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Identification of key genes and pathways revealing the central regulatory mechanism of brain-derived glucagon-like peptide-1 on obesity using bioinformatics analysis

OBJECTIVE: Central glucagon-like peptide-1 (GLP-1) is a target in treating obesity due to its effect on suppressing appetite, but the possible downstream key genes that GLP-1 regulated have not been studied in depth. This study intends to screen out the downstream feeding regulation genes of central...

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Autores principales: Shao, Yuwei, Tian, Jun, Yang, Yanan, Hu, Yan, Zhu, Ye, Shu, Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9389235/
https://www.ncbi.nlm.nih.gov/pubmed/35992905
http://dx.doi.org/10.3389/fnins.2022.931161
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author Shao, Yuwei
Tian, Jun
Yang, Yanan
Hu, Yan
Zhu, Ye
Shu, Qing
author_facet Shao, Yuwei
Tian, Jun
Yang, Yanan
Hu, Yan
Zhu, Ye
Shu, Qing
author_sort Shao, Yuwei
collection PubMed
description OBJECTIVE: Central glucagon-like peptide-1 (GLP-1) is a target in treating obesity due to its effect on suppressing appetite, but the possible downstream key genes that GLP-1 regulated have not been studied in depth. This study intends to screen out the downstream feeding regulation genes of central GLP-1 neurons through bioinformatics analysis and verify them by chemical genetics, which may provide insights for future research. MATERIALS AND METHODS: GSE135862 genetic expression profiles were extracted from the Gene Expression Omnibus (GEO) database. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were carried out. STRING database and Cytoscape software were used to map the protein-protein interaction (PPI) network of the differentially expressed genes (DEGs). After bioinformatics analysis, we applied chemogenetic methods to modulate the activities of GLP-1 neurons in the nucleus tractus solitarius (NTS) and observed the alterations of screened differential genes and their protein expressions in the hypothalamus under different excitatory conditions of GLP-1 neurons. RESULTS: A total of 49 DEGs were discovered, including 38 downregulated genes and 11 upregulated genes. The two genes with the highest expression scores were biglycan (Bgn) and mitogen-activated protein kinase activated protein kinase 3 (Mapkapk3). The results of GO analysis showed that there were 10 molecular functions of differential genes. Differential genes were mainly localized in seven regions around the cells, and enriched in 10 biology processes. The results of the KEGG signaling pathway enrichment analysis showed that differential genes played an important role in seven pathways. The top 15 genes selected according to the Cytoscape software included Bgn and Mapkapk3. Chemogenetic activation of GLP-1 in NTS induced a decrease in food intake and body mass, while chemogenetic inhibition induced the opposite effect. The gene and protein expression of GLP-1 were upregulated in NTS when activated by chemogenetics. In addition, the expression of Bgn was upregulated and that of Mapkapk3 was downregulated in the hypothalamus. CONCLUSION: Our data showed that GLP-1 could modulate the protein expression of Bgn and Mapkapk3. Our findings elucidated the regulatory network in GLP-1 to obesity and might provide a novel diagnostic and therapeutic target for obesity.
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spelling pubmed-93892352022-08-20 Identification of key genes and pathways revealing the central regulatory mechanism of brain-derived glucagon-like peptide-1 on obesity using bioinformatics analysis Shao, Yuwei Tian, Jun Yang, Yanan Hu, Yan Zhu, Ye Shu, Qing Front Neurosci Neuroscience OBJECTIVE: Central glucagon-like peptide-1 (GLP-1) is a target in treating obesity due to its effect on suppressing appetite, but the possible downstream key genes that GLP-1 regulated have not been studied in depth. This study intends to screen out the downstream feeding regulation genes of central GLP-1 neurons through bioinformatics analysis and verify them by chemical genetics, which may provide insights for future research. MATERIALS AND METHODS: GSE135862 genetic expression profiles were extracted from the Gene Expression Omnibus (GEO) database. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were carried out. STRING database and Cytoscape software were used to map the protein-protein interaction (PPI) network of the differentially expressed genes (DEGs). After bioinformatics analysis, we applied chemogenetic methods to modulate the activities of GLP-1 neurons in the nucleus tractus solitarius (NTS) and observed the alterations of screened differential genes and their protein expressions in the hypothalamus under different excitatory conditions of GLP-1 neurons. RESULTS: A total of 49 DEGs were discovered, including 38 downregulated genes and 11 upregulated genes. The two genes with the highest expression scores were biglycan (Bgn) and mitogen-activated protein kinase activated protein kinase 3 (Mapkapk3). The results of GO analysis showed that there were 10 molecular functions of differential genes. Differential genes were mainly localized in seven regions around the cells, and enriched in 10 biology processes. The results of the KEGG signaling pathway enrichment analysis showed that differential genes played an important role in seven pathways. The top 15 genes selected according to the Cytoscape software included Bgn and Mapkapk3. Chemogenetic activation of GLP-1 in NTS induced a decrease in food intake and body mass, while chemogenetic inhibition induced the opposite effect. The gene and protein expression of GLP-1 were upregulated in NTS when activated by chemogenetics. In addition, the expression of Bgn was upregulated and that of Mapkapk3 was downregulated in the hypothalamus. CONCLUSION: Our data showed that GLP-1 could modulate the protein expression of Bgn and Mapkapk3. Our findings elucidated the regulatory network in GLP-1 to obesity and might provide a novel diagnostic and therapeutic target for obesity. Frontiers Media S.A. 2022-08-05 /pmc/articles/PMC9389235/ /pubmed/35992905 http://dx.doi.org/10.3389/fnins.2022.931161 Text en Copyright © 2022 Shao, Tian, Yang, Hu, Zhu and Shu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Shao, Yuwei
Tian, Jun
Yang, Yanan
Hu, Yan
Zhu, Ye
Shu, Qing
Identification of key genes and pathways revealing the central regulatory mechanism of brain-derived glucagon-like peptide-1 on obesity using bioinformatics analysis
title Identification of key genes and pathways revealing the central regulatory mechanism of brain-derived glucagon-like peptide-1 on obesity using bioinformatics analysis
title_full Identification of key genes and pathways revealing the central regulatory mechanism of brain-derived glucagon-like peptide-1 on obesity using bioinformatics analysis
title_fullStr Identification of key genes and pathways revealing the central regulatory mechanism of brain-derived glucagon-like peptide-1 on obesity using bioinformatics analysis
title_full_unstemmed Identification of key genes and pathways revealing the central regulatory mechanism of brain-derived glucagon-like peptide-1 on obesity using bioinformatics analysis
title_short Identification of key genes and pathways revealing the central regulatory mechanism of brain-derived glucagon-like peptide-1 on obesity using bioinformatics analysis
title_sort identification of key genes and pathways revealing the central regulatory mechanism of brain-derived glucagon-like peptide-1 on obesity using bioinformatics analysis
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9389235/
https://www.ncbi.nlm.nih.gov/pubmed/35992905
http://dx.doi.org/10.3389/fnins.2022.931161
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