Amy L. Lane1, Felice de Jong2, Chris Beecher2
Amy L. Lane1, Felice de Jong2, Chris Beecher2
Yunping Qiu1, Felice de Jong2, Chris Beecher2, Irwin Kurland1
Robin H.J. Kemperman1, Chris W.W. Beecher1,2, Richard A. Yost1
Global Metabolomic Investigation of Tissues from Melanoma Patients with HRMS Using a Yeast Standard for Isotopic Ratio Outlier Analysis (IROA)Taylor Domenick1, Christopher Beecher1, Peter A. Kanetsky2, Richard A. Yost1, Nicholas Taylor3, John Koomen2, Timothy J. Garrett1
 Departments of Chemistry and Pathology, University of Florida, Gainesville, FL,  H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, [ 3] Texas A&M University, College Station, TX
Untargeted metabolomic and lipidomic analyses of tissue offer insight into the biochemistry of disease and help to potentially discover candidates for development as clinical biomarkers. Identification of metabolites and lipids that differentiate diseased tissue from adjacent tissue representing the tumor microenvironment can be a major challenge. Isotopic ratio outlier analysis (IROA) using 13C labeling offers a complementary approach to traditional strategies for these studies. These approaches have been applied to frozen tissue samples from melanoma patients, including primary tumors, metastatic tumors, and adjacent skin, to help identify and evaluate metabolic signatures of human melanoma. These methods offer new strategies for studying the mechanisms of disease onset and progression in melanoma, the most aggressive form of skin cancer causing over 10,000 deaths per year in the U.S. alone.
Type 1 Diabetes (T1D) is an incurable, auto-immune disease that results from the destruction of insulin-producing pancreatic beta cells by pathogenic T lymphocytes. These defective T cells can differentiate into CD4+ T cells that correlate with T1D progression. Of the few experimental designs targeted to identifying the metabolic profile of solely T1D, many incorporate animal models that fail to account for pathophysiological differences in humans. There is a need to better understand the metabolic and lipidomic signature of this disease using human samples. This work employs isotopic labeling LC-HRMS methodologies to identify the metabolic and lipidomic trends of immune dysregulation using primary T cells obtained from T1D patients compared to 1st degree relatives and healthy controls.
Metabolomic approaches have been documented to have great value in phenotyping and diagnostic analyses in plants1. The IROA® protocol2,3 was applied to determine the biochemical response of wheat metabolomes to water-stress during the grain filling growth stage. SS8641, a high-yield soft-red winter wheat, was grown under well-watered and drought conditions. In this IROA phenotypic analysis, controlled greenhouse-grown leaves containing carbon at natural abundance were compared to Standard wheat leaves that were grown to contain universally-distributed ~97% 13C; namely, a targeted analysis using a biologically-relevant Internal Standard. The IROA patterns allowed the identification of the isotopically labeled peaks and their 12C isotopomers, and the removal of artifacts, noise and extraneous peaks. By pooling experimental and Standard samples, variances introduced during sample-preparation and analysis were controlled.
Metabolomics of Hermaphroditic C. elegans via Isotopic Ratio Outlier Analysis using High-Resolution Accurate Mass LC/MS/MS
Caenorhabditis elegans is one of the best-studied animals in science. Despite this, metabolomic studies in C. elegans have only recently become active areas of research. The Isotopic Ratio Outlier Analysis (IROA) protocol uses 13C-isotopic signatures to identify and to quantitate metabolites. It reduces error introduced during sample preparation and analysis, including ionization suppression by the use of IROA standards. The marriage of IROA and high-resolution accurate mass (HRAM) LC/MS/MS with C. elegans metabolomics allows experiments which assess the biological response to stresses or stimuli. These experiments would conventionally be difficult due to interferences by metabolites of unlabeled organisms. With IROA labeling and HRAM detection, metabolites can be distinguished in an untargeted manner, quantitated and unambiguously identified to their chemical formulas.
Isotopic Ratio Outlier Analysis (IROA) of Myxobacteria using ultra high resolution mass spectrometry
Myxobacteria represent an important source of novel natural products exhibiting a wide range of biological activities. Some of these so-called secondary metabolites are investigated as potential leads for novel drugs. Traditional approaches to discovering natural products mainly employ bioassays and activity-guided isolation, but genomics-based strategies and “metabolome-mining” approaches become increasingly successful to reveal additional compounds. These newer methods hold great promise for uncovering novel secondary metabolites from myxobacterial strains, as the number of known compounds identified to date is often significantly lower than expected from genome sequence information. Analytical challenges for comprehensive MS-based profiling of myxobacteria include the need to reliably detect the significant differences between secondary metabolomes, e.g. as a consequence of gene knock-outs or regulatory effects, as well as the robust quantitation of known and unknown target compounds and their identification. The IROA protocol was applied to the analysis of myxobacterial secondary metabolomes.
Differential Metabolomic Profiling of Maize Genotypes under Drought-Stressed Conditions using IROA (Isotopic Ratio Outlier Analysis)
The IROA protocol has been applied in a phenotypic analysis of field grown maize (Zea mays) to understand the biochemical differences across selected genotypes when exposed to drought conditions. In this IROA phenotypic analysis, field-grown leaves containing carbon at natural abundance were compared to a standard maize leaf that was grown to contain universally-distributed ~97% 13C; becoming a targeted analysis using a biologically-relevant internal standard. At 97% 13C the IROA patterns were sufficient to find isotopically labeled peaks, identify their 12C isotopomers, and remove artifacts, noise and extraneous peaks. With accurate mass and IROA, the identification of observed component peaks to chemical formula is unambiguous. The benefit of IROA is it takes into account variances introduced during sample-preparation and analysis, including ion suppression.
Characterization and identification of unknown metabolites using Isotopic Ratio Outlier Analysis (IROA)
The identification of unknown metabolites is one of the biggest bottlenecks of metabolomics. The IROA protocol utilizes isotopically-defined media (in which all nutrients are labeled with either 5%13C, “C12 IROA media” (experimental), or 95%13C, “C13 IROA media” (control), to label all biological compounds with differing masses. Therefore, control and experimental samples can be analyzed as a single sample by LC-MS with all biological peaks uniquely paired. For any compound, the peak from the C12-media is mirrored by a second peak from the C13-media. The distance between these peaks is the number of carbons in the compound. The formula of the compound can be readily determined if the high-resolution mass and number of carbons is known.
Differential Metabolomic Profiling of Wheat Cultivars by IROA (Isotopic Ratio Outlier Analysis)
The interest in metabolomics to understand fundamental biology and applied biotechnology, especially in the field of plant science, has driven technology development. This study describes the use of a combined analytical and bioinformatic metabolomics technology applied to the understanding of plant metabolism. The diurnal metabolome changes exhibited in a cultivar of wheat, TX8544, were determined using the IROA protocol. Metabolomics plays an important role in how an organism adapts to change, in this case the diurnal pattern of heat and light. Here an isotopically-defined standard wheat sample is added to the experimental sample and is analyzed as a single sample, reducing suppression, and sample-to-sample variance, including variance introduced during preparation and analysis.