Abstract
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.
Highlights
Microarrays are the preferred technology in many biological applications ranging from functional characterization of genes and pathways to classification of disease signatures for diagnostic and prognostic purposes
We speculated that the difference in group behaviors between the knockout and wild type (WT) strains was a result of different compensatory mechanisms each knockout animal developed to compensate for the absence of pregnane X receptor (PXR) regulation
The present study describes a novel algorithm for analyzing gene expression data and its application in studying the mechanism of toxicity of a drug candidate
Summary
Microarrays are the preferred technology in many biological applications ranging from functional characterization of genes and pathways to classification of disease signatures for diagnostic and prognostic purposes. The growing number of applications and wide adoption of microarray data have in turn fueled the development of analysis methods devised to extract information from these datasets [5]. Even with highly improved technology, the microarray community continues to struggle with the analysis, interpretation, and extraction of biologically relevant knowledge from the large volume of expression measurements. Much work has been invested in developing models and algorithms for these purposes and their levels of complexity have tended to increase over time. The increased level of algorithmic complexity does not always translate to improved biological understanding [10]. While most existing statistical models perform well with simulated data, they often are too sensitive to what is generally considered an acceptable level of biological variation
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