Identification of platform-independent gene expression markers of cisplatin nephrotoxicity.

Within the International Life Sciences Institute Committee on Genomics, a working group was formed to focus on the application of microarray technology to preclinical assessments of drug-induced nephrotoxicity. As part of this effort, Sprague-Dawley rats were treated with the nephrotoxicant cisplatin at doses of 0.3-5 mg/kg over a 4- to 144-hr time course. RNA prepared from these animals was run on a variety of microarray formats at multiple sites. A set of 93 differentially expressed genes associated with cisplatin-induced renal injury was identified on the National Institute of Environmental Health Sciences (NIEHS) custom cDNA microarray platform using quadruplicate measurements of pooled animal RNA. The reproducibility of this profile of statistically significant gene changes on other platforms, in pooled and individual animal replicate samples, and in an independent study was investigated. A good correlation in response between platforms was found among the 48 genes in the NIEHS data set that could be matched to probes on the Affymetrix RGU34A array by UniGene identifier or sequence alignment. Similar results were obtained with genes that could be linked between the NIEHS and Incyte or PHASE-1 arrays. The degree of renal damage induced by cisplatin in individual animals was commensurate with the number of differentially expressed genes in this data set. These results suggest that gene profiles linked to specific types of tissue injury or mechanisms of toxicity and identified in well-performed replicated microarray experiments may be extrapolatable across platform technologies, laboratories, and in-life studies

A Novel Statistical Algorithm for Gene Expression Analysis Helps Differentiate Pregnane X Receptor-Dependent and Independent Mechanisms of Toxicity

Genome-wide gene expression profiling has become standard for assessing potential liabilities as well as for elucidating mechanisms of toxicity of drug candidates under development. Analysis of microarray data is often challenging due to the lack of a statistical model that is amenable to biological variation in a small number of samples. Here we present a novel non-parametric algorithm that requires minimal assumptions about the data distribution. Our method for determining differential expression consists of two steps: 1) We apply a nominal threshold on fold change and platform p-value to designate whether a gene is differentially expressed in each treated and control sample relative to the averaged control pool, and 2) We compared the number of samples satisfying criteria in step 1 between the treated and control groups to estimate the statistical significance based on a null distribution established by sample permutations. The method captures group effect without being too sensitive to anomalies as it allows tolerance for potential non-responders in the treatment group and outliers in the control group. Performance and results of this method were compared with the Significant Analysis of Microarrays (SAM) method. These two methods were applied to investigate hepatic transcriptional responses of wild-type (PXR+/+) and pregnane X receptor-knockout (PXR−/−) mice after 96 h exposure to CMP013, an inhibitor of β-secretase (β-site of amyloid precursor protein cleaving enzyme 1 or BACE1). Our results showed that CMP013 led to transcriptional changes in hallmark PXR-regulated genes and induced a cascade of gene expression changes that explained the hepatomegaly observed only in PXR+/+ animals. Comparison of concordant expression changes between PXR+/+ and PXR−/− mice also suggested a PXR-independent association between CMP013 and perturbations to cellular stress, lipid metabolism, and biliary transport

Use of a mixed tissue RNA design for performance assessments on multiple microarray formats

The comparability and reliability of data generated using microarray technology would be enhanced by use of a common set of standards that allow accuracy, reproducibility and dynamic range assessments on multiple formats. We designed and tested a complex biological reagent for performance measurements on three commercial oligonucleotide array formats that differ in probe design and signal measurement methodology. The reagent is a set of two mixtures with different proportions of RNA for each of four rat tissues (brain, liver, kidney and testes). The design provides four known ratio measurements of >200 reference probes, which were chosen for their tissue-selectivity, dynamic range coverage and alignment to the same exemplar transcript sequence across all three platforms. The data generated from testing three biological replicates of the reagent at eight laboratories on three array formats provides a benchmark set for both laboratory and data processing performance assessments. Close agreement with target ratios adjusted for sample complexity was achieved on all platforms and low variance was observed among platforms, replicates and sites. The mixed tissue design produces a reagent with known gene expression changes within a complex sample and can serve as a paradigm for performance standards for microarrays that target other species