Mining kidney toxicogenomic data by using gene co-expression modules

被引:18
作者
AbdulHameed, Mohamed Diwan M. [1 ]
Ippolito, Danielle L. [1 ]
Stallings, Jonathan D. [1 ]
Wallqvist, Anders [1 ]
机构
[1] US Army, Dept Def Biotechnol High Performance Comp, Software Applicat Inst, Telemed & Adv Technol Res Ctr,Med Res & Mat Comma, 504 Scott St, Ft Detrick, MD 21702 USA
来源
BMC GENOMICS | 2016年 / 17卷
关键词
Acute kidney injury; Toxicogenomics; Kidney co-expression modules; Gene signature; Havcr1; KIM-1; Frequently co-expressed genes; AKI pathways; Immunoproteasome; Cd44; ectodomain; AKI networks; ACUTE-RENAL-FAILURE; TOLL-LIKE RECEPTORS; UP-REGULATION; R/BIOCONDUCTOR PACKAGE; MOLECULAR-MECHANISMS; EXPRESSION; INJURY; BIOMARKERS; IMMUNOPROTEASOME; INFLAMMATION;
D O I
10.1186/s12864-016-3143-y
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Background: Acute kidney injury (AKI) caused by drug and toxicant ingestion is a serious clinical condition associated with high mortality rates. We currently lack detailed knowledge of the underlying molecular mechanisms and biological networks associated with AKI. In this study, we carried out gene co-expression analyses using DrugMatrix-a large toxicogenomics database with gene expression data from rats exposed to diverse chemicals-and identified gene modules associated with kidney injury to probe the molecular-level details of this disease. Results: We generated a comprehensive set of gene co-expression modules by using the Iterative Signature Algorithm and found distinct clusters of modules that shared genes and were associated with similar chemical exposure conditions. We identified two module clusters that showed specificity for kidney injury in that they 1) were activated by chemical exposures causing kidney injury, 2) were not activated by other chemical exposures, and 3) contained known AKI-relevant genes such as Havcr1, Clu, and Tff3. We used the genes in these AKI-relevant module clusters to develop a signature of 30 genes that could assess the potential of a chemical to cause kidney injury well before injury actually occurs. We integrated AKI-relevant module cluster genes with protein-protein interaction networks and identified the involvement of immunoproteasomes in AKI. To identify biological networks and processes linked to Havcr1, we determined genes within the modules that frequently co-express with Havcr1, including Cd44, Plk2, Mdm2, Hnmt, Macrod1, and Gtpbp4. We verified this procedure by showing that randomized data did not identify Havcr1 co-expression genes and that excluding up to 10 % of the data caused only minimal degradation of the gene set. Finally, by using an external dataset from a rat kidney ischemic study, we showed that the frequently co-expressed genes of Havcr1 behaved similarly in a model of non-chemically induced kidney injury. Conclusions: Our study demonstrated that co-expression modules and co-expressed genes contain rich information for generating novel biomarker hypotheses and constructing mechanism-based molecular networks associated with kidney injury.
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页数:17
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