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Proteomics. 2015 Aug;15(15):2678-90. doi: 10.1002/pmic.201400606. Epub 2015 Jun 11.

Machine learning reveals sex-specific 17β-estradiol-responsive expression patterns in white perch (Morone americana) plasma proteins.

Author information

1
Department of Applied Ecology, North Carolina State University, Raleigh, NC, USA.
2
W. M. Keck FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, NC, USA.
3
Department of Biological Sciences, North Carolina State University, Raleigh, NC, USA.
4
Center for Human Health and the Environment, North Carolina State University, Raleigh, NC, USA.
5
Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, NC, USA.
6
Center for Comparative Medicine and Translational Research, North Carolina State University, Raleigh, NC, USA.
7
Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA.
8
Faculty of Fisheries Sciences, Hokkaido University, Hakodate, Hokkaido, Japan.

Abstract

With growing abundance and awareness of endocrine disrupting compounds (EDCs) in the environment, there is a need for accurate and reliable detection of EDC exposure. Our objective in the present study was to observe differences within and between the global plasma proteomes of sexually mature male and female white perch (Morone americana) before (Initial Control, IC) and after 17β-estradiol (E2 ) induction. Semiquantitative nanoLC-MS/MS data were analyzed by machine learning support vector machines (SVMs) and by two-way ANOVA. By ANOVA, the expression levels of 44, 77, and 57 proteins varied significantly by gender, treatment, and the interaction of gender and treatment, respectively. SVMs perfectly classified male and female perch IC and E2 -induced plasma samples using the protein expression data. E2 -induced male and female perch plasma proteomes contained significantly higher levels of the yolk precursors vitellogenin Aa and Ab (VtgAa, VtgAb), as well as latrophilin and seven transmembrane domain-containing protein 1 (Eltd1) and kininogen 1 (Kng1). This is the first report that Eltd1 and Kng1 may be E2 -responsive proteins in fishes and therefore may be useful indicators of estrogen induction.

KEYWORDS:

Animal proteomics; Endocrine disrupting compounds; Machine learning; Plasma; Semiquantitative proteomics; Support vector machines

PMID:
25900664
PMCID:
PMC5765861
DOI:
10.1002/pmic.201400606
[Indexed for MEDLINE]
Free PMC Article

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