Ultrafine carbon black (ufCB) is a potential hazard to the lung. It causes changes in protein expression and it increases alveolar-capillary permeability in the lung. Label-free quantitative proteomic methods allow a sensitive and accurate analytical method for identifying and quantifying proteins in a protein mixture without chemically modifying the proteins. We used a label-free quantitative proteomic approach that combined and aligned LC-MS and LC-MS/MS spectra to analyze mouse bronchoalveolar lavage fluid (BALF) protein changes associated with exposure to ufCB. We developed a simple normalization method for quantification without spiking the internal standard. The intensities of unchanged peptides were used as normalization factors based on a statistical method to avoid the influence of peptides changed because of ufCB. LC-MS/MS spectra and then database searching were used to identify proteins. The relative abundances of the aligned peptides of identified proteins were determined using LC-MS spectra. We identified 132 proteins, of which 77 are reported for the first time. In addition, the expression of 15 inflammatory proteins and surfactant-associated proteins was regulated (i.e., 7 upregulated and 8 downregulated) compared with the controls. Several proteins not previously reported provide complementary information on the proteins present in mouse BALF, and they are potential biomarkers for the understanding of mechanisms involved in ufCB-induced lung disorders hypothesize that using the label-free quantitative proteomic approach introduced here is well suited for more rigorous, large-scale quantitative analysis of biological samples. We hypothesize that this label-free quantitative proteomic approach will be suited for a large-scale quantitative analysis of biological samples.
All Science Journal Classification (ASJC) codes
- Analytical Chemistry
- Environmental Chemistry