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How close are we to complete annotation of metabolomes?

Mark R. Viant 1,2, Irwin J. Kurland 3, Martin R. Jones1, Warwick B. Dunn 1,2
1 School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK, 2 Phenome Centre Birmingham, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK,3 Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
Current Opinion in Chemical Biology 2017, 36:64–69
ABSTRACT: The metabolome is critical for discovering the maximum amount of biochemical knowledge from metabolomics datasets. Yet no exhaustive experimental characterisation of any organismal metabolome has been reported to date, dramatically contrasting with the genome sequencing of thousands of plants, animals and microbes. Here, we review the status of metabolome annotation and describe advances in the analytical methodologies being applied. In part through new international coordination, we conclude that we are now entering a new era of metabolome annotation.


Isotopic Ratio Outlier Analysis (IROA) of the S. cerevisiae metabolome using accuate mass GC-TOF/MS: A new method for discovery 

Yunping Qiu 1, Robin Moir 1, Ian M. Willis 1, Chris Beecher 2, Yu-Hsuan Tsai 1, Timothy J. Garrett 3, Richard A. Yost 3, Irwin Jack Kurland 1
 1 Albert Einstein College of Medicine, Bronx, NY, USA, 2 IROA Technologies, Ann Arbor, MI, USA, 3  University of Florida, Gainesville, FL, USA
First report using IROA technology in combination with accurate mass GC/TOF-MS, used to examine the S. cerevisiae metabolome. An accuratemass CI IROA library containing 126 metabolites with retention times based on the Fiehn protocol was established. The combination of CI and EI IROA protocols identifies co-eluting metabolites and differentiates metabolite spectra from artifacts, and “known unknown” metabolites were easily separated, with the number of carbons identified from the IROA patterns.
Analytical Chemistry 88:5. doi. 10.1021/acs.analchem.5b04263, January 2016
ABSTRACT: Isotopic Ratio Outlier Analysis (IROA) is a 13C metabolomics profiling method that eliminates sample-to-sample variance, discriminates against noise and artifacts, and improves identification of compounds, previously done with accurate mass LC/MS. This is the first report using IROA technology in combination with accurate mass GC-TOFMS, here used to examine the S. cerevisiae metabolome. S. cerevisiae was grown in YNB media, containing randomized 95% 13C, or 5%13C glucose as the single carbon source, in order that the isotopomer pattern of all metabolites would mirror the labeled glucose. When these IROA experiments are combined, the abundance of the heavy isotopologues in the 5%13C extracts, or light isotopologues in the 95%13C extracts, follows the binomial distribution, showing mirrored peak pairs for the molecular ion. The mass difference between the 12C monoisotopic and the 13C monoisotopic equals the number of carbons in the molecules. The IROA-GC/MS protocol developed, using both Chemical and Electron Ionization, extends the information acquired from the isotopic peak patterns for formulae generation, a process that can be formulated as an algorithm, in which the number of carbons, as well as the number of methoximations and silylations, are used as search constraints. In Electron Impact (EI/IROA) spectra, the artifactual peaks are identified and easily removed, which has the potential to generate "clean" EI libraries. The combination of Chemical Ionization (CI) IROA and EI IROA affords a metabolite identification procedure that enables the identification of co-eluting metabolites, and allowed us to characterize 126 metabolites in the current study.

An overview of methods using 13C for improved compound identification in metabolomics and natural products

Chaevien S. Clendinen 1,2, Gregory S. Stupp 3, Ramadan Ajredini 1,2, Brittany Lee-McMullen 1,2, Chris Beecher 1,2,4 and Arthur S. Edison 1,2
1 Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL, USA, 2 Department of Biochemistry and Molecular Biology, University of Florida, Gainesville, FL, USA, 3 The Scripps Research Institute, La Jolla, CA, USA, 4IROA Technologies, Ann Arbor, MI, USA
Frontiers in Plant Science 6:611. doi. 10.3389/fpls.2015.00611, August 2015 
ABSTRACT: Compound identification is a major bottleneck in metabolomics studies. In nuclear magnetic resonance (NMR) investigations, resonance overlap often hinders unambiguous database matching or de novo compound identification. In liquid chromatography mass spectrometry (LC-MS), discriminating between biological signals and background artifacts and reliable determination of molecular formulae are not always straightforward.
We have designed and implemented several NMR and LC-MS approaches that utilize 13C, either enriched or at natural abundance, in metabolomics applications. For LC-MS applications, we describe a technique called isotopic ratio outlier analysis (IROA), which utilizes samples that are isotopically labeled with 5% (test) and 95% (control) 13C. This labeling strategy leads to characteristic isotopic patterns that allow the differentiation of biological signals from artifacts and yield the exact number of carbons, significantly reducing possible molecular formulae. The relative abundance between the test and control samples for every IROA feature can be determined simply by integrating the peaks that arise from the 5 and 95% channels. For NMR applications, we describe two 13Cbased approaches. For samples at natural abundance, we have developed a workflow to obtain 13C–13C and 13C–1H statistical correlations using 1D 13C and 1H NMR spectra.
For samples that can be isotopically labeled, we describe another NMR approach to obtain direct 13C–13C spectroscopic correlations. These methods both provide extensive information about the carbon framework of compounds in the mixture for either database matching or de novo compound identification. We also discuss strategies in which 13C NMR can be used to identify unknown compounds from IROA experiments. By 
combining technologies with the same samples, we can identify important biomarkers and corresponding metabolites of interest.

Metabolomics and Natural-Products Strategies to Study Chemical Ecology in Nematodes 

Arthur S. Edison1* , Chaevien S. Clendinen*, Ramadan Ajredini*,  Chris Beecher*,†, Francesca V. Ponce* and Gregory S. Stupp‡
*Department of Biochemistry and Molecular Biology and Southeast Center for Integrated Metabolomics, University of Florida, Gainesville, FL 32610-0245, USA; †IROA Technologies, Ann Arbor, MI, USA; ‡The Scripps Research Institute, Department of Molecular and Experimental Medicine, 10550 North Torrey Pines Road, La Jolla, CA 92037, USA 
Integrative and Comparative Biology DOI:10.1093/ICH/ICV077. Publication Date (Web): July 2, 2015
ABSTRACT: This review provides an overview of two complementary approaches to identify biologically active compounds for studies in chemical ecology. The first is activity-guided fractionation and the second is metabolomics, particularly focusing on a new liquid chromatography-mass spectrometry-based method called isotopic ratio outlier analysis. To illustrate examples using these approaches, we review recent experiments using Caenorhabditis elegans and related free-living nematodes.

Isotopic Ratio Outlier analysis Global Metabolomics of Caenorhabditis elegans

Gregory S. Stupp, Chaevien S. Clendinen, Ramadan Ajredini, Mark A. Szewc‡ , Timothy Garrett § , Robert F. Menger , Richard A. Yost, , Chris Beecher  , and Arthur S. Edison

†Department of Biochemistry and Molecular Biology,University of Florida, Gainesville, FL;  Thermo Fisher Scientific, Somerset, NJ; §Department of Pathology, Immunology, and Laboratory Medicine; Southeast Center for Integrated Metabolomics; Department of Chemistry,University of Florida, Gainesville, FL;  IROA Technologies, Ann Arbor, MI

 Anal. Chem. DOI: 10.1021/ac4025413. Publication Date (Web): November 25, 2013

 ABSTRACT: We demonstrate the global metabolic analysis of Caenorhabditis elegans stress responses using a mass-spectrometry-based technique called isotopic ratio outlier analysis (IROA). In an IROA protocol, control and experimental samples are isotopically labeled with 95 and 5% 13C, and the two sample populations are mixed together for uniform extraction, sample preparation, and LC-MS analysis. This labeling strategy provides several advantages over conventional approaches: (1) compounds arising from biosynthesis are easily distinguished from artifacts, (2) errors from sample extraction and preparation are minimized because the control and experiment are combined into a single sample, (3) measurement of both the molecular weight and the exact number of carbon atoms in each molecule provides extremely accurate molecular formulas, and (4) relative concentrations of all metabolites are easily determined. A heat-shock perturbation was conducted on C. elegans to demonstrate this approach. We identified many compounds that significantly changed upon heat shock, including several from the purine metabolism pathway. The metabolomic response information by IROA may be interpreted in the context of a wealth of genetic and proteomic information available for C. elegans. Furthermore, the IROA protocol can be applied to any organism that can be isotopically labeled, making it a powerful new tool in a global metabolomics pipeline.

A Tale of Two Matrix Factorizations

Paul Fogel, Douglas M. Hawkins‡, Chris Beecher, George Luta || and S. Stanley Young*

 Consultant, Paris;  School of Statistics, University of Minnesota, Minneapolis, MN; Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington DC;  IROA Technologies, Ann Arbor, MI; *National Institute of Statistical Sciences, Research Triangle Park, NC

 The American Statistician, Volume 67, Issue 4, 2013

ABSTRACT: In statistical practice, rectangular tables of numeric data are commonplace, and are often analyzed using dimension-reduced methods like the singular value decomposition and its close cousin, principal component analysis (PCA).  This analysis produces score and loading matrices representing the rows and columns of the original table and these matrices may be used for both prediction purposes and to gain structural understanding of the data.  In some tables, the data entries are necessarily nonnegative (apart, perhaps, from some small random noise), and so the matrix factors meant to represent them should arguably also contain nonnegative elements. This thinking, and the desire for parsimony, underlies such techniques as rotating factors in a search for "simple structure."  These attempts to transform score or loading matrices of mixed sign into nonnegative matrix factorization, or NMF, is an attractive alternative.  Rather than attempt to transform a loading or score matrix of mixed signs into one with only nonnegative elements, it directly seeks matrix factors containing only nonegative elements. The resulting factorization often leads to substantial improvements in interpretability of the factors.  We illustrate this potential by synthetic examples anda real dataset. The questions of exactly when NMF is effective is not fully resolved, but some indicators of its domain of success are given.  It is pointed out that the NMF factors can be used in much of the same way as those coming from PCA for such tasks as ordination, clustering, and prediction. Supplementary materials for this article are available on line.


Addressing the current bottlenecks of metabolomics: Isotopic Ratio Outlier Analysis, an Isotopic labeling technique for accurate biochemical profiling

Felice A. de Jong and Chris Beecher,  IROA Technologies, Ann Arbor, MI

 Bioanalysis, Volume 4, Issue 18, 2012

ABSTRACT: Metabolomics or biochemical profiling is a fast emerging science; however, there are still many associated bottlenecks to overcome before measurements will be considered robust. Advances in MS resolution and sensitivity, ultra-pressure LC MS, ESI, and isotopic approaches such as flux analysis and stable-isotope dilution, have made it easier to quantitate biochemicals. The digitization of mass spectrometers has simplified informatic aspects. However, issues of analytical variability, ion suppression and metabolite identification still plague metabolomics investigators. These hurdles need to be overcome for accurate metabolite quantitation not only for in vitro systems, but for complex matrices such as biofluids and tissues, before it is possible to routinely identify biomarkers that are associated with the early prediction and diagnosis of diseases. In this report, we describe a novel isotopic-labeling method that uses the creation of distinct biochemical signatures to eliminate current bottlenecks and enable accurate metabolic profiling.