Metabolomic network analysis of estrogen-stimulated MCF-7 cells: a comparison of overrepresentation analysis, quantitative enrichment analysis and pathway analysis versus metabolite network analysis

Abstract

In the context of the Human Toxome project, mass spectroscopy-based metabolomics characterization of estrogen-stimulated MCF-7 cells was studied in order to support the untargeted deduction of pathways of toxicity. A targeted and untargeted approach using overrepresentation analysis (ORA), quantitative enrichment analysis (QEA) and pathway analysis (PA) and a metabolite network approach were compared. Any untargeted approach necessarily has some noise in the data owing to artifacts, outliers and misidentified metabolites. Depending on the chemical analytical choices (sample extraction, chromatography, instrument and settings, etc.), only a partial representation of all metabolites will be achieved, biased by both the analytical methods and the database used to identify the metabolites. Here, we show on the one hand that using a data analysis approach based exclusively on pathway annotations has the potential to miss much that is of interest and, in the case of misidentified metabolites, can produce perturbed pathways that are statistically significant yet uninformative for the biological sample at hand. On the other hand, a targeted approach, by narrowing its focus and minimizing (but not eliminating) misidentifications, renders the likelihood of a spurious pathway much smaller, but the limited number of metabolites also makes statistical significance harder to achieve. To avoid an analysis dependent on pathways, we built a de novo network using all metabolites that were different at 24 h with and without estrogen with a p value <0.01 (53) in the STITCH database, which links metabolites based on known reactions in the main metabolic network pathways but also based on experimental evidence and text mining. The resulting network contained a "connected component" of 43 metabolites and helped identify non-endogenous metabolites as well as pathways not visible by annotation-based approaches. Moreover, the most highly connected metabolites (energy metabolites such as pyruvate and alpha-ketoglutarate, as well as amino acids) showed only a modest change between proliferation with and without estrogen. Here, we demonstrate that estrogen has subtle but potentially phenotypically important alterations in the acyl-carnitine fatty acids, acetyl-putrescine and succinoadenosine, in addition to likely subtle changes in key energy metabolites that, however, could not be verified consistently given the technical limitations of this approach. Finally, we show that a network-based approach combined with text mining identifies pathways that would otherwise neither be considered statistically significant on their own nor be identified via ORA, QEA, or PA.

Keywords: Bioinformatics; Computational toxicology; Endocrine disruption; Estrogen; Metabolomics; Pathway analysis.

Figure 1. Untargeted Metabolomics analyzed by Quantitative Enrichment Analysis from 24 hour 1nM estradiol-treated MCF-7 cells vs. control in EXP1 and EXP2

After metabolite identification, Metaboanalyst was used for Quantitative Enrichment Analysis. Using a Holmes adjusted p-value of .05, there were more pathways unique to each experiment than in common when QEA was used to analyze the data, despite being identical experiments.

Figure 1. Untargeted Metabolomics analyzed by Quantitative Enrichment Analysis from 24 hour 1nM estradiol-treated MCF-7 cells vs. control in EXP1 and EXP2

After metabolite identification, Metaboanalyst was used for Quantitative Enrichment Analysis. Using a Holmes adjusted p-value of .05, there were more pathways unique to each experiment than in common when QEA was used to analyze the data, despite being identical experiments.

Figure 2. Untargeted metabolomics analyzed by Pathway Analysis of 24 hour 1nM estradiol-treated MCF-7 cells vs. control in EXP1 and EXP2

After metabolite identification, Metaboanalyst was used for Pathway Analysis. Using a Holm-adjusted p-value of 0.05, only 5 pathways were common to both experiments while another 18 pathways were present in only one experiment, 6 in EXP1 and 12 in EXP2 using pathway impact analysis. Only one pathway was similar in both experiments using Quantitative Enrichment Analysis and Pathway Analysis.

Figure 3. Dihydoxyacetone subnetwork, for untargeted metabolomics analyzed by Pathway Analysis of 24 hour 1nM estradiol-treated MCF-7 cells (A) EXP1 and (B) EXP2

Dihydroxyacetone and all linked metabolites. While dihyxdroxyacetone was annotated to the methane pathway, a network analysis connected it to alanine and pyruvic acid in both experiments.

Figure 4. Metabolic network, plotted with Circular Neighborhood Connectivity layout, of targeted metabolomics of 24 hour 1nM estradiol-treated MCF-7 cells

Targeted metabolomicss was carried out at Cornell university. All metabolites statistically significantly different with a p-value less than .01 were used to create a network in STITCH Version 4 with a medium stringency of .40 and no additional proteins. All unconnected metabolites were discarded. The network was plotted in Cystoscape using Neighborhood Connectivity map. Node color = absolute value of the difference between the fold change of 0 vs 24 h with estrogen and without estrogen (i.e. if a metabolite increased 2.5-fold with estrogen from 0 to 24 hours, and 1.5 without, Absolute Fold Change would equal to 1, darker is equivalent to a higher change); node size is equivalent to “degree” (the number of connected metabolites).

Figure 5. Carnitine subnetwork cluster in targeted metabolomics of 24 hour 1nM estradiol-treated MCF-7 cells

Carnitine and carnitine derivatives were linked in the network, but were not connected via pathway databases but instead via text-mining (using simple PubMed co-ocurrence); only propionyl-L-carnitine was connected to carnitine via known reactions in the pathway databases. In addition to the carnitine derivatives, carnitine was a “hub” in the network and connected to many other metabolites (names not shown).

Figure 6. Reproducibility of fold-changes of 20 metabolites in three experiments using targeted metabolomics of 20 metabolites following 1nM estradiol treatment for 24h of MCF-7 cells

The three experiments with targeted metabolomics shown in Figure 6 analyzed first at Cornell and then restricted to 20 of the significant metabolites in Johns Hopkins are shown with regard to fold-change in metabolite.