing compounds between any two input compounds to be considered connected For example, we can deduce that only two compounds (model 1) from the input list are related as substrate and product of the same reaction If one missing compound is allowed, a maximum of four compounds from the input list are connected into a network (model 2) For example, model D5, which allows up to four intermediate compounds, covers 11 metabolites For each model, the P-value was estimated using a Monte Carlo procedure For the most significant model D5, the estimated P-value was < 0.01 This means that when we randomly sampled a list of 16 compounds 100 times (only compounds from the global compound network were used to sample a random list) and applied the network inference procedure to the random list, there was no case, whereas the size of the inferred model D5 from a random list is 11 In all these cases, it was less Thus, the P-value suggests that these 11 compounds represent a statistically valid metabolic network model TICL provides a number of online visualization capabilities The user can also download a preformatted text file and use the medusa package [34] to visualize the inferred model on a computer Figure illustrates a typical visualization output (model D5) Table 3, in Lu et al [9], reports 22 compounds whose levels were significantly different between SHR and WKY rats In total, 14 compounds can be mapped to the global compound network inferred from the KEGG Submission of this list to the KEGG atlas gives the graphical visualization presented in Fig Again, visualization of these compounds on the global metabolic network is not sufficient to obtain a full understanding of the quality of the relations among the compounds The report for the analyzed list is presented in Table From Table 2, we can see that the second set of compounds with significantly different levels between hypertensive (SHR) and control (WKY) rats does not define a statistically robust transformation network For example, model D6, which allows up to five missing compounds between any two compounds from the input list, covers only eight input metabolites The statistical significance of the inferred models (for all models D1, , D6) was insignificant (the most significant model, D5, covers seven compounds, P > 0.1) The identified compounds are related to each other, although no more so than randomly selected compounds Thus, in the first case (age-related differences), TICL provides statistically valid arguments that the identified metabolites represent a set of dependent compounds Most probably, the identified compounds reflect structural, age-related changes in metabolism, in which whole metabolic blocks function differently In the second case (differences between SHR and WKY rats), however, no indication of structural metabolic variations can be found We admit that the result might have been influenced by the incomplete information currently available for metabolic reactions Another reason might be that the identified markers not necessarily reflect structural metabolic variations, because there might be more complex mechanisms, not directly related to metabolism, which actually unite these compounds The next example considered is related to a clinical study [7] In this study, a set of 66 invasive ovarian carcinomas and borderline tumors of the ovary were analyzed by GC ⁄ time-of-flight MS After automated mass spectral deconvolution, 291 metabolites were detected, of which 114 (39.1%) were annotated as known compounds Using a t-test, 51 metabolites were identified to be significantly (P < 0.01) different between borderline tumors and carcinomas Table 1, in Denkert et al [7], reports 26 significantly different metabolites which are known, 21 of which are mapped to the global metabolic network The standard output FEBS Journal 276 (2009) 2084–2094 ª 2009 Helmholtz Zentrum Munchen – German Research Center for Environmental Health (GmbH) Journal compilation ª 2009 FEBS ¨ 2087 TICL – a tool for interpretation of compound lists A V Antonov et al Fig Visualization of the inferred network model D5 returned by TICL after submission of 20 compounds that have significantly increased or decreased levels from 10 to 18 weeks of age in both SHR and WKY rats Boxes are compounds from the input list, circles are intermediate compounds Colors are used to specify canonical KEGG metabolic pathways Table The quantitative report ‘Enriched subnetworks’ returned by TICL on submission of 21 known compounds found to have significantly different concentrations between borderline ovarian tumors and ovarian carcinomas Model Maximum distance between compounds No input compounds in the subnetwork P-value 6 15 19 19 19 < < < < < < 0.045 0.01 0.01 0.01 0.01 0.01 report from TICL for these compounds is given in Table If we consider the metabolite pathway membership, then only ‘Nitrogen metabolism’ is presented in the list more then twice Nevertheless, from Table we can see that almost all of the identified known metabolites are dependent For example, model D2, which allows only one missing metabolite, covers eight compounds from the input list Model D3, which allows only two missing metabolites, covers 15 input compounds and model D4 covers almost all (19 of 21) metabolites 2088 Figure illustrates a typical visualization output for model D4 The last example we consider is related to another clinical cancer study In this case, the target was colon carcinoma A set of paired samples of normal colon and colorectal cancer tissue was investigated by GC ⁄ time-of-flight MS, which allowed robust detection of a total of 206 metabolites Subsequent analysis revealed that 82 metabolites were significantly different Table presents TICL output for these 82 compounds We can see that almost all of the identified known metabolites are dependent For example, model D2, which allows only one missing metabolite, covers 37 compounds from the input list Model D3, which allows only two missing metabolites, covers 49 input compounds Figure illustrates a typical visualization output produced using TICL for model D3 In both cancer-related examples, TICL provides statistically valid arguments that the identified metabolites represent a set of dependent compounds Although the analyzed cases were related to different tissues (ovarian cancer and colon cancer), in both cases, the discovered metabolic markers were not independent; they define a related set of metabolic reactions which, in turn, define a semi-noninterrupted FEBS Journal 276 (2009) 2084–2094 ª 2009 Helmholtz Zentrum Munchen – German Research Center for Environmental Health (GmbH) Journal compilation ê 2009 FEBS ă A V Antonov et al TICL – a tool for interpretation of compound lists Fig The output returned by KEGG atlas after submission of 22 compounds that have levels significantly different between SHR and WKY rats Red points correspond to the submitted compounds FEBS Journal 276 (2009) 2084–2094 ª 2009 Helmholtz Zentrum Munchen – German Research Center for Environmental Health (GmbH) Journal compilation ª 2009 FEBS ă 2089 TICL a tool for interpretation of compound lists A V Antonov et al Fig Visualization of the inferred network model D4 returned by TICL after submission of 21 compounds found to have significantly different concentrations in borderline ovarian tumors and carcinomas Boxes are compounds from the input list, circles are intermediate compounds Colors are used to specify canonical KEGG metabolic pathways Table The quantitative report ‘Enriched subnetworks’ returned by TICL on submission of 82 known compounds found to have significantly different concentrations between normal colon tissue and colorectal cancer tissue in metabolic processes in cancer and presents statistical arguments validating these insights Discussion Model Maximum distance between compounds No input compounds in the subnetwork P-value 6 37 49 57 61 63 < < < < < < 0.01 0.01 0.01 0.01 0.01 0.01 network of metabolic transformations that covers most of the identified compounds Thus, in these two cases, TICL provides new biological insights into variations 2090 In addition to the ability to generate a large amount of data per experiment, high-throughput technologies also brought the challenge of translating such data into a better understanding of the underlying biological phenomena A number of tools in the field of transcriptomics and proteomics have been developed recently to interpret gene ⁄ protein lists in order to address this challenge High-throughput metabolomics has recently started to be instrumental in exploring metabolic variations on a genomic scale [7–10,35,36] The output produced by experimental metabolomics is similar to other ‘omics’ technologies in the sense that FEBS Journal 276 (2009) 2084–2094 ª 2009 Helmholtz Zentrum Munchen – German Research Center for Environmental Health (GmbH) Journal compilation ª 2009 FEBS ă A V Antonov et al TICL a tool for interpretation of compound lists Fig Visualization of the inferred network model D3 returned by TICL after submission of 82 compounds found to have significantly different concentrations in normal colon tissue and colorectal cancer tissue Boxes are compounds from the input list, circles are intermediate compounds Colors are used to specify canonical KEGG metabolic pathways it provides a list The difference is that it is not a gene ⁄ protein list, but a list of compounds, whose concentration differs between the considered cell (tissue) phenotypes The bioinformatics tools and procedures currently available in the field of metabolomics are more relevant for the annotation of mass peaks or for the interpretation of whole mass peaks spectra To our knowledge, there is currently no procedure or tool available that deals with a relatively sparse compound list found to be differentially present between different cell physiological conditions As demonstrated here, such lists can be translated into network models, which cover most metabolites from the supplied list However, the sparseness of the compound list presumes that the inferred models may have a lot of intermediate compounds (up to 2–5 intermediate compounds between any two compounds from the input list covered by the model) In this case, tools that offer only a visualization of compounds in the context of the global metabolic network are inefficient It is evident that if relaxing the number of possible missing compounds, sooner or later, one will be able to cover all input compounds It is essential to provide a model of the possible metabolic transformations that cover the input compound list, and also to estimate quantitatively the FEBS Journal 276 (2009) 2084–2094 ª 2009 Helmholtz Zentrum Munchen – German Research Center for Environmental Health (GmbH) Journal compilation ê 2009 FEBS ă 2091 TICL a tool for interpretation of compound lists quality of the produced model TICL is the first tool for the analysis of compound lists that implements such quality control by providing P-values for the inferred models Materials and methods Given a compound list found to be differentially present between biological samples, we translate this list into a network model In other words, we reconstruct the most probable transformation routes that unite compounds from the list In some sense, this task is similar to the problem of finding the shortest path between two compounds, but is extended to list of compounds [27,37] To restore the transformation routes, we use a global metabolic network inferred from the KEGG database The major advance of TICL is that it not only provides a model of possible compound transformations related to the input list, but also implements a robust statistical framework to estimate the significance of the inferred model In simple terms, the P-values inferred by Monte Carlo simulations [17,38,39] represent the probability of a random list having the same quality model Global compound network The KEGG REACTION database is a collection of chemical structure transformation patterns for substrate–product pairs (reactant pairs) We can build a global ‘reaction network’ (reactions are nodes, compounds are edges) by connecting edges and reactions that share the same compounds In general, a reaction consists of multiple reactant pairs, and the one that appears in a KEGG metabolic pathway is called a main pair To build a global reaction network, we used only compounds classified as main reaction pairs Network inference procedure At the start of the procedure, we have a list of compounds (the input list), on the one hand, and the global compound network, on the other hand The distance between two arbitrary compounds is computed as the minimum number of consecutive steps required to get from one compound to another by working through existing paths on the global compound network Distance means that the two compounds are directly connected (related as substrate and product of a metabolic reaction); distance means that the two compounds are connected via one intermediate compound; distance means that the two compounds are connected via two intermediate compounds, and so on Given a compound list, our purpose is to infer the network model (connect some pairs from the input list to get connected component) that minimizes the distance between each connected pair of compounds 2092 A V Antonov et al Initially, we map compounds from the input list onto the global compounds network At this point, all compounds from the input list are disconnected In the first step, all pairs of compounds with distance are connected by edges and we look for connected subnetworks The subnetwork with the maximal number of compounds is referred to as an inferred network model D1 In the second step, compounds (from the input list) with distance are connected by edges The subnetwork with the maximal number of compounds is inferred and referred to as network model D2 In a similar way, network models D3, D4, up to a specified number z (model Dz) are inferred Models D2, D3, , Dz incorporate compounds that are not from the input list but are added to connect input compounds in the network model We refer to these added compounds as intermediate or missing compounds Statistical treatment Let us assume that we have an input compound list of size N and using the network inference procedure described above we infer the network models D1, , Dz, which allow 0, 1, ., z - intermediate compounds to be added to the model Let us denote S1, S2, , Sz to be the number of input compounds in the inferred network models We also refer to S1, S2, , Sz as the sizes of the respective models D1, , Dz Given the size of the input compound list (N), we consider the sizes of the models (values S1, S2, , Sz) to be quality measures We have to estimate the probability of inferring models of the same or larger sizes from randomly generated compound lists of size N To estimate the significance of the inferred models, we compare the values S1, S2, , Sz with background distributions BD1, , BDz computed using Monte Carlo simulation [39] To generate the background distributions BD1, , BDz, we repeat the following simulation procedure k times, where k specifies the upper significance level A random gene list Lj of size N (equal to the size of the input list) is generated by sampling compounds from global compound network Index j = k specifies each of the k random simulations The network inference procedure described above is applied to the random list Lj and the network models D1, , Dz are inferred Let us denote the size (the number of input compounds) of the inferred models D1, , Dz for the random list Lj as R1j, ., Rzj Thus, after repeating the simulation procedure k times, we get the background distribution R1j (j = k) for models D1, the background distribution R2j (j = k) for models D2, and the background distribution Rzj (j = k) for models Dz To estimate significance of the inferred network model D1 for the input gene list, the value S1 is compared with the distribution R1j Let n be the number of values from the distribution R1j that are ‡ S1 The estimate of P of the inferred network model D1 is computed as P = (n + 1) ⁄ k FEBS Journal 276 (2009) 2084–2094 ª 2009 Helmholtz Zentrum Munchen – German 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bioinformatics to provide a statistically valid interpretation of compound lists produced experimentally Currently, several bioinformatics approaches are available for metabolomics. .. propose an analytical framework for the interpretation of molecular mechanisms that unite a list of compounds This analytical framework is implemented as the freely accessible web tool TICL As we... bioinformatics tools and procedures currently available in the field of metabolomics are more relevant for the annotation of mass peaks or for the interpretation of whole mass peaks spectra To