| Objective: In this study,we used Label Free quantitative proteomics to analyze the characteristics and patterns of proteomic changes in senescence samples and screen the differential proteins to provide a reference for the selection of senescence biomarkers and the mechanisms of metabolic pathway changes during senescence.Methods : Senescence models were constructed by using Caenorhabditis elegans culture 12 angels for their natural senescence,and by using β-galactose to induce PC12 cells,both methods.Differential proteins in the senescence model were analyzed using Label-Free quantitative proteomics.DEG differential analysis,GO enrichment analysis and KEGG pathway enrichment analysis was performed on the proteomics data using R to screen for differential proteins.Molecular biological functions were evaluated by PPI analysis and used to find possible biomarkers of aging.Results: In the senescence model constructed using Caenorhabditis elegans,there were 419 differential proteins;GO enrichment analysis revealed that the differential proteins were focused on growth and proliferation and reproduction functions;KEGG enrichment pathways were autophagy-animal and lysosomal pathways;PPI analysis showed that the proteins of high node degree were atg-13 and unc-51.In the senescent cell model,there were 651 differential proteins;GO enrichment analysis focused on intracellular organelle inner membrane function and ion transport function;KEGG enrichment pathways were mainly neurodegenerative disease-related pathways;PPI analysis of high node degree proteins,Rpl19,Rpl27,Rpl29,Rpl35,and other ribosomal proteins accounted for the highest percentage.The expression of genes RECQL,SUCLG1,TMCO1 and GCH1 were significantly different in both aging models.Conclusions: In this study,we found that atg-13 and unc-51 were significantly different and highly associated with lysosomal pathway and autophagic pathway in Caenorhabditis elegans senescence model.In the PPI analysis of PC12 cell senescence model,we found that among the high node degree proteins in the results,ribosomal proteins had the highest percentage,such as Rpl19,Rpl27,Rpl29,Rpl35,etc.;histone deacetylases HDAC1 and HDAC2 also had high correlation.In addition,Tbp,Mphosph6,Mrpl20,Polr2 f and other highly associated differential proteins lacked studies related to aging,and the association of these proteins with aging could be further investigated.Among the proteins with significantly different expressions in both aging models,SUCLG1 and TMCO1 have been less studied in the aging;GCH1 has been shown to influence the onset of Parkinson’s disease and may be a promising biomarker of aging. |
