Shi-Ming Huang1, Xia Zhao2, Xue-Mei Zhao3, Xiao-Ying Wang4, Shan-Shan Li5, Yu-Hui Zhu5. 1. Department of Urology, Qianfoshan Hospital Affiliated to Shandong Univercity Jinan 250014, China. 2. Department of Nephrology, Qianfoshan Hospital Affiliated to Shandong Univercity Jinan 250014, China. 3. Department of Anorecta, Qianfoshan Hospital Affiliated to Shandong Univercity Jinan 250014, China. 4. Chemical Defense Clusters Medical Teams of 74122 PLA Troops Jinan 250031, China. 5. Department of Nephrology, The 456th Hospital of Jinan Military Region Jinan 250031, China.
Abstract
OBJECTIVES: Renal transplantation is the preferred method for most patients with end-stage renal disease, however, acute renal allograft rejection is still a major risk factor for recipients leading to renal injury. To improve the early diagnosis and treatment of acute rejection, study on the molecular mechanism of it is urgent. METHODS: MicroRNA (miRNA) expression profile and mRNA expression profile of acute renal allograft rejection and well-functioning allograft downloaded from ArrayExpress database were applied to identify differentially expressed (DE) miRNAs and DE mRNAs. DE miRNAs targets were predicted by combining five algorithm. By overlapping the DE mRNAs and DE miRNAs targets, common genes were obtained. Differentially co-expressed genes (DCGs) were identified by differential co-expression profile (DCp) and differential co-expression enrichment (DCe) methods in Differentially Co-expressed Genes and Links (DCGL) package. Then, co-expression network of DCGs and the cluster analysis were performed. Functional enrichment analysis for DCGs was undergone. RESULTS: A total of 1270 miRNA targets were predicted and 698 DE mRNAs were obtained. While overlapping miRNA targets and DE mRNAs, 59 common genes were gained. We obtained 103 DCGs and 5 transcription factors (TFs) based on regulatory impact factors (RIF), then built the regulation network of miRNA targets and DE mRNAs. By clustering the co-expression network, 5 modules were obtained. Thereinto, module 1 had the highest degree and module 2 showed the most number of DCGs and common genes. TF CEBPB and several common genes, such as RXRA, BASP1 and AKAP10, were mapped on the co-expression network. C1R showed the highest degree in the network. These genes might be associated with human acute renal allograft rejection. CONCLUSIONS: We conducted biological analysis on integration of DE mRNA and DE miRNA in acute renal allograft rejection, displayed gene expression patterns and screened out genes and TFs that may be related to acute renal allograft rejection.
OBJECTIVES: Renal transplantation is the preferred method for most patients with end-stage renal disease, however, acute renal allograft rejection is still a major risk factor for recipients leading to renal injury. To improve the early diagnosis and treatment of acute rejection, study on the molecular mechanism of it is urgent. METHODS: MicroRNA (miRNA) expression profile and mRNA expression profile of acute renal allograft rejection and well-functioning allograft downloaded from ArrayExpress database were applied to identify differentially expressed (DE) miRNAs and DE mRNAs. DE miRNAs targets were predicted by combining five algorithm. By overlapping the DE mRNAs and DE miRNAs targets, common genes were obtained. Differentially co-expressed genes (DCGs) were identified by differential co-expression profile (DCp) and differential co-expression enrichment (DCe) methods in Differentially Co-expressed Genes and Links (DCGL) package. Then, co-expression network of DCGs and the cluster analysis were performed. Functional enrichment analysis for DCGs was undergone. RESULTS: A total of 1270 miRNA targets were predicted and 698 DE mRNAs were obtained. While overlapping miRNA targets and DE mRNAs, 59 common genes were gained. We obtained 103 DCGs and 5 transcription factors (TFs) based on regulatory impact factors (RIF), then built the regulation network of miRNA targets and DE mRNAs. By clustering the co-expression network, 5 modules were obtained. Thereinto, module 1 had the highest degree and module 2 showed the most number of DCGs and common genes. TF CEBPB and several common genes, such as RXRA, BASP1 and AKAP10, were mapped on the co-expression network. C1R showed the highest degree in the network. These genes might be associated with human acute renal allograft rejection. CONCLUSIONS: We conducted biological analysis on integration of DE mRNA and DE miRNA in acute renal allograft rejection, displayed gene expression patterns and screened out genes and TFs that may be related to acute renal allograft rejection.
Authors: Thangamani Muthukumar; Darshana Dadhania; Ruchuang Ding; Catherine Snopkowski; Rubina Naqvi; Jun B Lee; Choli Hartono; Baogui Li; Vijay K Sharma; Surya V Seshan; Sandip Kapur; Wayne W Hancock; Joseph E Schwartz; Manikkam Suthanthiran Journal: N Engl J Med Date: 2005-12-01 Impact factor: 91.245
Authors: L Agodoa; G Eknoyan; J Ingelfinger; W Keane; M Mauer; W Mitch; G Striker; C Wilcox Journal: Kidney Int Suppl Date: 1997-12 Impact factor: 10.545