Metabolomic network analysis of estrogen-stimulated MCF-7 cells: a comparison of overrepresentation analysis, quantitative enrichment analysis and pathway analysis versus metabolite network analysis
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), quantitat...
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description | 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 |
doi_str_mv | 10.1007/s00204-016-1695-x |
format | Article |
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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.</description><identifier>ISSN: 0340-5761</identifier><identifier>EISSN: 1432-0738</identifier><identifier>DOI: 10.1007/s00204-016-1695-x</identifier><identifier>PMID: 27039105</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Biomedical and Life Sciences ; Biomedicine ; Cells ; Chromatography, High Pressure Liquid ; Computational Biology ; Data Mining ; Databases, Factual ; Endocrine Disruptors - pharmacology ; Energy Metabolism - drug effects ; Environmental Health ; Estradiol - pharmacology ; Estrogens ; Estrogens - pharmacology ; Humans ; Mass spectrometry ; MCF-7 Cells ; Metabolites ; Metabolome - drug effects ; Metabolomics - methods ; Models, Biological ; Molecular Toxicology ; Occupational Medicine/Industrial Medicine ; Ora ; Pharmacology/Toxicology ; Quantitative genetics ; Reproducibility of Results ; Secondary Metabolism - drug effects ; Spectrometry, Mass, Electrospray Ionization ; Toxicology - methods</subject><ispartof>Archives of toxicology, 2017, Vol.91 (1), p.217-230</ispartof><rights>Springer-Verlag Berlin Heidelberg 2016</rights><rights>Archives of Toxicology is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c448t-1f72160b1947b946f316b1fc7285c7a2d53e662d34723851b91fedec3811b5aa3</citedby><cites>FETCH-LOGICAL-c448t-1f72160b1947b946f316b1fc7285c7a2d53e662d34723851b91fedec3811b5aa3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00204-016-1695-x$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00204-016-1695-x$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27039105$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Maertens, Alexandra</creatorcontrib><creatorcontrib>Bouhifd, Mounir</creatorcontrib><creatorcontrib>Zhao, Liang</creatorcontrib><creatorcontrib>Odwin-DaCosta, Shelly</creatorcontrib><creatorcontrib>Kleensang, Andre</creatorcontrib><creatorcontrib>Yager, James D.</creatorcontrib><creatorcontrib>Hartung, Thomas</creatorcontrib><title>Metabolomic network analysis of estrogen-stimulated MCF-7 cells: a comparison of overrepresentation analysis, quantitative enrichment analysis and pathway analysis versus metabolite network analysis</title><title>Archives of toxicology</title><addtitle>Arch Toxicol</addtitle><addtitle>Arch Toxicol</addtitle><description>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.</description><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Cells</subject><subject>Chromatography, High Pressure Liquid</subject><subject>Computational Biology</subject><subject>Data Mining</subject><subject>Databases, Factual</subject><subject>Endocrine Disruptors - pharmacology</subject><subject>Energy Metabolism - drug effects</subject><subject>Environmental Health</subject><subject>Estradiol - pharmacology</subject><subject>Estrogens</subject><subject>Estrogens - pharmacology</subject><subject>Humans</subject><subject>Mass spectrometry</subject><subject>MCF-7 Cells</subject><subject>Metabolites</subject><subject>Metabolome - drug effects</subject><subject>Metabolomics - methods</subject><subject>Models, Biological</subject><subject>Molecular Toxicology</subject><subject>Occupational Medicine/Industrial Medicine</subject><subject>Ora</subject><subject>Pharmacology/Toxicology</subject><subject>Quantitative genetics</subject><subject>Reproducibility of Results</subject><subject>Secondary Metabolism - 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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.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>27039105</pmid><doi>10.1007/s00204-016-1695-x</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Biomedical and Life Sciences Biomedicine Cells Chromatography, High Pressure Liquid Computational Biology Data Mining Databases, Factual Endocrine Disruptors - pharmacology Energy Metabolism - drug effects Environmental Health Estradiol - pharmacology Estrogens Estrogens - pharmacology Humans Mass spectrometry MCF-7 Cells Metabolites Metabolome - drug effects Metabolomics - methods Models, Biological Molecular Toxicology Occupational Medicine/Industrial Medicine Ora Pharmacology/Toxicology Quantitative genetics Reproducibility of Results Secondary Metabolism - drug effects Spectrometry, Mass, Electrospray Ionization Toxicology - methods |
title | Metabolomic network analysis of estrogen-stimulated MCF-7 cells: a comparison of overrepresentation analysis, quantitative enrichment analysis and pathway analysis versus metabolite network analysis |
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