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Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data

Hypoxia affects the tumor microenvironment and is considered important to metastasis progression and therapy resistance. Thus far, the majority of global analyses of tumor hypoxia responses have been limited to just a single omics level. Combining multiple omics data can broaden our understanding of...

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Autores principales: Tsai, I-Lin, Kuo, Tien-Chueh, Ho, Tsung-Jung, Harn, Yeu-Chern, Wang, San-Yuan, Fu, Wen-Mei, Kuo, Ching-Hua, Tseng, Yufeng Jane
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3730319/
https://www.ncbi.nlm.nih.gov/pubmed/24216987
http://dx.doi.org/10.3390/cancers5020491
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author Tsai, I-Lin
Kuo, Tien-Chueh
Ho, Tsung-Jung
Harn, Yeu-Chern
Wang, San-Yuan
Fu, Wen-Mei
Kuo, Ching-Hua
Tseng, Yufeng Jane
author_facet Tsai, I-Lin
Kuo, Tien-Chueh
Ho, Tsung-Jung
Harn, Yeu-Chern
Wang, San-Yuan
Fu, Wen-Mei
Kuo, Ching-Hua
Tseng, Yufeng Jane
author_sort Tsai, I-Lin
collection PubMed
description Hypoxia affects the tumor microenvironment and is considered important to metastasis progression and therapy resistance. Thus far, the majority of global analyses of tumor hypoxia responses have been limited to just a single omics level. Combining multiple omics data can broaden our understanding of tumor hypoxia. Here, we investigate the temporal change of the metabolite composition with gene expression data from literature to provide a more comprehensive insight into the system level in response to hypoxia. Nuclear magnetic resonance spectroscopy was used to perform metabolomic profiling on the MDA-MB-231 breast cancer cell line under hypoxic conditions. Multivariate statistical analysis revealed that the metabolic difference between hypoxia and normoxia was similar over 24 h, but became distinct over 48 h. Time dependent microarray data from the same cell line in the literature displayed different gene expressions under hypoxic and normoxic conditions mostly at 12 h or earlier. The direct metabolomic profiles show a large overlap with theoretical metabolic profiles deduced from previous transcriptomic studies. Consistent pathways are glycolysis/gluconeogenesis, pyruvate, purine and arginine and proline metabolism. Ten metabolic pathways revealed by metabolomics were not covered by the downstream of the known transcriptomic profiles, suggesting new metabolic phenotypes. These results confirm previous transcriptomics understanding and expand the knowledge from existing models on correlation and co-regulation between transcriptomic and metabolomics profiles, which demonstrates the power of integrated omics analysis.
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spelling pubmed-37303192013-08-05 Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data Tsai, I-Lin Kuo, Tien-Chueh Ho, Tsung-Jung Harn, Yeu-Chern Wang, San-Yuan Fu, Wen-Mei Kuo, Ching-Hua Tseng, Yufeng Jane Cancers (Basel) Article Hypoxia affects the tumor microenvironment and is considered important to metastasis progression and therapy resistance. Thus far, the majority of global analyses of tumor hypoxia responses have been limited to just a single omics level. Combining multiple omics data can broaden our understanding of tumor hypoxia. Here, we investigate the temporal change of the metabolite composition with gene expression data from literature to provide a more comprehensive insight into the system level in response to hypoxia. Nuclear magnetic resonance spectroscopy was used to perform metabolomic profiling on the MDA-MB-231 breast cancer cell line under hypoxic conditions. Multivariate statistical analysis revealed that the metabolic difference between hypoxia and normoxia was similar over 24 h, but became distinct over 48 h. Time dependent microarray data from the same cell line in the literature displayed different gene expressions under hypoxic and normoxic conditions mostly at 12 h or earlier. The direct metabolomic profiles show a large overlap with theoretical metabolic profiles deduced from previous transcriptomic studies. Consistent pathways are glycolysis/gluconeogenesis, pyruvate, purine and arginine and proline metabolism. Ten metabolic pathways revealed by metabolomics were not covered by the downstream of the known transcriptomic profiles, suggesting new metabolic phenotypes. These results confirm previous transcriptomics understanding and expand the knowledge from existing models on correlation and co-regulation between transcriptomic and metabolomics profiles, which demonstrates the power of integrated omics analysis. MDPI 2013-05-03 /pmc/articles/PMC3730319/ /pubmed/24216987 http://dx.doi.org/10.3390/cancers5020491 Text en © 2013 by the authors; licensee MDPI, Basel, Switzerland. http://creativecommons.org/licenses/by/3.0/ This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Tsai, I-Lin
Kuo, Tien-Chueh
Ho, Tsung-Jung
Harn, Yeu-Chern
Wang, San-Yuan
Fu, Wen-Mei
Kuo, Ching-Hua
Tseng, Yufeng Jane
Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data
title Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data
title_full Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data
title_fullStr Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data
title_full_unstemmed Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data
title_short Metabolomic Dynamic Analysis of Hypoxia in MDA-MB-231 and the Comparison with Inferred Metabolites from Transcriptomics Data
title_sort metabolomic dynamic analysis of hypoxia in mda-mb-231 and the comparison with inferred metabolites from transcriptomics data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3730319/
https://www.ncbi.nlm.nih.gov/pubmed/24216987
http://dx.doi.org/10.3390/cancers5020491
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