Page 143 - 2014 Printable Abstract Book
P. 143
(PS2-09) Metabolomic analysis reveals radiation disrupts essential nutrient signaling nodes. Jace W.
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Jones ; Keely Pierzchalski ; Jianshi Yu ; Claire L. Carter ; Fei Li ; Gregory Tudor ; Alexander Bennett ; Ann
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Farese ; Isabel L. Jackson ; Zeljko Vujaskovic ; Catherine Booth ; Thomas J. MacVittie ; Maureen A. Kane
University of Maryland, Baltimore, Baltimore, MD and Epistem, Ltd., Manchester, United Kingdom
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Vitamin A is an essential nutrient that is indispensable to numerous physiological processes
including proliferation, differentiation, apoptosis, immune response, development, cellular metabolism,
and regeneration. Vitamin A is metabolized into retinoic acid (RA), an active metabolite that controls gene
transcription through binding to nuclear receptors and can also function via non-genomic actions. As such,
RA signaling is an important node in cellular function and controls numerous cellular targets modulated
in radiation-induced damage including PI3K, Akt, TGFβ, IL-6, and collagen. Metabolomic profiles
generated with liquid chromatography – tandem mass spectrometry inform on cellular status, as
metabolites are the end products of biological processes and key signaling molecules. We used targeted
metabolomic analysis of the retinoid pathway in murine and non-human primate models after total body
irradiation (TBI) or whole thorax lung irradiation (WTLI). Our preliminary data indicates that key nutrient
signaling is disrupted after radiation exposure and provides molecular insight into the mechanism of this
dysregulation. Additionally, we investigated the physiological impact of aberrant retinoid metabolism by
using untargeted metabolomics and mass spectrometry imaging to identify key targets of vitamin A
signaling and radiation exposure. Identification and characterization of key signaling nodes that are
disrupted as a result of radiation-induced damage is essential to understanding the mechanisms of
radiation injury in model systems and necessary to identifying consensus mechanisms that might be
targeted therapeutically with medical countermeasures in the case of multi-organ injury. This work was
supported by NIAID contract HHSN272201000046C
(PS2-10) Metabolyzer: a novel statistical workflow for analyzing post-processed lc/ms radiation
metabolomics data. Tytus D. Mak; Evagelia C. Laiakis; Maryam Goudarzi; and Albert J. Fornace
Georgetown University Medical Center, Washington, DC
Metabolomics, the global study of small molecules in a particular system, has in the last few years
risen to become a primary -omics platform for the study of metabolic processes associated with radiation
injury. With the ever-increasing pool of quantitative data yielded from metabolomic research, specialized
methods and tools with which to analyze and extract meaningful conclusions from these data are
becoming more and more crucial. Furthermore, the depth of knowledge and expertise required to
undertake a metabolomics oriented study is a daunting obstacle to investigators new to the field. As such,
we have created a new statistical analysis workflow, MetaboLyzer, which aims to both simplify analysis
for investigators new to metabolomics, as well as provide experienced investigators the flexibility to
conduct sophisticated analysis. MetaboLyzer’s workflow is specifically tailored to the unique
characteristics and idiosyncrasies of post-processed liquid chromatography/mass spectrometry (LC/MS)
based metabolomic datasets. It utilizes a wide gamut of statistical tests, procedures, and methodologies
that belong to classical biostatistics, as well as several novel statistical techniques that we have developed
specifically for metabolomics data. Furthermore, MetaboLyzer conducts rapid putative ion identification
and putative biologically relevant analysis via incorporation of four major small molecule databases:
KEGG, HMDB, Lipid Maps, and BioCyc. MetaboLyzer incorporates these aspects into a comprehensive
workflow that outputs easy to understand statistically significant and potentially biologically relevant
141 | P a g e
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Jones ; Keely Pierzchalski ; Jianshi Yu ; Claire L. Carter ; Fei Li ; Gregory Tudor ; Alexander Bennett ; Ann
2
1
1
1
1
1
Farese ; Isabel L. Jackson ; Zeljko Vujaskovic ; Catherine Booth ; Thomas J. MacVittie ; Maureen A. Kane
University of Maryland, Baltimore, Baltimore, MD and Epistem, Ltd., Manchester, United Kingdom
1
2
Vitamin A is an essential nutrient that is indispensable to numerous physiological processes
including proliferation, differentiation, apoptosis, immune response, development, cellular metabolism,
and regeneration. Vitamin A is metabolized into retinoic acid (RA), an active metabolite that controls gene
transcription through binding to nuclear receptors and can also function via non-genomic actions. As such,
RA signaling is an important node in cellular function and controls numerous cellular targets modulated
in radiation-induced damage including PI3K, Akt, TGFβ, IL-6, and collagen. Metabolomic profiles
generated with liquid chromatography – tandem mass spectrometry inform on cellular status, as
metabolites are the end products of biological processes and key signaling molecules. We used targeted
metabolomic analysis of the retinoid pathway in murine and non-human primate models after total body
irradiation (TBI) or whole thorax lung irradiation (WTLI). Our preliminary data indicates that key nutrient
signaling is disrupted after radiation exposure and provides molecular insight into the mechanism of this
dysregulation. Additionally, we investigated the physiological impact of aberrant retinoid metabolism by
using untargeted metabolomics and mass spectrometry imaging to identify key targets of vitamin A
signaling and radiation exposure. Identification and characterization of key signaling nodes that are
disrupted as a result of radiation-induced damage is essential to understanding the mechanisms of
radiation injury in model systems and necessary to identifying consensus mechanisms that might be
targeted therapeutically with medical countermeasures in the case of multi-organ injury. This work was
supported by NIAID contract HHSN272201000046C
(PS2-10) Metabolyzer: a novel statistical workflow for analyzing post-processed lc/ms radiation
metabolomics data. Tytus D. Mak; Evagelia C. Laiakis; Maryam Goudarzi; and Albert J. Fornace
Georgetown University Medical Center, Washington, DC
Metabolomics, the global study of small molecules in a particular system, has in the last few years
risen to become a primary -omics platform for the study of metabolic processes associated with radiation
injury. With the ever-increasing pool of quantitative data yielded from metabolomic research, specialized
methods and tools with which to analyze and extract meaningful conclusions from these data are
becoming more and more crucial. Furthermore, the depth of knowledge and expertise required to
undertake a metabolomics oriented study is a daunting obstacle to investigators new to the field. As such,
we have created a new statistical analysis workflow, MetaboLyzer, which aims to both simplify analysis
for investigators new to metabolomics, as well as provide experienced investigators the flexibility to
conduct sophisticated analysis. MetaboLyzer’s workflow is specifically tailored to the unique
characteristics and idiosyncrasies of post-processed liquid chromatography/mass spectrometry (LC/MS)
based metabolomic datasets. It utilizes a wide gamut of statistical tests, procedures, and methodologies
that belong to classical biostatistics, as well as several novel statistical techniques that we have developed
specifically for metabolomics data. Furthermore, MetaboLyzer conducts rapid putative ion identification
and putative biologically relevant analysis via incorporation of four major small molecule databases:
KEGG, HMDB, Lipid Maps, and BioCyc. MetaboLyzer incorporates these aspects into a comprehensive
workflow that outputs easy to understand statistically significant and potentially biologically relevant
141 | P a g e
