We are analyzing https://link.springer.com/article/10.1007/s11306-005-0012-0.

Title:
Metabolomics-edited transcriptomics analysis of Se anticancer action in human lung cancer cells | Metabolomics
Description:
Transcriptomic analysis is an essential tool for systems biology but it has been stymied by a lack of global understanding of genomic functions, resulting in the inability to link functionally disparate gene expression events. Using the anticancer agent selenite and human lung cancer A549 cells as a model system, we demonstrate that these difficulties can be overcome by a progressive approach which harnesses the emerging power of metabolomics for transcriptomic analysis. We have named the approach Metabolomics-edited transcriptomic analysis (META). The main analytical engine was 13C isotopomer profiling using a combination of multi-nuclear 2-D NMR and GC-MS techniques. Using 13C-glucose as a tracer, multiple disruptions to the central metabolic network in A549 cells induced by selenite were defined. META was then achieved by coupling the metabolic dysfunctions to altered gene expression profiles to: (1) provide new insights into the regulatory network underlying the metabolic dysfunctions; (2) enable the assembly of disparate gene expression events into functional pathways that was not feasible by transcriptomic analysis alone. This was illustrated in particular by the connection of mitochondrial dysfunctions to perturbed lipid metabolism via the AMP-AMPK pathway. Thus, META generated both extensive and highly specific working hypotheses for further validation, thereby accelerating the resolution of complex biological problems such as the anticancer mechanism of selenite.
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Keywords {🔍}

google, scholar, article, pubmed, fan, cancer, analysis, journal, research, higashi, lane, twm, biology, nmr, metabolic, access, privacy, cookies, content, metabolomics, anticancer, lung, cells, transcriptomic, cell, spectroscopy, biochemistry, data, publish, search, metabolomicsedited, teresa, bandura, gene, expression, selenite, meta, university, van, louisville, usa, information, log, human, analytical, profiling, dysfunctions, metabolism, open, subjects,

Topics {✒️}

n-methyl-n-[tert-butyldimethylsilyl]trifluoroacetamide nmr 13c isotopomer profiling metabolomics-edited transcriptomics analysis month download article/chapter functional pathways metabolic network-based identification mass spectrometry cancer cell metabolism a549 cells induced gc-ms techniques article metabolomics aims perturbed lipid metabolism central metabolic network biophysica acta-general subjects nuclear magnetic resonance full article pdf privacy choices/manage cookies experimental cell research brown cancer center 13c-glucose ��13c]-glucose anticancer agent selenite 1h tocsy jg brown foundation mammalian genome regulatory network underlying cancer research cancer research 59 microarray data analysis amp-ampk pathway complex biological problems physiol endocrinol metab kluwer academic publishers reactive oxygen species 1007/s11306-005-0012-0 keywords conditions privacy policy metabolic dysfunctions se anticancer action european economic area check access instant access main analytical engine van der greef accepting optional cookies multiple disruptions author information authors main content log cell biology article fan transcriptomic analysis

Schema {🗺️}

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         description:Transcriptomic analysis is an essential tool for systems biology but it has been stymied by a lack of global understanding of genomic functions, resulting in the inability to link functionally disparate gene expression events. Using the anticancer agent selenite and human lung cancer A549 cells as a model system, we demonstrate that these difficulties can be overcome by a progressive approach which harnesses the emerging power of metabolomics for transcriptomic analysis. We have named the approach Metabolomics-edited transcriptomic analysis (META). The main analytical engine was 13C isotopomer profiling using a combination of multi-nuclear 2-D NMR and GC-MS techniques. Using 13C-glucose as a tracer, multiple disruptions to the central metabolic network in A549 cells induced by selenite were defined. META was then achieved by coupling the metabolic dysfunctions to altered gene expression profiles to: (1) provide new insights into the regulatory network underlying the metabolic dysfunctions; (2) enable the assembly of disparate gene expression events into functional pathways that was not feasible by transcriptomic analysis alone. This was illustrated in particular by the connection of mitochondrial dysfunctions to perturbed lipid metabolism via the AMP-AMPK pathway. Thus, META generated both extensive and highly specific working hypotheses for further validation, thereby accelerating the resolution of complex biological problems such as the anticancer mechanism of selenite.
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            GC-tandem MS
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            Cell Biology
            Developmental Biology
            Biomedicine
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         general
         Molecular Medicine
         Cell Biology
         Developmental Biology
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