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Title: Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics

Abstract

Untargeted metabolomics can detect more than 10 000 peaks in a single LC–MS run. The correspondence between these peaks and metabolites, however, remains unclear. Here in this study, we introduce a Peak Annotation and Verification Engine (PAVE) for annotating untargeted microbial metabolomics data. The workflow involves growing cells in 13C and 15N isotope-labeled media to identify peaks from biological compounds and their carbon and nitrogen atom counts. Improved deisotoping and deadducting are enabled by algorithms that integrate positive mode, negative mode, and labeling data. To distinguish metabolites and their fragments, PAVE experimentally measures the response of each peak to weak in-source collision induced dissociation, which increases the peak intensity for fragments while decreasing it for their parent ions. The molecular formulas of the putative metabolites are then assigned based on database searching using both m/z and C/N atom counts. Application of this procedure to Saccharomyces cerevisiae and Escherichia coli revealed that more than 80% of peaks do not label, i.e., are environmental contaminants. More than 70% of the biological peaks are isotopic variants, adducts, fragments, or mass spectrometry artifacts yielding ~2000 apparent metabolites across the two organisms. About 650 match to a known metabolite formula based on m/z and C/Nmore » atom counts, with 220 assigned structures based on MS/MS and/or retention time to match to authenticated standards. Lastly, PAVE enables systematic annotation of LC–MS metabolomics data with only ~4% of peaks annotated as apparent metabolites.« less

Authors:
 [1];  [1];  [1];  [1]; ORCiD logo [2]; ORCiD logo [1];  [1]; ORCiD logo [1]
  1. Princeton Univ., NJ (United States)
  2. Princeton Univ., NJ (United States); Rutgers Univ., New Brunswick, NJ (United States)
Publication Date:
Research Org.:
Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1491815
Grant/Contract Number:  
SC0018420; SC0018260; FG02-02ER15344
Resource Type:
Accepted Manuscript
Journal Name:
Analytical Chemistry
Additional Journal Information:
Journal Volume: 91; Journal Issue: 3; Journal ID: ISSN 0003-2700
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Wang, Lin, Xing, Xi, Chen, Li, Yang, Lifeng, Su, Xiaoyang, Rabitz, Herschel, Lu, Wenyun, and Rabinowitz, Joshua D. Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics. United States: N. p., 2018. Web. doi:10.1021/acs.analchem.8b03132.
Wang, Lin, Xing, Xi, Chen, Li, Yang, Lifeng, Su, Xiaoyang, Rabitz, Herschel, Lu, Wenyun, & Rabinowitz, Joshua D. Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics. United States. https://doi.org/10.1021/acs.analchem.8b03132
Wang, Lin, Xing, Xi, Chen, Li, Yang, Lifeng, Su, Xiaoyang, Rabitz, Herschel, Lu, Wenyun, and Rabinowitz, Joshua D. Wed . "Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics". United States. https://doi.org/10.1021/acs.analchem.8b03132. https://www.osti.gov/servlets/purl/1491815.
@article{osti_1491815,
title = {Peak Annotation and Verification Engine for Untargeted LC–MS Metabolomics},
author = {Wang, Lin and Xing, Xi and Chen, Li and Yang, Lifeng and Su, Xiaoyang and Rabitz, Herschel and Lu, Wenyun and Rabinowitz, Joshua D.},
abstractNote = {Untargeted metabolomics can detect more than 10 000 peaks in a single LC–MS run. The correspondence between these peaks and metabolites, however, remains unclear. Here in this study, we introduce a Peak Annotation and Verification Engine (PAVE) for annotating untargeted microbial metabolomics data. The workflow involves growing cells in 13C and 15N isotope-labeled media to identify peaks from biological compounds and their carbon and nitrogen atom counts. Improved deisotoping and deadducting are enabled by algorithms that integrate positive mode, negative mode, and labeling data. To distinguish metabolites and their fragments, PAVE experimentally measures the response of each peak to weak in-source collision induced dissociation, which increases the peak intensity for fragments while decreasing it for their parent ions. The molecular formulas of the putative metabolites are then assigned based on database searching using both m/z and C/N atom counts. Application of this procedure to Saccharomyces cerevisiae and Escherichia coli revealed that more than 80% of peaks do not label, i.e., are environmental contaminants. More than 70% of the biological peaks are isotopic variants, adducts, fragments, or mass spectrometry artifacts yielding ~2000 apparent metabolites across the two organisms. About 650 match to a known metabolite formula based on m/z and C/N atom counts, with 220 assigned structures based on MS/MS and/or retention time to match to authenticated standards. Lastly, PAVE enables systematic annotation of LC–MS metabolomics data with only ~4% of peaks annotated as apparent metabolites.},
doi = {10.1021/acs.analchem.8b03132},
journal = {Analytical Chemistry},
number = 3,
volume = 91,
place = {United States},
year = {2018},
month = {12}
}

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Works referenced in this record:

Emerging applications of metabolomics in drug discovery and precision medicine
journal, March 2016


Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses
journal, June 2014

  • Gowda, Harsha; Ivanisevic, Julijana; Johnson, Caroline H.
  • Analytical Chemistry, Vol. 86, Issue 14
  • DOI: 10.1021/ac500734c

Systems biology guided by XCMS Online metabolomics
journal, April 2017

  • Huan, Tao; Forsberg, Erica M.; Rinehart, Duane
  • Nature Methods, Vol. 14, Issue 5
  • DOI: 10.1038/nmeth.4260

XCMS:  Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification
journal, February 2006

  • Smith, Colin A.; Want, Elizabeth J.; O'Maille, Grace
  • Analytical Chemistry, Vol. 78, Issue 3
  • DOI: 10.1021/ac051437y

MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data
journal, January 2006


MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data
journal, July 2010

  • Pluskal, Tomáš; Castillo, Sandra; Villar-Briones, Alejandro
  • BMC Bioinformatics, Vol. 11, Issue 1
  • DOI: 10.1186/1471-2105-11-395

MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis
journal, May 2015

  • Tsugawa, Hiroshi; Cajka, Tomas; Kind, Tobias
  • Nature Methods, Vol. 12, Issue 6
  • DOI: 10.1038/nmeth.3393

Annotation: A Computational Solution for Streamlining Metabolomics Analysis
journal, November 2017

  • Domingo-Almenara, Xavier; Montenegro-Burke, J. Rafael; Benton, H. Paul
  • Analytical Chemistry, Vol. 90, Issue 1
  • DOI: 10.1021/acs.analchem.7b03929

Bioinformatics: The Next Frontier of Metabolomics
journal, November 2014

  • Johnson, Caroline H.; Ivanisevic, Julijana; Benton, H. Paul
  • Analytical Chemistry, Vol. 87, Issue 1
  • DOI: 10.1021/ac5040693

Analytical Methods in Untargeted Metabolomics: State of the Art in 2015
journal, March 2015

  • Alonso, Arnald; Marsal, Sara; Julià, Antonio
  • Frontiers in Bioengineering and Biotechnology, Vol. 3
  • DOI: 10.3389/fbioe.2015.00023

CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data Sets
journal, December 2011

  • Kuhl, Carsten; Tautenhahn, Ralf; Böttcher, Christoph
  • Analytical Chemistry, Vol. 84, Issue 1
  • DOI: 10.1021/ac202450g

Stable Isotope Assisted Assignment of Elemental Compositions for Metabolomics
journal, September 2007

  • Hegeman, Adrian D.; Schulte, Christopher F.; Cui, Qiu
  • Analytical Chemistry, Vol. 79, Issue 18
  • DOI: 10.1021/ac070346t

NTFD--a stand-alone application for the non-targeted detection of stable isotope-labeled compounds in GC/MS data
journal, March 2013


Metabolomic Analysis and Visualization Engine for LC−MS Data
journal, December 2010

  • Melamud, Eugene; Vastag, Livia; Rabinowitz, Joshua D.
  • Analytical Chemistry, Vol. 82, Issue 23
  • DOI: 10.1021/ac1021166

PeakML/mzMatch: A File Format, Java Library, R Library, and Tool-Chain for Mass Spectrometry Data Analysis
journal, April 2011

  • Scheltema, Richard A.; Jankevics, Andris; Jansen, Ritsert C.
  • Analytical Chemistry, Vol. 83, Issue 7
  • DOI: 10.1021/ac2000994

mzMatch–ISO: an R tool for the annotation and relative quantification of isotope-labelled mass spectrometry data
journal, November 2012


X 13 CMS: Global Tracking of Isotopic Labels in Untargeted Metabolomics
journal, January 2014

  • Huang, Xiaojing; Chen, Ying-Jr; Cho, Kevin
  • Analytical Chemistry, Vol. 86, Issue 3
  • DOI: 10.1021/ac403384n

MetExtract: a new software tool for the automated comprehensive extraction of metabolite-derived LC/MS signals in metabolomics research
journal, January 2012


MetExtract II: A Software Suite for Stable Isotope-Assisted Untargeted Metabolomics
journal, August 2017


Systems-Level Annotation of a Metabolomics Data Set Reduces 25 000 Features to Fewer than 1000 Unique Metabolites
journal, September 2017


Extraction and Quantitation of Nicotinamide Adenine Dinucleotide Redox Cofactors
journal, January 2018

  • Lu, Wenyun; Wang, Lin; Chen, Li
  • Antioxidants & Redox Signaling, Vol. 28, Issue 3
  • DOI: 10.1089/ars.2017.7014

Employing ProteoWizard to Convert Raw Mass Spectrometry Data
journal, June 2014


Interferences and contaminants encountered in modern mass spectrometry
journal, October 2008


HMDB: the Human Metabolome Database
journal, January 2007

  • Wishart, D. S.; Tzur, D.; Knox, C.
  • Nucleic Acids Research, Vol. 35, Issue Database
  • DOI: 10.1093/nar/gkl923

Metabolite Measurement: Pitfalls to Avoid and Practices to Follow
journal, June 2017


Internal energy and fragmentation of ions produced in electrospray sources
journal, January 2005

  • Gabelica, Valérie; Pauw, Edwin De
  • Mass Spectrometry Reviews, Vol. 24, Issue 4
  • DOI: 10.1002/mas.20027

Avoiding Misannotation of In-Source Fragmentation Products as Cellular Metabolites in Liquid Chromatography–Mass Spectrometry-Based Metabolomics
journal, January 2015

  • Xu, Yi-Fan; Lu, Wenyun; Rabinowitz, Joshua D.
  • Analytical Chemistry, Vol. 87, Issue 4
  • DOI: 10.1021/ac504118y

An accelerated workflow for untargeted metabolomics using the METLIN database
journal, September 2012

  • Tautenhahn, Ralf; Cho, Kevin; Uritboonthai, Winnie
  • Nature Biotechnology, Vol. 30, Issue 9
  • DOI: 10.1038/nbt.2348

LIPID MAPS online tools for lipid research
journal, May 2007

  • Fahy, E.; Sud, M.; Cotter, D.
  • Nucleic Acids Research, Vol. 35, Issue Web Server
  • DOI: 10.1093/nar/gkm324

YMDB: the Yeast Metabolome Database
journal, November 2011

  • Jewison, T.; Knox, C.; Neveu, V.
  • Nucleic Acids Research, Vol. 40, Issue D1
  • DOI: 10.1093/nar/gkr916

ECMDB: The E. coli Metabolome Database
journal, October 2012

  • Guo, An Chi; Jewison, Timothy; Wilson, Michael
  • Nucleic Acids Research, Vol. 41, Issue D1
  • DOI: 10.1093/nar/gks992

Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli
journal, June 2009

  • Bennett, Bryson D.; Kimball, Elizabeth H.; Gao, Melissa
  • Nature Chemical Biology, Vol. 5, Issue 8
  • DOI: 10.1038/nchembio.186

Glucose feeds the TCA cycle via circulating lactate
journal, October 2017

  • Hui, Sheng; Ghergurovich, Jonathan M.; Morscher, Raphael J.
  • Nature, Vol. 551, Issue 7678
  • DOI: 10.1038/nature24057

Noninvasive liquid diet delivery of stable isotopes into mouse models for deep metabolic network tracing
journal, November 2017


Lactate Metabolism in Human Lung Tumors
journal, October 2017


Metabolomics and Isotope Tracing
journal, May 2018


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CROP: correlation-based reduction of feature multiplicities in untargeted metabolomic data
journal, January 2020


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journal, December 2019


Deep annotation of untargeted LC-MS metabolomics data with Binner
journal, October 2019


Untargeted metabolomics links glutathione to bacterial cell cycle progression
journal, February 2020