Methodology Optimization And Application Of C57BL/6J Mouse Liver Phosphoproteome

Protein phosphorylation is one of the most important post-translational modifications. Phosphorylation and de-phosphorylation process are the most general and important regulations [1] of intracellular signal transduction, gene expression, and many other life processes, which draw the widespread attention in the biological function research.Mass spectrometry as the core technology in phosphoproteomic research approaches can conduct large-scale quantitive and qualitive analysis systematically on phosphoproteins in complex system and thus to provide quantitative technology platform and highly valuable data sets for a variety of biological functions related to protein phosphorylation. However, low stoichiometry and poor ionization making detection of phosphorylated peptides in MS difficult. And a large number of high abundance of non-phosphorylated peptides in complex biological samples would further inhibit the presence of phosphopeptide signal. A direct mass spectrometry analysis of phosphopeptides would result in low detection rate, so a relative separation and enrichment of phosphopeptide before MS analysis is one of the essential steps.Metal-oxide enrichment, especially titanium dioxide enrichment is inexpensive, easy to operate, and effective to the enrichment of phosphopeptides. It is the most widely used phosphopeptide enrichment method. However, titanium dioxide absorbs not only phosphate groups but also acidic amino acids such as aspartic acid (D) and glutamic acid (E) in certain degree. In order to reduce these non-specific combinations some inhibitor is often added into the sample loading buffer to enhance the selectivity of enrichment.This study will introduce optimized titanium dioxide enrichment method by introducing aspartic acid as a new type of non-phosphorylated peptide absorption inhibitor, and the evaluation of this optimization on complex biological samples. Firstly, model organism C57/BL6J mouse liver phosphoproteomic data set was obtained and then large-scale of mouse liver sub-cellular phosphoproteome data sets. The corresponding bioinformatics analysis was further performed. This type of model organism's studies of phosphoproteome in the liver is still blank, data acquisition and the corresponding data analysis has laid a solid foundation in-depth study of protein phosphorylation's important role in liver physiological processes.The first part of the paper, based on the preliminary work, discussed further optimization and evaluation on titanium dioxide enrichment method, and confirmed aspartic acid as a novel non-phosphorylated peptide absorption inhibitor. Using three and nine standard protein mixture as the object of study to compare different non-phosphorylated peptide absorption inhibitors under same reaction conditions have proved aspartic acid's effectiveness and superiority as a non-phosphorylated peptide adsorption inhibitor. Thereafter, the optimized method is used in enriching mouse liver whole protein lysate, also achieved good results. In comparison with non-optimized method the enrichment selectivity has been increased markedly. This further confirmed the reliability of this optimized enrichment in complex sample application. The second part of the paper used SDS-PAGE method as pre-separation method, combined with optimized titanium dioxide enrichment method to get the C57BL/6J mouse liver phosphoproteome data set. Using preparative gel electrophoresis to separate mouse liver whole protein lysate, and Coomassie brilliant blue staining, the gel was divided into 15 slices and digested separately, then used the optimized enrichment mentioned in the first part on the peptides extracted from gel for following mass spectrometry detection. In order to obtain a more comprehensive and accurate data set, we have the data search conditions optimized and ultimately determined the best data search condition is: precursor ion mass tolerance set to"10ppm", enzyme set to"Trypsin"and MS/MS tolerance set to"0.8Da". And under such condition we obtained 1778 non-redundant phosphorylated peptides, 2417 phosphorylation sites and 1031 phosphorylated proteins. The proportion of phosphorylated serine, threonine and tyrosine is 85%, 13% and 2%, respectively which matches literature report. Besides, GO annotation and other data analyses were also performed. The experiment and analysis results of this chapter show that this platform can be used for obtaining large-scale phosphoproteome datasets.The third part of the paper, we used the technical platform that has been established and successfully obtained the whole phosphoproteome data sets in mouse liver, to separate C57BL/6J mouse liver sub-cellular components and obtain sub-cellular phosphoproteome data set. Together with whole phosphoproteome, we totally obtained 2442 non-redundant phosphorylated peptides, 3394 phosphorylation sites and 1402 phosphorylated proteins. Depth bioinformatic analyses were conducted i.e. data sets comparison, GO annotation, Motif-X analysis, pathway analysis, novel phosphorylation sites and proteins function analysis etc.