Proteomic profiling in the sera of workers occupationally exposed to arsenic and lead: identification of potential biomarkers

Arsenic (As) and lead (Pb) are important inorganic toxicants in the environment. Frequently, humans are exposed to the mixtures of As and Pb, but little is known about the expression of biomarkers resulting from such mixed exposures. In this study, we analyzed serum proteomic profiles in a group of...

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Bibliographic Details
Published in:Biometals 2005-12, Vol.18 (6), p.603-613
Main Authors: Zhai, Rihong, Su, Suhua, Lu, Xin, Liao, Ruiqing, Ge, Xianmin, He, Min, Huang, Yuanjiao, Mai, Sui, Lu, Xi, Christiani, David
Format: Article
Language:English
Subjects:
Adult
Arsenic
Arsenic - blood
Arsenic - urine
Bioinformatics
Biomarkers
Biomarkers - blood
Humans
Ionization
Lead
Lead - blood
Lead - urine
Male
Occupational Exposure
Peptides
Peptides - blood
Predictive Value of Tests
Proteins - analysis
Proteomics - methods
Sensitivity and Specificity
Smelters
Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization - methods
Toxicants
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Summary:Arsenic (As) and lead (Pb) are important inorganic toxicants in the environment. Frequently, humans are exposed to the mixtures of As and Pb, but little is known about the expression of biomarkers resulting from such mixed exposures. In this study, we analyzed serum proteomic profiles in a group of smelter workers with the aim of identifying protein biomarkers of mixed As and Pb exposure. Forty-six male workers co-exposed to As and Pb were studied. Forty-five age-matched male office workers were chosen as controls. Urine As and blood Pb concentrations were determined. Serum proteomic profiles were analyzed by Surface-Enhanced Laser Desorption/Ionization Time-Of-Flight (SELDI-TOF) mass spectrometer on the WCX2 ProteinChip. Using Recursive support vector machine (RSVM) algorithm, a panel of five peptides/proteins (2097 Da, 2953 Da, 3941 Da, 5338 Da, and 5639 Da) was selected based on their collective contribution to the optional separation between higher metal mixture exposure and non-exposure controls. Among these five selected markers, the 3941 Da was down-regulated and the four other proteins were up-regulated. Descriptive statistics confirmed that these five proteins differed significantly between metal exposure and non-exposure. Interestingly, the combined use of the five selected biomarkers could achieve higher discriminative power than single marker. These results demonstrated that proteomic technology, in conjunction with bioinformatics tools, could facilitate the discovery of new and better biomarkers of mixed metal exposure.
ISSN:0966-0844
1572-8773
DOI:10.1007/s10534-005-3001-x