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. Author manuscript; available in PMC: 2013 Sep 3.
Published in final edited form as: Curr Metabolomics. 2013 Jan 1;1(1):83–91. doi: 10.2174/2213235X11301010084

Analyzing LC/MS metabolic profiling data in the context of existing metabolic networks

Tianwei Yu 1, Yun Bai 2
PMCID: PMC3760437  NIHMSID: NIHMS480801  PMID: 24010053

Abstract

Metabolic profiling is the unbiased detection and quantification of low molecular-weight metabolites in a living system. It is rapidly developing in biological and translational research, contributing to disease mechanism elucidation, environmental chemical surveillance, biomarker detection, and health outcome prediction. Recent developments in experimental and computational technology allow more and more known metabolites to be detected and quantified from complex samples. As the coverage of the metabolic network improves, it has become feasible to examine metabolic profiling data from a systems perspective, i.e. interpreting the data and performing statistical inference in the context of pathways and genome-scale metabolic networks. Recently a number of methods have been developed in this area, and much improvement in algorithms and databases are still needed. In this review, we survey some methods for the analysis of metabolic profiling data based on metabolic networks.

Background

The complex biological system consists of multiple functional levels – genome, transcriptome, proteome, and metabolome. Probing the metabolome offers a unique perspective in understanding the linkage between genotype and phenotype 1. It is the most informative level in the study of environmental effects on the organism 2-4. In clinical settings, metabolic profiling offers critical information about a person’s metabolic status, which compensates genomic, proteomic and other information to paint the overall picture of a person’s health status and disease risk 5. When a pharmacological compound is involved, its interaction with the human system can be revealed by metabolomics studies 6-7. The field of metabolomics has been rapidly developing in the post-genome era 8. In addition to utilizing metabolomic technology alone to study mechanisms and find biomarkers, more and more studies integrate metabolomics with other omics technologies to bring insights into the complex regulatory networks of the multi-level system 9-12.

There are two major platforms in metabolomic studies – nuclear magnetic resonance (NMR) and mass spectrometry (MS) 13. In NMR data, many of the signals overlap, making it difficult to identify and quantify individual metabolites 14. The signal overlap issue can be reduced by multidimensional techniques. Still the method is very useful in measuring abundant metabolites and following their dynamics. On the other hand, mass spectrometry, when coupled with separation techniques such as liquid chromatography, can provide quantification to thousands of metabolites in a single sample 13. Due to its feasibility for complex biological samples, e.g. plasma and urine, liquid chromatography – mass spectrometry (LC/MS) has become a favored technique in metabolic profiling 8, 13, 15-16.

Bottom-up and top-down metabolomics

Bottom-up metabolomics start with known metabolites and pathways. By quantifying the metabolites longitudinally or measuring their fluxes in a targeted manner, the bottom-up approaches attempt to build dynamic models that can simulate and predict complex behaviors of key pathways 17. Complementing the bottom-up approaches, the top-down approaches start with untargeted profiling of low molecular weight components in a system. The identified features may correspond to various ionic forms of known metabolites, previously uncharacterized metabolites, food components, and environmental chemicals. After detection and quantification, they can be matched to databases to find their identities, or further characterized using techniques such as LC/MS/MS. The top-down approach helps discover previously unknown metabolites and their relations with components of the known metabolic network, as well as previously undiscovered patterns between metabolites 18-19.

Recently, top-down profiling of human metabolome is gaining wider and wider use in biomedical research. In a typical liquid chromatograph-Fourier Transform mass spectrometry (LC/FTMS) analysis, roughly 4,000 features can be identified from a single human plasma sample 17. While a large portion of the features are not matched to the KEGG database, those that match to KEGG cover about 25% of known human metabolites in the KEGG database 20. When a dual chromatography LC/FTMS procedure is used, a greater number (~7,000) features can be detected 17. Major efforts have been made to improve the feature extraction, quantification, and identification. A number of algorithms and software have been developed that greatly facilitate the data processing of LC/MS data 21-37. As this is not the focus of the review, we will skip the details. In the following discussion, we assume the data has been properly preprocessed, and high confidence matches between detected features and known metabolites are available.

Although the capability of metabolic profiling of complex samples has been greatly improved, there are still major difficulties in the study of human metabolome. Firstly, the plasma/urine metabolome is heavily impacted by food intake, the presence of medicine and environmental chemicals. Secondly, when studying metabolomics in a cohort of subjects, variations of the metabolome along the time of day, such as diurnal variation, may cause an increase in measurement noise. Thirdly, the plasma/urine metabolome represent an average effect of multiple tissues, rather than being the product of a homogeneous group of cells. These and other factors cause large variations between subjects, as well as difficulty in mechanistic interpretations of results. With these problems, the analysis of metabolic profiling data could greatly benefit from the utilization of existing knowledge on genome-scale metabolic network and pre-defined metabolic pathways. By utilizing such information, information is pooled from metabolites that are functionally related, and the impact of measurement noise can be reduced.

Pathway/metabolite set testing

In the related field of gene expression analysis, a number of methods were designed for the so-called “gene set analysis”. Groups of functionally related genes are first determined based on functional annotations. Then at the gene set level, statistical testing is conducted in order to determine if a gene set is associated with the outcome variable 38-44. In the past few years, similar strategies have been applied to metabolomics data (Fig. 1). The methods are largely borrowed from gene set analysis. Such methods use functional grouping of metabolites, but ignore the detailed topological relations within a pathway.

Figure 1.

Figure 1

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An illustration of the typical workflow of metabolite set analysis and network testing using metabolic profiling data.

In the field of gene (or feature) set analysis, there are generally two types of methods (Fig. 2). A number of methods were designed to compare the distribution of certain test statistics that reflect the gene set-wise behavior in response to a certain outcome variable 41-44. Gene sets are not homogeneous, i.e. not all genes in a gene set are regulated at the expression level in a given physiological condition. Some methods were developed to select subsets of genes simultaneously with the generation of gene set statistics 45-46, utilize covariance structure in testing 47, or incorporate regulatory network connectivity 48. Some of the gene set analysis methods have been previously reviewed and compared 38-40, 49-52. Most of the gene set differential expression analysis methods use permutation tests to assess the significance of gene sets. They can be further classified into two sub-classes – those based on gene label permutation (competitive hypotheses), and those based on sample permutation (self-contained hypotheses) 38-40. The methods using competitive hypotheses have been criticized for their properties of ignoring within-gene set correlation structure, tendency to yield false-positives on gene sets with high internal correlation, and difficulty in statistical interpretation 38-40. On the other hand, methods using self-contained hypotheses require certain sample size to reach enough significance level, and could identify too many gene sets as significant when the number of differentially expressed genes is large 40. A second class of gene set analyses, which we call gene set differential coordination analysis, tries to capture relationships that are not revealed by the analysis of differential expression, by testing within-gene set co-expression changes between treatment groups 53, differential coexpression between pairs of gene sets under different conditions 54, or changes in coordination between a gene set with all other genes 55.

Figure 2.

Figure 2

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Types of feature set level analyses.

All the aforementioned gene set methods can potentially be applied to the analysis of metabolite sets. Some software packages have been published specifically for metabolomics data, most of which involve functionality of matching features to known metabolites by database search. MBRole applies the enrichment analysis to metabolite sets 56. MPEA considers the many-to-many relations when matching features to known metabolites 57. MSEA and MetaboAnalyst use multiple gene set analysis methods 58-59. MetPA applies gene set analysis methods, and considers network topology by incorporating centrality measures 60. PAPi is a new algorithm specifically designed for metabolic pathways, which defines a pathway activity score for each treatment condition 61.

Testing on the network

The methods mentioned in the previous section consider each pathway or metabolite set independently from other parts of the genome-scale metabolic network. However, the metabolic network is highly inter-connected, and some pathways are artificially carved out from the entire network. Thus there is a strong interest in analyzing high-throughput data in the context of the entire network, without pre-determining sub-networks to study (Fig. 1). To achieve this goal, computational techniques have been developed in both transcriptomics and metabolomics. Again, the two disciplines can borrow models and insights from each other.

In order to incorporate network topology into the analysis of gene expression data, a number of methods were proposed. A graph-structured two sample test finds if a subgraph is differentially expressed under different treatment conditions 62. A Markov Random Fields model was proposed to use the pathway structures in identifying differentially expressed genes and important sub-networks 63. This work was later extended to consider longitudinal behavior of genes to analyze time course gene expression data 64. Bayesian linear regression models were developed to use the dependence structure of transcription factors and/or genes in the network to aid gene selection 65-66. Network-based penalized regression model was developed for subnetwork selection 67. The Omis Response Group (ORG) method starts with a list of selected genes, potentially generated from between-treatment group testing, and seeks pathways (response groups) that are highly connected in a certain direction to the list of query genes. Using metabolomics data, Cakir et al proposed a method that finds “reporter reactions”, which are reactions whose neighboring genes on average show high levels of change between treatment groups 68.

With current technology, there are still major drawbacks in testing metabolomics data on the genome-scale metabolic network. One major issue is the limited coverage. Cakir et al attempted to address this issue by producing a reduced genome-scale model, both eliminating unmeasured metabolites and reducing enzyme sets into virtual single reactions 68. Another issue is the uncertainty in the mapping of detected features to known metabolites. Due to the sharing of m/z values, there are often instances of one-to-multi or multi-to-multi matching. This issue is yet to be addressed in network testing. A third issue is sample origin. Metabolomics data in translational research is most often generated from blood plasma. While each tissue type activates different portions of the overall metabolic network 69, the plasma metabolome is influenced by multiple tissues. Thus it becomes questionable whether the behaviors of two metabolites on the genome-scale network are indeed mechanistically linked.

Joint analysis of metabolomics and transcriptomics data

Metabolite concentrations are regulated by the activities of enzymes that catalyze corresponding reactions. Both metabolites and enzymes are intrinsic parts of the metabolic network. Gene expression data is also linked to other networks, such as the transcriptional regulatory network 70-71. With the development of both metabolic and gene expression profiling techniques, there is a strong interest in analyzing metabolomics data jointly with gene expression data, in order to boost the statistical power to detect perturbed pathways, and shed light on the regulation resulting in the changes of metabolite levels (Fig. 3).

Figure 3.

Figure 3

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An illustration of the multi-layer network subject to joint-analysis. For simplicity, this picture is an over-simplified illustration. The gene/protein level can be further separated into several layers, e.g. genetic variations, gene expression, and protein level.

A simple approach is analyzing the two types of data separately, match each to pathways, and then merge the results at the pathway level 72-73. Joint testing of pathways using the hypergeometric distribution and independence assumption between different data types was also proposed 74. In the analysis of time course data, the Granger causality was used to find time lag effects between gene expression and metabolite levels 75. A method using the orthogonal partial least square discriminant analysis (OPLS-DA) to combine multiple coresponse statistics between transcripts and metabolites was proposed to detect relations between genes and metabolic pathways 76. The integrated metabolome and interactome mapping (iMIM) method incorporates metabolic network with protein interaction network and protein-RNA interactions, to reach a model linking a set of differential metabolites and a target gene 77. A web service, IMPaLA, is available for the joint enrichment analysis of pathways using user-specified metabolite and gene lists 78.

Besides the methods mentioned above, a number of publications deal with interpreting transcriptomics data in conjunction with the genome-scale metabolic network. In term of their goals, these methods partially overlap with those mentioned in the previous two sections. However these methods are focused on properties of the metabolic network, and are more adapted to its characteristics. The mixture model on graphs (MMG) is a Bayesian method for sub-network selection that can incorporate weights and directionality of the network 79. The KEGG spider maps differentially expressed gene list onto the KEGG network, and finds sub-graphs by connecting the genes through the shortest path between them on the graph 80. Tissue-specific metabolic behavior was predicted by maximizing the agreement between the expression levels of the enzyme genes and the predicted flux activity in the tissue 81. Patil and Nielsen defined “reporter metabolites” as those surrounded by differentially expressed enzymes 82.

Metabolic network databases and network reconstruction efforts

A number of databases are available summarizing current knowledge of the genome-scale metabolic network of multiple organisms. The Kyoto Encyclopedia of Genes and Genomes (KEGG) 20 may be the most widely used in metabolomics data analysis. Its pathway database contains small molecule reactions, as well as organism-specific enzymes that are known to catalyze the reactions. The ConsensusPathDB is an integration of different types of interactions, built from various sources 83. It contains not only metabolic reactions, but also protein interactions, signal transduction pathways etc. The small molecule pathway database (SMPDB) is focused on human pathways 84. Detailed information, such as tissue specificity, subcellular locations, and cofactors etc are available in the database. Pathway Commons is a comprehensive pathway database incorporating multiple types of biological networks 85. The UniPathway 86 links metabolic reactions to Uniprot 87 enzyme database. Other relevant databases that can help annotate metabolites include the human metabolome database (HMDB) 88, Madison Metabolomics Consortium Database (MMCD) 89, and the METLIN Metabolite Database 90.

The existing network databases represent the current state of the knowledge on the metabolic networks. There are on-going efforts to refine and improve the knowledge based on new experimental and computational developments. An important theme is to utilize genomic sequence information to refine the knowledge on metabolic networks. To name a few examples, Duarte et al reported a human metabolic network reconstruction based on both the literature and genomic information, in which compartmentalization was predicted by targeting signals in gene sequences and protein localization data 91. Hao et al reconstructed the Edinburgh Human Metabolic Network (EHMN) by including transport reactions and compartmentalization predicted by subcellular location of enzymes 92. For the human metabolic network, efforts were made in tissue-specific network reconstruction 93-94. Herrgard et al built a consensus yeast metabolic network by merging information about yeast metabolism from several sources 95.

Visualization of metabolomics data using the genome-scale metabolic network

Visual examination is an intuitive approach for the analysis of high-throughput data. Several software are available for the visualization of metabolomics data, often in conjunction with relatively simple testing capabilities. Metscape 96-97 is a visualization tool associated with the popular Cytoscape software 98. It allows users to upload gene and metabolite lists with measurement values, and builds network based on the relations between the entities stored in the database. It also conducts enrichment analysis using pathway information. Another software, VANTED, conducts data analysis such as Pearson’s correlation, self-organizing map (SOM), and two-sample testing 99. Then the results are visualized in the network context. The Omics Viewer is a component of the Pathway Tools visualization software 100. The software includes metabolic, signal transduction and transport pathways. The Omics Viewer paints metabolomics measurements and/or gene expression onto the network. Paintomics is a web service that visualizes gene expression and metabolomics data jointly onto the KEGG pathway maps 101.

Conclusion

The booming field of metabolomics represents tremendous opportunities of new knowledge discovery, including potential disease biomarkers, drug targets, and environmental impacts on human health. Improvements in metabolic profiling technologies yield more and more comprehensive and accurate coverage of the metabolome. Being able to analyze the high-throughput data from the systems biology perspective is critical to maximize information retrieval from such data. The development and application of new computational techniques generate new insights into the regulation of the metabolic system and it association with diseases. Advances in other omics areas are opening up new opportunities for the development of joint modeling methods.

Acknowledgments

This work was partially supported by NIH grants P20 HL113451 and P01 ES016731.

Contributor Information

Tianwei Yu, Email: tianwei.yu@emory.edu, Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA.

Yun Bai, Email: yunba@pcom.edu, Department of Pharmaceutical Sciences, School of Pharmacy, Philadelphia College of Osteopathic Medicine, Suwanee, GA.

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