VAMP


Abstract

Metabolomics shows great promise as a tool for the discovery of biomarkers of mutagen exposure and early tumorigenesis. With the ever increasing pool of quantitative data yielded from metabolomic research, methods and tools with which to analyze and extract meaningful conclusions from these data are becoming more and more crucial. Methods currently in wide use are limited in either the scope and depth of analysis or are not specifically tailored to metabolomic datasets. In light of this, a new methodology has been developed and presented here that is specifically tailored to characteristics unique to post-processed LC/MS metabolomic data acquired from biofluid samples. Twenty-four-hour urine samples were collected from male rats before and after whole body exposures to doses of gamma radiation ranging from 0.5 to 10 Gy (n = 20 per dose). Urine metabolomics data were acquired by Ultra-Performance Liquid Chromatography coupled to time-of-flight mass spectrometry, operated in both positive and negative electrospray ionization modes, and pre-processed using MarkerLynx software (Waters, Inc.). Relative abundances of urinary ions were normalized to corresponding creatinine relative abundances to reduce the probability of confounding by variation in renal function. A visualization tool effective in qualitatively illustrating an overall metabolome response to radiation exposure was developed. 3D plots were created, on which each plotted point represents a metabolite, specified by its mass/charge (X axis) and retention time (Y axis), with its pre- to post-exposure change in regulation (Z axis) colorized with regard to the magnitude of change. Data filters and data augmentation schemes were developed in order to yield results that were more quantitatively robust, from which stronger conclusions can be drawn about the experiment, such as identifying specific metabolites of interest. With the development of this methodology, the repertoire of analytical tools available to metabolomics researchers will be enhanced, facilitating more rapid and meaningful biomarker discovery.


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AllRatData.tar.gz
(9830k)
Tytus Mak,
Oct 14, 2014, 1:18 PM
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confidence.m
(2k)
Tytus Mak,
Jun 27, 2014, 1:17 AM
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plotpct_noise_rank.m
(7k)
Tytus Mak,
Jun 27, 2014, 1:17 AM
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