Identification of six hub genes and two key pathways in two rat renal fibrosis models based on bioinformatics and RNA-seq transcriptome analyses

Yueqin Cai; Jingan Chen; Jingyan Liu; Keyan Zhu; Zhixing Xu; Jianan Shen; Dejun Wang; Lisheng Chu

doi:10.3389/fmolb.2022.1035772

Identification of six hub genes and two key pathways in two rat renal fibrosis models based on bioinformatics and RNA-seq transcriptome analyses

Front Mol Biosci. 2022 Nov 9:9:1035772. doi: 10.3389/fmolb.2022.1035772. eCollection 2022.

Authors

Yueqin Cai^{1

2}, Jingan Chen³, Jingyan Liu⁴, Keyan Zhu², Zhixing Xu⁵, Jianan Shen⁵, Dejun Wang², Lisheng Chu⁶

Affiliations

¹ School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
² Academy of Chinese Medical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
³ College of Pharmaceutical Sciences, Zhejiang Chinese Medical University, Hangzhou, China.
⁴ Laboratory Animal Resources Center, Westlake University, Hangzhou, China.
⁵ The First Medical College, Zhejiang Chinese Medical University, Hangzhou, China.
⁶ Department of Physiology, Zhejiang Chinese Medical University, Hangzhou, China.

Abstract

Renal fibrosis (RF) is the common pathological manifestation and central treatment target of multiple chronic kidney diseases with high morbidity and mortality. Currently, the molecular mechanisms underlying RF remain poorly understood, and exploration of RF-related hub targets and pathways is urgently needed. In this study, two classical RF rat models (adenine and UUO) were established and evaluated by HE, Masson and immunohistochemical staining. To clear molecular mechanisms of RF, differentially expressed genes (DEGs) were identified using RNA-Seq analysis, hub targets and pathways were screened by bioinformatics (functional enrichment analyses, PPI network, and co-expression analysis), the screening results were verified by qRT-PCR, and potential drugs of RF were predicted by network pharmacology and molecular docking. The results illustrated that renal structures were severely damaged and fibrotic in adenine- and UUO-induced models, as evidenced by collagen deposition, enhanced expressions of biomarkers (TGF-β1 and α-SMA), reduction of E-cadherin biomarker, and severe renal function changes (significantly decreased UTP, CREA, Ccr, and ALB levels and increased UUN and BUN levels), etc. 1189 and 1253 RF-related DEGs were screened in the adenine and UUO models, respectively. Two key pathways (AGE-RAGE and NOD-like receptor) and their hub targets (Tgfb1, Col1a1, Nlrc4, Casp4, Trpm2, and Il18) were identified by PPI networks, co-expressed relationships, and qRT-PCR verification. Furthermore, various reported herbal ingredients (curcumin, resveratrol, honokiol, etc.) were considered as important drug candidates due to the strong binding affinity with these hub targets. Overall, this study mainly identified two key RF-related pathways (AGE-RAGE and NOD-like receptor), screened hub targets (Tgfb1, Col1a1, Nlrc4, Casp4, Trpm2, and Il18) that involved inflammation, ECM formation, myofibroblasts generation, and pyroptosis, etc., and provided referable drug candidates (curcumin, resveratrol, honokiol, etc.) in basic research and clinical treatment of RF.

Keywords: RNA-seq; drug prediction; hub targets; molecular mechanism; renal fibrosis.