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Integrative Analysis Workflow for Untargeted Metabolomics in Translational Research

Robinder Gauba1, Subha Madhavan1,2*, Robert Clarke2 and Yuriy Gusev1

1Innovation Center for Biomedical Informatics, Georgetown University Medical Center, Washington, DC, USA

2Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Washington, DC, USA

*Corresponding Author:
Subha Madhavan
Innovation Center for Biomedical Informatics
Georgetown University Medical Center, Washington, DC, USA
Tel: 2026873294
Fax: 2026875011
E-mail: [email protected]

Received date: January 17, 2014; Accepted date: February 25, 2014; Published date: March 02, 2014

Citation: Gauba R, Madhavan S, Clarke R, Gusev Y (2014) Integrative Analysis Workflow for Untargeted Metabolomics in Translational Research. Metabolomics 4:130. doi: 10.4172/2153-0769.1000130

Copyright: © 2014 Gauba R, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Background: Metabolomics is an emerging ‘omics’ science that has demonstrated its fast gaining importance as a powerful profiling tool for determining an individual’s response to a foreign stimulus such as a drug, toxin, or environmental change; or as an indicator of disease progression. Such small molecule profiles can used as biological markers of disease, and provide an indicator of drug efficacy or toxicity.Several studies have demonstrated that the results of any single ‘omics’ analysis may not be sufficient to decode extremely complex biological mechanisms. Methods: We have developed a translational research workflow to enable researchers to perform cutting-edge integrative analysis of metabolomics data with transcriptomics (gene expression) data using knowledge-driven networks. This network based view of interconnected functional partners can provide valuable new insight about the underlying biochemical processes and pathways associated with the phenotype of interest. To enhance and simplify metabolomics annotation we built a fully cross-referenced database called MetPlus DB, which integrates data from the three most comprehensive metabolite databases tailored largely towards mammalian metabolomics: HMDB, HUAMNCYC, and LIPID MAPS.Cross-referencing information is provided for linking to several other mainstream cheminformatics/bioinformatics repositories including KEGG, METLIN, ChEBI, FooDB, Pubchem, and Chemspider to provide unambiguous knowledge on clinically and physiologically relevant metabolites. We have made the integrated database available freely to the research community ( Results: To demonstrate the usefulness and strength of our methodology, we have tested it on a multi-omics profiling dataset from NCI-60 breast cancer cell lines to explore the biological dynamics of breast cancer. Conclusions: Our results demonstrate a streamlined approach for the comprehensive annotation of metabolites using MetPlus DB, and the subsequent integration of metabolomics and transcriptomics data to explore potentially relevant biological interactions and candidate biomarkers associated with disease phenotype.


Untargeted metabolomics; Integrative analysis; Systems biology; Knowledge-driven network; NCI-60


With the recent advances in technology related to ultra-high performance mass spectrometry, especially those concerned with liquid chromatography/mass spectrometry (LC/MS), it is now possible to perform a comprehensive assessment of thousands of physiochemically distinct small molecules such as lipids, central carbon metabolites, sugars and amino acids, both exogenous or endogenous in nature, to gain insights into cellular and physiological responses. The field of metabolomics is showing tremendous promise for providing fast, accurate, and nonbiased profiling methods to elucidate underlying disease mechanisms and diagnosis, develop new effective strategies for the treatment, and for other potential applications in diverse clinical areas such as personalized medicine, nutritional metabolomics, population profiling, and molecular epidemiology.

Metabolomics analyses are commonly practiced in two fundamental ways. A targeted metabolomics study with the help of internal standards and specific mass spectrometer (MS) conditions aims at measuring previously identified metabolites for achieving better quantitation and validation. Untargeted metabolomics research largely focuses on discovery to detect and quantify any small molecule (<1500 Da) that can be ionized by mass spectrometry to understand biochemical function and change within a biological system.

The ability to easily quantify changes in the biological system makes metabolomics an attractive translational research tool that can help identify biomarkers of disease through non-invasive measurements of biofluids or tissue [1]. In combination with other molecular measurements it has shown great potential for accurate diagnosis, prognosis, and evaluation of therapeutic response and drug efficacy [2].

The cancer metabolome may be the most studied to date, particularly in the area of breast cancer diagnostics [1]. Several breast cancer studies using multivariate analysis of metabolite profiles from tissue have shown very high sensitivity and specificity in discriminating a malignant phenotype from benign tissue, and even cancer grade and hormone status [3-5]. Numerous studies have also indicated that changes in lipid metabolism can be an indicator of cancer (often characterized by an elevation of total choline-containing compounds (tCho) and phosphocholine). Choline levels have been extensively studied as a biomarker to stratify various cancers, including brain tumor types and grade [6,7], and distinguish between benign and malignant breast tumors [8,9], and prostate tumors [10,11].

The use of a multi-omics, systems biology approach to study cellular metabolism is key to understanding disease etiology and outcome. Integration of omics data types can help identify biological pathways affected and provides a more complete picture disease biology and prognosis, and can even provide new drug targets. One recent study integrated the metabolomics profile of tissue from breast cancer patients with protein expression data for glycerol-3-phosphate acyltransferase (GPAM), an enzyme involved in lipid biosynthesis. This data was overlaid with gene expression data to show that GPAM expression in breast cancer is associated with changes in the cellular metabolism, especially an increased synthesis of phospholipids [12]. Another study integrated transcriptomics and metabolomics data from NCI60 cell lines to show biological pathways associated with drug sensitivity, which demonstrates potential as a methodology to predict response to drug therapy [13].

Several approaches have been recently introduced to integrate metabolomics data with gene expression data [14,15]. Some of the current methods utilize a data-driven approach based on a statistical framework of correlation or multivariate analysis [16] producing computationally derived associations between the abundance of metabolites and level of gene expression. Such methods have proven to be effective in elucidating possible connections between changes observed in metabolomics data and gene expression data. The existence of correlated changes, however, does not necessarily represent real biological interactions; for instance, correlated metabolomics and gene expression data may indicate independent co-regulation by upstream regulators or environmental factors (e.g. hypoxia etc.). These computationally derived associations do not provide a clear path to biologically meaningful conclusions about molecular interactions and require further downstream analysis for biological interpretation. Recent studies have been also reported on applications of pathway enrichment analysis for gene expression and metabolomics data [17,18] utilizing biological pathway information for integration of metabolomics and transcriptomics for biomarker discovery.

Metabolite identification and systematic curation of public resources to obtain high confidence annotations is one of the biggest challenges faced by researchers working in the field of metabolomics. One of the current methods to address this challenge is to use consensus based reporting Metabosearch [19], and the Manchester Metabolomics Database (MMD) [20] by merging annotations from multiple resources to obtain annotations of higher confidence and also achieve widespread coverage since none of the existing bioinformatics/ cheminformatics resources claims to cover the entire metabolome [21]. Another useful approach to identify metabolites is to carry out a restrictive search using organism-specific metabolomics databases since each species is characterized by a biochemically distinguishable and unique metabolome. Disregarding the source of origin of metabolites can lead to false and inconsequential identifications leading to wasted downstream validation efforts [22,23].

We describe a streamlined biological context-driven workflow for turning crude MS information into reliable and actionable knowledge applicable to profiling studies focusing on untargeted metabolomics. As part of this workflow we introduce a metabolite annotation database, MetPlus DB, to address gaps in the current tools and provide the scientific community with a freely available resource for reliable annotation of metabolites. MetPlus DB is a fully cross-referenced database that integrates data from the three most comprehensive metabolite databases: HMDB [24], LIPID MAPS [25] and HUMANCYC [26]. Another aspect of this workflow is the ability to combine the information from complementary ‘omics’ technologies, particularly metabolomics and transcriptomics, to build integrative, knowledge-driven networks. This method provides a novel systems biology based way to explore metabolite-to-gene interactions that can facilitate biological interpretation of the findings and generation of new hypotheses and be incorporated into an overall systems biology approach to metabolite profiling.

Materials and Methods

Integrative analysis workflow

The entire workflow for carrying out integrative analysis of metabolomics datasets can be broadly categorized into 4 steps – 1) data acquisition and processing, 2) metabolite identification, 3) data integration and 4) data interpretation. The workflow diagram with all the major steps is shown in Figure 1.


Figure 1: The bioinformatics workflow. Outlining major steps for constructing the integrative knowledge-driven network and its biological interpretation using complimentary ‘omics’ datasets.

The first step in the workflow involves extraction of highresolution LC/MS signals. Several frameworks are being developed for feature detection (and alignment) including MarkerLynx (Waters, commercial), MathDAMP [27], MetAlign [28], XCMS [29] and MZmine [30] (open source, freely-available). Using various univariate and multivariate statistical techniques the screening involves identification of hundreds to thousands of features that are differentially regulated between two or more sample groups or those strongly associated with the phenotype of interest. While the first step has now become straightforward and routine practice there is a pressing need for improvements in the latter steps of the workflow (i.e. metabolite identification and data interpretation especially in the case of multi-omics studies). Our method allows for omics data integration to provide a more comprehensive biological context for interpretation of metabolomics findings.

Database design and implementation

MetPlus DB is implemented as a SQLite database to deliver high confidence annotations with significantly reduced redundancy and eliminates the complexity of extracting metabolite annotations from individual databases. The data is primarily integrated from three comprehensive metabolite databases including HMDB (Version: 3.0), HUMANCYC (Version: 16.0) and LIPID MAPS (Updated: April 12, 2012) by merging records based on IUPAC International Chemical Identifier (InChIKey). The InChIKey is a fixed-length hashed version (27-character, including two hyphens) of the full InChI with five distinct components: 1) the first block (14-characters) encodes molecular skeleton (connectivity); 2) the second block (8-characters) contributes to proton positions (tautomers), isotopomers and stereochemistry; 3) a single flag character; 4) a single version character; and 5) a deprotonation indicator (

The InChIKey works very effectively in linking the chemical information together across multiple databases. It also provides a viable strategy for eliminating redundancy, inconsistency and inadequate representation of metabolite entry pulled from different sources. Since untargeted metabolomics experiments aims at identifying putative annotations for m/z signals through mass based search within a specified mass tolerance (ppm), we designed MetPlus DB exclusively to include records searchable through molecular weights preferably monoisotopic weights. However, mass-based queries can result in multiple numbers of putative identifications for any single m/z signal. It is always advised to further investigate and validate all putative annotations by UPLC-ESI MS/MS protocol to determine the metabolite identity. For the HUMANCYC database where only InChI string is available, a conversion to InChIKey was carried out using InChI resolver service ( provided by Chemspider. In the case of LIPID MAPS, records are included for integration having either valid corresponding Pubchem identifiers and/or HMDB identifiers. Metabolites with more than one primary identifier having the same InChIKey are merged as a single record to minimize redundancy. Cross-referencing information for linking to other mainstream cheminformatics/bioinformatics databases (e.g. ChEBI, Pubchem and Chemspider) is based on matching information on InChIKey. Additionally, HMDB and LIPID MAPS are used to provide pre-existing cross-referencing information in the case of METLIN, Foodb, and KEGG.

Knowledge-driven network construction

In this workflow, we have created an integrated knowledge-driven network by combining two profiles (metabolomics and transcriptomics) using STITCH (‘Search Tool for Interactions of Chemicals’) (Version: 3.1) [31] a repository of data that captures the publicly available knowledge on protein–chemical associations. STITCH is an aggregated database of interactions connecting over 300,000 chemicals and 2.6 million proteins from 1,133 organisms. As an exploratory tool STITCH includes 254,000 high-confidence human protein–chemical edges/interactions (confidence score ≥ 0.7). A confidence score is assigned taking into account both level of significance and certainty of an interaction [31]. Following metabolite annotation using MetPlus DB, a combined list of identified metabolites and genes/proteins that are strongly associated with the phenotype of interest was uploaded to the STITCH database to produce an interaction network. Edges in the network depict knowledge-driven interactions (e.g. based on text mining or experimental evidence) between genes and/or proteins. Hypotheses generated from this approach enables datadriven knowledge discovery. Next, the Comparative Toxicogenomics Database (CTD) [32] is used as an additional source of information to annotate both metabolites and proteins as molecular markers or therapeutic targets in the context of given phenotype or disease. A complete workflow for the data integration and interpretation steps of the pipeline resulting in a gene-metabolite knowledge-driven interaction network is shown in Figure 2.


Figure 2: A detailed stepwise workflow for assessing the biological significance of knowledge-driven integrative network of gene and metabolite interactions. Step-1 Upload a combined list of differentially identified metabolites and genes/proteins into STITCH; STEP-2 Retrieval of high-confidence human protein–chemical interactions (confidence score ≥ 0.9); STEP-3 Use the Comparative Toxicogenomics Database (CTD) as an additional source of information to annotate both metabolites and proteins that are molecular markers or therapeutic targets in the context of given phenotype or disease; STEP-4 Sub-set the interaction network by filtering out the gene-to-gene and metabolite-to-metabolite interactions; STEP-5 Re-construct the knowledge driven network based on direct interacting metabolites and genes/proteins; Step-6 Prepare the list of interacting genes/proteins and metabolites from sub-network constructed in the previous step; Step-7 Perform pathway enrichment analysis using CPDB (ConsensusPathDB) database; Step-8 Perform Fisher exact test (ORA Analysis) for both list of interacting metabolites and genes/proteins; Step-9 Scan for pathways populated with both metabolites and genes; Step-10 Investigate the list of overlapping biochemical pathways to assess the biological significance.

This network view of interacting functional partners can provide new insights about their association with the phenotype of interest and a more granular understanding of interdependence and interconnectivity between underlying biochemical processes and pathways at a systems level.

Case study

We tested our computational workflow on two breast cancer cell lines: BT-549 (basal breast cancer) and MCF-7 (luminal breast cancer) using a combination of two complimentary ‘omics’ datasets: 1) transcriptomics dataset –expression profiles were measured on the Affymetrix U133 Plus 2.0 microarray platform downloaded from the Gene Expression Omnibus (GEO; accession number GSE32474 [33]); and 2) metabolomics dataset –the NCI60 metabolomics data was downloaded from DTP web portal (; August 2010 Data Release).

Case study data processing

The metabolomics data was generated by Metabolon Inc. ( and consists of measurements on 352 metabolites with 154 putatively identified. After removing metabolites having no variation across samples and excluding retired annotations 137 metabolites were selected. The raw gene expression data from Affymetrix U133 Plus 2.0 chip was pre-processed using RMA normalization [34] equating to 54,675 probes. The processed data was log transformed and non-specific filtering was performed using nsfilter function from the Bioconductor package genefilter [35] to remove probe sets that have no Entrez Gene identifier and have low intergroup variability, as these are likely to be uninformative. To ensure that each probe set maps to exactly one Entrez Gene ID, if multiple probes were found that mapped to the same Entrez Gene ID a probe with the largest IQR was selected for downstream analysis.

Case study statistical analysis

Statistical group comparison was performed using a t-test (Bioconductor). A total of 54 differential metabolites with adjusted p-value cut-off 0.05 were identified between basal (BT-549) vs. luminal (MCF-7) breast cancer cell lines (based on three biological replicates for each cell line). Similarly, 428 DEGs were identified with an adjusted p-value cut-off at 0.01 and absolute log-ratio of 2. The multiple testing corrections were computed using the Bioconductor package qvalue [36] (Supplemental File 1).

Case study network inference

The differentially identified metabolites (using KEGG identifiers) and differentially expressed genes (using HGNC Symbols) were used as input to query chemical-gene interactions using the STITCH database (illustrated in Supplemental File 2). Using a confidence score of ≥ 0.9, a set of highest-confidence interactions was outputted and the resultant interaction network was downloaded locally in text format (Supplemental File 3). Furthermore, the Comparative Toxicogenomics Database (CTD) was used as an additional source to provide manually curated information on interacting metabolites and genes that are annotated as molecular markers or therapeutic targets in the context of breast cancer. The network-based representation of metabolite – protein interactions derived from STITCH and CTD was created using cytoscape [37].

Case study pathway enrichment analysis

To assess the biological significance of an integrative network in the context of significantly affected biological pathways, a pathway enrichment analysis was performed on the list of 26 differentially identified metabolites annotated by MetPlus DB to acquire external database identifiers including KEGG, HMDB etc. and 54 differentially expressed genes that are interconnected through gene-to-metabolite interactions using ConsensusPathDB [38]. A generalized workflow outlining steps for assessing the significance of integrative network in the context of biological pathways is illustrated in Figure 2.

Pathway analysis was performed on the ConsensusPathDB website using overrepresentation analysis based on Fisher’s exact test. The lists of significantly enriched pathways with an adjusted p-value cut-off of 0.05 were retained. The enrichment analysis was performed using KEGG, Reactome, SMPDB, EHMN, and Humancyc. By intersecting the two separate lists of enriched pathways we obtained overlapping biochemical pathways containing both metabolite and gene functional partners found to be altered in the study sets.


We have developed a novel pipeline for integrative analysis of untargeted metabolomics data in conjunction with gene expression data derived from the same samples. A complete workflow for this pipeline is presented in Figure 1.

This methodology includes several consecutive steps for streamlining a process of metabolite annotation (MetPlus DB) as well as downstream systems biologyanalysis of networks and pathways by incorporating known information about gene-metabolite interactions. This pipeline has been already utilized in our current translational research projects on multi-omics profiling of breast and colorectal cancer (data unpublished).

To illustrate the utility of this methodology we have applied it to the analysis of open access breast cancer data from the NCI-60 collection.

Implementation of MetPlus DB

We have built a fully cross-referenced database (MetPlus DB) by integrating the data from the three most comprehensive metabolite databases tailored largely towards mammalian metabolomics: HMDB, HUAMNCYC and LIPID MAPS. MetPlus DB has cross-referencing information for linking to several other mainstream cheminformatics/ bioinformatics repositories including KEGG [39], METLIN [40], ChEBI [41], FooDB (, Pubchem [42] and Chemspider ( to provide unambiguous knowledge on clinically and physiologically relevant metabolites. Technical details of database construction are presented in Methods section. MetPlus DB is available for download at (

There are several features that have made MetPlus DB a customizable and unique alternative to current annotation resources [19,20]: 1) Non-dependency on remote database availability and schema. Remote databases often change their database schema when releasing newer versions thereby making older versions obsolete. Under such situations, web-based applications with a dependency on remote databases often require changes in their software architecture and/or computational module to tune-in with these resources from time to time. Consequently, reproducibility of the same results from one time or another cannot be guaranteed; 2) Fully customizable, extensible, accessible, and tailored towards supporting a variety of systems biology workflows for annotating massive amounts of metabolomics data; 3) Assembly of a large amount of information useful for searching annotations specific to mammalians thereby reducing the chances of hitting spurious and inconsequential identification of metabolites of different origins; and 4) Flexible architecture that not only allows for mass-based or external identifier specific searches but also enables searching for metabolites based on a common chemical name, IUPAC name, systematic or trade name. In short, MetPlus DB saves a large amount of time for other research activities that would otherwise be devoted to manually annotate metabolites across multiple sources to filter down to meaningful results for downstream validation purposes.

To demonstrate the utility of our pipeline we used MetPlus DB to re-annotate a list of 26 differentially present metabolites in the case study presented below. MetPlus DB acquires multiple external database identifiers including (e.g. KEGG, HMDB) as shown in Table 1 and enables downstream analysis of metabolite pathway enrichment.

Common name Inchikey Monoisotopic weight Formula HMDB id LMDB id Humancyc Compound name Pubchem CID Chemspider id Chebi id Metlin id KEGG id foodb id
NADP XJLXINKUBYWONI-NNYOXOHSSA-O 744.0833 C21H29N7O17P3 HMDB00217 -- NADP 5886 5675 18009 5227 C00006 FDB021908
Galactitol FBPFZTCFMRRESA-GUCUJZIJSA-N 182.079 C6H14O6 HMDB00107 -- -- 11850 11357 16813 5148 C01697 FDB006453
L-Aspartic acid CKLJMWTZIZZHCS-REOHCLBHSA-N 133.0375 C4H7NO4 HMDB00191 -- -- 5960; 44367445 5745 17053 5206 C00049 FDB012567
Uracil ISAKRJDGNUQOIC-UHFFFAOYSA-N 112.0273 C4H4N2O2 HMDB00300 -- Uracil 1174 1141 17568 258 C00106 FDB006426
Glycerol PEDCQBHIVMGVHV-UHFFFAOYSA-N 92.04734 C3H8O3 HMDB00131 -- Glycerol 753 733 17522; 17754 105 C00116 FDB000756
Succinic acid KDYFGRWQOYBRFD-UHFFFAOYSA-N 118.0266 C4H6O4 HMDB00254 LMFA01170043 Succinic acid 1110; 21952380 1078 15741 114 C00042 FDB001931
Phosphate NBIIXXVUZAFLBC-UHFFFAOYSA-K 94.95342 O4P HMDB01429 -- Phosphate 1061 1032 18367 3231 C00009 FDB022617
L-Glutamic acid WHUUTDBJXJRKMK-VKHMYHEASA-N 147.0532 C5H9NO4 HMDB00148 -- L-Glutamic acid 33032; 44272391 30572 16015 5174 C00025 FDB012535
S-Adenosylhomocysteine ZJUKTBDSGOFHSH-WFMPWKQPSA-N 384.1216 C14H20N6O5S HMDB00939 -- -- 439155; 25246222 388301 16680; 57856 296 C00021 FDB022327
Glutathione RWSXRVCMGQZWBV-WDSKDSINSA-N 307.0838 C10H17N3O6S HMDB00125 -- -- 124886; 25246407 111188 16856; 60836 44 C00051 FDB001498
Niacinamide DFPAKSUCGFBDDF-UHFFFAOYSA-N 122.048 C6H6N2O HMDB01406 -- Niacinamide 936 911 17154 1497 C00153 FDB012485
Cytidine UHDGCWIWMRVCDJ-XVFCMESISA-N 243.0855 C9H13N3O5 HMDB00089 -- -- 6175 5940 17562 3376 C00475 FDB021809
L-Kynurenine YGPSJZOEDVAXAB-QMMMGPOBSA-N 208.0848 C10H12N2O3 HMDB00684 -- -- 161166; 6971029 141580 16946; 57959 72 C00328 FDB022181
Uridine DRTQHJPVMGBUCF-XVFCMESISA-N 244.0695 C9H12N2O6 HMDB00296 -- Uridine 6029 5807 16704 90 C00299 FDB007411
N-Acetylneuraminic acid SQVRNKJHWKZAKO-PFQGKNLYSA-N 309.106 C11H19NO9 HMDB00230 -- -- 445063 392810 45744 3321 C19910 FDB001209
Hydroxyproline PMMYEEVYMWASQN-DMTCNVIQSA-N 131.0582 C5H9NO3 HMDB00725 -- -- 5810; 6971053 5605 18095; 58375 257 C01157 FDB013511
L-Valine KZSNJWFQEVHDMF-BYPYZUCNSA-N 117.079 C5H11NO2 HMDB00883 -- L-Valine 6287; 6971018 6050 16414; 57762 5842 C00183 FDB000465
Cholesterol HVYWMOMLDIMFJA-DPAQBDIFSA-N 386.3549 C27H46O -- LMST01010001 -- 5997 5775 16113 -- C00187 --
Cholesterol HVYWMOMLDIMFJA-FNOPAARDSA-N 386.3549 C27H46O HMDB00067 -- -- 11025495 9200676 -- 163 C00187 FDB013269
Cholesterol(d7) HVYWMOMLDIMFJA-IFAPJKRJSA-N 393.3988 C27H39D7O -- LMST01010093 -- 5314029 4473448 -- -- C00187 --
5'-Methylthioadenosine WUUGFSXJNOTRMR-IOSLPCCCSA-N 297.0896 C11H15N5O3S HMDB01173 -- 5'-Methylthioadenosine 439176 388321 17509 3425 C00170 FDB022465
L-Alanine QNAYBMKLOCPYGJ-REOHCLBHSA-N 89.04768 C3H7NO2 HMDB00161 -- L-Alanine 5950; 7311724 5735 16977; 57972 -- C00041 FDB000556
L-Leucine ROHFNLRQFUQHCH-YFKPBYRVSA-N 131.0946 C6H13NO2 HMDB00687, HMDB13773 -- -- 6106; 7045798 5880 15603; 57427 24 C00123 FDB001946
L-Glutamine ZDXPYRJPNDTMRX-VKHMYHEASA-N 146.0691 C5H10N2O3 HMDB00641 -- -- 5961; 6992086 5746 18050; 58359 5614 C00064 FDB012164
Guanosine NYHBQMYGNKIUIF-UUOKFMHZSA-N 283.0917 C10H13N5O5 HMDB00133 -- -- 6802 6544 16750 87 C00387 FDB003632
Inosine UGQMRVRMYYASKQ-KQYNXXCUSA-N 268.0808 C10H12N4O5 HMDB00195 -- Inosine 6021 5799 17596 84 C00294 FDB011802
Glucose 6-phosphate NBSCHQHZLSJFNQ-GASJEMHNSA-N 260.0297 C6H13O9P HMDB01401 -- -- 5958 5743; 17216117 4170 145 C00092 FDB021818
Beta-Alanine UCMIRNVEIXFBKS-UHFFFAOYSA-N 89.04768 C3H7NO2 HMDB00056 -- Beta-Alanine 239; 4755801 234 16958; 57966 5119 C00099 FDB002253

Table 1: Breast cancer metabolites annotated using MetPlus DB.

Case Study: Integrative analysis of metabolomics and transcriptomics data for NCI-60 breast cancer cell lines

We tested our computational workflow on two breast cancer cell lines - BT-549 (basal breast cancer) and MCF-7 (luminal breast cancer) using a combination of two complimentary ‘omics’ datasets – transcriptomics (microarray - gene expression) and metabolomics. The technical details of data processing and network and pathway analysis for this study are presented in the Methods section.

A knowledge-driven network of interactions was generated between differentially expressed genes (DEGs) and differentially identified metabolites (Supplemental File 1) using literature-derived information on chemical-gene interactions from the STITCH knowledge base as illustrated in (Supplemental File 2) and redrawn using cytoscape in the circular layout in Figure 3A. To focus specifically on knowledge-driven networks connected through metabolite and gene interactions, we filtered out the gene-to-gene and metabolite-to-metabolite interactions and re-constructed the network as shown in Figure 3B retaining only metabolite-to-gene interactions. This interaction network indicated significant differences in metabolism and gene expression between two breast cancer cell lines of basal and luminal subtypes.


Figure 3: The integrative knowledge-driven network combining two ‘omics’ technologies including microarray gene expression and metabolomics.A) the circular view of entire network showing gene-to-metabolite, gene-to-gene and metabolite-to-metabolite interactions. The network is color-coded with nodes in red color corresponds to metabolites and cyan color genes. Node label highlighted in red color corresponds to CTD curated genes and metabolites associated with breast cancer. Grey color solid edges corresponds to undirected gene-to-gene and metabolite-to-metabolite interactions, and dotted red color undirected gene-to-metabolites interactions. B) Sub-network view showing exclusive gene-to-metabolite interactions to gain novel insights into the molecular mechanisms of breast cancer dynamics.

The network allowed us to clearly identify several interacting functional modules (Figure 3B). The maximum number of connections in this network involved phosphate (24 edges connected to genes) followed by glycerol (12 edges). Identification of metabolites with multiple gene connections could provide additional criteria for selection of potential candidates for targeted metabolomics validation experiments.

Genes in the network were found to have smaller number of connections to metabolites in a range from 1 to 5 metabolites per gene with the majority of genes (34 out of 54 genes) connected to only one metabolite. The maximum number of connections was for NT5E (5 edges) followed by UPP1 and FNP1 (4 edges each). Genes connected to several metabolites could also help identify potential candidate metabolites for validation experiments.

Joint pathway enrichment analysis

To assess the biological significance of an integrative network in the context of significantly affected biological pathways, a pathway enrichment analysis was performed on the list of 26 differentially identified metabolites and 54 differentially expressed genes that are interconnected through gene-to-metabolite interactions using ConsensusPathDB [38]. The metabolites were annotated by MetPlus DB to acquire external database identifiers as shown in Table 1.

The lists of significantly enriched pathways were generated using KEGG, Reactome, SMPDB, EHMN, and Humancyc databases. By intersecting the two lists, overlapping biochemical pathways were obtained and populated with both functional partners (i.e. metabolites and genes as shown in Figure 4). The results of joint pathway analysis presented as a heat-map showing presence of metabolites (highlighted in green color) and genes (highlighted in blue color) in the enriched pathways. Each column represents individual pathways and each row – metabolites or genes.


Figure 4: Diagram of Significantly Enriched Overlapping Pathways. Adj. P-value < 0.05 identified by Pathway Enrichment Analysis for metabolites and genes. Colored cells indicate pathways containing corresponding metabolite (green) or genes (blue).

Noticeably, only few genes were mapped to a large number of enriched metabolic pathways: ABAT (16 pathways), ALDHSA1 (12 Pathways), NT5E (9 pathways). Similarly, only few metabolites were mapped to a large number of pathways: Glutamate (20 pathways), Succinate (15 pathways), beta-Alanine (15 pathways).

Enriched pathways were primarily represented by metabolites belonging to known metabolic pathways. However, several more specific pathways were also represented by multiple molecules, including transmembrane transport of small molecules from Reactome (23 molecules), metabolism of nucleotides (17 molecules), metabolism of amino acids and derivatives (17 molecules), and Urea cycle and metabolism of arginine, proline, glutamate, aspartate and asparagine from the Edinburgh Human Metabolic Network (16 molecules).


We discuss a pipeline using open source tools for metabolomics analysis that allows for metabolite integration with transcriptomics data. The integration of both molecular data types provides a more accurate understanding of the biological processes implicated in disease. The novelty of our methodology is the sequential execution of analytical steps allowing for both annotation of metabolomics peaks, and knowledge driven integration of annotated metabolites with gene expression data in a flexible, reusable, and configurable workflow.

The computational workflow is based on loosely coupled analytical modules to support custom data analysis in a rapidly changing environment. The pipeline is easy to implement and reusable for users with some programming skills including database handling and writing basic SQL (Structured Query Language) queries. It utilizes our new resource for annotation of metabolites (MetPlus DB) and knowledge-driven workflow for integrating metabolite abundance data with gene expression data into interaction networks based on prior biological knowledge. A final step of this pipeline allows to us to identify biological pathways that are significantly enriched within these subsets of interacting genes and metabolites. This methodology streamlines the identification of biologically meaningful subsets of data and assists in focusing on potential biological mechanisms that are relevant to observed phenotypic changes.

To demonstrate utility of this approach a case study was conducted on publicly available NCI-60 metabolomics profiling and gene expression data from breast cancer cell lines. The data were compared between two breast cancer cell lines with known distinct phenotypes of basal and luminal types of breast tumors.

A network integrating genes and metabolites was constructed based on the STITCH knowledge base (Figure 3). After subtraction of gene-gene and metabolite-metabolite interactions a gene-metabolite network emerged that allowed us to clearly identify a subset of metabolites connected to multiple DEGs (ranging from 1 to 24 edges). In contrast, the genes were connected to markedly less metabolites (ranging from 1 to 5 per gene) with majority of genes (34 out of 54 genes) connected to only one metabolite. Identification of metabolites with high connectivity to genes provides selection criteria for potential small molecule biomarkers associated with phenotype differences between basal and luminal breast cancer cell lines. In this case study we found that phosphate has the largest number of connections in the network with 26 genes. Differences in phosphate metabolite levels has been reported in the early 31P NMR study [43] showing that human breast cancer cells with the phenotype of pleiotropic drug resistance (derivative from wt MCF7 cell line) exhibited significantly elevated levels of phosphate metabolites as compared to the wild-type, drugsensitive parent cell line of wt MCF7 cells. Interestingly, in our case study we have compared the MCF7 cell line with the BT-549, a basal subtype B triple negative cell line [44] that has been reported to have a multi-drug resistant phenotype [45].

The differential expression of genes connected to multiple metabolites (such as NT5E with 5 connections) could indicate a key role in the alteration of several metabolic pathways associated with specific breast cancer cell line phenotypes. Connectivity of genes might serve as additional criteria for designing follow up experiments.

The last step of our pipeline, a joint analysis of pathway enrichment, enables the identification of specific metabolic and regulatory pathways that are significantly altered between the two cell lines. Such an approach provides an opportunity to expand analysis beyond interactions between single entities and identify those genes and metabolites that are involved in the same pathways where there might be no direct association or interaction between them (Figure 4). Using this approach for analysis of breast cancer cell lines we have identified several genes that were mapped to many enriched pathways: ABAT (16 pathways), ALDHSA1 (12 Pathways), NT5E (9 pathways). While some of these genes have multiple connections in metabolite-gene networks as well (such as NT5E), other genes mapped to multiple pathways did not have large number of connections to metabolites in the network (ABAT – only 3 connections).

During a recent validation study on 42 breast cancer cell lines the role of ABAT expression in breast cancer has been reported as part of multi-gene predictors for use in capturing response to chemotherapy [46]. ABAT was also shown to contribute to the mechanisms of mammary tumor progression [47].

These observations have clearly demonstrated the complementarity of the two integrative analysis steps incorporated into our pipeline. In addition, the joint pathway enrichment analysis step helps to reveal relatively important (and not obvious) roles for genes and metabolites participating in multiple pathways relevant to a specific phenotype. Such differences can only be obtained by integrative analysis of two data types together.

While specific findings from this case study require follow up validation, the results clearly demonstrate that such a systems biology approach for the analysis of metabolomics data offers a unique way to connect data within a biological context. The methodology also provides novel criteria for selection of candidate biomarkers based on joint analysis of differentially expressed genes and metabolites.

Future Directions

Our novel integrative analysis pipeline provides a streamlined approach for integrating untargeted metabolomics data with transcriptomics data. The key strength of the pipeline is in the potential for broader applicability for integrating metabolomics data with other complementary ‘omics’ data types to allow for rapid generation of new testable hypotheses based on the interactions of functional partners (genes, proteins, miRNA and metabolites etc.) in the knowledge-driven network. The workflow with some modifications can be extended to integration of metabolomics data with other omics data such as proteomics and genomics (e.g. DNA-seq variant data and microRNA profiling data).

Several steps of pipeline presented here have been recently incorporated into the metabolomics analysis module of the Georgetown Database of Cancer (G-DOC). Downstream analysis steps will eventually be incorporated into G-DOC and made available for public use on cancer datasets currently in G-DOC. We are also exploring the analysis of human metabolomics data from bio-fluids (blood, urine) and plan to integrate this with other types of omics data types.

MetPlus DB as a standalone SQLite database is developed to facilitate external identifier or mass-based searches from the collection of comprehensive metabolite databases. The data is primarily integrated by merging records based on IUPAC International Chemical Identifier (InChIKey) to deliver high confidence annotations with a significantly reduced redundancy and eliminates the complexity of extracting metabolite annotations from individual databases. To further extend the coverage on mammalian specific metabolites of clinical and physiological importance, more databases can be integrated to provide annotation-rich and curated knowledgebase of mammalian metabolomes for putative identification of metabolites.

Although several current methods are available for accurate metabolite identification and annotation, the process is cumbersome and involves curation using multiple databases. We describe novel computational solutions for streamlining the process of metabolite annotation using MetPlus DB, and the subsequent integration of metabolomics and transcriptomics data to explore potentially relevant biological interactions and candidate biomarkers associated with disease phenotype.

Availability of Supporting Data

The data sets supporting the results of this article are available in the DTP Web portal: .html; (August 2010 Data Release) and expression profiles downloaded from Gene Expression Omnibus (GEO; accession number GSE32474.

MetPlus DB is located in GitHub: Instructions for using MetPlusdb are available on Github to load and run the SQLite database, and examples are provided for querying annotations.


This research was supported by the National Cancer Institute (NCI) of the National Institutes of Health under Cooperative Agreement: U54-CA149147; and the NCI in silico Research Center of Excellence (ISRCE) contract: HHSN26120080000IE.

We would also like to thank Dr. Laura Sheahan for editing the manuscript and providing invaluable advice and support.


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