Bioinformatics prediction and experimental verification of key biomarkers for diabetic kidney disease based on transcriptome sequencing in mice

PeerJ. 2022 Sep 20;10:e13932. doi: 10.7717/peerj.13932. eCollection 2022.


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.

Keywords: Bioinformatics analysis; Biomarker; Diabetic kidney disease (DKD); Differentially expressed genes (DEGs); RNA-seq.

Associated data

  • figshare/10.6084/m9.figshare.19823302.v2

Grant support

This work was supported by the National Natural Science Foundation of China (No. 81960142), Youth Science and Technology Fund Program of Gansu Province (No. 21JR1RA157), Talent Innovation and Entrepreneurship Project of Lanzhou City, Gansu Province (2021-RC-94), Lanzhou Chengguan District Talent Entrepreneurship and Innovation Project (2021RCCX0027), Lanzhou University Second Hospital Youth Fund (CY2021-QN-B01), and Project of Department of Education of Gansu Province (2022B-050). Meanwhile, our experiments are supported by the Clinical Medical Research Center of Gansu Province (21JR7RA436). There was no additional external funding received for this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.