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Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration
BACKGROUND: Type 2 diabetes (T2D) onset is a complex, organized biological process with multilevel regulation, and its physiopathological mechanisms are yet to be elucidated. This study aims to find out the key drivers and pathways involved in the pathogenesis of T2D through multi-omics analysis. ME...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9756634/ https://www.ncbi.nlm.nih.gov/pubmed/36527108 http://dx.doi.org/10.1186/s12967-022-03826-5 |
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author | Liu, Jiachen Liu, Shenghua Yu, Zhaomei Qiu, Xiaorui Jiang, Rundong Li, Weizheng |
author_facet | Liu, Jiachen Liu, Shenghua Yu, Zhaomei Qiu, Xiaorui Jiang, Rundong Li, Weizheng |
author_sort | Liu, Jiachen |
collection | PubMed |
description | BACKGROUND: Type 2 diabetes (T2D) onset is a complex, organized biological process with multilevel regulation, and its physiopathological mechanisms are yet to be elucidated. This study aims to find out the key drivers and pathways involved in the pathogenesis of T2D through multi-omics analysis. METHODS: The datasets used in the experiments comprise three groups: (1) genomic (2) transcriptomic, and (3) epigenomic categories. Then, a series of bioinformatics technologies including Marker set enrichment analysis (MSEA), weighted key driver analysis (wKDA) was performed to identify key drivers. The hub genes were further verified by the Receiver Operator Characteristic (ROC) Curve analysis, proteomic analysis, and Real-time quantitative polymerase chain reaction (RT-qPCR). The multi-omics network was applied to the Pharmomics pipeline in Mergeomics to identify drug candidates for T2D treatment. Then, we used the drug-gene interaction network to conduct network pharmacological analysis. Besides, molecular docking was performed using AutoDock/Vina, a computational docking program. RESULTS: Module-gene interaction network was constructed using MSEA, which revealed a significant enrichment of immune-related activities and glucose metabolism. Top 10 key drivers (PSMB9, COL1A1, COL4A1, HLA-DQB1, COL3A1, IRF7, COL5A1, CD74, HLA-DQA1, and HLA-DRB1) were selected by wKDA analysis. Among these, COL5A1, IRF7, CD74, and HLA-DRB1 were verified to have the capability to diagnose T2D, and expression levels of PSMB9 and CD74 had significantly higher in T2D patients. We further predict the co-expression network and transcription factor (TF) binding specificity of the key driver. Besides, based on module interaction networks and key driver networks, 17 compounds are considered to possess T2D-control potential, such as sunitinib. CONCLUSIONS: We identified signature genes, biomolecular processes, and pathways using multi-omics networks. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions of T2D. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03826-5. |
format | Online Article Text |
id | pubmed-9756634 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-97566342022-12-17 Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration Liu, Jiachen Liu, Shenghua Yu, Zhaomei Qiu, Xiaorui Jiang, Rundong Li, Weizheng J Transl Med Research BACKGROUND: Type 2 diabetes (T2D) onset is a complex, organized biological process with multilevel regulation, and its physiopathological mechanisms are yet to be elucidated. This study aims to find out the key drivers and pathways involved in the pathogenesis of T2D through multi-omics analysis. METHODS: The datasets used in the experiments comprise three groups: (1) genomic (2) transcriptomic, and (3) epigenomic categories. Then, a series of bioinformatics technologies including Marker set enrichment analysis (MSEA), weighted key driver analysis (wKDA) was performed to identify key drivers. The hub genes were further verified by the Receiver Operator Characteristic (ROC) Curve analysis, proteomic analysis, and Real-time quantitative polymerase chain reaction (RT-qPCR). The multi-omics network was applied to the Pharmomics pipeline in Mergeomics to identify drug candidates for T2D treatment. Then, we used the drug-gene interaction network to conduct network pharmacological analysis. Besides, molecular docking was performed using AutoDock/Vina, a computational docking program. RESULTS: Module-gene interaction network was constructed using MSEA, which revealed a significant enrichment of immune-related activities and glucose metabolism. Top 10 key drivers (PSMB9, COL1A1, COL4A1, HLA-DQB1, COL3A1, IRF7, COL5A1, CD74, HLA-DQA1, and HLA-DRB1) were selected by wKDA analysis. Among these, COL5A1, IRF7, CD74, and HLA-DRB1 were verified to have the capability to diagnose T2D, and expression levels of PSMB9 and CD74 had significantly higher in T2D patients. We further predict the co-expression network and transcription factor (TF) binding specificity of the key driver. Besides, based on module interaction networks and key driver networks, 17 compounds are considered to possess T2D-control potential, such as sunitinib. CONCLUSIONS: We identified signature genes, biomolecular processes, and pathways using multi-omics networks. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions of T2D. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-022-03826-5. BioMed Central 2022-12-16 /pmc/articles/PMC9756634/ /pubmed/36527108 http://dx.doi.org/10.1186/s12967-022-03826-5 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Liu, Jiachen Liu, Shenghua Yu, Zhaomei Qiu, Xiaorui Jiang, Rundong Li, Weizheng Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration |
title | Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration |
title_full | Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration |
title_fullStr | Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration |
title_full_unstemmed | Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration |
title_short | Uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration |
title_sort | uncovering the gene regulatory network of type 2 diabetes through multi-omic data integration |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9756634/ https://www.ncbi.nlm.nih.gov/pubmed/36527108 http://dx.doi.org/10.1186/s12967-022-03826-5 |
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