Prediction and validation of potential targets for the glucagon-like peptide-1 receptor agonist in the treatment of Alzheimer’s disease

doi:10.12307/2024.306

Abstract

Abstract: BACKGROUND: In the process of exploring the mechanism of Alzheimer’s disease, the important role of bioinformatics for common target screening has been revealed, enabling the use of its screening results as a basis for exploring the therapeutic effects of drugs on the disease.
OBJECTIVE: To predict the targets of liraglutide, a glucagon-like peptide-1 receptor agonist, in the treatment of Alzheimer’s disease by bioinformatics and molecular biology.
METHODS: DisGeNET database and SEA database were used to obtain the common genes of Alzheimer’s disease and liraglutide. GO/KEGG enrichment analysis of common targets was conducted using DAVID online database. Protein-protein interaction networks were constructed using STRING database. The optimal dosage of liraglutide was determined using cell counting kit-8 assay. Expression of key proteins was analyzed using immunofluorescence and immunoblotting techniques. The mouse hippocampal neuron HT22 cell line was used for ex vivo experiments, and the cells were randomly divided into three groups: HT22 group, HT22+Aβ group, and HT22+Aβ+Lir group. No special treatment was done in the HT22 group, while Aβ1-42 was used to intervene in the HT22 cell line for 24 hours to construct an Aβ injury cell model in the HT22+Aβ group. In additional to modeling, liraglutide was added to the HT22+Aβ+Lir group for 12 hours.
RESULTS AND CONCLUSION: A total of 3 333 genes associated with Alzheimer’s disease were screened from DisGeNET database. Then 147 potential targets of liraglutide were obtained from SEA database. Finally, 64 common targets of Alzheimer’s disease and Liraglutide were determined using R packets. GO/KEGG analysis of common targets using DAVID online database suggested that common targets were mainly enriched in the following biological processes: neuroactive ligand-receptor interaction, renin-angiotensin system, bladder cancer, endopeptidase activity, peptide receptor activity, G protein-coupled peptide receptor activity, and transport vesicles. The obtained 64 common target proteins were imported into SRTING online database for protein-protein interaction network construction, and the top three genes, matrix metalloproteinases 2, 9 and interleukin 1β, were obtained. The activity of cultured cells was detected by the cell counting kit-8 kit. Liraglutide at 100 nmol/L was the optimal concentration for antagonizing Aβ1-42. In the western blot and immunofluorescence assays, the expression of matrix metalloproteinases 2, 9 and interleukin 1β was significantly increased in the HT22+Aβ group compared with the HT22 group (P < 0.05) but significantly decreased in the HT22+Aβ+Lir group compared with the HT22+Aβ group (P < 0.05). To conclude, the above bioinformatics data and secondary validation of differential genes in the GEO database suggest that both matrix metalloproteinases 2,9 and interleukin 1β could be potential targets of liraglutide in the treatment of Alzheimer’s disease.

Key words: Alzheimer’s disease, liraglutide, glucagon-like peptide-1 receptor, bioinformatics, DisGeNET database, SEA database, GEO database, MMP9, MMP2, interleukin 1β

CLC Number:

Han Weina, Xu Xiaoqing, Shi Jinning, Li Xinru, Cai Hongyan. Prediction and validation of potential targets for the glucagon-like peptide-1 receptor agonist in the treatment of Alzheimer’s disease[J]. Chinese Journal of Tissue Engineering Research, 2024, 28(16): 2568-2573.

Figures/Tables 7

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