| Literature DB >> 33023073 |
Nailis Syifa1,2, Jhih-Tian Yang3, Chang-Shiann Wu4, Miao-Hsia Lin5, Wan-Ling Wu6, Cheng-Wei Lai1, Sheng-Hsuan Ku1, Suh-Yuen Liang6, Yu-Chun Hung1, Chia-Te Chou1, Chien-Sheng Wang1, Yasushi Ishihama5, Jiahn-Haur Liao6, Shih-Hsiung Wu6, Tzu-Hua Wu1,7,8.
Abstract
Protein phosphorylation can induce signal transduction to change sperm motility patterns during sperm capacitation. However, changes in the phosphorylation of sperm proteins in mice are still incompletely understood. Here, capacitation-related phosphorylation in mouse sperms were firstly investigated by label-free quantitative (LFQ) phosphoproteomics coupled with bioinformatics analysis using ingenuity pathway analysis (IPA) methods such as canonical pathway, upstream regulator, and network analysis. Among 1632 phosphopeptides identified at serine, threonine, and tyrosine residues, 1050 novel phosphosites, corresponding to 402 proteins, were reported. Gene heatmaps for IPA canonical pathways showed a novel role for GSK-3 in GP6 signaling pathways associated with capacitation for 60 min. At the same time, the reduction of the abundant isoform-specific GSK-3α expression was shown by western blot (WB) while the LFQ pY of this isoform slightly decreased and then increased. The combined results from WB and LFQ methods explain the less inhibitory phosphorylation of GSK-3α during capacitation and also support the predicted increases in its activity. In addition, pAKAP4 increased at the Y156 site but decreased at the Y811 site in a capacitated state, even though IPA network analysis and WB analysis for overall pAKAP revealed upregulated trends. The potential roles of GSK-3 and AKAP4 in fertility are discussed.Entities:
Keywords: AKAP4; GSK-3; IPA; bioinformatics; capacitation; mouse; sperm
Mesh:
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Year: 2020 PMID: 33023073 PMCID: PMC7582274 DOI: 10.3390/ijms21197283
Source DB: PubMed Journal: Int J Mol Sci ISSN: 1422-0067 Impact factor: 5.923
Figure 1Gene ontology (GO) analysis of the sperm capacitation phosphoproteome. The top 15 selected significant GO slim categories (p-value < 0.05) and the ranked frequencies of GO slim categories within the (a) cellular component, (b) molecular function, and (c) biological process gene ontologies are shown. The analyzed phosphoprotein datasets came from all datasets for the three capacitation times (0, 60, and 90 min).
Figure 2Profiling of protein phosphorylation during mouse sperm capacitation. (a) Numbers of phosphopeptides with probabilities > 0.75 for S, T, and Y phosphorylation identified during different capacitation times (0, 60, and 90 min). (b) Western blot analysis of Y-phosphorylated mouse sperm proteins after different capacitation times (0, 60, and 90 min) with a pY1000 antibody. (c) Label-free quantitation (LFQ) of mouse sperm AKAP4 before or after BSA-induced capacitation. (d) LFQ of phosphopeptide level changes from those at capacitation time zero after BSA-induced capacitation. The normalized ion intensities of Y-phosphorylated phosphopeptides corresponding to identified phosphoproteins are plotted versus capacitation time (X-axis). * indicates significant differences between the changes at time 60 vs. time 90 (p < 0.05).
Figure 3Differential phosphoproteomic analysis using ingenuity pathway analysis (IPA) for the Cap 60/0 and Cap 90/0 datasets. (a) Representative bar chart of the IPA-revealed canonical pathways for the Cap 60/0 and Cap 90/0 datasets. The orange lines represent the ratios of changed genes to the total number of genes in specific pathways. The threshold (set to 1.3) is scored as the −log p-value from Fisher’s exact test and indicates the minimum significance level. The ratio indicates the number of molecules in the dataset that mapped to the pathway listed divided by the total number of molecules that mapped to the canonical pathway within the IPA database. (b) Representative bar charts determined by IPA showing the biological functions associated with the phosphoproteins for the Cap 60/0 and Cap 90/0 datasets. (c) Heatmaps generated through IPA canonical pathway analysis for comparison among all datasets (Cap 60/0, Cap 90/0, and Cap 90/60). Upregulated pathways are shaded orange, and downregulated pathways are shaded blue. The intensity indicates the degree to which each gene was upregulated or downregulated as determined by the IPA-determined z-score.
Figure 4(a) Upstream regulators and their corresponding inhibition as predicted by IPA. F2 was predicted to be inhibited (z-score −2.147 and p-value 0.011). In this picture, activated upstream regulators are highlighted in orange, while inhibited upstream regulators are in blue. Colors in red and green indicated upregulated and downregulated proteins, respectively, and the color depth is correlated to the fold change. Dashed lines with arrows in orange and blue indicate indirect activation and inhibition, respectively. Yellow dashed lines with arrows indicated inconsistent effects, while gray dashed lines with arrows indicated no prediction. (b) IPA-based network of proteins involved in cell signaling, cellular movement, and reproductive system development and function for the Cap 60/0 datasets. In the figure, red represents upregulation and green represents downregulation. The color intensity represents the relative magnitude of the change in protein expression. Direct and indirect interactions are indicated by solid and dashed lines, respectively.
Figure 5Validation of MS results by western blot analysis for selected proteins. (a) Western blot analysis of GSK-3α, GSK-3β, alpha tubulin, AKAP4, and beta actin in noncapacitated (Con) and capacitated (Cap 60 and Cap 90) mouse sperm. (b) Relative expression of GSK-3α, GSK-3β, and AKAP4, (fold changes). * indicates significant differences compared to control (Con) group of each selected protein (p < 0.05).