Using untargeted metabolomics for detecting exposome compounds

Abstract The exposome is the summary of all chemical and non-chemical exposures over an individual's lifetime that collectively describe all non-genetic factors that may influence phenotype. While advances in genomics have significantly improved the understanding of chronic disease, they have also highlighted the need for better characterization of exposure. Untargeted metabolomics should complement targeted methods for quantitative and reliable analysis of exposome compounds in biological matrices. Using an existing workflow consisting of untargeted instrumental acquisition, analyte annotation using library matching, unknown identification, and data visualization, environmental effects on endogenous metabolites can be assessed by accurate and comprehensive exposure analysis.

[1]  Gang Liu,et al.  Effects of cigarette smoke on the human airway epithelial cell transcriptome. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[2]  Oliver Fiehn,et al.  Toward Merging Untargeted and Targeted Methods in Mass Spectrometry-Based Metabolomics and Lipidomics. , 2016, Analytical chemistry.

[3]  Oliver Fiehn,et al.  MINEs: open access databases of computationally predicted enzyme promiscuity products for untargeted metabolomics , 2015, Journal of Cheminformatics.

[4]  T. K. Jensen,et al.  Current exposure of 200 pregnant Danish women to phthalates, parabens and phenols. , 2014, Reproduction.

[5]  D. Allaway,et al.  Ultra high performance liquid chromatography–high resolution mass spectrometry plasma lipidomics can distinguish between canine breeds despite uncontrolled environmental variability and non-standardized diets , 2017, Metabolomics.

[6]  E. Jablonka,et al.  Why the missing heritability might not be in the DNA , 2017, BioEssays : news and reviews in molecular, cellular and developmental biology.

[7]  C. N. Coleman,et al.  Discovering clinical biomarkers of ionizing radiation exposure with serum proteomic analysis. , 2006, Cancer research.

[8]  L. James,et al.  Targeted metabolomic approach for assessing human synthetic cannabinoid exposure and pharmacology. , 2013, Analytical chemistry.

[9]  Nicolas J Cabaton,et al.  Effects of Low Doses of Bisphenol A on the Metabolome of Perinatally Exposed CD-1 Mice , 2013, Environmental health perspectives.

[10]  A. Peters,et al.  Identification of Serum Metabolites Associated With Risk of Type 2 Diabetes Using a Targeted Metabolomic Approach , 2013, Diabetes.

[11]  Giorgis Isaac,et al.  Enhanced lipid isomer separation in human plasma using reversed-phase UPLC with ion-mobility/high-resolution MS detection[S] , 2014, Journal of Lipid Research.

[12]  M. Allen,et al.  The validated sun exposure questionnaire: association of objective and subjective measures of sun exposure in a Danish population‐based sample , 2017, The British journal of dermatology.

[13]  Ann Marie Schmidt,et al.  Advanced glycation end products: sparking the development of diabetic vascular injury. , 2006, Circulation.

[14]  Dieter Jahn,et al.  Comprehensive comparison of in silico MS/MS fragmentation tools of the CASMI contest: database boosting is needed to achieve 93% accuracy , 2017, Journal of Cheminformatics.

[15]  M. Glibetić,et al.  Validity of the Food Frequency Questionnaire Assessing the Folate Intake in Women of Reproductive Age Living in a Country without Food Fortification: Application of the Method of Triads , 2017, Nutrients.

[16]  Eleazar Eskin,et al.  Discovering genes involved in disease and the mystery of missing heritability , 2015, Commun. ACM.

[17]  Stephen M Rappaport,et al.  Environment and Disease Risks , 2010, Science.

[18]  Gabi Kastenmüller,et al.  Questionnaire-based self-reported nutrition habits associate with serum metabolism as revealed by quantitative targeted metabolomics , 2011, European Journal of Epidemiology.

[19]  C. Wild,et al.  The exposome: from concept to utility. , 2012, International journal of epidemiology.

[20]  Oliver Fiehn,et al.  MS2Analyzer: A Software for Small Molecule Substructure Annotations from Accurate Tandem Mass Spectra , 2014, Analytical chemistry.

[21]  Karl Fraser,et al.  Predicting retention time in hydrophilic interaction liquid chromatography mass spectrometry and its use for peak annotation in metabolomics , 2014, Metabolomics.

[22]  J. V. Sancho,et al.  Prediction of Collision Cross-Section Values for Small Molecules: Application to Pesticide Residue Analysis. , 2017, Analytical chemistry.

[23]  B. McManus,et al.  The Human Serum Metabolome , 2011, PloS one.

[24]  P. Limbach,et al.  Multiple Enzymatic Digestions and Ion Mobility Separation Improve Quantification of Bacterial Ribosomal Proteins by Data Independent Acquisition Liquid Chromatography−Mass Spectrometry , 2014, Analytical chemistry.

[25]  S. Böcker,et al.  Searching molecular structure databases with tandem mass spectra using CSI:FingerID , 2015, Proceedings of the National Academy of Sciences of the United States of America.

[26]  A. Naska,et al.  A metabolomic study of biomarkers of meat and fish intake. , 2017, The American journal of clinical nutrition.

[27]  Masanori Arita,et al.  MS-DIAL: Data Independent MS/MS Deconvolution for Comprehensive Metabolome Analysis , 2015, Nature Methods.

[28]  W. Dunn,et al.  Measuring the metabolome: current analytical technologies. , 2005, The Analyst.

[29]  David S. Wishart,et al.  CFM-ID: a web server for annotation, spectrum prediction and metabolite identification from tandem mass spectra , 2014, Nucleic Acids Res..

[30]  H. Søndergaard,et al.  Genetic and environmental determinants of 25-hydroxyvitamin D levels in multiple sclerosis , 2015, Multiple sclerosis.

[31]  S. Harlow,et al.  Agreement between questionnaire data and medical records. The evidence for accuracy of recall. , 1989, American journal of epidemiology.

[32]  R. Weinshilboum,et al.  Pharmacometabolomics: Implications for Clinical Pharmacology and Systems Pharmacology , 2014, Clinical pharmacology and therapeutics.

[33]  Paolo Vineis,et al.  The Blood Exposome and Its Role in Discovering Causes of Disease , 2014, Environmental health perspectives.

[34]  E. Birney,et al.  A small cell lung cancer genome reports complex tobacco exposure signatures , 2009, Nature.

[35]  Chien-Chung Lin,et al.  Interactions between household air pollution and GWAS-identified lung cancer susceptibility markers in the Female Lung Cancer Consortium in Asia (FLCCA) , 2015, Human Genetics.

[36]  A. Korte,et al.  The advantages and limitations of trait analysis with GWAS: a review , 2013, Plant Methods.

[37]  Oliver Fiehn,et al.  Diacetylspermine Is a Novel Prediagnostic Serum Biomarker for Non-Small-Cell Lung Cancer and Has Additive Performance With Pro-Surfactant Protein B. , 2015, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[38]  Dorret I. Boomsma,et al.  The continuing value of twin studies in the omics era , 2012, Nature Reviews Genetics.

[39]  B. Brunekreef,et al.  The impact of ambient air pollution on the human blood metabolome , 2017, Environmental research.

[40]  Svati H Shah,et al.  Recommendations for Improving Identification and Quantification in Non-Targeted, GC-MS-Based Metabolomic Profiling of Human Plasma , 2017, Metabolites.

[41]  A. Amberbir,et al.  A metabolomic analytical approach permits identification of urinary biomarkers for Plasmodium falciparum infection: a case–control study , 2017, Malaria Journal.

[42]  S. Rappaport.  Implications of the exposome for exposure science , 2011, Journal of Exposure Science and Environmental Epidemiology.

[43]  N. Goncharov,et al.  Toxicometabolomics: Identification of markers of chronic exposure to low doses of aliphatic hydrocarbons , 2017, Journal of Evolutionary Biochemistry and Physiology.

[44]  C. Ranabhat,et al.  The Correlation of Different Cotinine Levels With Questionnaire Results , 2015, Asia-Pacific journal of public health.

[45]  C. Wild.  Complementing the Genome with an “Exposome”: The Outstanding Challenge of Environmental Exposure Measurement in Molecular Epidemiology , 2005, Cancer Epidemiology Biomarkers & Prevention.

[46]  D. Hochstrasser,et al.  Evaluation and identification of dioxin exposure biomarkers in human urine by high-resolution metabolomics, multivariate analysis and in vitro synthesis. , 2016, Toxicology letters.

[47]  Kazuki Saito,et al.  Hydrogen Rearrangement Rules: Computational MS/MS Fragmentation and Structure Elucidation Using MS-FINDER Software. , 2016, Analytical chemistry.

[48]  L. Birnbaum,et al.  Monitoring Indoor Exposure to Organophosphate Flame Retardants: Hand Wipes and House Dust , 2014, Environmental health perspectives.

[49]  P. He,et al.  Biomarker identification and pathway analysis of preeclampsia based on serum metabolomics. , 2017, Biochemical and biophysical research communications.

[50]  Kaj Bo Veiersted,et al.  Validity of Questionnaire and Representativeness of Objective Methods for Measurements of Mechanical Exposures in Construction and Health Care Work , 2016, PloS one.