Proteomics-based evaluation of the mechanism underlying vascular injury via DNA interstrand crosslinks, glutathione perturbation, mitogen-activated protein kinase, and Wnt and ErbB signaling pathways induced by crotonaldehyde

Abstract

Crotonaldehyde (CRA)-one of the major environmental pollutants from tobacco smoke and industrial pollution-is associated with vascular injury (VI). We used proteomics to systematically characterize the presently unclear molecular mechanism of VI and to identify new related targets or signaling pathways after exposure to CRA. Cell survival assays were used to assess DNA damage, whereas oxidative stress was determined using colorimetric assays and by quantitative fluorescence study; additionally, cyclooxygenase-2, mitogen-activated protein kinase pathways, Wnt3a, β-catenin, phospho-ErbB2, and phospho-ErbB4 were assessed using ELISA. Proteins were quantitated via tandem mass tag-based liquid chromatography-mass spectrometry and bioinformatics analyses, and 34 differentially expressed proteins were confirmed using parallel reaction monitoring, which were defined as new indicators related to the mechanism underlying DNA damage; glutathione perturbation; mitogen-activated protein kinase; and the Wnt and ErbB signaling pathways in VI based on Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and protein-protein interaction network analyses. Parallel reaction monitoring confirmed significant (p < 0.05) upregulation (> 1.5-fold change) of 23 proteins and downregulation (< 0.667-fold change) of 11. The mechanisms of DNA interstrand crosslinks; glutathione perturbation; mitogen-activated protein kinase; cyclooxygenase-2; and the Wnt and ErbB signaling pathways may contribute to VI through their roles in DNA damage, oxidative stress, inflammation, vascular dysfunction, endothelial dysfunction, vascular remodeling, coagulation cascade, and the newly determined signaling pathways. Moreover, the Wnt and ErbB signaling pathways were identified as new disease pathways involved in VI. Taken together, the elucidated underlying mechanisms may help broaden existing understanding of the molecular mechanisms of VI induced by CRA.

Keywords: Crotonaldehyde; Differentially expressed proteins; Indicators; Vascular injury; Wnt and ErbB signaling pathways.

Fig. 1

The effects of crotonaldehyde (CRA) on human aortic endothelial cells (HAECs). A DNA damage induced by CRA in HAECs deficient in the FANC pathway. HAECs were exposed to a range of CRA concentrations—0, 0.5, 1, 2, 3, and 4 μM—and incubated for a period of 8 days. Drug lethal dose (LD₅₀) values were identified from data generated using survival curves and have been indicated by an arrow. Data are expressed as mean ± standard deviation of five independent experiments. B Effect of CRA on the depletion of mitochondrial membrane potential. C Effect of CRA on GSH levels. D Activation of COX-2 by CRA. E Activation of MAPK pathways, including P-ERK, P-JNK, and P-P38, by CRA. Data are expressed as mean ± standard deviation of three independent experiments (B–E); statistically significant differences are indicated by an asterisk; p < 0.05 was considered statistically significant

Fig. 2

Effects of crotonaldehyde (CRA) on the Wnt and ErbB signaling pathways. A Activation of β-catenin by CRA. B Activation of Wnt3a by CRA. C Activation of phospho-ErbB2 by CRA. D Activation of phospho-ErbB4 by CRA. *p < 0.05

Fig. 3

Differentially expressed proteins determined using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations. A The abscissa represents the percentage of genes; the ordinate lists the KEGG pathways. B The blue and red lines indicate p < 0.05, and p < 0.01, respectively

Fig. 4

Expression of differentially expressed proteins was analyzed using Gene Ontology (GO) enrichment analysis. The top ten biological process (A), molecular function (B), and cellular component (C) enrichment terms are shown. The abscissa represents the percentage of genes, while the ordinate indicates the details of the enrichment terms (p < 0.01)

Fig. 5

Protein–protein interactions between the differentially expressed proteins were determined using the STRING database. Nodes represent the proteins, and lines represent protein–protein interactions