Prediction of nephrotoxicant action and identification of candidate toxicity-related biomarkers

被引:110
作者
Thukral, SK [1 ]
Nordone, PJ [1 ]
Hu, R [1 ]
Sullivan, L [1 ]
Galambos, E [1 ]
Fitzpatrick, VD [1 ]
Healy, L [1 ]
Bass, MB [1 ]
Cosenza, ME [1 ]
Afshari, CA [1 ]
机构
[1] Amgen Inc, Dept Toxicol, Thousand Oaks, CA 91320 USA
关键词
biomarkers; glomerulus; kidney; proximal-tubule necrosis; nephrotoxicity; toxicogenomics;
D O I
10.1080/01926230590927230
中图分类号
R36 [病理学];
学科分类号
100104 ;
摘要
A vast majority of pharmacological compounds and their metabolites are excreted via the urine, and within the complex structure of the kidney. the proximal tubules are a main target site of nephrotoxic compounds. We used the model nephrotoxicants mercuric chloride, 2-bromoethylamine hydrobromide. hexachlorobutadiene, mitomycin, amphotericin, and puromycin to elucidate time- and close-dependent global gene expression changes associated with proximal tubular toxicity. Male Sprague-Dawley rats were closed via intraperitoneal injection once daily for mercuric chloride and amphotericin (LIP to 7 doses). while a single dose was given for all other Compounds. Animals were exposed to 2 different doses of these compounds and kidney tissues were collected on day 1, 1 and 7 postdosing. Gene expression profiles were generated from kidney RNA using 17K rat cDNA dual dye microarray and analyzed in conjunction with histopathology. Analysis of gene expression profiles showed that the profiles Clustered based on similarities in the severity and type of pathology of individual annuals. Further, the expression changes were indicative Of tubular toxicity showing hallmarks of tubular degeneration/regeneration and necrosis. Use of gene expression data in predicting the type of nephrotoxicity was then tested with a support vector machine (SVM)-based approach. A SVM prediction module was trained using 120 profiles of total profiles divided into four classes based on the severity of pathology and clustering. Although mitomycin C and amphotericin B treatments did not Cause toxicity, their expression profiles were included in the SVM prediction module to increase the sample size. Using this classifier, the SVM predicted the type of pathology of 28 test profiles with 100% selectivity and 82% sensitivity. These data indicate that valid predictions could be made based on gene expression changes from a small set of expression profiles. A set of potential biomarkers showing a time- and dose-response with respect to the progression of proximal tubular toxicity were identified. These include several transporters (Slc21a2 Slc15, Slc34a2), Kim1, IGFbp-1, osteopontin, alpha-fibrinogen, and Gst alpha.
引用
收藏
页码:343 / 355
页数:13
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