Title:Acute Kidney Injury (AKI) in COVID-19: In silico Identification of
LncRNA-MiRNA-Gene Networks and Key Transcription Factors
Volume: 29
Issue: 24
Author(s): Somayeh Hashemi Sheikhshabani, Zeinab Amini-Farsani, Nesa Kazemifard, Parastoo Modarres, Sharareh Khazaei Feyzabad, Zahra Amini-Farsani, Nasibeh Shaygan, Mir Davood Omrani and Soudeh Ghafouri-Fard*
Affiliation:
- Student Research Committee, Department of Medical Genetics, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Keywords:
COVID-19, kidney, inflammation, miRNA-Lnc network, transcriptional regulatory network (TRN), gene regulatory network (GRN).
Abstract:
Purpose: Acute kidney injury (AKI) accounts for up to 29% of severe COVID-19 cases and increases
mortality among these patients. Viral infections participate in the pathogenesis of diseases by changing the
expression profile of normal transcriptome. This study attempts to identify LncRNA-miRNA-gene and TF-gene
networks as gene expression regulating networks in the kidney tissues of COVID-19 patients.
Methods: In this analysis, four kidney libraries from the GEO repository were considered. To conduct the preprocessing,
Deseq2 software in R was used for the purpose of data normalization and log2 transformation. In addition,
pre- and post-normalization, PCA and box plots were developed using ggplot2 software in R for quality
control. The expression profiles of the kidney samples of COVID-19 patients and control individuals were compared
using DEseq2 software in R. The considered significance thresholds for DEGs were Adj P value < 0.05
and |logFC| >2. Then, to predict molecular interactions in lncRNA-miRNA-gene networks, different databases,
including DeepBase v3.0, miRNATissueAtlas2, DIANA-LncBase v3, and miRWalk, were used. Furthermore,
by employing ChEA databases, interactions at the TF-Gene level were obtained. Finally, the obtained networks
were plotted using Stringdb and Cytoscape v8.
Results: Results obtained from the comparison of the post-mortem kidney tissue samples of the COVID-19 patients
with the healthy kidney tissue samples showed significant changes in the expression of more than 2000
genes. In addition, predictions regarding the miRNA-gene interaction network based on DEGs obtained from
this meta-analysis showed that 11 miRNAs targeted the obtained DEGs. Interestingly, in the kidney tissue, these
11 miRNAs interacted with LINC01874, LINC01788, and LINC01320, which have high specificity for this
tissue. Moreover, four transcription factors of EGR1, SMAD4, STAT3, and CHD1 were identified as key transcription
factors regulating DEGs. Taken together, the current study showed several dysregulated genes in the
kidney of patients affected with COVID-19.
Conclusion: This study suggests lncRNA-miRNA-gene networks and key TFs as new diagnostic and therapeutic
targets for experimental and preclinical studies.
