PMID- 36157062
OWN - NLM
STAT- MEDLINE
DCOM- 20230118
LR  - 20230118
IS  - 2167-8359 (Print)
IS  - 2167-8359 (Electronic)
IS  - 2167-8359 (Linking)
VI  - 10
DP  - 2022
TI  - Bioinformatics prediction and experimental verification of key biomarkers for 
      diabetic kidney disease based on transcriptome sequencing in mice.
PG  - e13932
LID - 10.7717/peerj.13932 [doi]
LID - e13932
AB  - 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.
CI  - (c)2022 Zhao et al.
FAU - Zhao, Jing
AU  - Zhao J
AD  - Lanzhou University, Lanzhou, China.
AD  - Lanzhou University Second Hospital, Lanzhou, China.
FAU - He, Kaiying
AU  - He K
AD  - Lanzhou University, Lanzhou, China.
AD  - Lanzhou University Second Hospital, Lanzhou, China.
FAU - Du, Hongxuan
AU  - Du H
AD  - Lanzhou University, Lanzhou, China.
AD  - Lanzhou University Second Hospital, Lanzhou, China.
FAU - Wei, Guohua
AU  - Wei G
AD  - Lanzhou University Second Hospital, Lanzhou, China.
FAU - Wen, Yuejia
AU  - Wen Y
AD  - Lanzhou University, Lanzhou, China.
AD  - Lanzhou University Second Hospital, Lanzhou, China.
FAU - Wang, Jiaqi
AU  - Wang J
AD  - Lanzhou University, Lanzhou, China.
FAU - Zhou, Xiaochun
AU  - Zhou X
AD  - Lanzhou University Second Hospital, Lanzhou, China.
FAU - Wang, Jianqin
AU  - Wang J
AD  - Lanzhou University Second Hospital, Lanzhou, China.
LA  - eng
SI  - figshare/10.6084/m9.figshare.19823302.v2
PT  - Journal Article
PT  - Research Support, Non-U.S. Gov't
DEP - 20220920
PL  - United States
TA  - PeerJ
JT  - PeerJ
JID - 101603425
RN  - 0 (Peroxisome Proliferator-Activated Receptors)
RN  - 0 (Biomarkers)
MH  - Mice
MH  - Animals
MH  - *Diabetic Nephropathies/genetics
MH  - Transcriptome/genetics
MH  - Gene Expression Profiling/methods
MH  - *Diabetes Mellitus, Type 2/genetics
MH  - Peroxisome Proliferator-Activated Receptors/genetics
MH  - Biomarkers
MH  - Computational Biology/methods
PMC - PMC9504448
OTO - NOTNLM
OT  - Bioinformatics analysis
OT  - Biomarker
OT  - Diabetic kidney disease (DKD)
OT  - Differentially expressed genes (DEGs)
OT  - RNA-seq
COIS- The authors declare there are no competing interests.
EDAT- 2022/09/27 06:00
MHDA- 2022/09/27 06:01
PMCR- 2022/09/20
CRDT- 2022/09/26 17:01
PHST- 2022/05/24 00:00 [received]
PHST- 2022/07/31 00:00 [accepted]
PHST- 2022/09/26 17:01 [entrez]
PHST- 2022/09/27 06:00 [pubmed]
PHST- 2022/09/27 06:01 [medline]
PHST- 2022/09/20 00:00 [pmc-release]
AID - 13932 [pii]
AID - 10.7717/peerj.13932 [doi]
PST - epublish
SO  - PeerJ. 2022 Sep 20;10:e13932. doi: 10.7717/peerj.13932. eCollection 2022.