Refinement of a Methodology for Untargeted Detection of Serum Albumin Adducts in Human Populations
Author(s) -
George W. Preston,
Michelle Plusquin,
Osman Sözeri,
Karin van Veldhoven,
Lilian Bastian,
Tim S. Nawrot,
Marc ChadeauHyam,
David H. Phillips
Publication year - 2017
Publication title -
chemical research in toxicology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.031
H-Index - 156
eISSN - 1520-5010
pISSN - 0893-228X
DOI - 10.1021/acs.chemrestox.7b00186
Subject(s) - adduct , albumin , chemistry , computational biology , proteomics , serum albumin , human serum albumin , biochemistry , biology , combinatorial chemistry , gene , organic chemistry
Covalently modified blood proteins (e.g., serum albumin adducts) are increasingly being viewed as potential biomarkers via which the environmental causes of human diseases may be understood. The notion that some (perhaps many) modifications have yet to be discovered has led to the development of untargeted adductomics methods, which attempt to capture entire populations of adducts. One such method is fixed-step selected reaction monitoring (FS-SRM), which analyses distributions of serum albumin adducts via shifts in the mass of a tryptic peptide [Li et al. (2011) Mol. Cell. Proteomics 10, M110.004606]. Working on the basis that FS-SRM might be able to detect biological variation due to environmental factors, we aimed to scale the methodology for use in an epidemiological setting. Development of sample preparation methods led to a batch workflow with increased throughput and provision for quality control. Challenges posed by technical and biological variation were addressed in the processing and interpretation of the data. A pilot study of 20 smokers and 20 never-smokers provided evidence of an effect of smoking on levels of putative serum albumin adducts. Differences between smokers and never-smokers were most apparent in putative adducts with net gains in mass between 105 and 114 Da (relative to unmodified albumin). The findings suggest that our implementation of FS-SRM could be useful for studying other environmental factors with relevance to human health.
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