UPLC-ESI-TOFMS-based metabolomics and gene expression dynamics inspector self-organizing metabolomic maps as tools for understanding the cellular response to ionizing radiation.
Global transcriptomic and proteomic profiling platforms have yielded important insights into the complex response to ionizing radiation (IR). Nonetheless, little is known about the ways in which small cellular metabolite concentrations change in response to IR. Here, a metabolomics approach using ultraperformance liquid chromatography coupled with electrospray time-of-flight mass spectrometry was used to profile, over time, the hydrophilic metabolome of TK6 cells exposed to IR doses ranging from 0.5 to 8.0 Gy. Multivariate data analysis of the positive ions revealed dose- and time-dependent clustering of the irradiated cells and identified certain constituents of the water-soluble metabolome as being significantly depleted as early as 1 h after IR. Tandem mass spectrometry was used to confirm metabolite identity. Many of the depleted metabolites are associated with oxidative stress and DNA repair pathways. Included are reduced glutathione, adenosine monophosphate, nicotinamide adenine dinucleotide, and spermine. Similar measurements were performed with a transformed fibroblast cell line, BJ, and it was found that a subset of the identified TK6 metabolites were effective in IR dose discrimination. The GEDI (Gene Expression Dynamics Inspector) algorithm, which is based on self-organizing maps, was used to visualize dynamic global changes in the TK6 metabolome that resulted from IR. It revealed dose-dependent clustering of ions sharing the same trends in concentration change across radiation doses. "Radiation metabolomics," the application of metabolomic analysis to the field of radiobiology, promises to increase our understanding of cellular responses to stressors such as radiation.
Patterson
Andrew D
AD
Laboratory of Metabolism, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, Maryland, USA.
Li
Henghong
H
Eichler
Gabriel S
GS
Krausz
Kristopher W
KW
Weinstein
John N
JN
Fornace
Albert J
AJ
Gonzalez
Frank J
FJ
Idle
Jeffrey R
JR
D000249:Adenosine Monophosphate / Q000378:metabolism; D045744:Cell Line, Tumor / Q000378:metabolism / Q000473:pathology / Q000528:radiation effects; D002853:Chromatography, Liquid / Q000379:methods; D004260:DNA Repair / Q000502:physiology / Q000528:radiation effects*; D004307:Dose-Response Relationship, Radiation; D005786:Gene Expression Regulation / Q000502:physiology / Q000528:radiation effects*; D005978:Glutathione / Q000378:metabolism; D006801:Humans; D015999:Multivariate Analysis; D009243:NAD / Q000378:metabolism; D018384:Oxidative Stress / Q000502:physiology / Q000528:radiation effects*; D011839:Radiation, Ionizing; D021241:Spectrometry, Mass, Electrospray Ionization / Q000379:methods; D019032:Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization / Q000379:methods; D013096:Spermine / Q000378:metabolism; D053719:Tandem Mass Spectrometry / Q000379:methods; D013997:Time Factors
D009243:NAD; D013096:Spermine; D000249:Adenosine Monophosphate; D005978:Glutathione