Comparison of proteome alterations during aging in the temporal lobe of humans and rhesus macaques.
Xia WangKang ChenMeng PanWei GeZhanlong HePublished in: Experimental brain research (2020)
Rhesus macaques are widely used as animal models for studies of the nervous system; however, it is unknown whether the alterations in the protein profile of the brain during aging are conserved between humans and rhesus macaques. In this study, temporal cortex samples from old and young humans (84 vs. 34 years, respectively) or rhesus macaques (20 vs. 6 years, respectively) were subjected to tandem mass tag-labeled proteomic analysis followed by bioinformatic analysis. A total of 3861 homologous pairs of proteins were identified during the aging process. The conservatively upregulated proteins (n = 190) were involved mainly in extracellular matrix (ECM), focal adhesion and coagulation; while, the conservatively downregulated proteins (n = 56) were enriched in ribosome. Network analysis showed that these conservatively regulated proteins interacted with each other with respect to protein synthesis and cytoskeleton-ECM connection. Many proteins in the focal adhesion, blood clotting, complement and coagulation, and cytoplasmic ribosomal protein pathways were regulated in the same direction in human and macaque; while, proteins involved in oligodendrocyte specification and differentiation pathways were downregulated during human aging, and many proteins in the electron transport chain pathway showed differences in the altered expression profiles. Data are available via ProteomeXchange with identifier PXD013597. Our findings suggest similarities in some changes in brain protein profiles during aging both in humans and macaques, although other changes are unique to only one of these species.
Keyphrases
- extracellular matrix
- endothelial cells
- network analysis
- transcription factor
- white matter
- multiple sclerosis
- escherichia coli
- binding protein
- machine learning
- protein protein
- staphylococcus aureus
- pseudomonas aeruginosa
- deep learning
- artificial intelligence
- electronic health record
- cystic fibrosis
- induced pluripotent stem cells
- solar cells