Candice Z. Ulmer 1, Christopher Beecher 2, Timothy J. Garrett 3, Jing Chen 3, Clayton Matthews 3, Richard A. Yost 1,3
1Department of Chemistry, University of Florida, Gainesville, FL; 2IROA Technologies, Ann Arbor, MI; 3Department of Pathology, Immunology and Laboratory Medicine, College of Medicine, University of Florida, Gainesville, FL
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.
Chris Beecher1; Felice de Jong1; Amrita Cheema2; Tyrone Dowdy2; Giuseppe Astarita3
1IROA Technologies LLC, Ann Arbor, MI, 2Georgetown University, Washington, DC, 3Waters Corporation, Milford, MA
Metabolite identification represents the bottleneck of most metabolomics studies. This is aggravated by the presence of noise signals, impurities due to sample collection and extraction procedures and other non-biological relevant information. Isotopic Ratio Outlier Analysis (IROA)1,2 protocol mitigates several of these commonly encountered sources of variance by using specific isotopic signature. Once the biological relevant analytes have been identified, the characterization of their structure often relies only on accurate mass and isotopic pattern. Here, we propose a metabolomics approach using IROA in combination with UHPLC-QTOF in data-independent acquisition (DIA) mode for a rapid screening of the metabolome and the simultaneously collection of both qualitative and quantitative information of known and unknown metabolites.
Metabolic effect of drought stress during the grain filling growth stage in wheat measured by Isotopic Ratio Outlier Analysis (IROA)
Felice de Jong1, Chris Beecher2, Masum Akond3, John Ericson3, Md Ali Babar3
1IROA Technologies LLC, Bolton, MA, 2University of Florida, Gainsville, FL, Dept of Chemistry, 3University of Florida, Gainsville, FL, Dept of Agronomy
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.
Untargeted metabolomic analysis of the yeast lipin phophatidate phosphatase deletion using IROA and LC-HRMS
Yu-Hsuan Tsai;1 Timothy J. Garrett;2 Yunping Qiu;3 Robyn Moir;5 Ian Willis;5 Chris Beecher;4 Richard A. Yost;1,2 Irwin Kurland3
1Department of Chemistry, University of Florida, Gainesville, FL; 2Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL; 3 Department of Medicine, Albert Einstein College of Medicine, Bronx, NY; 4 IROA Technologies, Ann Arbor, Michigan; 5 Department of Biochemistry, Albert Einstein College of Medicine, Bronx, NY
Lipins are phosphatidate phosphatases that generate diacylglycerol (DAG) from phosphatidic acid (PA), regulating a pathway key for production of triglycerides (PA->DAG->TAG). Absence of the mammalian lipin results in lipodystrophy, and yeast lipin (Pah1p)controls the formation of cytosolic lipid droplets. Depletion of PAH1 (pah1Δ) has been shown to result in a dramatic decrease in lipid droplet number. Metabolic pathways that might mediate this effect, and possibly have relevance to mammalian lipin-dependent lipodystrophy, were examined using Isotopic Ratio Outlier Analysis (IROA). IROA is a mass spectrometry based metabolomic profiling method using 13C labeling to eliminate sample-to-sample variance, discriminate against noise and artifacts, and improve compound identification. This work utilized IROA with LC-HRMS and investigated the metabolomic profiles from WT yeast vs pah1Δ.
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.