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Zeng Y, Zhang A, Yang X, Xing C, Zhai J, Wang Y, Cai B, Shi S, Zhang Y, Shen Z, Fu TM, Zhu L, Shen H, Ye J, Wang C. Internal exposure potential of water-soluble organic molecules in urban PM 2.5 evaluated by non-covalent adductome of human serum albumin. ENVIRONMENT INTERNATIONAL 2024; 184:108492. [PMID: 38350258 DOI: 10.1016/j.envint.2024.108492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 02/06/2024] [Accepted: 02/06/2024] [Indexed: 02/15/2024]
Abstract
Water-soluble organic molecules (WSOMs) in inhaled PM2.5 can readily translocate from the lungs into the blood circulation, facilitating their distribution to and health effects on distant organs and tissues in the human body. Human serum albumin (HSA), the most abundant protein carrier in the blood, readily binds exogenous substances to form non-covalent adducts and subsequently transports them throughout the circulatory system, thereby indicating their internal exposure. The direct internal exposure of WSOMs in PM2.5 needs to be understood. In this study, the non-covalent HSA-WSOM adductome was developed as a dosimeter to evaluate the internal exposure potential of WSOMs in urban PM2.5. The WSOM composition was acquired from non-target high-resolution mass spectrometry analysis coupled with multiple ionizations. The binding level of HSA-WSOM non-covalent adducts was obtained from surface plasma resonance. Machine learning combined WSOM composition and the binding level of HSA-WSOM non-covalent adducts to screen bindable (also internalizable) WSOMs. The concentration of WSOM ranged from 4 to 13 μg/m3 during our observation period. Of the 17,513 mass spectral features detected, 9,484 contributed to the non-covalent adductome and possessed the internal exposure potential. 102 major contributors accounted for 90.6 % of the HSA-WSOM binding level. The fraction of internalizable WSOMs in PM2.5 varied from 11.9 % to 61.3 %, averaging 26.2 %. WSOMs that have internal exposure potential were primarily lignin-like and lipid-like substances. The HSA-WSOMs non-covalent adductome represents direct internal exposure potential, which can provide crucial insights into the molecular diagnosis of PM2.5 exposure and precise assessments of PM2.5 health effects.
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Affiliation(s)
- Yaling Zeng
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China
| | - Antai Zhang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China
| | - Xin Yang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China.
| | - Chunbo Xing
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China
| | - Jinghao Zhai
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China
| | - Yixiang Wang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China
| | - Baohua Cai
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China
| | - Shao Shi
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China
| | - Yujie Zhang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China
| | - Zhenxing Shen
- Department of Environmental Science and Engineering, Xi'an Jiaotong University, Xi'an 710049, China
| | - Tzung-May Fu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China
| | - Lei Zhu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China
| | - Huizhong Shen
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China
| | - Jianhuai Ye
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China
| | - Chen Wang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China; Guangdong Provincial Observation and Research Station for Coastal Atmosphere and Climate of the Greater Bay Area, Shenzhen 518055, China
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Punzalan C, Wang L, Bajrami B, Yao X. Measurement and utilization of the proteomic reactivity by mass spectrometry. MASS SPECTROMETRY REVIEWS 2024; 43:166-192. [PMID: 36924435 DOI: 10.1002/mas.21837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Chemical proteomics, which involves studying the covalent modifications of proteins by small molecules, has significantly contributed to our understanding of protein function and has become an essential tool in drug discovery. Mass spectrometry (MS) is the primary method for identifying and quantifying protein-small molecule adducts. In this review, we discuss various methods for measuring proteomic reactivity using MS and covalent proteomics probes that engage through reactivity-driven and proximity-driven mechanisms. We highlight the applications of these methods and probes in live-cell measurements, drug target identification and validation, and characterizing protein-small molecule interactions. We conclude the review with current developments and future opportunities in the field, providing our perspectives on analytical considerations for MS-based analysis of the proteomic reactivity landscape.
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Affiliation(s)
- Clodette Punzalan
- Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA
| | - Lei Wang
- Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA
- AD Bio US, Takeda, Lexington, Massachusetts, 02421, USA
| | - Bekim Bajrami
- Chemical Biology & Proteomics, Biogen, Cambridge, Massachusetts, USA
| | - Xudong Yao
- Department of Chemistry, University of Connecticut, Storrs, Connecticut, USA
- Institute for Systems Biology, University of Connecticut, Storrs, Connecticut, USA
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Ebbels TMD, van der Hooft JJJ, Chatelaine H, Broeckling C, Zamboni N, Hassoun S, Mathé EA. Recent advances in mass spectrometry-based computational metabolomics. Curr Opin Chem Biol 2023; 74:102288. [PMID: 36966702 PMCID: PMC11075003 DOI: 10.1016/j.cbpa.2023.102288] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Revised: 02/16/2023] [Accepted: 02/21/2023] [Indexed: 04/03/2023]
Abstract
The computational metabolomics field brings together computer scientists, bioinformaticians, chemists, clinicians, and biologists to maximize the impact of metabolomics across a wide array of scientific and medical disciplines. The field continues to expand as modern instrumentation produces datasets with increasing complexity, resolution, and sensitivity. These datasets must be processed, annotated, modeled, and interpreted to enable biological insight. Techniques for visualization, integration (within or between omics), and interpretation of metabolomics data have evolved along with innovation in the databases and knowledge resources required to aid understanding. In this review, we highlight recent advances in the field and reflect on opportunities and innovations in response to the most pressing challenges. This review was compiled from discussions from the 2022 Dagstuhl seminar entitled "Computational Metabolomics: From Spectra to Knowledge".
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Affiliation(s)
- Timothy M D Ebbels
- Section of Bioinformatics, Department of Metabolism, Digestion & Reproduction, Imperial College London, Burlington Danes Building, Hammersmith Hospital, Du Cane Road, London W12 0NN, UK.
| | - Justin J J van der Hooft
- Bioinformatics Group, Wageningen University & Research, Wageningen 6708 PB, the Netherlands; Department of Biochemistry, University of Johannesburg, Auckland Park, Johannesburg 2006, South Africa
| | - Haley Chatelaine
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA
| | - Corey Broeckling
- Bioanalysis and Omics Center, Analytical Resources Core, Colorado State University, Fort Collins, CO, USA
| | - Nicola Zamboni
- Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Soha Hassoun
- Department of Computer Science, Tufts University, Medford, MA, USA; Department of Chemical and Biological Engineering, Tufts University, Medford, MA, USA
| | - Ewy A Mathé
- Informatics Core, Division of Preclinical Innovation, National Center for Advancing Translational Sciences, Rockville, MD, USA.
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Smith JW, O'Meally RN, Burke SM, Ng DK, Chen JG, Kensler TW, Groopman JD, Cole RN. Global Discovery and Temporal Changes of Human Albumin Modifications by Pan-Protein Adductomics: Initial Application to Air Pollution Exposure. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:595-607. [PMID: 36939690 DOI: 10.1021/jasms.2c00314] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Assessing personal exposure to environmental toxicants is a critical challenge for predicting disease risk. Previously, using human serum albumin (HSA)-based biomonitoring, we reported dosimetric relationships between adducts at HSA Cys34 and ambient air pollutant levels (Smith et al., Chem. Res. Toxicol. 2021, 34, 1183). These results provided the foundation to explore modifications at other sites in HSA to reveal novel adducts of complex exposures. Thus, the Pan-Protein Adductomics (PPA) technology reported here is the next step toward an unbiased, comprehensive characterization of the HSA adductome. The PPA workflow requires <2 μL serum/plasma and uses nanoflow-liquid chromatography, gas-phase fractionation, and overlapping-window data-independent acquisition high-resolution tandem mass spectrometry. PPA analysis of albumin from nonsmoking women exposed to high levels of air pollution uncovered 68 unique location-specific modifications (LSMs) across 21 HSA residues. While nearly half were located at Cys34 (33 LSMs), 35 were detected on other residues, including Lys, His, Tyr, Ser, Met, and Arg. HSA adduct relative abundances spanned a ∼400 000-fold range and included putative products of exogenous (SO2, benzene, phycoerythrobilin) and endogenous (oxidation, lipid peroxidation, glycation, carbamylation) origin, as well as 24 modifications without annotations. PPA quantification revealed statistically significant changes in LSM levels across the 84 days of monitoring (∼3 HSA lifetimes) in the following putative adducts: Cys34 trioxidation, β-methylthiolation, benzaldehyde, and benzene diol epoxide; Met329 oxidation; Arg145 dioxidation; and unannotated Cys34 and His146 adducts. Notably, the PPA workflow can be extended to any protein. Pan-Protein Adductomics is a novel and powerful strategy for untargeted global exploration of protein modifications.
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Affiliation(s)
- Joshua W Smith
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21205, United States
| | - Robert N O'Meally
- Department of Biological Chemistry, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21205, United States
| | - Sean M Burke
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21205, United States
| | - Derek K Ng
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, United States
| | - Jian-Guo Chen
- Qidong Liver Cancer Institute, Qidong People's Hospital, Affiliated Qidong Hospital of Nantong University, Qidong, Jiangsu 226200, P. R. China
| | - Thomas W Kensler
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, United States
| | - John D Groopman
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland 21205, United States
| | - Robert N Cole
- Department of Biological Chemistry, School of Medicine, Johns Hopkins University, Baltimore, Maryland 21205, United States
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VoPham T, Jones RR. State of the science on outdoor air pollution exposure and liver cancer risk. ENVIRONMENTAL ADVANCES 2023; 11:100354. [PMID: 36875691 PMCID: PMC9984166 DOI: 10.1016/j.envadv.2023.100354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Background There is emerging evidence that air pollution exposure increases the risk of developing liver cancer. To date, there have been four epidemiologic studies conducted in the United States, Taiwan, and Europe showing generally consistent positive associations between ambient exposure to air pollutants, including particulate matter <2.5 μm in aerodynamic diameter (PM2.5) and nitrogen dioxide (NO2), and liver cancer risk. There are several research gaps and thus valuable opportunities for future work to continue building on this expanding body of literature. The objectives of this paper are to narratively synthesize existing epidemiologic literature on the association between air pollution exposure and liver cancer incidence and describe future research directions to advance the science of understanding the role of air pollution exposure in liver cancer development. Future research directions include 1) accounting for potential confounding by established risk factors for the predominant histological subtype, hepatocellular carcinoma (HCC); 2) examination of incident primary liver cancer outcomes with consideration of potential differential associations according to histology; 3) air pollution exposure assessments considering early-life and/or historical exposures, residential histories, residual confounding from other sources of air pollution (e.g., tobacco smoking), and integration of geospatial ambient exposure modeling with novel biomarker technologies; 4) examination of air pollution mixtures experienced in the exposome; 5) consideration of increased opportunities for exposure to outdoor air pollution due to climate change (e.g., wildfires); and 6) consideration of modifying factors for air pollution exposure, such as socioeconomic status, that may contribute to disparities in liver cancer incidence. Conclusions In light of mounting evidence demonstrating that higher levels of air pollution exposure increase the risk for developing liver cancer, methodological considerations primarily concerning residual confounding and improved exposure assessment are warranted to robustly demonstrate an independent association for air pollution as a hepatocarcinogen.
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Affiliation(s)
- Trang VoPham
- Epidemiology Program, Public Health Sciences Division, Fred Hutchinson Cancer Center, 1100 Fairview Avenue N, Seattle, Washington 98109, United States
- Department of Epidemiology, University of Washington, 3980 15th Avenue NE, Seattle, Washington 98195, United States
| | - Rena R. Jones
- Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, 9609 Medical Center Drive MSC 9776, Bethesda, Maryland 20850, United States
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6
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Lockridge O. Overview of Adductomics in Toxicology. Curr Protoc 2023; 3:e672. [PMID: 36799690 PMCID: PMC9942099 DOI: 10.1002/cpz1.672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
Adductomics is epidemiology at the molecular level. Untargeted adductomics compares levels of chemical adducts on albumin, hemoglobin, and DNA between healthy and exposed individuals. The goal is to determine a cause-and-effect relationship between chemical exposure and illness. Chemical exposures are not necessarily due to synthetic chemicals but are often due to oxidation products of naturally occurring lipids, for example, 4-hydroxynonenal and acrolein produced by lipid peroxidation of arachidonic and linoleic acids. The preferred method used in adductomics is ultra-high pressure liquid chromatography coupled to with nanoelectrospray tandem mass spectrometry. The mass of the adduct indicates its structure and identifies the chemical. The advantages of molecular epidemiology include information about the many toxicants to which a person is exposed over a period of weeks or months and the relative exposure levels. The disadvantage is the absence of information about the mechanism of toxicity. Untargeted adductomics examines albumin and hemoglobin adducts, which serve as biomarkers of exposure but do not identify the proteins and genes responsible for the toxicity. Targeted adductomics is used when the origin of the toxicity is known. This can be either an adducted protein, such as the butyrylcholinesterase protein modified by nerve agents, or a toxicant, such as acetaminophen. Untargeted adductomics methods have identified potential protein adduct biomarkers of breast cancer, colorectal cancer, childhood leukemia, and lung cancer. Adductomics is a new research area that offers structural insights into chemical exposures and a platform for the discovery of disease biomarkers. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.
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Smith JW, Matchado AJ, Wu LSF, Arnold CD, Burke SM, Maleta KM, Ashorn P, Stewart CP, Shaikh S, Ali H, Labrique AB, West KP, Christian P, Dewey KG, Groopman JD, Schulze KJ. Longitudinal Assessment of Prenatal, Perinatal, and Early-Life Aflatoxin B 1 Exposure in 828 Mother-Child Dyads from Bangladesh and Malawi. Curr Dev Nutr 2022; 6:nzab153. [PMID: 35155983 PMCID: PMC8829025 DOI: 10.1093/cdn/nzab153] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 12/14/2021] [Accepted: 12/22/2021] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND In utero or early-life exposure to aflatoxin, which contaminates staple crops in disadvantaged settings, may compromise pregnancy and infant outcomes, but investigations into the extent, persistence, and determinants of aflatoxin exposure at these life stages have lacked longitudinal data collection and broad geographic representation. OBJECTIVES Aflatoxin exposure and selected determinants thereof were characterized in mother-child dyads with serial plasma/serum samples in prenatal, perinatal, and early life in Malawi and Bangladesh. METHODS Circulating aflatoxin B1 (AFB1)-lysine albumin adducts were measured in dyads from Bangladesh (n = 573; maternal first and third trimester, 3 mo postpartum, cord blood, infant 24 mo) and Malawi (n = 255; maternal second and third trimester, 6 mo postpartum, infant 6 and 18 mo) with isotope dilution mass spectrometry. We examined AFB1-lysine adduct magnitude, persistence, seasonality, and associations with infant feeding, and estimated daily AFB1 intake. RESULTS Maternal AFB1-lysine was higher in Malawi (98% detectable; median: 0.469, IQR: 0.225-1.027 pg/µL) than in Bangladesh (59%; 0.030, nondetectable [nd]-0.077 pg/µL). Although estimated dietary exposure in Malawi was temporally stable (648 ng AFB1/day), estimated intake in Bangladesh was reduced by 94% between rainy and winter seasons (98 to 6 ng/day). AFB1-lysine was low in cord blood from Bangladesh (15% detectable; 0.045, 0.031-0.088 pg/µL among detectable) and in Malawian infants at 6 mo of age (0.072, nd-0.236 pg/µL), but reached maternal concentrations by 18 or 24 mo (Bangladesh: 0.034, nd-0.063 pg/µL; Malawi: 0.370, 0.195-0.964 pg/µL). In Malawian infants, exclusive breastfeeding at 3 mo was associated with 58% lower AFB1-lysine concentrations at 6 mo compared with other feeding modes (P = 0.010). CONCLUSIONS Among pregnant women, aflatoxin exposure was persistently high in Malawi, while lower and seasonal in Bangladesh. Infants were partially protected from exposure in utero and with exclusive breastfeeding, but exposures reached adult levels by 18-24 mo of age. The Bangladesh and Malawi trials are registered at clinicaltrials.gov as NCT00860470 and NCT01239693.
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Affiliation(s)
- Joshua W Smith
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Andrew J Matchado
- Department of Nutrition and Institute for Global Nutrition, University of California, Davis, Davis, CA, USA
- School of Public Health and Family Medicine, College of Medicine, University of Malawi, Blantyre, Malawi
| | - Lee S-F Wu
- Center for Human Nutrition, Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Charles D Arnold
- Department of Nutrition and Institute for Global Nutrition, University of California, Davis, Davis, CA, USA
| | - Sean M Burke
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Kenneth M Maleta
- School of Public Health and Family Medicine, College of Medicine, University of Malawi, Blantyre, Malawi
| | - Per Ashorn
- Tampere University, Faculty of Medicine and Health Technology, Center for Child, Adolescent and Maternal Health Research and Tampere University Hospital, Department of Pediatrics, Tampere, Finland
| | - Christine P Stewart
- Department of Nutrition and Institute for Global Nutrition, University of California, Davis, Davis, CA, USA
| | - Saijuddin Shaikh
- Center for Human Nutrition, Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- The JiVitA Project of Johns Hopkins University, Bangladesh, Gaibandha, Bangladesh
| | - Hasmot Ali
- Center for Human Nutrition, Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- The JiVitA Project of Johns Hopkins University, Bangladesh, Gaibandha, Bangladesh
| | - Alain B Labrique
- Center for Human Nutrition, Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Keith P West
- Center for Human Nutrition, Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Parul Christian
- Center for Human Nutrition, Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Kathryn G Dewey
- Department of Nutrition and Institute for Global Nutrition, University of California, Davis, Davis, CA, USA
| | - John D Groopman
- Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Kerry J Schulze
- Center for Human Nutrition, Department of International Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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Abstract
Chemicals are measured regularly in air, food, the environment, and the workplace. Biomonitoring of chemicals in biological fluids is a tool to determine the individual exposure. Blood protein adducts of xenobiotics are a marker of both exposure and the biologically effective dose. Urinary metabolites and blood metabolites are short term exposure markers. Stable hemoglobin adducts are exposure markers of up to 120 days. Blood protein adducts are formed with many xenobiotics at different sites of the blood proteins. Newer methods apply the techniques developed in the field of proteomics. Larger adducted peptides with 20 amino acids are used for quantitation. Unfortunately, at present the methods do not reach the limits of detection obtained with the methods looking at single amino acid adducts or at chemically cleaved adducts. Therefore, to progress in the field new approaches are needed.
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