Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice
Background Diabetic kidney disease (DKD) is the leading cause of death in people with type 2 diabetes mellitus (T2DM). The main objective of this study is to find the potential biomarkers for DKD. Materials and Methods Two datasets (GSE86300 and GSE184836) retrieved from Gene Expression Omnibus (GEO...
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PeerJ Inc.
2022-09-01
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| Online Access: | https://peerj.com/articles/13932.pdf |
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| author | Jing Zhao Kaiying He Hongxuan Du Guohua Wei Yuejia Wen Jiaqi Wang Xiaochun Zhou Jianqin Wang |
| author_facet | Jing Zhao Kaiying He Hongxuan Du Guohua Wei Yuejia Wen Jiaqi Wang Xiaochun Zhou Jianqin Wang |
| author_sort | Jing Zhao |
| collection | DOAJ |
| description | Background Diabetic kidney disease (DKD) is the leading cause of death in people with type 2 diabetes mellitus (T2DM). The main objective of this study is to find the potential biomarkers for DKD. Materials and Methods Two datasets (GSE86300 and GSE184836) retrieved from Gene Expression Omnibus (GEO) database were used, combined with our RNA sequencing (RNA-seq) results of DKD mice (C57 BLKS-32w db/db) and non-diabetic (db/m) mice for further analysis. After processing the expression matrix of the three sets of data using R software “Limma”, differential expression analysis was performed. The significantly differentially expressed genes (DEGs) (—logFC— > 1, p-value < 0.05) were visualized by heatmaps and volcano plots respectively. Next, the co-expression genes expressed in the three groups of DEGs were obtained by constructing a Venn diagram. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were further analyzed the related functions and enrichment pathways of these co-expression genes. Then, qRT-PCR was used to verify the expression levels of co-expression genes in the kidney of DKD and control mice. Finally, protein-protein interaction network (PPI), GO, KEGG analysis and Pearson correlation test were performed on the experimentally validated genes, in order to clarify the possible mechanism of them in DKD. Results Our RNA-seq results identified a total of 125 DEGs, including 59 up-regulated and 66 down-regulated DEGs. At the same time, 183 up-regulated and 153 down-regulated DEGs were obtained in GEO database GSE86300, and 76 up-regulated and 117 down-regulated DEGs were obtained in GSE184836. Venn diagram showed that 13 co-expression DEGs among the three groups of DEGs. GO analysis showed that biological processes (BP) were mainly enriched inresponse to stilbenoid, response to fatty acid, response to nutrient, positive regulation of macrophage derived foam cell differentiation, triglyceride metabolic process. KEGG pathway analysis showed that the three major enriched pathways were cholesterol metabolism, drug metabolism–cytochrome P450, PPAR signaling pathway. After qRT-PCR validation, we obtained 11 genes that were significant differentially expressed in the kidney tissues of DKD mice compared with control mice. (The mRNA expression levels of Aacs, Cpe, Cd36, Slc22a7, Slc1a4, Lpl, Cyp7b1, Akr1c14 and Apoh were declined, whereas Abcc4 and Gsta2 were elevated). Conclusion Our study, based on RNA-seq results, GEO databases and qRT-PCR, identified 11 significant dysregulated DEGs, which play an important role in lipid metabolism and the PPAR signaling pathway, which provide novel targets for diagnosis and treatment of DKD. |
| first_indexed | 2024-03-09T08:20:41Z |
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| language | English |
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| spelling | doaj.art-61ed199a59f04e64926c5bc77b47be0a2023-12-02T21:50:31ZengPeerJ Inc.PeerJ2167-83592022-09-0110e1393210.7717/peerj.13932Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in miceJing Zhao0Kaiying He1Hongxuan Du2Guohua Wei3Yuejia Wen4Jiaqi Wang5Xiaochun Zhou6Jianqin Wang7Lanzhou University, Lanzhou, ChinaLanzhou University, Lanzhou, ChinaLanzhou University, Lanzhou, ChinaLanzhou University Second Hospital, Lanzhou, ChinaLanzhou University, Lanzhou, ChinaLanzhou University, Lanzhou, ChinaLanzhou University Second Hospital, Lanzhou, ChinaLanzhou University Second Hospital, Lanzhou, ChinaBackground Diabetic kidney disease (DKD) is the leading cause of death in people with type 2 diabetes mellitus (T2DM). The main objective of this study is to find the potential biomarkers for DKD. Materials and Methods Two datasets (GSE86300 and GSE184836) retrieved from Gene Expression Omnibus (GEO) database were used, combined with our RNA sequencing (RNA-seq) results of DKD mice (C57 BLKS-32w db/db) and non-diabetic (db/m) mice for further analysis. After processing the expression matrix of the three sets of data using R software “Limma”, differential expression analysis was performed. The significantly differentially expressed genes (DEGs) (—logFC— > 1, p-value < 0.05) were visualized by heatmaps and volcano plots respectively. Next, the co-expression genes expressed in the three groups of DEGs were obtained by constructing a Venn diagram. In addition, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were further analyzed the related functions and enrichment pathways of these co-expression genes. Then, qRT-PCR was used to verify the expression levels of co-expression genes in the kidney of DKD and control mice. Finally, protein-protein interaction network (PPI), GO, KEGG analysis and Pearson correlation test were performed on the experimentally validated genes, in order to clarify the possible mechanism of them in DKD. Results Our RNA-seq results identified a total of 125 DEGs, including 59 up-regulated and 66 down-regulated DEGs. At the same time, 183 up-regulated and 153 down-regulated DEGs were obtained in GEO database GSE86300, and 76 up-regulated and 117 down-regulated DEGs were obtained in GSE184836. Venn diagram showed that 13 co-expression DEGs among the three groups of DEGs. GO analysis showed that biological processes (BP) were mainly enriched inresponse to stilbenoid, response to fatty acid, response to nutrient, positive regulation of macrophage derived foam cell differentiation, triglyceride metabolic process. KEGG pathway analysis showed that the three major enriched pathways were cholesterol metabolism, drug metabolism–cytochrome P450, PPAR signaling pathway. After qRT-PCR validation, we obtained 11 genes that were significant differentially expressed in the kidney tissues of DKD mice compared with control mice. (The mRNA expression levels of Aacs, Cpe, Cd36, Slc22a7, Slc1a4, Lpl, Cyp7b1, Akr1c14 and Apoh were declined, whereas Abcc4 and Gsta2 were elevated). Conclusion Our study, based on RNA-seq results, GEO databases and qRT-PCR, identified 11 significant dysregulated DEGs, which play an important role in lipid metabolism and the PPAR signaling pathway, which provide novel targets for diagnosis and treatment of DKD.https://peerj.com/articles/13932.pdfDiabetic kidney disease (DKD)RNA-seqBioinformatics analysisDifferentially expressed genes (DEGs)Biomarker |
| spellingShingle | Jing Zhao Kaiying He Hongxuan Du Guohua Wei Yuejia Wen Jiaqi Wang Xiaochun Zhou Jianqin Wang Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice PeerJ Diabetic kidney disease (DKD) RNA-seq Bioinformatics analysis Differentially expressed genes (DEGs) Biomarker |
| title | Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice |
| title_full | Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice |
| title_fullStr | Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice |
| title_full_unstemmed | Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice |
| title_short | Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice |
| title_sort | bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice |
| topic | Diabetic kidney disease (DKD) RNA-seq Bioinformatics analysis Differentially expressed genes (DEGs) Biomarker |
| url | https://peerj.com/articles/13932.pdf |
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