Biomonitoring in the Era of the Exposome

Kristine K Dennis, Elizabeth Marder, David M Balshaw, Yuxia Cui, Michael A Lynes, Gary J Patti, Stephen M Rappaport, Daniel T Shaughnessy, Martine Vrijheid, Dana Boyd Barr, Kristine K Dennis, Elizabeth Marder, David M Balshaw, Yuxia Cui, Michael A Lynes, Gary J Patti, Stephen M Rappaport, Daniel T Shaughnessy, Martine Vrijheid, Dana Boyd Barr

Abstract

Background: The term "exposome" was coined in 2005 to underscore the importance of the environment to human health and to bring research efforts in line with those on the human genome. The ability to characterize environmental exposures through biomonitoring is key to exposome research efforts.

Objectives: Our objectives were to describe why traditional and nontraditional (exposomic) biomonitoring are both critical in studies aiming to capture the exposome and to make recommendations on how to transition exposure research toward exposomic approaches. We describe the biomonitoring needs of exposome research and approaches and recommendations that will help fill the gaps in the current science.

Discussion: Traditional and exposomic biomonitoring approaches have key advantages and disadvantages for assessing exposure. Exposomic approaches differ from traditional biomonitoring methods in that they can include all exposures of potential health significance, whether from endogenous or exogenous sources. Issues of sample availability and quality, identification of unknown analytes, capture of nonpersistent chemicals, integration of methods, and statistical assessment of increasingly complex data sets remain challenges that must continue to be addressed.

Conclusions: To understand the complexity of exposures faced throughout the lifespan, both traditional and nontraditional biomonitoring methods should be used. Through hybrid approaches and the integration of emerging techniques, biomonitoring strategies can be maximized in research to define the exposome.

Conflict of interest statement

K.K.D. and D.B.B are supported by Emory University’s Health and Exposome Research Center: Understanding Lifetime Exposures (HERCULES; grant P30 ES019776). M.A.L. has a patent that he shares with Ciencia, Inc. for a biomonitoring instrument that uses both grating-coupled surface plasmon resonance (GCSPR) imaging and grating-coupled surface plasmon–coupled emission (GCSPCE) imaging in a microarray format for the analysis of functional cell phenotyping. M.A.L. has consulted for Ciencia, Inc. in the past, but is not currently compensated as a consultant. M.A.L. also has had (and currently has) NIH/NIEHS support to develop this technology. G.J.P. is a scientific advisory board member for Cambridge Isotope Laboratories. All other authors declare they have no actual or potential competing financial interests.

References

    1. Andra SS, Austin C, Arora M. 2015. Tooth matrix analysis for biomonitoring of organic chemical exposure: current status, challenges, and opportunities. Environ Res 142 387 406, doi:10.1016/j.envres.2015.06.032
    1. Arora M, Bradman A, Austin C, Vedar M, Holland N, Eskenazi B, et al. 2012. Determining fetal manganese exposure from mantle dentine of deciduous teeth. Environ Sci Technol 46 5118 5125, doi:10.1021/es203569f
    1. Baker SE, Olsson AO, Needham LL, Barr DB. High-performance liquid chromatography–tandem mass spectrometry method for quantifying sulfonylurea herbicides in human urine: reconsidering the validation process. Anal Bioanal Chem. 2005;383:963–976.
    1. Barr DB, Wang RY, Needham LL. 2005. Biologic monitoring of exposure to environmental chemicals throughout the life stages: requirements and issues for consideration for the National Children’s Study. Environ Health Perspect 113 1083 1091, doi:10.1289/ehp.7617
    1. Beane J, Vick J, Schembri F, Anderlind C, Gower A, Campbell J, et al. 2011. Characterizing the impact of smoking and lung cancer on the airway transcriptome using RNA-Seq. Cancer Prev Res (Phila) 4 803 817, doi:10.1158/1940-6207
    1. CDC (Centers for Disease Control and Prevention) Fourth National Report on Human Exposure to Environmental Chemicals. Department of Health and Human Services, CDC. 2009 [accessed 24 August 2015]
    1. CDC. Chemical Selection, National Report on Human Exposure to Environmental Chemicals, Chemical Selection. 2012 [accessed 7 October 2015]
    1. CDC. Fourth National Report on Human Exposure to Environmental Chemicals Updated Tables. Department of Health and Human Services, CDC. 2015 [accessed 1 September 2015]
    1. CHEAR (Children’s Health Exposure Analysis Resource) Lab Analysis Services. Targeted Analysis. 2016a [accessed 30 April 2016]
    1. CHEAR. Lab Analysis Services. Untargeted Analysis. Metabolomics. 2016b [accessed 30 April 2016]
    1. Chin-Chan M, Navarro-Yepes J, Quintanilla-Vega B. 2015. Environmental pollutants as risk factors for neurodegenerative disorders: Alzheimer and Parkinson diseases. Front Cell Neurosci 9 124, doi:10.3389/fncel.2015.00124
    1. CORDIS (Community Research and Development Information Service. EXPOSOMICS (Enhanced Exposure Assessment and Omic Profiling for High Priority Environmental Exposures in Europe.). 2014 [accessed 14 April 2016]
    1. CORDIS. Periodic Report Summary 1 – HEALS (Health and Environment-wide Associations based on Large population Surveys). 2015 [accessed 14 April 2016]
    1. da Silva RR, Dorrestein PC, Quinn RA. 2015. Illuminating the dark matter in metabolomics. Proc Natl Acad Sci U S A 112 41 12549 12550, doi:10.1073/pnas.1516878112
    1. Dennis KK, Auerbach SS, Balshaw DM, Cui Y, Fallin MD, Smith MT, et al. 2016. The importance of the biological impact of exposure to the concept of the exposome. Environ Health Perspect 124 1504 1510, doi:10.1289/EHP140
    1. Go EP. 2010. Database resources in metabolomics: an overview. J Neuroimmune Pharmacol 5 1 18 30, doi:10.1007/s11481-009-9157-3
    1. Go YM, Uppal K, Walker DI, Tran V, Dury L, Strobel FH, et al. 2014. Mitochondrial metabolomics using high-resolution Fourier-transform mass spectrometry. Methods Mol Biol 1198 43 73, doi:10.1007/978-1-4939-1258-2_4
    1. Go YM, Walker DI, Liang Y, Uppal K, Soltow QA, Tran V, et al. 2015. Reference standardization for mass spectrometry and high-resolution metabolomics applications to exposome research. Toxicol Sci 148 2 531 543, doi:10.1093/toxsci/kfv198
    1. Grigoryan H, Li H, Iavarone AT, Williams ER, Rappaport SM. 2012. Cys34 adducts of reactive oxygen species in human serum albumin. Chem Res Toxicol 25 1633 1642, doi:10.1021/tx300096a
    1. Hu H, Shih R, Rothenberg S, Schwartz BS. 2007. The epidemiology of lead toxicity in adults: measuring dose and consideration of other methodologic issues. Environ Health Perspect 115 455 462, doi:10.1289/ehp.9783
    1. Ivanisevic J, Zhu ZJ, Plate L, Tautenhahn R, Chen S, O’Brien PJ, et al. 2013. Toward ‘omic scale metabolite profiling: a dual separation–mass spectrometry approach for coverage of lipid and central carbon metabolism. Anal Chem 85 6876 6884, doi:10.1021/ac401140h
    1. Johnson CH, Ivanisevic J, Benton HP, Siuzdak G. 2015. Bioinformatics: the next frontier of metabolomics. Anal Chem 87 1 147 156, doi:10.1021/ac5040693
    1. Johnson JM, Yu T, Strobel FH, Jones DP. 2010. A practical approach to detect unique metabolic patterns for personalized medicine. Analyst 135 11 2864 2870, doi:10.1039/c0an00333f
    1. Jones DP. Sequencing the exposome: a call to action. Toxicol Rep. 2016;3:29–45.
    1. Kanehisa M. The KEGG database. Novartis Found Symp. 2002;247:91–101.
    1. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000;28:27–30.
    1. Kaufman AS, Zhou X, Reynolds MR, Kaufman NL, Green GP, Weiss LG. 2014. The possible societal impact of the decrease in U.S. blood lead levels on adult IQ. Environ Res 132 413 420, doi:10.1016/j.envres.2014.04.015
    1. Lee JW, Devanarayan V, Barrett YC, Weiner R, Allinson J, Fountain S, et al. Fit-for-purpose method development and validation for successful biomarker measurement. Pharm Res. 2006;23(2):312–328.
    1. Li S, Todor A, Luo R. 2015. Blood transcriptomics and metabolomics for personalized medicine. Comput Struct Biotechnol J 14 1 7, doi:10.1016/j.csbj.2015.10.005
    1. LIPID MAPS (LIPID Metabolites and Pathways Strategy) Lipodomics Gateway. Resources Overview. 2015 [accessed 24 August 2015]
    1. Lu C, Anderson LC, Morgan MS, Fenske RA. Salivary concentrations of atrazine reflect atrazine plasma levels in rats. J Toxicol Environ Health A. 1998;53:283–292.
    1. Mahieu NG, Huang X, Chen YJ, Patti GJ. 2014. Credentialing features: a platform to benchmark and optimize untargeted metabolomic methods. Anal Chem 86 9583 9589, doi:10.1021/ac503092d
    1. Marshall AG, Hendrickson CL. 2008. High resolution mass spectrometers. Annu Rev Anal Chem (Palo Alto Calif) 1 579 599, doi:10.1146/annurev.anchem.1.031207.112945
    1. Miller GW, Jones DP. 2014. The nature of nurture: refining the definition of the exposome. Toxicol Sci 137 1 1 2, doi:10.1093/toxsci/kft251
    1. NIEHS (National Institute of Environmental Health Sciences) Children’s Health Exposure Analysis Resource (CHEAR). 2015 [accessed 1 September 2015]
    1. Panuwet P, Hunter RE, Jr, D’Souza PE, Chen X, Radford SA, Cohen JR, et al. 2016. Biological matrix effects in quantitative tandem mass spectrometry-based analytical methods: advancing biomonitoring. Crit Rev Anal Chem 46 2 93 105, doi:10.1080/10408347.2014.980775
    1. Rager JE, Strynar MJ, Liang S, McMahen RL, Richard AM, Grulke CM, et al. 2016. Linking high-resolution mass spectrometry data with exposure and toxicity forecasts to advance high-throughput environmental monitoring. Environ Int 88 269 280, doi:10.1016/j.envint.2015.12.008
    1. Rappaport SM. 2012. Biomarkers intersect with the exposome. Biomarkers 17 483 489, doi:10.3109/1354750X.2012.691553
    1. Rappaport SM. 2016. Genetic factors are not the major causes of chronic diseases. PloS One 11 4 e0154387, doi:10.1371/journal.pone.0154387
    1. Rappaport SM, Barupal DK, Wishart D, Vineis DP, Scalbert A. 2014. The blood exposome and its role in discovering causes of disease. Environ Health Perspect 122 769 774, doi:10.1289/ehp.1308015
    1. Rappaport SM, Li H, Grigoryan H, Funk WE, Williams ER. 2012. Adductomics: characterizing exposures to reactive electrophiles. Toxicol Lett 213 83 90, doi:10.1016/j.toxlet.2011.04.002
    1. Rappaport SM, Smith MT. 2010. Environment and disease risks. Science 330 460 461, doi:10.1126/science.1192603
    1. Rosas LG, Eskenazi B. 2008. Pesticides and child neurodevelopment. Curr Opin Pediatr 20 191 197, doi:10.1097/MOP.0b013e3282f60a7d
    1. Smith CA, O’Maille G, Want EJ, Qin C, Trauger SA, Brandon TR, et al. METLIN: a metabolite mass spectral database. Ther Drug Monit. 2005;27:747–751.
    1. Soltow QA, Strobel FH, Mansfield KG, Wachtman L, Park Y, Jones DP. High-performance metabolic profiling with dual chromatography-Fourier-transform mass spectrometry (DC-FTMS) for study of the exposome. Metabolomics. 2013;9(1) suppl:S132–S143.
    1. Southam AD, Lange A, Al-Salhi R, Hill EM, Tyler CR, Viant MT. Distinguishing between the metabolome and xenobiotic exposome in environmental field samples analyses by direct-infusion mass spectrometry based metabolomics and lipidomics. Metabolomics. 2014;10(6):1050–1058.
    1. Spira A, Beane J, Schembri F, Liu G, Ding C, Gilman S, et al. Noninvasive method for obtaining RNA from buccal mucosa epithelial cells for gene expression profiling. Biotechniques. 2004;36:484–487.
    1. Tautenhahn R, Cho K, Uritboonthai W, Zhu Z, Patti GJ, Siuzdak G. 2012. An accelerated workflow for untargeted metabolomics using the METLIN database. Nat Biotechnol 30 826 828, doi:10.1038/nbt.2348
    1. Vrijheid M, Slama R, Robinson O, Chatzi L, Coen M, van den Hazel P, et al. 2014. The Human Early-Life Exposome (HELIX): project rationale and design. Environ Health Perspect 122 535 544, doi:10.1289/ehp.1307204
    1. Wambaugh JF, Setzer RW, Reif DM, Gangwal S, Mitchell-Blackwood J, Arnot JA, et al. 2013. High-throughput models for exposure-based chemical prioritization in the ExpoCast project. Environ Sci Technol 47 15 8479 8488, doi:10.1021/es400482g
    1. Wei R, Li G, Seymour AB. 2010. High-throughput and multiplexed LC/MS/MRM method for targeted metabolomics. Anal Chem 82 13 5527 5533, doi:10.1021/ac100331b
    1. Wild CP. Complementing the genome with an “exposome”: the outstanding challenge of environmental exposure measurement in molecular epidemiology. Cancer Epidemiol Biomarkers Prev. 2005;14:1847–1850.
    1. Wild CP. 2012. The exposome: from concept to utility. Int J Epidemiol 41 24 32, doi:10.1093/ije/dyr236
    1. Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, et al 2013. HMDB 3.0—the Human Metabolome Database in 2013. Nucleic Acids Res 41 database issue D801 D807, doi: 10.1093/nar/gks1065
    1. Wishart DS, Knox C, Guo AC, Eisner R, Young N, Gautam B, et al 2009. HMDB: a knowledgebase for the human metabolome. Nucleic Acids Res 37 database issue D603 D610, doi: 10.1093/nar/gkn810
    1. Yu T, Park Y, Li S, Jones DP. 2013. Hybrid feature detection and information accumulation using high-resolution LC-MS metabolomics data. J Proteome Res 12 3 1419 1427, doi:10.1021/pr301053d
    1. Zhang J, Shen H, Xu W, Xia Y, Barr DB, Mu X, et al. 2014. Urinary metabolomics revealed arsenic internal dose-related metabolic alterations: a proof-of-concept study in a Chinese male cohort. Environ Sci Technol 48 20 12265 12274, doi:10.1021/es503659w
    1. Zhang X, Sebastiani P, Liu G, Schembri F, Zhang X, Dumas YM, et al. 2010. Similarities and differences between smoking-related gene expression in nasal and bronchial epithelium. Physiol Genomics 41 1 8, doi:10.1152/physiolgenomics.00167.2009
    1. Zhou B, Xiao JF, Tuli L, Ressom HW. 2012. LC-MS-based metabolomics. Mol Biosyst 8 2 470 481, doi:10.1039/c1mb05350g

Source: PubMed

Sponsor e collaboratori

Condizioni mediche

Interventi farmacologici

3
Sottoscrivi