Abstract
We aim to identify the key protein interaction networks and implicated pathways of BK virus nephropathy (BKVN) via bioinformatic methods. The microarray data GSE75693 of 30 patients with stable kidney transplantation and 15 with BKVN were downloaded and analyzed by using the limma package to identify differentially expressed genes (DEGs). Then the gene ontology (GO) functional enrichment analysis and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were done to investigate the molecular function (MF), biological process (BP), cellular components (CC) and pathways of DEGs. Finally, protein-protein interactions (PPIs) were constructed, and the hub proteins were identified. As a result, 249 up-regulated genes and 253 down-regulated genes of BKVN patients were selected based on criteria of P > 0.01 and fold change >2.0. GO and KEGG showed that DEGs were mainly located in nucleus and cytosol, and were implicated in the immune responses. In the PPI analysis, 26 up-regulated and 8 down-regulated proteins composed the pivotal interaction network. CXCL10, EGF and STAT1 were identified as hub proteins in BKVN. In conclusion, CXCL10, EGF and STAT1 may induce kidney injuries by promoting inflammation and prohibiting reparation of tissue damage in BKVN.
Highlights
The BK virus (BKV) is a double-stranded DNA virus, belonging to the Polyomaviridae family[1]
We aimed at finding out the key protein interaction networks in BK virus nephropathy (BKVN) after kidney transplantation
The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses show the important role of innate immune system in BKVN
Summary
The BK virus (BKV) is a double-stranded DNA virus, belonging to the Polyomaviridae family[1]. Once the primary infection occurs, BKV persists latently in the renal epithelium[3]. BKVN is one of the main causes of graft dysfunction and morbidity in renal-transplant recipients[6,7]. Leflunomide combined with everolimus or intravenous immunoglobulin may be safe rescue therapies of BKVN6,11. These therapies have not been proved in preclinical experiments or large randomized controlled studies. Previous studies demonstrated that expression levels of these cell factors were changed (Supplementary Table S1), www.nature.com/scientificreports/. Gene expression analysis by bioinformatic methods has been widely used in genomics and biomedical research, providing insights into the molecular events underlying human biology and disease[18]. Data mining of the available microarray datasets could help scientists to narrow down the research scope and to carry out targeted experiments
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